Design of saccharide based organic binder for low-grade iron ore pelletization using atomistic simulations and machine learning methods

Inorganic binders like bentonite, used for pelletization of low-grade iron ore, generate iron ore slimes with comparatively high silica and alumina content necessitating extra steps for their removal during iron making process. This demands the usage of organic binders as full or partial replacement of bentonite for iron ore pelletization. In this work, adsorption of organic binders with saccharides skeleton and -H, -OH, -CH2OH and -CH2CH2OH as polar substituents, on goethite surface was studied using density functional theory, molecular dynamics and machine learning. It was observed that adsorption energy of binders on goethite surface had weak dependence on number of hydrogen bonds between them. With this favorable interaction in mind, a library containing 64 organic binders was constructed and adsorption energy of 30 of these binders was computed using molecular dynamics, followed by training of a linear regression model, which was then used to predict the adsorption energy of rest of the binders in the library. It was found that the introduction of -CH2CH2OH at R2 position resulted in statistically significant higher adsorption energy. Binder34 and Binder44 were identified as viable candidates for both goethite and hematite ore pelletization and adsorption of their n-mers on goethite and hematite surfaces was also quantified.

Prognostic modeling in early rheumatoid arthritis: reconsidering the predictive role of disease activity scores

OBJECTIVE

In this prospective cohort study, we provide several prognostic models to predict functional status as measured by the modified Health Assessment Questionnaire (mHAQ). The early adoption of the treat-to-target strategy in this cohort offered a unique opportunity to identify predictive factors using longitudinal data across 20 years.

METHODS

A cohort of 397 patients with early RA was used to develop statistical models to predict mHAQ score measured at baseline, 12 months, and 18 months post diagnosis, as well as serially measured mHAQ. Demographic data, clinical measures, autoantibodies, medication use, comorbid conditions, and baseline mHAQ were considered as predictors.

RESULTS

The discriminative performance of models was comparable to previous work, with an area under the receiver operator curve ranging from 0.64 to 0.88. The most consistent predictive variable was baseline mHAQ. Patient-reported outcomes including early morning stiffness, tender joint count (TJC), fatigue, pain, and patient global assessment were positively predictive of a higher mHAQ at baseline and longitudinally, as was the physician global assessment and C-reactive protein. When considering future function, a higher TJC predicted persistent disability while a higher swollen joint count predicted functional improvements with treatment.

CONCLUSION

In our study of mHAQ prediction in RA patients receiving treat-to-target therapy, patient-reported outcomes were most consistently predictive of function. Patients with high disease activity due predominantly to tenderness scores rather than swelling may benefit from less aggressive treatment escalation and an emphasis on non-pharmacological therapies, allowing for a more personalized approach to treatment. Key Points • Long-term use of the treat-to-target strategy in this patient cohort offers a unique opportunity to develop prognostic models for functional outcomes using extensive longitudinal data. • Patient reported outcomes were more consistent predictors of function than traditional prognostic markers. • Tender joint count and swollen joint count had discordant relationships with future function, adding weight to the possibility that disease activity may better guide treatment when the components are considered separately.

Regulatory role of PDK1 via integrated gene analysis of mitochondria-immune response in periodontitis

AIMS

To investigate the association between mitochondrial events and immune response in periodontitis and related regulatory genes.

MAIN METHODS

Gene expression profiles in gingival tissues were retrieved from the Gene Expression Omnibus. Mitochondria-immune response-related differentially expressed genes (MIR-DEGs) between the healthy and periodontitis samples were determined. WGCNA, GO, and KEGG were used to investigate the function and the enriched pathways of MIR-DEGs. The correlation between MIR-DEGs expression and clinical probing pocket depth was analyzed. The MIR-DEGs were further identified and verified in animal samples. A periodontitis model was established in C57BL/6 mice with silk ligation. Micro-computed tomography was used to assess alveolar bone loss. Western blot, quantitative real-time polymerase chain reaction, and immunohistochemical analyses further validated the differential expression of the MIR-DEGs.

KEY FINDINGS

A total of ten MIR-DEGs (CYP24A1, PRDX4, GLDC, PDK1, BCL2A1, CBR3, ARMCX3, BNIP3, IFI27, and UNG) were identified, the expression of which could effectively distinguish patients with periodontitis from the healthy controls. Enhanced immune response was detected in the periodontitis group with that in the healthy controls, especially in B cells. PDK1 was a critical MIR-DEG correlated with B cell immune response and clinical periodontal probing pocket depth. Both animal and clinical periodontal samples presented higher gene and protein expression of PDK1 than the control samples. Additionally, PDK1 colocalized with B cells in both animal and clinical periodontal tissues.

SIGNIFICANCE

Mitochondria participate in the regulation of the immune response in periodontitis. PDK1 may be the key mitochondria-related gene regulating B-cell immune response in periodontitis.

Comparison of the different methods of width estimation in unerupted canine and premolars

BACKGROUND

There are different methods for determining the required space for unerupted teeth. However, the accuracy of these techniques varies depending on ethnic differences. Therefore, the current study was performed to compare the accuracy of four methods for estimating the mesiodistal width of unerupted canines and premolars in a population of northern Iran.

METHODS

The present cross-sectional study was conducted on 50 pairs of dental casts of patients aged 12-24 years old. The mesiodistal width of the teeth was measured with a digital caliper by two observers (ICC < 0.9), and the mean value was recorded. The space required for eruption of canines and premolars was obtained by the Tanaka-Johnson formula and the Moyers tables and compared with the actual value by paired t test.

RESULTS

The Tanaka-Johnson formula had overestimation in the maxilla and mandible, which was statistically significant (p < 0.001). The values obtained from the Moyers tables in different confidence levels were not accurate. However, the 65% level for the mandible had almost no difference from the actual value (P = 0.996 and r2 = 0.503). Furthermore, linear regression was obtained based on the total mesiodistal width of the maxillary first molar and mandibular central incisor (maxilla: Yx= 0.613X + 2.23 and mandible: Ym= 0.618X + 1.6) and the total mesiodistal width of the mandibular first molar and maxillary central incisor in each jaw (maxilla: Yx = 0.424X + 5.021 and mandible: Ym = 0.447X + 3.631).

CONCLUSION

The Tanaka-Johnson method was overestimated in the population of northern Iran. The 85% and 75% confidence levels of the Moyers table have the best clinical results for the maxilla and mandible, respectively. Regression based on maxillary first molars and mandibular central incisors has better results.

Machine learning regression algorithms to predict emissions from steam boilers

Currently, the modeling of complex chemical-physical processes is drastically influencing industrial development. Therefore, the analysis and study of the combustion process of the boilers using machine learning (ML) techniques are vital to increase the efficiency with which this equipment operates and reduce the pollution load they contribute to the environment. This work aims to predict the emissions of CO, CO2, NOx, and the temperature of the exhaust gases of industrial boilers from real data. Different ML algorithms for regression analysis are discussed. The following are input variables: ambient temperature, working pressure, steam production, and the type of fuel used in around 20 industrial boilers. Each boiler's emission data was collected using a TESTO 350 Combustion Gas Analyzer. The modeling, with a machine learning approach using the Gradient Boosting Regression algorithm, showed better performance in the predictions made on the test data, outperforming all other models studied. It was achieved with predicted values showing a mean absolute error of 0.51 and a coefficient of determination of 99.80%. Different regression models (DNN, MLR, RFR, GBR) were compared to select the most optimal. Compared to models based on Linear Regression, the DNN model has better prediction performance. The proposed model provides a new method to predict CO2, CO, NOx emissions, and exhaust gas outlet temperature.

Interaction models matter: an efficient, flexible computational framework for model-specific investigation of epistasis

PURPOSE

Epistasis, the interaction between two or more genes, is integral to the study of genetics and is present throughout nature. Yet, it is seldom fully explored as most approaches primarily focus on single-locus effects, partly because analyzing all pairwise and higher-order interactions requires significant computational resources. Furthermore, existing methods for epistasis detection only consider a Cartesian (multiplicative) model for interaction terms. This is likely limiting as epistatic interactions can evolve to produce varied relationships between genetic loci, some complex and not linearly separable.

METHODS

We present new algorithms for the interaction coefficients for standard regression models for epistasis that permit many varied models for the interaction terms for loci and efficient memory usage. The algorithms are given for two-way and three-way epistasis and may be generalized to higher order epistasis. Statistical tests for the interaction coefficients are also provided. We also present an efficient matrix based algorithm for permutation testing for two-way epistasis. We offer a proof and experimental evidence that methods that look for epistasis only at loci that have main effects may not be justified. Given the computational efficiency of the algorithm, we applied the method to a rat data set and mouse data set, with at least 10,000 loci and 1,000 samples each, using the standard Cartesian model and the XOR model to explore body mass index.

RESULTS

This study reveals that although many of the loci found to exhibit significant statistical epistasis overlap between models in rats, the pairs are mostly distinct. Further, the XOR model found greater evidence for statistical epistasis in many more pairs of loci in both data sets with almost all significant epistasis in mice identified using XOR. In the rat data set, loci involved in epistasis under the XOR model are enriched for biologically relevant pathways.

CONCLUSION

Our results in both species show that many biologically relevant epistatic relationships would have been undetected if only one interaction model was applied, providing evidence that varied interaction models should be implemented to explore epistatic interactions that occur in living systems.

Determination of the most significant rubber components influencing the hardness of natural rubber (NR) using various statistical methods

Manufacturers use a large number of components in the production of modern rubber products. The selection of the constituents of the rubber recipe is primarily determined by the purpose of use. The different fields of applications of rubbers require the presence of appropriate mechanical properties. In this respect, it can be useful to know which substances forming the rubber recipe have significant influence on the different mechanical properties. In this study, the statistical analysis of the influence of rubber components on the hardness of natural rubber (NR) is proposed based on literature review. Based on the literature data, various statistical analyses, like linear regression, constrained linear regression, Ridge regression, Ridge sparse regression and binary classification decision trees were performed to determine which rubber components have the most significant effect on the hardness. In the statistical analyses, the effect of a total of 42 constituents of rubber compound on hardness was investigated. Most of the applied statistical methods confirmed that the traditional frequently used rubber components, such as carbon black and sulfur, have a primary effect on the hardness. However, the substances forming the rubber compound that are not widely used in practice or newly developed components appear differently in the lists of significant additives obtained by the different statistical methods.

Adaptive terminal synergetic-backstepping technique based machine learning regression algorithm for MPPT control of PV systems under real climatic conditions

This paper deals with a comparative evaluation of nonlinear controllers based on the linear regression technique, which is a machine learning algorithm for maximum power point tracking. In the past decade, most photovoltaic systems have been equipped with classical algorithms such as perturb and observe, hill climbing, and incremental conductance. The simplicity of these techniques and their ease of implementation were seen as the main reasons for their utilization in photovoltaic systems. However, researchers' attention has recently been attracted by artificial intelligence-based techniques such as linear regression, which offer better performance within the bounds of the nonlinearity of photovoltaic system characteristics. An adaptive terminal synergetic backstepping controller is developed in this paper for a single-ended primary inductance converter. This control scheme is based on the combination of a non-singular terminal synergetic technique with an integral backstepping technique and equally a neural network for the approximation of unmeasured or inaccessible variables that guarantees the finite-time convergence. The proposed controller was further verified under virtual and real environmental conditions, and the numerical results obtained from Matlab/Simulink software under various test conditions, including load variations, show that the adaptive terminal synergetic backstepping controller gives satisfactory performance compared to the adaptive integral backstepping controller used in the same climatic conditions.

A comprehensive review of the development of land use regression approaches for modeling spatiotemporal variations of ambient air pollution: A perspective from 2011 to 2023

Land use regression (LUR) models are widely used in epidemiological and environmental studies to estimate humans' exposure to air pollution within urban areas. However, the early models, developed using linear regressions and data from fixed monitoring stations and passive sampling, were primarily designed to model traditional and criteria air pollutants and had limitations in capturing high-resolution spatiotemporal variations of air pollution. Over the past decade, there has been a notable development of multi-source observations from low-cost monitors, mobile monitoring, and satellites, in conjunction with the integration of advanced statistical methods and spatially and temporally dynamic predictors, which have facilitated significant expansion and advancement of LUR approaches. This paper reviews and synthesizes the recent advances in LUR approaches from the perspectives of the changes in air quality data acquisition, novel predictor variables, advances in model-developing approaches, improvements in validation methods, model transferability, and modeling software as reported in 155 LUR studies published between 2011 and 2023. We demonstrate that these developments have enabled LUR models to be developed for larger study areas and encompass a wider range of criteria and unregulated air pollutants. LUR models in the conventional spatial structure have been complemented by more complex spatiotemporal structures. Compared with linear models, advanced statistical methods yield better predictions when handling data with complex relationships and interactions. Finally, this study explores new developments, identifies potential pathways for further breakthroughs in LUR methodologies, and proposes future research directions. In this context, LUR approaches have the potential to make a significant contribution to future efforts to model the patterns of long- and short-term exposure of urban populations to air pollution.

Generating accurate 3D gaze vectors using synchronized eye tracking and motion capture

Assessing gaze behavior during real-world tasks is difficult; dynamic bodies moving through dynamic worlds make gaze analysis difficult. Current approaches involve laborious coding of pupil positions. In settings where motion capture and mobile eye tracking are used concurrently in naturalistic tasks, it is critical that data collection be simple, efficient, and systematic. One solution is to combine eye tracking with motion capture to generate 3D gaze vectors. When combined with tracked or known object locations, 3D gaze vector generation can be automated. Here we use combined eye and motion capture and explore how linear regression models generate accurate 3D gaze vectors. We compare spatial accuracy of models derived from four short calibration routines across three pupil data inputs: the efficacy of calibration routines was assessed, a validation task requiring short fixations on task-relevant locations, and a naturalistic object interaction task to bridge the gap between laboratory and "in the wild" studies. Further, we generated and compared models using spherical and Cartesian coordinate systems and monocular (left or right) or binocular data. All calibration routines performed similarly, with the best performance (i.e., sub-centimeter errors) coming from the naturalistic task trials when the participant is looking at an object in front of them. We found that spherical coordinate systems generate the most accurate gaze vectors with no differences in accuracy when using monocular or binocular data. Overall, we recommend 1-min calibration routines using binocular pupil data combined with a spherical world coordinate system to produce the highest-quality gaze vectors.

Disentangling Socioeconomic Status and Race in Infant Brain, Birth Weight, and Gestational Age at Birth: A Neural Network Analysis

Background

Race is commonly used as a proxy for multiple features including socioeconomic status. It is critical to dissociate these factors, to identify mechanisms that affect infant outcomes, such as birth weight, gestational age, and brain development, and to direct appropriate interventions and shape public policy.

Methods

Demographic, socioeconomic, and clinical variables were used to model infant outcomes. There were 351 participants included in the analysis for birth weight and gestational age. For the analysis using brain volumes, 280 participants were included after removing participants with missing magnetic resonance imaging scans and those matching our exclusion criteria. We modeled these three different infant outcomes, including infant brain, birth weight, and gestational age, with both linear and nonlinear models.

Results

Nonlinear models were better predictors of infant birth weight than linear models (R2 = 0.172 vs. R2 = 0.145, p = .005). In contrast to linear models, nonlinear models ranked income, neighborhood disadvantage, and experiences of discrimination higher in importance than race while modeling birth weight. Race was not an important predictor for either gestational age or structural brain volumes.

Conclusions

Consistent with the extant social science literature, the findings related to birth weight suggest that race is a linear proxy for nonlinear factors related to structural racism. Methods that can disentangle factors often correlated with race are important for policy in that they may better identify and rank the modifiable factors that influence outcomes.

Spatio-temporal dynamics of land use transitions associated with human activities over Eurasian Steppe: Evidence from improved residual analysis

We presented a framework to evaluate the land use transformations over the Eurasian Steppe (EUS) driven by human activities from 2000 to 2020. Framework involves three main components: (1) evaluate the spatial-temporal dynamics of land use transitions by utilizing the land change modeler (LCM) and remote sensing data; (2) quantifying the individual contributions of climate change and human activities using improved residual trend analysis (IRTA) and pixel-based partial correlation coefficient (PCC); and (3) quantifying the contributions of land use transitions to Leaf Area Index Intensity (LAII) by using the linear regression. Research findings indicate an increase in cropland (+1.17 % = 104,217 km2) over EUS, while a - 0.80 % reduction over Uzbekistan and - 0.16 % over Tajikistan. From 2000 to 2020 a slight increase in grassland was observed over the EUS region by 0.05 %. The detailed findings confirm an increase (0.24 % = 21,248.62 km2) of grassland over the 1st half (2000-2010) and a decrease (-0.19 % = -16,490.50 km2) in the 2nd period (2011-2020), with a notable decline over Kazakhstan (-0.54 % = 13,690 km2), Tajikistan (-0.18 % = 1483 km2), and Volgograd (-0.79 % = 4346 km2). Area of surface water bodies has declined with an alarming rate over Kazakhstan (-0.40 % = 10,261 km2) and Uzbekistan (-2.22 % = 8943 km2). Additionally, dominant contributions of human activities to induced LULC transitions were observed over the Chinese region, Mongolia, Uzbekistan, and Volgograd regions, with approximately 87 %, 83 %, 92 %, and 47 %, respectively, causing effective transitions to 12,997 km2 of cropland, 24,645 km2 of grassland, 16,763 km2 of sparse vegetation in China, and 12,731.2 km2 to grassland and 15,356.1 km2 to sparse vegetation in Mongolia. Kazakhstan had mixed climate-human impact with human-driven transitions of 48,568 km2 of bare land to sparse vegetation, 27,741 km2 to grassland, and 49,789 km2 to cropland on the eastern sides. Southern regions near Uzbekistan had climatic dominancy, and 8472 km2 of water bodies turned into bare soil. LAII shows an increasing trend rate of 0.63 year-1, particularly over human-dominant regions. This study can guide knowledge of oscillations and reduce adverse impacts on ecosystems and their supply services.

Indexing and segment-level mapping of soil quality in a spatially complex watershed in northern Iran

Soil quality (SQ) modeling and mapping is a leading research field aiming to provide reproducible and cost-effective yet accurate SQ predictions at the landscape level. This endeavor was conducted in a complex watershed in northern Iran. We classified the region into spectrally and topographically homogenous land units (average area of 48 ± 23 ha) using object-based segmentation analysis. Following the physicochemical analysis of soil samples from 98 stations, the Nemoro soil quality index (SQIn) was produced using the minimum dataset procedure and a non-linear sigmoid scoring function. SQIn values averaged 0.21 ± 0.06 and differed statistically between major land uses. To predict and map SQIn for each land unit, the best-performing regression model (F(3, 84) = 45.57, p = 0.00, R2 = 0.62) was built based on the positive contribution of the mean Landsat 8-OLI band 5, and negative influence of land surface temperature retrieved from Landsat 8-OLI band 10 and surface slope (t-test p-values < 0.01). Results showed that dense-canopy woodlands located in low-slope land units exhibit higher SQIn while regions characterized by either low-vegetation or steep-sloped land units had SQ deficits. This study provides insights into SQ prediction and mapping across spatially complex large-scale landscapes.

Soil health implications of some d-block metals in selected agricultural soils in Southeast Nigeria

Soil fertility, soil health and environmental management through the estimation of background concentration of potentially toxic elements is required for environmental safety. This study aims at investigating the concentration, fertility and potential health risks of some d-block metals (Ti, V, Fe, Mn, and Mo) in some agricultural soils, and establishes the relationship between the metals and some soil properties. Eight elevation ranges resulted from the digital elevation models of the study area; two in Ishibori (NG1, NG2), three each in Agoi-Ibami (CG1, CG2, CG3) and Mfamosing (SG1, SG2 and SG3). One soil profile pit was sunk along each of the elevations. Thirty-five composite soil samples were collected at 0-30, 30-60, 60-90, 90-120, 120-150, 150-180 and 180-200 cm depending on soil depth. Only the profile means of Mn (660.82 ± 612.89 mg/kg) and Mo (2.61 ± 0.73 mg/kg) exceeded permissible concentrations and would pose threats to the environment. Also, the concentrations of the d-block metals exceeded permissible values in Ishibori making them prone to toxicity. The metals were irregularly distributed with depth; however, Mn and Fe were concentrated in the subsurface soils. Clay and sand contents correlated positively and negatively, respectively with all the d-block metals at p < 0.05. The linear model was more efficient in estimating V and Mo via soil properties with adjusted R2 of 33 - 67% for the metals. In conclusion, agricultural activities and geology may influence the accumulation of d-block metals, hence the call for environmental monitoring to curtail metals' assimilation by crops. HIGHLIGHTS: • Mn and Mo threaten the environment the most. • Soils in the Southern Guinea Savannah are most prone to d-block metals contamination. • BD, pH, Mg, and CEC are the best predictors of d-block metals in the soils. • The linear model was best performing in the estimation of V and Mo, respectively.

Five or more gender- and size-diverse customizations of distal femur prostheses are needed to improve fit for Chinese knees

PURPOSE

Mismatch between partial imported prostheses and Chinese anatomy affects the clinical outcome of the procedure. The purpose of this study was to measure the anatomical dimensions of Chinese distal femurs to provide guidance for the design of more compatible distal femoral prostheses.

METHODS

A total of 406 healthy distal femurs were reconstructed and measured. Consistency of these measurements and differences in sides, gender, and populations were examined. Parameter correlations were analyzed, and pairs with strong correlations underwent linear regression analysis. The design of distal femoral prostheses was referenced from the results of K-means and hierarchical clustering analysis.

RESULTS

Ten parameters were measured, including the trans-epicondylar axis, width of the distal femur (ML), anteroposterior diameter of the distal femur (AP), etc. The intra-class correlation coefficient ranged from 0.795 to 0.999 for intra-observer consistency, and from 0.796 to 0.998 for inter-observer consistency. Males exhibited significantly larger parameters than females, except for the posterior condylar angle (all P values < 0.05). Compared to other populations, substantial differences were observed for most parameters, such as ML, AP, width of lateral femoral condyle, etc. (all P values < 0.05). Clustering analysis suggested that distal femoral prostheses should include at least five sizes to adequately accommodate the sampled population. ML sizes for males were 68, 70, 83, 73, and 89 mm, and for females 64, 65, 71, 67, and 77 mm. AP sizes for males were 56, 60, 60, 64, and 64 mm, and for females 48, 52, 54, 57, and 58 mm.

CONCLUSIONS

Chinese distal femur morphology, as analyzed using 3D techniques, varies significantly between genders and when compared with international data. For improved patient fit, the creation of five or more distal femur prostheses, diversified by gender and size and informed by the associated morphological parameters, is recommended.

LEVEL OF EVIDENCE

IV.

Multiple univariate analysis of radiologic and clinical features on 168 patients with lumbar spinal stenosis: what is the role of the erector spinae in the development of a patient's disability?

BACKGROUND

The weakening of paraspinal muscles in the paravertebral area may play a role in developing central lumbar spinal stenosis, resulting in lower back discomfort.

OBJECTIVE

The study thoroughly examined the correlation between the Oswestry Disability Index, Dural Sac cross-sectional area, Schizas grading Scale, Body Mass Index, and the cross-sectional areas of Erector Spinae, Multifidus, and Psoas muscles. The findings were also compared between patients with central Lumbar Spinal Stenosis and healthy individuals.

STUDY DESIGN

Retrospective monocentric observational study.

METHODS

The study recruited 168 consecutive patients aged 60 or older diagnosed with central Lumbar Spinal Stenosis between January 2020 and July 2022. The patients' condition was evaluated by administering a preoperative Oswestry Disability Index questionnaire, measuring their Body Mass Index, and performing preoperative Magnetic Resonance Imaging. The analyzed parameters were the cross-sectional area of paraspinal muscles at the L4-L5 level, dural sac cross-sectional area, and Schizas grading Scale at the most stenotic level, using multiple linear univariate analyses. Two groups of healthy individuals were recruited: Group A (under 60 years old) and Group B (over 60 years old). The same data extrapolated from these groups were compared with those of patients with central lumbar stenosis using a two-tailed Mann-Whitney test.

RESULTS

As the Erector Spinae degenerates, the Oswestry Disability Index tends to increase. Similarly, an increase in Body Mass Index is often accompanied by a decrease in the cross-sectional area of the Erector Spinae. Low dural sac cross-sectional area is statistically linked to a reduced Multifidus cross-sectional area. Interestingly, the Schizas grading scale does not appear to correlate with changes in the cross-sectional area of the paraspinal muscles. Additionally, there is no significant difference in the cross-sectional area of the Psoas muscle between individuals with central lumbar spinal stenosis and healthy individuals.

CONCLUSIONS

Our study found that degeneration of the Erector Spinae plays a crucial role in the progression of perceived disability in Lumbar Spinal Stenosis. Prospective studies should investigate the long-term evolution of paraspinal muscles in decompressed patients.

In Vitro Virulence Contrast of Seven Genetically Distinct Toxoplasma gondii Isolates After Rejuvenation In Vivo

BACKGROUND

In the past for more than 100 years at least 300 genotypes of Toxoplasma gondii were recorded and several traditional isolates such as RH, GT1, ME49, PRU and VEG were used repeatedly to clarify the pathogenic mechanisms and the epidemiological significance to human, but for if their virulence was mutative post-iterative passages it remains confused.

OBJECTIVE

Therefore, in the study, seven genetically distinct T. gondii including C7 and PYS previously discovered in China were reidentified by sequencing the head of hsp40 locus to distinguish their virulence in vitro post-rejuvenation in vivo.

RESULTS

Our data showed the nucleotides were different in 18 positions and 7 of them can be used to type T. gondii isolates. Total 634 plaques of T. gondii without two or more overlaps indicated that RH and GT1 tachyzoites possess stronger power than other five isolates in vitro (p < 0.001), followed by ME49, PRU, C7, PYS, and the weakest VEG. Based on the shapes of plaques, we found the ratio of their width/length was associated with the virulence of T. gondii, and speculated it could be used to judge T. gondii tachyzoites in vitro, whereas the data of simple linear regression analyses did not agree.

CONCLUSIONS

Together, virulence of seven genetically distinct T. gondii isolates that can be distinguished by seven mutative nucleotides in hsp40 was redefined in vitro post-rejuvenation in vivo, and it cannot be directly judged just according to the shapes of plaques formed in vitro.

Prediction of the SF-6D utility score from Lung cancer FACT-L: a mapping study in China

OBJECTIVE

To develop a mapping algorithm for generating the Short Form Six-Dimension (SF-6D) utility score based on the Functional Assessment of Cancer Therapy-Lung (FACT-L) of lung cancer patients.

METHODS

Data were collected from 625 lung cancer patients in mainland China. The Spearman rank correlation coefficient and principal component analysis were used to evaluate the conceptual overlap between the FACT-L and SF-6D. Five model specifications and four statistical techniques were used to derive mapping algorithms, including ordinary least squares (OLS), Tobit and beta-mixture regression models, which were used to directly estimate health utility, and ordered probit regression was used to predict the response level. The prediction performance was evaluated using the correlations between the root mean square error (RMSE), mean absolute error (MAE), concordance correlation coefficient (CCC), Akaike information criterion (AIC) and Bayesian information criterion (BIC) and the observed and predicted SF-6D scores. A five-fold cross-validation method was used to test the universality of each model and select the best model.

RESULTS

The average FACT-L score was 103.024. The average SF-6D score was 0.774. A strong correlation was found between FACT-L and SF-6D scores (ρ = 0.797). The ordered probit regression model with the total score of each dimension and its square term, as well as age and sex as covariates, was most suitable for mapping FACT-L to SF-6D scores (5-fold cross-validation: RMSE = 0.0854; MAE = 0.0655; CCC = 0.8197; AEs > 0.1 (%) = 53.44; AEs > 0.05 (%) = 21.76), followed by beta-mixture regression for direct mapping. The Bland‒Altman plots showed that the ordered probit regression M5 had the lowest proportion of prediction scores outside the 95% agreement limit (-0.166, 0.163) at 4.96%.

CONCLUSIONS

The algorithm reported in this paper enables lung cancer data from the FACT-L to be mapped to the utility of the SF-6D. The algorithm allows the calculation of quality-adjusted life years for cost-utility analyses of lung cancer.

Prediction of beef tenderness and juiciness using consumer and descriptive sensory attributes

The impact of different cooking methods, degree of doneness, cuts, and marbling scores on beef juiciness and tenderness have been examined. However, relationships between tenderness and juiciness, the two major components of beef texture, for descriptive and consumer sensory data with Warner-Bratzler shear force (WBSF) and overall consumer liking have not been elucidated using US consumers recently. The objective was to use two data sets that measured consumer sensory and beef descriptive tenderness and juiciness attributes to understand relationships between consumer and trained descriptive tenderness and juiciness attributes, and Warner-Bratzler shear force (WBSF) and overall consumer liking. Data were analyzed in two sets, top loin steaks (n = 119) or beef cuts (n = 276) that included top loin steaks, tenderloin steaks, top sirloin steaks, and bottom round roasts. Average WBSF values for top loin steaks and beef cuts were 26.0 and 28.5 N, respectively. Consumer attributes were not strong predictors of WBSF. WBSF was more highly related to descriptive tenderness ratings (R2 = 0.37 for beef cuts). Overall liking was correlated to consumer attributes, most strongly to flavor liking (R2 = 0.94 for beef cuts). Descriptive and consumer juiciness ratings did not appreciably improve predictability of regression equations for either WBSF or consumer overall liking. These results indicated that using a WBSF value of 28 N or less for beef cuts would provide assurance for moderately tender beef as defined by descriptive sensory evaluation, and WBSF values between 30 and 32 N were slightly tender (as defined by descriptive sensory evaluation). Beef with WBSF values of 40 or higher were defined as slightly tough or tougher.

Machine learning-based time series models for effective CO2 emission prediction in India

China, India, and the USA are the countries with the highest energy consumption and CO2 emissions globally. As per the report of datacommons.org , CO2 emission in India is 1.80 metric tons per capita, which is harmful to living beings, so this paper presents India's detrimental CO2 emission effect with the prediction of CO2 emission for the next 10 years based on univariate time-series data from 1980 to 2019. We have used three statistical models; autoregressive-integrated moving average (ARIMA) model, seasonal autoregressive-integrated moving average with exogenous factors (SARIMAX) model, and the Holt-Winters model, two machine learning models, i.e., linear regression and random forest model and a deep learning-based long short-term memory (LSTM) model. This paper brings together a variety of models and allows us to work on data prediction. The performance analysis shows that LSTM, SARIMAX, and Holt-Winters are the three most accurate models among the six models based on nine performance metrics. Results conclude that LSTM is the best model for CO2 emission prediction with the 3.101% MAPE value, 60.635 RMSE value, 28.898 MedAE value, and along with other performance metrics. A comparative study also concludes the same. Therefore, the deep learning-based LSTM model is suggested as one of the most appropriate models for CO2 emission prediction.

Plasma metabolomics profiles in Black and White participants of the Adventist Health Study-2 cohort

BACKGROUND

Black Americans suffer disparities in risk for cardiometabolic and other chronic diseases. Findings from the Adventist Health Study-2 (AHS-2) cohort have shown associations of plant-based dietary patterns and healthy lifestyle factors with prevention of such diseases. Hence, it is likely that racial differences in metabolic profiles correlating with disparities in chronic diseases are explained largely by diet and lifestyle, besides social determinants of health.

METHODS

Untargeted plasma metabolomics screening was performed on plasma samples from 350 participants of the AHS-2, including 171 Black and 179 White participants, using ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and a global platform of 892 metabolites. Differences in metabolites or biochemical subclasses by race were analyzed using linear regression, considering various models adjusted for known confounders, dietary and/or other lifestyle behaviors, social vulnerability, and psychosocial stress. The Storey permutation approach was used to adjust for false discovery at FDR < 0.05.

RESULTS

Linear regression revealed differential abundance of over 40% of individual metabolites or biochemical subclasses when comparing Black with White participants after adjustment for false discovery (FDR < 0.05), with the vast majority showing lower abundance in Blacks. Associations were not appreciably altered with adjustment for dietary patterns and socioeconomic or psychosocial stress. Metabolite subclasses showing consistently lower abundance in Black participants included various lipids, such as lysophospholipids, phosphatidylethanolamines, monoacylglycerols, diacylglycerols, and long-chain monounsaturated fatty acids, among other subclasses or lipid categories. Among all biochemical subclasses, creatine metabolism exclusively showed higher abundance in Black participants, although among metabolites within this subclass, only creatine showed differential abundance after adjustment for glomerular filtration rate. Notable metabolites in higher abundance in Black participants included methyl and propyl paraben sulfates, piperine metabolites, and a considerable proportion of acetylated amino acids, including many previously found associated with glomerular filtration rate.

CONCLUSIONS

Differences in metabolic profiles were evident when comparing Black and White participants of the AHS-2 cohort. These differences are likely attributed in part to dietary behaviors not adequately explained by dietary pattern covariates, besides other environmental or genetic factors. Alterations in these metabolites and associated subclasses may have implications for the prevention of chronic diseases in Black Americans.

Caffeine intake is associated with less severe depressive symptoms in noncancer populations: an analysis based on NHANES 2007-2016

Depression and cancer are both prevalent diseases worldwide. Numerous cancer patients experience psychological illnesses, especially depression, following a malignancy's dismal prognosis. Although some research has suggested that caffeine may be protective against depressive symptoms, it is still unclear how caffeine and cancer patients are related. Thus, we hypothesized that moderate daily caffeine intake may reduce the risk of depression in both the cancer and noncancer populations. Data were extracted and combined from the National Health and Nutrition Examination Survey from 2007 to 2016. After controlling for potential confounding factors, interaction effects analysis was used to clarify the interaction between caffeine and cancer on depressive symptoms. Linear regression analysis and restricted cubic splines were used to further analyze the relationship between caffeine and depression in cancer and noncancer populations. A total of 24,145 participants were included in the analysis. In the noncancer population, the quartile 3 group of caffeine intake showed a negative association between caffeine intake and Patient Health Questionnaire-9 (PHQ-9) scores (β = -0.23, 95% confidence interval, -0.45 to -0.01; P = .041). No association between caffeine intake and PHQ-9 scores was observed in the cancer population. In both cancer and noncancer populations, restricted cubic splines indicated a nonlinear trend between caffeine and PHQ-9 scores, with the lowest PHQ-9 scores when caffeine intake was 119.52 mg. In the noncancer population, moderate daily caffeine intake (quartile 3 group; range, 119.5-236.5 mg) was associated with reduced depressive symptoms, whereas in the cancer population, no association was found between caffeine intake and depression.

Challenges and progresses in the detailed estimation of sediment export in agricultural watersheds in Navarra (Spain) after two decades of experience

Soil erosion is a very serious environmental problem worldwide, with agriculture considered the main source of sediment in inland waters. In order to determine the extent and importance of soil erosion in the Spanish region of Navarra, in 1995 the Government of Navarra established the Network of Experimental Agricultural Watersheds (NEAWGN), which consists of five small watersheds representative of local conditions. In each watershed, key hydrometeorological variables, including turbidity, were recorded every 10 min, and daily samples were taken to determine suspended sediment concentration. In 2006, the frequency of suspended sediment sampling was increased during hydrologically relevant events. The main objective of this study is to explore the possibility of obtaining long and accurate time series of suspended sediment concentration in the NEAWGN. To this end, simple linear regressions between sediment concentration and turbidity are proposed. In addition, supervised learning models incorporating a larger number of predictive variables are used for the same purpose. A series of indicators are proposed to objectively characterize the intensity and timing of sampling. It was not possible to obtain a satisfactory model for estimating the concentration of suspended sediment. This would be mainly due to the large temporal variability found of the physical and mineralogical characteristics of the sediment, which would be affecting the turbidity value, independently of the sediment concentration, per se. This fact would be particularly important in small river watersheds such as those of this study, and especially if their physical conditions are spatially and temporally radically disturbed by agricultural tillage and by a constant modification of the vegetation cover, as is the case in cereal basins. Our findings suggest that better results could be obtained by including in the analysis variables such as soil texture and exported sediment texture, rainfall erosivity, and the state of vegetation cover and riparian vegetation.

ƩexcessA1C index, the sum of yearly excess HbA1c values during the total period of diabetes, may have the potential to predict retinopathy by a linear regression setting regardless of duration in type 1 diabetes: a subgroup analysis of DCCT/EDIC data

Aims

To find an index of glycemic exposure that predicts retinopathy by a simple regression setting regardless of duration in type 1 diabetes which might be useful for the care of diabetes.

Materials and methods

To exclude the possible disturbing effect of metabolic memory, we examined a subgroup of patients with glycohemoglobin A1c (A1C) data for the total period of type 1 diabetes selected from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications data. Three indices-(1) mean value of yearly A1C (mA1C), (2) sum of yearly A1C values (ƩA1C), and (3) sum of yearly A1C values above 6.5% (ƩexcessA1C)-were assessed as potential candidates. Development of retinopathy was defined by ≥ 3-steps' progression of retinopathy from baseline.

Results

The areas under the receiver operating characteristics curves of the indices for development of retinopathy at years 5, 9, and 13 after the onset of diabetes were the same: 0.8481, 0.8762, and 0.8213, respectively, indicating that each index was substantially capable of predicting development of retinopathy at each timepoint. Linear regression analyses showed that each index had significant and substantial linear relations to retinopathy at each timepoint: all P < 0.0001 for slopes; contribution rate R2 = 0.21 (year 5), 0.46 (year 9), and 0.48 (year 13) for each index. But only ƩexcessA1C index appeared to have similar linear relations to retinopathy at all three timepoints (interactions by timepoint: for slopes: P = 0.1393; for intercepts: P = 0.9366).

Conclusion

ƩexcessA1C may have the potential to predict retinopathy by just one linear regression setting regardless of duration in type 1 diabetes.

Investigation of frequency components embedded in EEG recordings underlying neuronal mechanism of cognitive control and attentional functions

Attentional cognitive control regulates the perception to enhance human behaviour. The current study examines the atltentional mechanisms in terms of time and frequency of EEG signals. The cognitive load is higher for processing local attentional stimulus, thereby demanding higher response time (RT) with low response accuracy (RA). On the other hand, the global attentional mechanisms broadly promote the perception while demanding a low cognitive load with faster RT and high RA. Attentional mechanisms refer to perceptual systems that afford and allocate the adaptive behaviours for prioritizing the processing of relevant stimuli based on the local and global features. The early sensory component of C1, which was associated with the local attentional mechanism, showed higher amplitudes than the global attentional mechanisms in parieto-occipital regions. Further, the local attentional mechanisms were also sustained in N2 and P3 components increasing higher amplitude in the left and right hemispheric sides of temporal regions (T7 and T8). Theta band frequency had shown higher power spectrum density (PSD) values while processing local attentional mechanisms. However, the significance of other frequency bands was noticeably minute. Hence, integrating the attentional mechanisms in terms of ERP and frequency signatures, a hybrid custom weight allocation model (CWAM) was built to assess and predict the contribution of insignificant channels to significant ones. The CWAM model was formulated based on the computational linear regression derivatives. All the derivatives are computationally derived the significant score while channelizing the hierarchical performance of each channel with respect to the frequent and deviant occurrences of global-local stimulus. This model enables us to configure the neural dynamicity of cognitive allocation of resources within the different locations of the human brain while processing the attentional stimulus. CWAM is reported to be the first model to evaluate the performance of the non-significant channels for enhancing the response of significant channels. The findings of the CWAM model suggest that the brain's performance may be determined by the underlying contribution of the non-significant channels.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-022-09888-x.

A mathematical framework for improved weight initialization of neural networks using Lagrange multipliers

A good weight initialization is crucial to accelerate the convergence of the weights in a neural network. However, training a neural network is still time-consuming, despite recent advances in weight initialization approaches. In this paper, we propose a mathematical framework for the weight initialization in the last layer of a neural network. We first derive analytically a tight constraint on the weights that accelerates the convergence of the weights during the back-propagation algorithm. We then use linear regression and Lagrange multipliers to analytically derive the optimal initial weights and initial bias of the last layer, that minimize the initial training loss given the derived tight constraint. We also show that the restrictive assumption of traditional weight initialization algorithms that the expected value of the weights is zero is redundant for our approach. We first apply our proposed weight initialization approach to a Convolutional Neural Network that predicts the Remaining Useful Life of aircraft engines. The initial training and validation loss are relatively small, the weights do not get stuck in a local optimum, and the convergence of the weights is accelerated. We compare our approach with several benchmark strategies. Compared to the best performing state-of-the-art initialization strategy (Kaiming initialization), our approach needs 34% less epochs to reach the same validation loss. We also apply our approach to ResNets for the CIFAR-100 dataset, combined with transfer learning. Here, the initial accuracy is already at least 53%. This gives a faster weight convergence and a higher test accuracy than the benchmark strategies.

[Influencing factors on operating times for metal bar removal after Nuss repair]

BACKGROUND

Metal bar removal after the Nuss repair procedure is prone to be cancelled in cases of operating time shortages due it being suitable to be postponed without harming patients. Consequently, planning operation times as exactly as possible could be one solution.

OBJECTIVE

Statistical modelling of operation times of metal bar removal after Nuss repair using the prespecified independent predictors of age, sex, intraoperative complications, and number of implanted metal bars.

MATERIAL AND METHODS

We included all patients whose operation notes included an operation time, which was modelled via linear regression and subject to internal validation via bootstrap. Exploratory analyses also consisted of the surgeon's experience, the number of stabilizers, the body mass index, and preceding re-do surgery for bar dislocation.

RESULTS

We included 265 patients (14% ♀) with a median age of 19 years (interquartile range 17-20 years), of whom 81% had 1 and 17% had 2 metal bars removed. The prespecified regression model was statistically significant (likelihood ratio 56; df = 5; P < 0.001) and had a bias corrected R2 of 0.148. Patient age influenced operation times by 2.1min per year of life (95% confidence interval 1.3-2.9min; P < 0.001) and 16min per explanted metal bar (95% confidence interval: 10-22min; P < 0.001).

CONCLUSION

The patient-specific factors of age and the number of explanted metal bars influenced the operation times and can be included into scheduling operation times.

Factors Predicting the Health Status of Women with Ovarian Cancer During Five Treatment Phases

OBJECTIVE

The combined impact of disease status and treatment phase on the quality of life (QoL) of women with ovarian cancer has not been fully considered. Therefore, this clinical, epidemiologic study compared the QoL of patients with ovarian cancer between five different treatment phases and identified the factors predicting their QoL through multivariate modeling.

DATA SOURCES

This study had a cross-sectional survey design. The participants total of 183 were recruited from the inpatient and outpatient departments of the medical center in northern Taiwan. QoL was measured using the Quality of Life Scales QLQ-C30 and QLQ-OV28 and the Pittsburgh Sleep Quality Index. The patient's clinical characteristics data were obtained from the databank of the Taiwan Gynecologic Cancer Network, a registry of active patients being treated with gynecologic cancer.

CONCLUSION

Chemotherapeutic agents were the major predictors of poor global health status in patients with ovarian cancer. However, good sleep was beneficial to patients' QoL. The study results can be used as a reference to adjust oncological treatment regimens for more effective symptom management and to promote patient education to improve patients' QoL.

IMPLICATIONS FOR NURSING PRACTICE

The predicting factors can be considered by physicians and nurses to adjust treatment regimens and enhance patient education.

Exploring vegetation trends and restoration possibilities in Pakistan by using Hurst exponent

Vegetation cover change and its interaction with climate are significant to study as it has impact on ecosystem stability. We used the Normalized Difference Vegetation Index (NDVI) and climatic factors (temperature and rainfall) for investigating the relationship between vegetation and climate. We also traced spatiotemporal changes in the vegetation in Pakistan from 2000 to 2020; we used the Hurst exponent to estimate future vegetation trends in Pakistan. Our results show an increase in vegetation throughout Pakistan, and the Punjab Province is showing the highest significant vegetation trend at 88.51%. Our findings reveal that the response of vegetation to climate change varies by region and is influenced by local climatic conditions. However, the relationship between rainfall and annual NDVI is stronger than the temperature in the study area-Pakistan. The Hurst exponent value is above 0.5 in all four provinces, that is, the indication of consistent vegetation trends in the future. The highest values are observed in Punjab and Khyber Pakhtunkhwa (KPK). In the Punjab Province, 88.41% of the area showed positive development, with forests in particular showing a significant positive effect on land use classes. On the other hand, the Sindh Province has the highest negative result at 2.87%, with urban areas showing the highest negative development. To sum up, the NDVI pattern and change attribute suggest vegetation restoration in Pakistan.

Investigating changes in travel behavior over time in response to the COVID-19 pandemic

The COVID-19 pandemic has significantly affected travel behavior, including the frequency and mode of travel, with the magnitude and nature of these effects varying over time. This study investigates the nature of these relationships by examining changes in various measures of travel behavior, including weekly driving hours, as well as the frequency of telecommuting, use of ride-sharing services, travel for medical purposes, and use of food delivery services. Self-reported travel data from a representative statewide survey of Michigan residents were used to assess changes in these metrics during the early stages of the pandemic, as well as one year thereafter. Random effects linear regression and ordered logit regression models were estimated and the findings show that various changes in behavior had long-lasting effects, while other behaviors generally reverted back toward pre-pandemic levels. In addition, these changes were found to vary across individuals. For example, significant differences were observed based on socio-demographic characteristics, between urban and rural areas, and amongst individuals with differing views on COVID-19 and related government interventions. In general, the pandemic tended to have less pronounced and sustained effects among younger adults as compared to older age groups. Further, those individuals who were opposed to mandatory COVID-19 vaccines were less likely to change their travel behavior, during both the early and latter stages of the pandemic. Changes were observed consistently across most of the travel metrics of interest. Among these, overall driving hours, travel for medical purposes, and ride-sharing were still lower during the latter stages of the pandemic, while telecommuting and the use of food delivery services reverted nearer to pre-pandemic levels.

Limited sampling strategies for therapeutic drug monitoring of anti-tuberculosis medications: A systematic review of their feasibility and clinical utility

Therapeutic drug monitoring (TDM) is recommended for medications with high inter-individual variability, narrow therapeutic index drugs, possible drug-drug interactions, drug toxicity, and subtherapeutic concentrations, as well as to assess noncompliance. The area under the plasma concentration-time curve (AUC) is a significant pharmacokinetic parameter since it calculates the drug's total systematic exposure in the body. However, multiple blood samples from the patient are required to calculate the area under the curve, which is inconvenient for both the patient and the healthcare professional. To alleviate the issue, the limited sampling strategy (LSS) was devised, in which sampling is minimized while obtaining complete and precise findings to anticipate the area under the curve. One can reduce costs, labor, and discomfort for patients and healthcare workers by applying this limited sampling strategy. This article examines a systematic evaluation of all the limited sampling done in anti-tuberculosis (anti-TB) medications resulting from the literature search of several research papers. This article also briefly describes the two methodologies: Multiple regression analysis (MRA) and the Bayesian approach used to develop a limited sampling strategy model. Anti-TB medications have been found to have considerable inter-individual variability, and isoniazid has a narrow therapeutic index, both of which are criteria for therapeutic drug monitoring. To avoid multi-drug resistance and therapy failure, it is proposed that limited sampling strategy-based therapeutic drug monitoring of anti-TB medications be undertaken to generate an individualized dose regimen, particularly for individuals at high risk of treatment failure or delayed response.

Formant-Aware Spectral Analysis of Sustained Vowels of Pathological Breathy Voice

OBJECTIVES

This paper reports the effectiveness of formant-aware spectral parameters to predict the perceptual breathiness rating. A breathy voice has a steeper spectral slope and higher turbulent noise than a normal voice. Measuring spectral parameters of acoustic signals over lower formant regions is a known approach to capture the properties related to breathiness. This study examines this approach by testing the contemporary spectral parameters and algorithms within the framework, alternate frequency band designs, and vowel effects.

METHODS

Sustained vowel recordings (/a/, /i/, and /u/) of speakers with voice disorders in the German Saarbrueken Voice Database were considered (n: 367). Recordings with signal irregularities, such as subharmonics or with roughness perception, were excluded from the study. Four speech language pathologists perceptually rated the recordings for breathiness on a 100-point scale, and their averages were used in the analysis. The acoustic spectra were segmented into four frequency bands according to the vowel formant structures. Five spectral parameters (intraband harmonics-to-noise ratio, HNR; interband harmonics ratio, HHR; interband noise ratio, NNR; and interband glottal-to-noise energy, GNE, ratio) were evaluated in each band to predict the perceptual breathiness rating. Four HNR algorithms were tested.

RESULTS

Multiple linear regression models of spectral parameters, led by the HNRs, were shown to explain up to 85% of the variance in perceptual breathiness ratings. This performance exceeded that of the acoustic breathiness index (82%). Individually, the HNR over the first two formants best explained the variances in the breathiness (78%), exceeding the smoothed cepstrum peak prominence (74%). The performance of HNR was highly algorithm dependent (10% spread). Some vowel effects were observed in the perceptual rating (higher for /u/), predictability (5% lower for /u/), and model parameter selections.

CONCLUSIONS

Strong per-vowel breathiness acoustic models were found by segmenting the spectrum to isolate the portion most affected by breathiness.

Introduction to the Use of Linear and Nonlinear Regression Analysis in Quantitative Biological Assays

Biological assays are essential tools in biomedical and pharmaceutical research. In simplest terms, such an assay is an analytical method used to measure or predict a response in a biological system in the presence of a given stimulus (e.g., drug). The inherent complexity involved in evaluating a biological system requires the use of rigorous and appropriate tools for data analysis. Linear and nonlinear regression models represent critically important statistical analyses used to define the relationships between variables of interest in biological systems. Recent challenges relating to the reproducibility of published data suggest the absence of standardized and routine use of statistics to support experimental results across a wide range of scientific disciplines. The current situation warrants an introductory review of basic regression concepts using current, practical examples, along with references to in-depth resources. The goal is to provide the necessary information to help standardize the analysis of biological assays in academic research and drug discovery and development, elevating their utility and increasing data transparency and reproducibility. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC.

Predicting Success: An Examination of the Predictive Validity of a Measure of Motivational-Developmental Dimensions in College Admissions

Amid the COVID-19 pandemic, an unprecedented number of higher education institutions adopted test-optional admissions policies. The proliferation of these policies and the criticism of standardized admissions tests as unreliable predictors of applicants' postsecondary educational promise have prompted the reimagining of evaluative methodologies in college admissions. However, few institutions have designed and implemented new measures of applicants' potential for success, rather opting to redistribute the weight given to other variables such as high school course grades and high school GPA. We use multiple regression to investigate the predictive validity of a measure of non-cognitive, motivational-developmental dimensions implemented as part of a test-optional admissions policy at a large urban research university in the United States. The measure, composed of four short-answer essay questions, was developed based on the social-cognitive motivational and developmental-constructivist perspectives. Our findings suggest that scores derived from the measure make a statistically significant but small contribution to the prediction of undergraduate GPA and 4-year bachelor's degree completion. We also find that the measure does not make a statistically significant nor practical contribution to the prediction of 5-year graduation.

NASA POWER satellite meteorological system is a good tool for obtaining estimates of the temperature-humidity index under Brazilian conditions compared to INMET weather stations data

Heat stress negatively affects livestock, with undesirable effects on animals' production and reproduction. Temperature and humidity index (THI) is a climatic variable used worldwide to study the effect of heat stress on farm animals. Temperature and humidity data can be obtained in Brazil through the National Institute of Meteorology (INMET), but complete data may not be available due to temporary failures on weather stations. An alternative to obtaining meteorological data is the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER) satellite-based weather system. We aimed to compare THI estimates obtained from INMET weather stations and NASA POWER meteorological information sources using Pearson correlation and linear regression. After quality check, data from 489 INMET weather stations were used. The hourly, average daily and maximum daily THI were evaluated. We found greater correlations and better regression evaluation metrics when average daily THI values were considered, followed by maximum daily THI, and hourly THI. NASA POWER satellite-based weather system is a suitable tool for obtaining the average and maximum THI values using information collected from Brazil, showing high correlations with THI estimates from INMET and good regression evaluation metrics, and can assist studies that aim to analyze the impact of heat stress on livestock production in Brazil, providing additional data to complement the existing information available in the INMET database.

Prediction of postoperative lung function in lung cancer patients using machine learning models

Background

: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models.

Methods

We extracted data from the Clinical Data Warehouse (CDW) and developed 3 sets: set Ⅰ, the linear regression model; set Ⅱ, machine learning models omitting the missing data: and set Ⅲ, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in one second (FEV1) measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set Ⅲ. Predictive performance was evaluated by R2 and mean squared error (MSE) in the 3 sets.

Results

A total of 1,487 patients were included in sets Ⅰ and Ⅲ and 896 patients were included in set Ⅱ. In set Ⅰ, the R2 value was 0.27 and in set Ⅱ, LightGBM was the best model with the highest R2 value of 0.5 and the lowest MSE of 154.95. In set Ⅲ, LightGBM was the best model with the highest R2 value of 0.56 and the lowest MSE of 174.07.

Conclusion

The LightGBM model showed the best performance in predicting postoperative lung function.

Pulmonary function in a cohort of heart-healthy individuals from Northern Sweden-a comparison with discordant reference values

BACKGROUND

Dynamic spirometry is an important investigation to differentiate between impaired and normal lung function. This study aimed to evaluate the results of lung function testing in a cohort of subjects from Northern Sweden without any known heart or pulmonary disease. Our focus was to compare with two reference materials that have showed differences in the age-dependency of lung function in Swedish subjects.

METHODS

The study population consisted of 285 healthy adults (148 males, 52%) between 20-90 years of age. The subjects had been randomly selected from the population register for inclusion in a study investigating cardiac function in heart-healthy subjects, but were also assessed with dynamic spirometry. At least seven percent reported smoking. Sixteen subjects presented with pulmonary functional impairments and were excluded from the current study. The sex-specific age-dependency in lung volumes was estimated using the LMS model, where non-linear equations were derived for the mean value (M), the location (L) or skewness, and the scatter (S) or coefficient of variation. This model of the observed lung function data was compared with reference values given by the original LMS model published by the Global Lung Initiative (GLI), and with the model from the recent Obstructive Lung Disease In Norrbotten (OLIN) study, where higher reference values were presented for Swedish subjects than those given by the GLI model.

RESULTS

No differences were found in the age-dependency of pulmonary function between the LMS model developed in the study and the OLIN model. Although the study group included smokers, the original GLI reference values suggested significantly lower normal values of FEV₁ (forced expiratory volume) and FVC (forced vital capacity), and consequently fewer subjects below the lower limit of normality, than both the rederived LMS and OLIN models.

CONCLUSIONS

Our results are in line with previous reports and support that the original GLI reference values underestimate pulmonary function in the adult Swedish population. This underestimation could be reduced by updating the coefficients in the underlying LMS model based on a larger cohort of Swedish citizens than was available in this study.

A new metric for characterizing limb loading dynamics in post anterior cruciate ligament reconstruction individuals

BACKGROUND

Unresolved neuromuscular deficits often persist in post-anterior cruciate ligament reconstruction (ACLR) individuals manifesting as altered impact and active peak force production during running that can contribute to detrimental limb loading. Elevated impact and active peaks are common in pathological populations indicating a stiffer limb loading strategy. Although impact and active peaks are sensitive to changes in limb loading, to our knowledge, there are no established, standardized measures or cutoff criteria to differentiate between healthy and pathological limb loading. However, prior studies have demonstrated that the ratio between traditional biomechanical measures can be used to successfully establish quantifiable and graphical ranges to delineate between healthy and pathological movement.

RESEARCH QUESTION

Therefore, this study sought to exploit the impact-to-active peak ratio to generate a new, standardized metric to quantify and characterize limb loading dynamics in healthy controls and post-ACLR individuals during running.

METHODS

Twenty-eight post-ACLR individuals and 18 healthy controls performed a running protocol. Impact peak and active peak data were extracted from their strides as they ran at a self-selected speed. A linear regression model was fit to the healthy control data and the models 95 % prediction intervals were used to define a boundary region of healthy limb loading dynamics.

RESULTS

The post-ACLR individuals produced a higher impact-to-active peak ratio than the healthy controls indicating that they adopted a stiffer limb loading strategy. The boundary regions derived from the impact and active peak model successfully classified the healthy controls and post-ACLR individual's limb loading dynamics with an accuracy, sensitivity, and specificity of 89 %, 100 %, and 75 %, respectively.

SIGNIFICANCE

The ability to effectively evaluate limb loading dynamics using impact and active peaks can provide clinicians with a new, non-invasive metric to quantify and characterize healthy and pathological movement in a clinical setting.

Identification of the joint line in revision total knee arthroplasty using a multiple linear regression model: a cadaveric study

INTRODUCTION

The results of revision total knee arthroplasty (rTKA) may be compromised by excessive joint line (JL) elevation. It is critical but challenging in reestablishing the JL in rTKA. Previous studies have confirmed that, biomechanically and clinically, JL elevation should not exceed 4 mm. Image-based studies described several approaches to locate the JL intraoperatively, however magnification errors could occur. In this cadaveric study, we aim to define an accurate and reliable method to determine the JL.

MATERIALS AND METHODS

Thirteen male and eleven female cadavers were used, with an average age of death being 48.3 years. The transepicondylar width (TEW), the distance from the medial (MEJL) and lateral (LEJL) epicondyle, adductor tubercle (ATJL), fibular head (FHJL) and tibial tubercle (TTJL) to the JL were measured in 48 knees. Intra- and interobserver reliability and validity were tested prior to any additional analysis. Pearson correlation and linear regression analysis were used to examine the correlations between landmark-JL distances (LEJL, MEJL, ATJL, FHJL and TTJL) and the TEW, and to further derive models for intraoperative JL determination. The accuracy of different models, quantified by errors between estimated and measured landmark-JL distances, was compared using the Friedman and post hoc Dunn tests.

RESULTS

The intra- and inter-observer measurements for TEW, MEJL, LEJL, ATJL, TTJL and FHJL did not differ significantly (p > 0.05). Between genders, significant differences were found on TEW, MEJL, LEJL, ATJL, FHJL and TTJL (p < 0.05). There was no association between TEW and either FHJL or TTJL (p > 0.05), while ATJL, MEJL, and LEJL were found to be correlated with TEW (p < 0.05). Six models were derived: (1) MEJL = 0.37*TEW (r = 0.384), (2) LEJL = 0.28*TEW (r = 0.380), (3) ATJL = 0.47*TEW (r = 0.608), (4) MEJL = 0.413*TEW - 4.197 (R² = 0.473), (5) LEJL = 0.236*TEW + 3.373 (R² = 0.326), (6) ATJL = 0.455*TEW + 1.440 (R² = 0.556). Errors were defined as deviations between estimated and actual landmark-JL distances. The mean absolute value of the errors, created by Model 1-6 was 3.18 ± 2.25, 2.53 ± 2.15, 2.64 ± 2.2, 1.85 ± 1.61, 1.60 ± 1.59 and 1.71 ± 1.5, respectively. The error could be limited to 4 mm in 72.9%, 83.3%, 72.9%, 87.5%, 87.5%, and 93.8% of the cases by referencing Model 1-6, respectively.

CONCLUSION

Compared to previous image-based measurements, the current cadaveric study most closely resembles a realistic view of intraoperative settings and could circumvents magnification errors. We recommend using Model 6, the JL can be best estimated by referencing the AT and the ATJL can be calculated as ATJL (mm) = 0.455*TEW (mm) + 1.440 (mm).

The effect of dichotomization of skewed adjustment covariates in the analysis of clinical trials

Baseline imbalance in covariates associated with the primary outcome in clinical trials leads to bias in the reporting of results. Standard practice is to mitigate that bias by stratifying by those covariates in the randomization. Additionally, for continuously valued outcome variables, precision of estimates can be (and should be) improved by controlling for those covariates in analysis. Continuously valued covariates are commonly thresholded for the purpose of performing stratified randomization, with participants being allocated to arms such that balance between arms is achieved within each stratum. Often the thresholding consists of a simple dichotomization. For simplicity, it is also common practice to dichotomize the covariate when controlling for it at the analysis stage. This latter dichotomization is unnecessary, and has been shown in the literature to result in a loss of precision when compared with controlling for the covariate in its raw, continuous form. Analytic approaches to quantifying the magnitude of the loss of precision are generally confined to the most convenient case of a normally distributed covariate. This work generalises earlier findings, examining the effect on treatment effect estimation of dichotomizing skew-normal covariates, which are characteristic of a far wider range of real-world scenarios than their normal equivalents.

Comparative study of lumbar bone mineral content using DXA and CT Hounsfield unit values in chest CT

BACKGROUND

Bone mineral content (BMC) values in certain bones and changes in BMC over time are key features for diagnosing osteoporosis. This study examined those features using morphometric texture analysis in chest computational tomography (CT) by comparing a dual-energy X-ray absorptiometry (DXA)-based BMC. An accessible approach for screening osteoporosis was suggested by accessing BMC using only Hounsfield units (HU).

METHODOLOGY

The study included a total of 510 cases (255 patients) acquired between May 6, 2012, and June 30, 2020, at a single institution. Two cases were associated with two chest CT scans from one patient with a scan interval of over two years, and each scan was followed soon after by a DXA scan. Axial cuts of the first lumbar vertebra in CT and DXA-based L1 BMC values were corrected for each case. The maximum trabecular area was selected from the L1 spine body, and 45 texture features were extracted from the region using gray-level co-occurrence matrices. A regression model was employed to estimate the absolute BMC value in each case using 45 features. Also, an additional regression model was used to estimate the change in BMC between two scans for each patient using 90 features from the corresponding cases.

RESULTS

The correlation coefficient (CC) and mean absolute error (MAE) between estimates and DXA references were obtained for the evaluation of regressors. In the case of the BMC estimation, CC and MAE were 0.754 and 1.641 (g). In the case of the estimation of change in BMC, CC and MAE were 0.680 and 0.528 (g).

CONCLUSION

The modality using morphometric texture analysis with CT HUs can indirectly help screening osteoporosis because it provides estimates of BMC and BMC change that show moderate positive correlations with DXA measures.

An Application of Machine Learning to Logistics Performance Prediction: An Economics Attribute-Based of Collective Instance

In this work, a machine learning application was constructed to predict the logistics performance index based on economic attributes. The prediction procedure employs both linear and non-linear machine learning algorithms. The macroeconomic panel dataset is used in this investigation. Furthermore, it was combined with the microeconomic panel dataset obtained through the data envelopment analysis method for evaluating financial efficiency. The procedure was implemented in six ASEAN member countries. The non-linear algorithm of an artificial neural network performed best on the complex pattern of a collective instance of these six countries, followed by the penalized linear of the Ridge regression method. Due to the limited amount of training data for each country, the artificial neural network prediction procedure is only applicable to the datasets of Singapore, Malaysia, and the Philippines. Ridge regression fits the Indonesia, Thailand and Vietnam datasets. The results provide precise trend forecasting. Macroeconomic factors are driving up the logistics performance index in Vietnam in 2020. Malaysia logistics performance is influenced by the logistics business's financial efficiency. The results at the country level can be used to track, improve, and reform the country's short-term logistics and supply chain policies. This can bring significant gains in national logistics and supply chain capabilities, as well as support for global trade collaboration, all for the long-term development of the region.

Wrist autonomy based on upper-limb synergy: a pilot study

Incorporating an electrically powered wrist can largely improve the dexterity of a prosthetic hand when grasping various objects; however, it also intensifies the difficulty of the hand's operation due to the introduction of extra degrees of freedom (DOFs). The mechanism of multi-joint synergy in human body movements provides a new sight to solve this problem. In this paper, focusing on four typical manipulation activities of daily life (ADLs), 10 upper-limb joint angles were collected and analyzed first to verify the existence of synergy. Then, a linear regression model was established to predict the wrist rotation angle from the shoulder and elbow joints, which can be directly used as a control reference for achieving wrist autonomy. For both healthy and amputee subjects, experimental platforms were established and control tests were conducted, wherein the task completion time and compensatory movement during the four ADLs were evaluated. The results show that our synergy-based wrist autonomy method can effectively improve the completion efficiency of multiple ADLs without increasing the control complexity. Also, it can significantly reduce the compensatory movements of multiple joints compared to traditional prostheses using an idle wrist.

Replicability of proton MR spectroscopic imaging findings in mild traumatic brain injury: Implications for clinical applications

PURPOSE

Proton magnetic resonance spectroscopy (1H MRS) offers biomarkers of metabolic damage after mild traumatic brain injury (mTBI), but a lack of replicability studies hampers clinical translation. In a conceptual replication study design, the results reported in four previous publications were used as the hypotheses (H1-H7), specifically: abnormalities in patients are diffuse (H1), confined to white matter (WM) (H2), comprise low N-acetyl-aspartate (NAA) levels and normal choline (Cho), creatine (Cr) and myo-inositol (mI) (H3), and correlate with clinical outcome (H4); additionally, a lack of findings in regional subcortical WM (H5) and deep gray matter (GM) structures (H6), except for higher mI in patients' putamen (H7).

METHODS

26 mTBI patients (20 female, age 36.5 ± 12.5 [mean ± standard deviation] years), within two months from injury and 21 age-, sex-, and education-matched healthy controls were scanned at 3 Tesla with 3D echo-planar spectroscopic imaging. To test H1-H3, global analysis using linear regression was used to obtain metabolite levels of GM and WM in each brain lobe. For H4, patients were stratified into non-recovered and recovered subgroups using the Glasgow Outcome Scale Extended. To test H5-H7, regional analysis using spectral averaging estimated metabolite levels in four GM and six WM structures segmented from T1-weighted MRI. The Mann-Whitney U test and weighted least squares analysis of covariance were used to examine mean group differences in metabolite levels between all patients and all controls (H1-H3, H5-H7), and between recovered and non-recovered patients and their respectively matched controls (H4). Replicability was defined as the support or failure to support the null hypotheses in accordance with the content of H1-H7, and was further evaluated using percent differences, coefficients of variation, and effect size (Cohen's d).

RESULTS

Patients' occipital lobe WM Cho and Cr levels were 6.0% and 4.6% higher than controls', respectively (Cho, d = 0.37, p = 0.04; Cr, d = 0.63, p = 0.03). The same findings, i.e., higher patients' occipital lobe WM Cho and Cr (both p = 0.01), but with larger percent differences (Cho, 8.6%; Cr, 6.3%) and effect sizes (Cho, d = 0.52; Cr, d = 0.88) were found in the comparison of non-recovered patients to their matched controls. For the lobar WM Cho and Cr comparisons without statistical significance (frontal, parietal, temporal), unidirectional effect sizes were observed (Cho, d = 0.07 - 0.37; Cr, d = 0.27 - 0.63). No differences were found in any metabolite in any lobe in the comparison between recovered patients and their matched controls. In the regional analyses, no differences in metabolite levels were found in any GM or WM region, but all WM regions (posterior, frontal, corona radiata, and the genu, body, and splenium of the corpus callosum) exhibited unidirectional effect sizes for Cho and Cr (Cho, d = 0.03 - 0.34; Cr, d = 0.16 - 0.51).

CONCLUSIONS

We replicated findings of diffuse WM injury, which correlated with clinical outcome (supporting H1-H2, H4). These findings, however, were among the glial markers Cho and Cr, not the neuronal marker NAA (not supporting H3). No differences were found in regional GM and WM metabolite levels (supporting H5-H6), nor in putaminal mI (not supporting H7). Unidirectional effect sizes of higher patients' Cho and Cr within all WM analyses suggest widespread injury, and are in line with the conclusion from the previous publications, i.e., that detection of WM injury may be more dependent upon sensitivity of the 1H MRS technique than on the selection of specific regions. The findings lend further support to the corollary that clinic-ready 1H MRS biomarkers for mTBI may best be achieved by using high signal-to-noise-ratio single-voxels placed anywhere within WM. The biochemical signature of the injury, however, may differ and therefore absolute levels, rather than ratios may be preferred. Future replication efforts should further test the generalizability of these findings.

Improving the accuracy of beef cattle methane inventories in Latin America and Caribbean countries

On-farm methane (CH₄) emissions need to be estimated accurately so that the mitigation effect of recommended practices can be accounted for. In the present study prediction equations for enteric CH₄ have been developed in lieu of expensive animal measurement approaches. Our objectives were to: (1) compile a dataset from individual beef cattle data for the Latin America and Caribbean (LAC) region; (2) determine main predictors of CH₄ emission variables; (3) develop and cross-validate prediction models according to dietary forage content (DFC); and (4) compare the predictive ability of these newly-developed models with extant equations reported in literature, including those currently used for CH₄ inventories in LAC countries. After outlier's screening, 1100 beef cattle observations from 55 studies were kept in the final dataset (∼ 50 % of the original dataset). Mixed-effects models were fitted with a random effect of study. The whole dataset was split according to DFC into a subset for all-forage (DFC = 100 %), high-forage (94 % ≥ DFC ≥ 54 %), and low-forage (50 % ≥ DFC) diets. Feed intake and average daily gain (ADG) were the main predictors of CH₄ emission (g d^-1), whereas this was feeding level [dry matter intake (DMI) as % of body weight] for CH₄ yield (g kg^-1 DMI). The newly-developed models were more accurate than IPCC Tier 2 equations for all subsets. Simple and multiple regression models including ADG were accurate and a feasible option to predict CH₄ emission when data on feed intake are not available. Methane yield was not well predicted by any extant equation in contrast to the newly-developed models. The present study delivered new models that may be alternatives for the IPCC Tier 2 equations to improve CH₄ prediction for beef cattle in inventories of LAC countries based either on more or less readily available data.

Seasonal agricultural workers' personal well-being and preventive behaviors about Covid- 19 in Turkey

BACKGROUND

Seasonal agricultural workers working and living in inappropriate sanitary conditions are at great risk for public health. This study aimed to determine the relationships between the sociodemographic variables and life satisfaction of seasonal agricultural workers, and their knowledge, risk perception, and protective behaviors about the COVID-19 pandemic.

METHODS

This is a cross-sectional study, that included agricultural workers who are 18 years of age or older and worked seasonally in Yozgat, Turkey, during the period between August 2020 and October 2020. The well-being level was measured using the Personal Wellbeing Index-Adult form (PWIA). The data were collected using the face-to-face survey method and with 739 workers who voluntarily participated in the research.

RESULTS

All participants disclosed having insufficient information about Covid-19 and indicated their peers and television as their sources of information. The vast majority of the workers stated that they complied with the mask mandates, social distancing, and hand hygiene. No correlations were found between knowledge, attitudes, and behaviors about Covid-19 and the level of wellbeing. The mean PWIA score of the workers was low (53.7) while they were mostly satisfied with their personal relationships (96.6) and health (76.1). The multivariable linear regression analysis revealed that being male (β = 0.245) and not having an ongoing health issue (β = 0.689) were associated with more PWIA; on the other hand, having more children (β = -0.52) was related to less PWIA.

CONCLUSIONS

The well-being level of seasonal workers was lower while it was not associated with knowledge, attitudes, and behaviors about Covid-19.

Multiple linear regression model for improving accuracy of capsulorhexis size calculation in femtosecond laser-assisted cataract surgery for adults: a retrospective single-center study

BACKGROUND

Differences between programmed capsulorhexis diameter and actual resulting capsulorhexis diameter (ARCD) are commonly encountered in femtosecond laser-assisted cataract surgery (FLACS). The purpose of this study was to identify the preoperative ophthalmic variables influencing capsulorhexis diameter index (CDI) in FLACS for adults and create a multiple linear regression model for obtaining a more accurate capsulorhexis diameter.

METHODS

This retrospective study involved sixty-seven eyes of 44 patients who received FLACS and intraocular lens implantation. The ARCD was measured using anterior segment swept-source optical coherence tomography (CASIA 2). Keratometry (K1, K2 and average K), anterior chamber depth (ACD), lens thickness (LT), anterior chamber width (ACW), white-to-white (WTW), curvature radius of anterior lens capsule (Front R) and axial length (AL) were all measured preoperatively. Based on the derived data, LT/ACW, LT/AL, LT/ACD and LT/ACW/Front R were calculated. The ratio of the programmed capsulorhexis diameter and ARCD was defined as the CDI. Correlation analysis was conducted to examine the relationship between preoperative variables listed above and the CDI. Multiple linear regression analysis was applied to select the most influential preoperative variables on CDI.

RESULTS

ACD, LT, ACW, Front R, AL, LT/ACW, LT/AL, LT/ACD, and LT/ACW/Front R showed significant correlation with CDI. Front R and LT/ACW/Front R were selected as constants in the multiple linear regression model using stepwise variable selection. The following equation represents the multiple linear regression model: CDI = 1.306-4.516 × LT/ACW/FrontR-0.011 × Front R, when P < 0.0001, adjusted R-squared = 0.919, variance inflation factor = 8.389, and Durbin-Watson ratio = 1.846. Predicted postoperative capsulorhexis diameter (PPCD) equation was created based on CDI equation as follows: PPCD = programmed capsulorhexis diameter × 1.306-4.516 × LT/ACW/FrontR-0.011 × Front R.

CONCLUSION

Front R and LT/ACW/Front R were found to be the most significant influencing factors of capsulorhexis size. CDI and PPCD calculation equations presented in this study may be useful in setting up more accurate programmed capsulorhexis diameter for FLACS in adults, resulting in a precise ARCD.

Empirical evidence of effects of stringency amid Covid-19 pandemic spread

The objective of this paper is to provide an insight on effect of stringency in Covid-19 spread in India especially in Chennai, a city were more lockdown, and restrictions was imposed to control the infection. Even though the restriction was imposed in the country by the end of March 2020, the growth reduction was seen in the mid of June as the awareness was increased. The average Covid-19 case growth was got reduce from 3.43 to 2.62% by July mid. To analysis the impact of stringency, a detailed analysis was done on Chennai city which was imposed with more repeated lockdowns to flatten the curve. We tried to fit a regression line with three difference scenario of data. The results show a promising R-squared and p value, with a right skewed distribution normal probability plot. The impact of lockdown in people's lives in different sectors were also discussed in this paper.

Model-based estimation of the leaf area of ozone-indicator tobacco (Nicotiana tabacum L.) plants under ambient ozone conditions

Bel-W3 is an ozone-sensitive tobacco (Nicotiana tabacum L.) cultivar widely used worldwide for ozone biomonitoring. Despite its extensive use, there is no comprehensive predictive model to non-destructively estimate the leaf area using only a common ruler, yet leaf area is a major evaluative trait in plants under ozone stress and of economic value in tobacco plants. In this method, we aimed at developing a predictive model to estimate leaf area using the product between leaf length and leaf width. To this end, we conducted a field experiment with ground-grown Bel-W3 plants treated with different solutions under ambient ozone conditions. The solutions were water, the antiozonant ethylenediurea (EDU; 500 ppm), and the antitranspirant pinolene (Vapor Gard; 1%, 5%, 10%). The chemical treatments were introduced to enhance leaves pool and capture different conditions that can occur in ozone biomonitoring projects.•A simple linear predictive model was developed and validated using data from a previous chamber experiment with small seedlings.•Overestimation of the model led to the integration of data from both experiments and development of another simple linear predictive model.•This integrated model provides improved estimation of leaf area and can be used for representative estimation of the area of Bel-W3 leaves of any sizes.

Robust Prediction and Protein Selection with Adaptive PENSE

Adaptive PENSE is a method that can be used to build models for predicting clinical outcomes from a small subset of a potentially large number of candidate proteins. Adaptive PENSE is designed to give reliable results under two common challenges often encountered in these kinds of studies: (1) the number of samples with known clinical outcome and proteomic data is small, while the number of candidate proteins is large and/or (2) proteomic data and the clinical outcome measurements suffer from data quality issues in a small fraction of samples. Even in the presence of these challenges, adaptive PENSE reliably identifies proteins relevant for prediction and estimates accurate predictive models. Adaptive PENSE is designed to be resilient to data quality issues in up to 50% of samples. Almost half of the samples could have aberrant values in the measured protein levels and clinical outcome values without causing severe detrimental effects to the estimated predictive model. The method is implemented as an R package and supports the user in the model selection process by automating most steps and providing diagnostic visualizations to guide the user. Users can choose among several predictive models to select the model with high prediction accuracy and an appropriate number of selected proteins.