Screening biomarkers for spinal cord injury using weighted gene co-expression network analysis and machine learning

JOURNAL/nrgr/04.03/01300535-202412000-00028/figure1/v/2024-04-08T165401Z/r/image-tiff Immune changes and inflammatory responses have been identified as central events in the pathological process of spinal cord injury. They can greatly affect nerve regeneration and functional recovery. However, there is still limited understanding of the peripheral immune inflammatory response in spinal cord injury. In this study, we obtained microRNA expression profiles from the peripheral blood of patients with spinal cord injury using high-throughput sequencing. We also obtained the mRNA expression profile of spinal cord injury patients from the Gene Expression Omnibus (GEO) database (GSE151371). We identified 54 differentially expressed microRNAs and 1656 differentially expressed genes using bioinformatics approaches. Functional enrichment analysis revealed that various common immune and inflammation-related signaling pathways, such as neutrophil extracellular trap formation pathway, T cell receptor signaling pathway, and nuclear factor-κB signal pathway, were abnormally activated or inhibited in spinal cord injury patient samples. We applied an integrated strategy that combines weighted gene co-expression network analysis, LASSO logistic regression, and SVM-RFE algorithm and identified three biomarkers associated with spinal cord injury: ANO10, BST1, and ZFP36L2. We verified the expression levels and diagnostic performance of these three genes in the original training dataset and clinical samples through the receiver operating characteristic curve. Quantitative polymerase chain reaction results showed that ANO10 and BST1 mRNA levels were increased and ZFP36L2 mRNA was decreased in the peripheral blood of spinal cord injury patients. We also constructed a small RNA-mRNA interaction network using Cytoscape. Additionally, we evaluated the proportion of 22 types of immune cells in the peripheral blood of spinal cord injury patients using the CIBERSORT tool. The proportions of naïve B cells, plasma cells, monocytes, and neutrophils were increased while the proportions of memory B cells, CD8+ T cells, resting natural killer cells, resting dendritic cells, and eosinophils were markedly decreased in spinal cord injury patients increased compared with healthy subjects, and ANO10, BST1 and ZFP26L2 were closely related to the proportion of certain immune cell types. The findings from this study provide new directions for the development of treatment strategies related to immune inflammation in spinal cord injury and suggest that ANO10, BST1, and ZFP36L2 are potential biomarkers for spinal cord injury. The study was registered in the Chinese Clinical Trial Registry (registration No. ChiCTR2200066985, December 12, 2022).

VIBES: A consensus subtyping of the vaginal microbiota reveals novel classification criteria

This study aimed to develop a robust classification scheme for stratifying patients based on vaginal microbiome. By employing consensus clustering analysis, we identified four distinct clusters using a cohort that includes individuals diagnosed with Bacterial Vaginosis (BV) as well as control participants, each characterized by unique patterns of microbiome species abundances. Notably, the consistent distribution of these clusters was observed across multiple external cohorts, such as SRA022855, SRA051298, PRJNA208535, PRJNA797778, and PRJNA302078 obtained from public repositories, demonstrating the generalizability of our findings. We further trained an elastic net model to predict these clusters, and its performance was evaluated in various external cohorts. Moreover, we developed VIBES, a user-friendly R package that encapsulates the model for convenient implementation and enables easy predictions on new data. Remarkably, we explored the applicability of this new classification scheme in providing valuable insights into disease progression, treatment response, and potential clinical outcomes in BV patients. Specifically, we demonstrated that the combined output of VIBES and VALENCIA scores could effectively predict the response to metronidazole antibiotic treatment in BV patients. Therefore, this study's outcomes contribute to our understanding of BV heterogeneity and lay the groundwork for personalized approaches to BV management and treatment selection.

Development of a processing factor prediction model for pesticides in processed tomato foods using elastic net regularization

A novel regularized elastic net regression model was developed to predict processing factor (PF) for pesticide residues, which represents a change in the residue levels during food processing. The PF values for tomato juice, wet pomace and dry pomace in the evaluations and reports published by the Joint FAO/WHO Meeting on Pesticide Residues significantly correlated with the physicochemical properties of pesticides, and subsequently the correlation was observed in the present tomato processing study. The elastic net regression model predicted the PF values using the physicochemical properties as predictor variables for both training and test data within a 2-fold range for 80-100% of the pesticides tested in the tomato processing study while overcoming multicollinearity. These results suggest that the PF values are predictable at a certain degree of accuracy from the unique sets of physicochemical properties of pesticides using the developed model based on a processing study with representative pesticides.

HK2 promotes migration and invasion of intrahepatic cholangiocarcinoma via enhancing cancer stem-like cells' resistance to anoikis

Cancer stem-like cells (CSLCs) and anoikis resistance play crucial roles in the metastasis of cancers. However, it remains unclear whether CSLCs are related to anoikis resistance in intrahepatic cholangiocarcinoma (ICC). Here we identified a group of stemness-related anoikis genes (SRAGs) via bioinformatic analysis of public data. Accordingly, a novel anoikis-related classification was established and it divided ICC into C1 and C2 type. Different type ICC displayed distinct prognosis, molecular as well immune characteristics. Furthermore, we found one key SRAGs via several machine learning algorithms. HK2 was up-regulated in tumor-repopulating cells (TRCs) of ICC, a kind of CSLCs with a potent resistance to anoikis. Its up-regulation may be caused by the activation of MTORC1 signaling in ICC-TRCs. And inhibition of HK2 significantly increased anoikis and decreased migration as well invasion in ICC-TRCs. Our studies provide an insight into the molecular mechanism underlying the resistance of ICC-TRCs to anoikis and enhance the evidences for targeting HK2 in ICC.

Predictive value of peripheral blood leukocytes-based methylation of Long non-coding RNA MALAT1 and H19 in the chemotherapy effect and prognosis of gastric cancer

BACKGROUND

The predictive value of the methylation of Long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and H19 promoters in peripheral blood leukocytes as a non-invasive biomarker for the chemotherapy effect and prognosis gastric cancer (GC) is unclear.

METHODS

The DNA methylation of H19 and MALAT1 between chemotherapy-sensitive and non-sensitive groups and between groups with better and worse survival of GC was compared using regression analyses. Several predictive nomograms were constructed. The genetic alteration of MALAT1 and H19 and the association between gene expression and immune status in GC were also investigated using bioinformatics analysis.

RESULTS

Higher genetic methylations in peripheral blood were noticed in GC groups with poorer survival. The constructed nomograms presented strong predictive values for the chemotherapy effect and 3-year survival of disease-free survival, progression-free survival, and overall survival, with the area under the curve as 0.838, 0.838, 0.912, and 0.925, respectively. Significant correlations between MALAT1 or H19 expression and marker genes of immune checkpoints and immune pathways were noticed. The high infiltration of macrophages in H19-high and low infiltration of CD8+ T cells in MALAT1-high groups were associated with worse survival of GC.

CONCLUSIONS

MALAT1 and H19 have the potential to predict the chemotherapy response and clinical outcomes of GC.

Anoikis-related lncRNA signature predicts prognosis and is associated with immune infiltration in hepatocellular carcinoma

Anoikis is a programmed cell death process triggered when cells are dislodged from the extracellular matrix. Numerous long noncoding RNAs (lncRNAs) have been identified as significant factors associated with anoikis resistance in various tumor types, including glioma, breast cancer, and bladder cancer. However, the relationship between lncRNAs and the prognosis of hepatocellular carcinoma (HCC) has received limited research attention. Further research is needed to investigate this potential link and understand the role of lncRNAs in the progression of HCC. We developed a prognostic signature based on the differential expression of lncRNAs implicated in anoikis in HCC. A co-expression network of anoikis-related mRNAs and lncRNAs was established using data obtained from The Cancer Genome Atlas (TCGA) for HCC. Cox regression analyses were conducted to formulate an anoikis-related lncRNA signature (ARlncSig) in a training cohort, which was subsequently validated in both a testing cohort and a combined dataset comprising the two cohorts. Receiver operating characteristic curves, nomograms, and decision curve analyses based on the ARlncSig score and clinical characteristics demonstrated robust predictive ability. Moreover, gene set enrichment analysis revealed significant enrichment of several immune processes in the high-risk group compared to the low-risk group. Furthermore, significant differences were observed in immune cell subpopulations, expression of immune checkpoint genes, and response to chemotherapy and immunotherapy between the high- and low-risk groups. Lastly, we validated the expression levels of the five lncRNAs included in the signature using quantitative real-time PCR. In conclusion, our ARlncSig model holds substantial predictive value regarding the prognosis of HCC patients and has the potential to provide clinical guidance for individualized immunotherapy. In this study, we obtained 36 genes associated with anoikis from the Gene Ontology and Gene Set Enrichment Analysis databases. We also identified 22 differentially expressed lncRNAs that were correlated with these genes using data from TCGA. Using Cox regression analyses, we developed an ARlncSig in a training cohort, which was then validated in both a testing cohort and a combined cohort comprising data from both cohorts. Additionally, we collected eight pairs of liver cancer tissues and adjacent tissues from the Affiliated Tumor Hospital of Nantong University for further analysis. The aim of this study was to investigate the potential of ARlncSig as a biomarker for liver cancer prognosis. The study developed a risk stratification system called ARlncSig, which uses five lncRNAs to categorize liver cancer patients into low- and high-risk groups. Patients in the high-risk group exhibited significantly lower overall survival rates compared to those in the low-risk group. The model's predictive performance was supported by various analyses including the receiver operating characteristic curve, nomogram calibration, clinical correlation analysis, and clinical decision curve. Additionally, differential analysis of immune function, immune checkpoint, response to chemotherapy, and immune cell subpopulations revealed significant differences between the high- and low-risk groups. Finally, quantitative real-time PCR validated the expression levels of the five lncRNAs. In conclusion, the ARlncSig model demonstrates critical predictive value in the prognosis of HCC patients and may provide clinical guidance for personalized immunotherapy.

The efficacy of Chinese herbal drugs for adults with angina pectoris: Bayesian network meta-analysis of 331 RCTs involving 36,467 individuals

ETHNOPHARMACOLOGICAL RELEVANCE

Hundreds of randomized controlled trials (RCT) on Chinese herbal drugs (CHDs) including Shexiang baoxin pill (BXP), compound Danshen dripping pill (DSP), compound Danshen tablet (DST), Suxiao jiuxin pill (JXP), Naoxintong capsule (NXT), Tongxinluo capsule (TXL), and Di'ao xinxuekang capsule (XXK) and conventional chemical drugs, such as isosorbide dinitrate (ISDN), for angina pectoris are available but have not been evaluated by a PRISMA-compliant network meta-analysis (NMA).

AIM OF THE STUDY

This study aimed to compare the efficacy of nine anti-anginal drugs through NMA on RCTs.

METHODS

RCTs of drug treatment for adult patients with angina pectoris for improvements in symptoms and electrocardiography were retrieved. Odds ratios and 95% credible intervals were computed to measure effect sizes. RCT quality was evaluated with the Cochrane risk of bias tool. Evidence synthesis was performed with Bayesian NMA. Essential analyses including subgroup analysis, sensitivity analysis, meta-regression analysis, publication bias analysis, and ranking analysis were conducted to assess the robustness of efficacies. Evidence strength was assessed with the GRADE approach.

RESULTS

A total of 331 RCTs with 36,467 participants were eligible. The overall quality of all included RCTs was low. Overall efficacy estimates from different approaches of evidential synthesis found that BXP, TXL, and DSP were more efficacious than DST and ISDN. Essential analyses indicated consistent efficacy estimates, insignificant publication bias, and corroborative ranking results. The overall GRADE evidence strength was low.

CONCLUSION

This comprehensive Bayesian NMA found BXP, TXL, and DSP to be the top three candidates among the seven tested CHDs for treating adults suffering from angina pectoris. However, the quality and the evidence strength of eligible RCTs were low. Further high-quality RCTs with more outcome measures and their NMAs are warranted.

REGISTRATION

PROSPERO CRD42014007035.

Connectivity at the human-wildlife interface: starling movements relate to carriage of E. coli

Synanthropic bird species in human, poultry or livestock environments can increase the spread of pathogens and antibiotic-resistant bacteria between wild and domestic animals. We present the first telemetry-based spatial networks for a small songbird. We quantified landscape connectivity exerted by spotless starling movements, and aimed to determine if connectivity patterns were related to carriage of potential pathogens. We captured 28 starlings on a partridge farm in 2020 and tested them for Avian influenza virus, West Nile virus WNV, Avian orthoavulavirus 1, Coronavirus, Salmonella spp. and Escherichia coli. We did not detect any viruses or Salmonella, but one individual had antibodies against WNV or cross-reacting Flaviviruses. We found E. coli in 61 % (17 of 28) of starlings, 76 % (13 of 17) of which were resistant to gentamicin, 12 % (2 of 17) to cefotaxime/enrofloxacin and 6 % (1 of 17) were phenotypic extended spectrum beta-lactamase (ESBL) carriers. We GPS-tracked 17 starlings and constructed spatial networks showing how their movements (i.e. links) connect different farms with nearby urban and natural habitats (i.e. nodes with different attributes). Using E. coli carriage as a proxy for acquisition/dispersal of bacteria, we found differences across spatial networks constructed for E. coli positive (n = 7) and E. coli negative (n = 9) starlings. We used Exponential Random Graph Models to reveal significant differences between networks. In particular, an urban roost was more connected to other sites by movements of E. coli positive than by movements of E. coli negative starlings. Furthermore, an open pine forest used mainly for roosting was more connected to other sites by movements of E. coli negative than by movements of E. coli positive starlings. Using E. coli as a proxy for a potential pathogen carried by starlings, we reveal the pathways of spread that starlings could provide between farms, urban and natural habitats.

Genetic Variation in Male Aggression Is Influenced by Genotype of Prior Social Partners in Drosophila melanogaster

AbstractSocial behaviors can be influenced by the genotypes of interacting individuals through indirect genetic effects (IGEs) and can also display developmental plasticity. We investigated how developmental IGEs, which describe the effects of a prior social partner's genotype on later behavior, can influence aggression in male Drosophila melanogaster. We predicted that developmental IGEs cannot be estimated by simply extending the effects of contextual IGEs over time and instead have their own unique effects on behavior. On day 1 of the experiment, we measured aggressive behavior in 15 genotypic pairings (n = 600 males). On day 2, each of the males was paired with a new opponent, and aggressive behavior was again measured. We found contextual IGEs on day 1 of the experiment and developmental IGEs on day 2 of the experiment: the influence of the day 1 partner's genotype on the focal individual's day 2 behavior depended on the genotypic identity of both the day 1 partner and the focal male. Importantly, the developmental IGEs in our system produced fundamentally different dynamics than the contextual IGEs, as the presence of IGEs was altered over time. These findings represent some of the first empirical evidence demonstrating developmental IGEs, a first step toward incorporating developmental IGEs into our understanding of behavioral evolution.

Sleep duration is associated with overnight changes in perceived fatigue in elite women soccer players

The current study aimed to describe sleep habits and analyze the associations between sleep indices, changes in perceived fatigue (Δ Fatigue) and external training load measures in women soccer players during a 7-day training camp. Sixteen elite women soccer players (age: 25.4 ± 3.6 years; mean ± SD) from the Portuguese Women's National Team participated in the study. Sleep indices (sleep duration and efficiency) were measured using a wrist-worn accelerometer. External training loads measures were measured using GPS devices. Players also reported perceived fatigue using a Likert scale (1 - very, very low to 7 - very, very high) before and immediately after sleep. Players' within-subject coefficient of variation for sleep duration was 5.6%, and 4.6% for sleep efficiency. Individually, 8 players (50%) slept less than 7 h per night throughout the training camp, and the same number of athletes had a sleep efficiency lower than 85%. Similar values (p<0.05) were measured for sleep duration and efficiency between training and match days. A moderate negative within-subjects correlation was found between Δ Fatigue and sleep duration (adjusted for pre-sleep fatigue) [r = -0.32; 95% Confidence Interval (-0.51 - -0.08); p = 0.04]. These findings reinforce the importance of sleep in the recovery process of elite women soccer players, showing that more sleep may help to attenuate fatigue.

Post-game recovery of isometric hamstring strength in a high-risk hamstring strain injury group throughout an Australian Football League season

This study sought to investigate post-game hamstring strength recovery of 26 Australian Football League (AFL) players with a previous hamstring strain injury (HSI) across an AFL season. Maximal unilateral isometric knee flexion strength was assessed using an externally fixed dynamometer, and inter-session reliability was measured during the pre-season period. Linear mixed effects models investigated the influence of numerous variables on post-game hamstring strength decrement (relative change between initial weekly test and individual baseline) and individual within-week strength change following gameplay. The test demonstrated good inter-tester reliability (ICC = 0.81-0.88; CV = 6.73-7.33), and an acceptable level of error (MAE = 5.77-7.14%). Player as a random effect strongly influenced post-game strength decrement and within-week strength change (marginal R2 = 0.185-0.407; conditional R2 = 0.455-0.654). Within-week hamstring strength change was strongly determined by post-game strength decrement alone (estimate = 0.51, 95% CI = -0.66- -0.36 ; η2 = 0.32; P=<0.001) and in interaction with number of days post-game (estimate = 0.43, 95% CI = 0.20-0.66; η2 = 0.096; P=<0.001). This study shows the importance of early individual assessment of post-game hamstring strength in players with prior hamstring injury and could be valuable to inform post-game hamstring recovery in future applications.

Lower versus higher blood pressure targets in comatose patients resuscitated from out-of-hospital cardiac arrest-Protocol for a secondary Bayesian analysis of the box trial

BACKGROUND

The management of blood pressure targets during intensive care after out-of-hospital cardiac arrest (OHCA) remains a topic of debate. The blood Pressure and Oxygenation Targets After OHCA (BOX) trial explored the efficacy of two different blood pressure targets in 789 patients during intensive care after OHCA. In the primary frequentist analysis, no statistically significant differences were found for neurological outcome after 90 days.

METHODS

This protocol outlines secondary Bayesian analyses of 365-day all-cause mortality and two secondary outcomes: neurological outcome after 365 days, and plasma neuron-specific enolase, a biomarker of brain injury, after 48 h. We will employ adjusted Bayesian logistic and linear regressions, presenting results as relative and absolute differences with 95% confidence intervals. We will use weakly informative priors for the primary analyses, and skeptical and evidence-based priors (where available) in sensitivity analyses. Exact probabilities for any benefit/harm will be presented for all outcomes, along with probabilities of clinically important benefit/harm (risk differences larger than 2%-points absolute) and no clinically important differences for the binary outcomes. We will assess whether heterogeneity of treatment effects on mortality is present according to lactate at admission, time to return of spontaneous circulation, primary shockable rhythm, age, hypertension, and presence of ST-elevation myocardial infarction.

DISCUSSION

This secondary analysis of the BOX trial aim to complement the primary frequentist analysis by quantifying the probabilities of beneficial or harmful effects of different blood pressure targets. This approach seeks to provide clearer insights for researchers and clinicians into the effectiveness of these blood pressure management strategies in acute medical conditions, particularly focusing on mortality, neurological outcomes, and neuron-specific enolase.

The Association Between Child Maltreatment and Loneliness Across the Lifespan: A Systematic Review and Multilevel Meta-Analysis

While there is evidence that child maltreatment is positively associated with loneliness, the strength of this association is not yet clear. It is also unclear whether the magnitude and statistical significance of this association varies across groups of individuals. Therefore, this meta-analysis examines whether there are differences in loneliness between individuals with and without maltreatment histories, and which factors may influence the association between child maltreatment and loneliness. A three-level meta-analysis was conducted on 52 studies reporting 116 effect sizes (N = 1,705,493; Mage = 30.93; 49.6% females). Results showed a medium overall effect (g = 0.45, p < .001, 95% CI [0.36, 0.53]), indicating that individuals with maltreatment histories, on average, feel lonelier than individuals without maltreatment histories. Moderator analyses showed that effect sizes were larger for emotional abuse and emotional neglect as compared to other types of child maltreatment and decreased when participants were older at the time of loneliness assessment. These findings suggest that individuals with maltreatment histories, especially those who have been emotionally abused and/or emotionally neglected, are vulnerable to experiencing loneliness across the lifespan. The results also suggest that feelings of loneliness warrant attention in prevention and intervention programs for individuals with maltreatment histories.

Influence of Betrayal Trauma on Schizotypal Personality Pathology

Schizotypal personality pathology (SZP) is a persistent and debilitating problem for a substantial number of people. Research on SZP has typically emphasized its biological and more specifically genetic origins. However, recent research has highlighted the potential influence of trauma on SZP. This research is promising, although it has thus far focused primarily on type of trauma (e.g., different types of abuse vs. neglect in childhood) rather than who perpetrated the trauma. Previous studies on both personality pathology in general and psychotic-spectrum experiences characteristic of SZP in particular have highlighted the influence of trauma perpetrated by someone with whom the trauma survivor was close (i.e., betrayal trauma), although this has not yet been examined with respect to SZP specifically. In this study we examined this, evaluating the influence of trauma with varying degrees of betrayal on SZP in a sample of adults (N = 364) using structural equation modeling. Results suggest that interpersonal trauma in general was associated with higher levels of SZP. Findings further indicate that for women but not men, trauma with a high degree of betrayal was uniquely associated with SZP. These results underscore the potential role of trauma in SZP and have implications for future research on and intervention with people with high levels of SZP.

Fully automated whole brain segmentation from rat MRI scans with a convolutional neural network

BACKGROUND

Whole brain delineation (WBD) is utilized in neuroimaging analysis for data preprocessing and deriving whole brain image metrics. Current automated WBD techniques for analysis of preclinical brain MRI data show limited accuracy when images present with significant neuropathology and anatomical deformations, such as that resulting from organophosphate intoxication (OPI) and Alzheimer's Disease (AD), and inadequate generalizability.

METHODS

A modified 2D U-Net framework was employed for WBD of MRI rodent brains, consisting of 27 convolutional layers, batch normalization, two dropout layers and data augmentation, after training parameter optimization. A total of 265 T2-weighted 7.0 T MRI scans were utilized for the study, including 125 scans of an OPI rat model for neural network training. For testing and validation, 20 OPI rat scans and 120 scans of an AD rat model were utilized. U-Net performance was evaluated using Dice coefficients (DC) and Hausdorff distances (HD) between the U-Net-generated and manually segmented WBDs.

RESULTS

The U-Net achieved a DC (median[range]) of 0.984[0.936-0.990] and HD of 1.69[1.01-6.78] mm for OPI rat model scans, and a DC (mean[range]) of 0.975[0.898-0.991] and HD of 1.49[0.86-3.89] for the AD rat model scans.

COMPARISON WITH EXISTING METHODS

The proposed approach is fully automated and robust across two rat strains and longitudinal brain changes with a computational speed of 8 seconds/scan, overcoming limitations of manual segmentation.

CONCLUSIONS

The modified 2D U-Net provided a fully automated, efficient, and generalizable segmentation approach that achieved high accuracy across two disparate rat models of neurological diseases.

Methylation-based markers for the estimation of age in African cheetah, Acinonyx jubatus

Age is a key demographic in conservation where age classes show differences in important population metrics such as morbidity and mortality. Several traits, including reproductive potential, also show senescence with ageing. Thus, the ability to estimate age of individuals in a population is critical in understanding the current structure as well as their future fitness. Many methods exist to determine age in wildlife, with most using morphological features that show inherent variability with age. These methods require significant expertise and become less accurate in adult age classes, often the most critical groups to model. Molecular methods have been applied to measuring key population attributes, and more recently epigenetic attributes such as methylation have been explored as biomarkers for age. There are, however, several factors such as permits, sample sovereignty, and costs that may preclude the use of extant methods in a conservation context. This study explored the utility of measuring age-related changes in methylation in candidate genes using mass array technology. Novel methods are described for using gene orthologues to identify and assay regions for differential methylation. To illustrate the potential application, African cheetah was used as a case study. Correlation analyses identified six methylation sites with an age relationship, used to develop a model with sufficient predictive power for most conservation contexts. This model was more accurate than previous attempts using PCR and performed similarly to candidate gene studies in other mammal species. Mass array presents an accurate and cost-effective method for age estimation in wildlife of conservation concern.

Examining the Contribution of Physical Cues for Same- and Cross-Race Face Individuation

Face individuation involves sensitivity to physical characteristics that provide information about identity. We examined whether Black and White American faces differ in terms of individuating information, and whether Black and White perceivers differentially weight information when judging same-race and cross-race faces. Study 1 analyzed 20 structural metrics (e.g., eye width, nose length) of 158 Black and White faces to determine which differentiate faces within each group. High-utility metrics (e.g., nose length, eye height, chin length) differentiated faces of both groups, low-utility metrics (e.g., face width, eye width, face length) offered less individuating information. Study 2 (N = 4,510) explored Black and White participants' sensitivity to variation on structural metrics using similarity ratings. High-utility metrics affected perceived dissimilarity more than low-utility metrics. This relationship was non-significantly stronger for same-race faces rather than cross-race faces. Perceivers also relied more on features that were racially stereotypic of the faces they were rating.

Influence of microarchitecture on stressed volume and mechanical fatigue behaviour of equine subchondral bone

Fractures of the equine metacarpophalangeal (MCP) joint are among the most common and fatal injuries experienced by racehorses. These bone injuries are a direct result of repetitive, high intensity loading of the skeleton during racing and training and there is consensus that they represent a mechanical fatigue phenomenon. Existing work has found the fatigue life of bone to be strongly determined by bone microarchitecture and the resulting stressed volume (i.e., the volume of bone stressed above assumed yield). The purpose of this study was to quantify the influence of bone microarchitecture on the mechanical fatigue behaviour of equine subchondral bone from the MCP joint across a wide variety of sample types. Forty-eight subchondral bone samples were prepared from the third metacarpal (MC3) and proximal phalanx (P1) of 8 horses and subsequently imaged using high resolution micro-computed tomography (μCT) to quantify microarchitectural features of interest, including bone volume fraction, tissue mineral density, pore size, pore spacing, and pore number. Samples were cyclically loaded in compression to a stress of 70 MPa, and fatigue life was defined as the number of cycles until failure. Finite element models were created from the μCT images and used to quantify stressed volume. Based on the expected log point-wise predictive density, stressed volume was a strong predictor of fatigue life in both the MC3 and P1. A regional analysis indicated fatigue life was more strongly associated with bone volume fraction in the superficial (r2 = 0.32, p < 0.001) and middle (r2 = 0.70, p < 0.001) regions of the subchondral bone, indicating the prominent role that the cortical plate played in the fatigue resistance of equine subchondral bone. By improving our understanding of the variance in fatigue life measurements, this research helps clarify the underlying mechanisms of the mechanical fatigue process and provides a basic understanding of subchondral bone injuries in the equine fetlock joint.

Endometrial cancer: predictors and oncological safety of tumor tissue manipulation

PURPOSE

The main goal of this study is to assess the impact of tumor manipulation on the presence of lympho-vascular space invasion and its influence on oncological results.

METHODS

We performed a retrospective multi-centric study amongst patients who had received primary surgical treatment for apparently early-stage endometrial cancer. A multivariate statistical analysis model was designed to assess the impact that tumor manipulation (with the use of uterine manipulator or preoperative hysteroscopy) has on lympho-vascular development (LVSI) in the final surgical specimen.

RESULTS

A total of 2852 women from 15 centers were included and divided into two groups based on the lympho-vascular status in the final surgical specimen: 2265 (79.4%) had no LVSI and 587 (20.6%) presented LVSI. The use of uterine manipulator was associated with higher chances of lympho-vascular involvement regardless of the type used: Balloon manipulator (HR: 95% CI 4.64 (2.99-7.33); p < 0.001) and No-Balloon manipulator ([HR]: 95% CI 2.54 (1.66-3.96); p < 0.001). There is no evidence of an association between the use of preoperative hysteroscopy and higher chances of lympho-vascular involvement (HR: 95% CI 0.90 (0.68-1.19); p = 0.479).

CONCLUSION

Whilst performing common gynecological procedures, iatrogenic distention and manipulation of the uterine cavity are produced. Our study suggests that the use of uterine manipulator increases the rate of LVSI and, therefore, leads to poorer oncological results. Conversely, preoperative hysteroscopy does not show higher rates of LVSI involvement in the final surgical specimen and can be safely used.

Comparing Subjective Similarity of Automated Driving Styles to Objective Distance-Based Similarity

OBJECTIVE

This study explores subjective and objective driving style similarity to identify how similarity can be used to develop driver-compatible vehicle automation.

BACKGROUND

Similarity in the ways that interaction partners perform tasks can be measured subjectively, through questionnaires, or objectively by characterizing each agent's actions. Although subjective measures have advantages in prediction, objective measures are more useful when operationalizing interventions based on these measures. Showing how objective and subjective similarity are related is therefore prudent for aligning future machine performance with human preferences.

METHODS

A driving simulator study was conducted with stop-and-go scenarios. Participants experienced conservative, moderate, and aggressive automated driving styles and rated the similarity between their own driving style and that of the automation. Objective similarity between the manual and automated driving speed profiles was calculated using three distance measures: dynamic time warping, Euclidean distance, and time alignment measure. Linear mixed effects models were used to examine how different components of the stopping profile and the three objective similarity measures predicted subjective similarity.

RESULTS

Objective similarity using Euclidean distance best predicted subjective similarity. However, this was only observed for participants' approach to the intersection and not their departure.

CONCLUSION

Developing driving styles that drivers perceive to be similar to their own is an important step toward driver-compatible automation. In determining what constitutes similarity, it is important to (a) use measures that reflect the driver's perception of similarity, and (b) understand what elements of the driving style govern subjective similarity.

Impasse-Driven problem solving: The multidimensional nature of feeling stuck

This study reports findings across four preregistered experiments (total N = 856) that establish the multidimensional nature of impasse and resolve two paradoxes implicit in the problem-solving literature: how a state of impasse can be at once necessary to solve a problem with insight yet also have appear to have a catastrophic effect on solution rates, and why individuals such as problem-solving and gaming enthusiasts seem to seek out this apparently aversive state. We introduce a new way of measuring impasse based on qualitative reports and subsequently confirmed through quantitative analysis that exploits two aspects of impasse: its dynamic and unstable nature (it can be resolved or unresolved) and its multidimensionality in terms of feelings of cognitive speediness, motivation, and affect. The feeling of being stuck varies between resolved and unresolved impasse in terms of feelings of speediness and positive affect, but not motivation, which remains constant. We demonstrate that the feeling of insight can be reliably elicited by experiencing and resolving impasse but also in the absence of impasse, which suggests that there is more than one path to an insight experience. This adds depths to current proposals of the cognitive mechanisms underlying both insight problem-solving and impasse. Our findings are robust across a range of problem types. The novel conception of impasse in this paper as dynamic and multidimensional has implications for theories of insight problem solving, and also wider implications for understanding how impasse can be resolved across different domains such as education and design.

Understanding crop colonization of oilseed rape crops by the cabbage stem flea beetle (Psylliodes chrysocephala L. (Coleoptera: Chrysomelidae))

BACKGROUND

Development of accurate pest monitoring systems is essential for the establishment of integrated pest management strategies. Information about the pest behavior during the colonization process, as well as the sex and reproductive status of the colonizing population often are lacking and hinder their development. The cabbage stem flea beetle (CSFB, Psylliodes chrysocephala) can cause the complete destruction of oilseed rape crops (OSR, Brassica napus). In the present study, the colonization process of OSR fields by the CSFB was studied.

RESULTS

More individuals were caught on the outward facing side of the traps than the side of the trap facing towards the crop at the field border and catches were higher on the trapping units at the center of the field than at its border, suggesting that more beetles were entering than leaving the crop. Catches were higher on lower traps placed near to the crop than on those positioned further from the ground and also were higher during the day than late afternoon and night. The sex-ratio of individuals caught was skewed towards males and sexual maturity was acquired for females during the experiment. Integration of sampling data with local meteorological data showed that the catches correlated mostly with air temperature and relative humidity.

CONCLUSION

This study provides new information about the dispersion of the CSFB in OSR fields during the colonization process, and highlights correlations between local meteorological factors and activity of the CSFB, and represent a new step towards implementing monitoring strategies against this pest. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Relative efficiency of methods to estimate cabbage stem flea beetle (Psylliodes chrysocephala) larval infestation in oilseed rape (Brassica napus)

BACKGROUND

The cabbage stem flea beetle (CSFB; Psylliodes chrysocephala) is an important pest of winter oilseed rape (OSR) crops in Europe. Damage is caused by adults feeding on young leaves and by larvae mining in the leaves and stems. The most common method to estimate the severity of CSFB larval infestation is by plant dissection, which is time consuming and labour intensive. Two alternative methods have been proposed: extraction of larvae from desiccating plants and counting the percentage of leaves with scars left by larvae on plant petioles. These methods are easy to implement and less time consuming than plant dissection but have not been properly validated.

RESULTS

OSR plants were sampled in five different experiments and the two alternative methods tested; assessment of the total number of scars per plant as a predictor of the number of larvae was also tested. The number of larvae remaining in plants following various periods of desiccation was checked via plant dissection. We found that the desiccation method is efficient, giving reliable results after 7 days (76% of the total larvae extracted) with good accuracy in estimating the number of larvae per plant (±0.38 larvae per plant). The total number of scars also gives a reliable estimation of the number of larvae (r = 0.76, accuracy of ±1.01 larvae per plant).

CONCLUSION

OSR plant desiccation and the total number of scars per plant are reliable methods to estimate CSFB larval infestation and will facilitate monitoring of this cryptic pest stage. © 2022 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Anoikis patterns via machine learning strategy and experimental verification exhibit distinct prognostic and immune landscapes in melanoma

BACKGROUND

Anoikis is a cell death programmed to eliminate dysfunctional or damaged cells induced by detachment from the extracellular matrix. Utilizing an anoikis-based risk stratification is anticipated to understand melanoma's prognostic and immune landscapes comprehensively.

METHODS

Differential expression genes (DEGs) were analyzed between melanoma and normal skin tissues in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data sets. Next, least absolute shrinkage and selection operator, support vector machine-recursive feature elimination algorithm, and univariate and multivariate Cox analyses on the 308 DEGs were performed to build the prognostic signature in the TCGA-melanoma data set. Finally, the signature was validated in GSE65904 and GSE22155 data sets. NOTCH3, PIK3R2, and SOD2 were validated in our clinical samples by immunohistochemistry.

RESULTS

The prognostic model for melanoma patients was developed utilizing ten hub anoikis-related genes. The overall survival (OS) of patients in the high-risk subgroup, which was classified by the optimal cutoff value, was remarkably shorter in the TCGA-melanoma, GSE65904, and GSE22155 data sets. Low-risk patients exhibited low immune cell infiltration and high expression of immunophenoscores and immune checkpoints. They also demonstrated increased sensitivity to various drugs, including dasatinib and dabrafenib. NOTCH3, PIK3R2, and SOD2 were notably associated with OS by univariate Cox analysis in the GSE65904 data set. The clinical melanoma samples showed remarkably higher protein expressions of NOTCH3 (P = 0.003) and PIK3R2 (P = 0.009) than the para-melanoma samples, while the SOD2 protein expression remained unchanged.

CONCLUSIONS

In this study, we successfully established a prognostic anoikis-connected signature using machine learning. This model may aid in evaluating patient prognosis, clinical characteristics, and immune treatment modalities for melanoma.

The associations of sexual desire, daily stress, and intimacy in gay men in long-term relationships

We investigated the associations between daily stress, sexual desire and intimacy in gay men in long-term relationships. A daily diary method of data collection was used. Twenty-two adult participants completed brief questionnaires ten times per day during seven consecutive days. Multilevel regression analyses revealed significant negative associations of daily stress with sexual desire (p = 0.003); higher stress came with lower levels of sexual desire, and vice versa. The hypothesized moderation effect of the associations of stress with sexual desire by intimacy was found significant (p = 0.003). However, the effect was not as predicted: at higher levels of intimacy the negative effect of stress on sexual desire was shown to be stronger than at lower levels. Most temporal associations of stress and intimacy with sexual desire were not significant. Nevertheless, the present findings implicate that daily stress and intimacy are important factors in sexual functioning of gay men in long-term relationships.

Epistemic uncertainty in Bayesian predictive probabilities

Bayesian predictive probabilities have become a ubiquitous tool for design and monitoring of clinical trials. The typical procedure is to average predictive probabilities over the prior or posterior distributions. In this paper, we highlight the limitations of relying solely on averaging, and propose the reporting of intervals or quantiles for the predictive probabilities. These intervals formalize the intuition that uncertainty decreases with more information. We present four different applications (Phase 1 dose escalation, early stopping for futility, sample size re-estimation, and assurance/probability of success) to demonstrate the practicality and generality of the proposed approach.

Increase in Linguistic Complexity in Older Adults During COVID-19

The reported psychological impact of the COVID-19 pandemic and related public health measures included a decline in cognitive functioning in older adults. Cognitive functioning is known to correlate with the lexical and syntactic complexity of an individual's linguistic productions. We examined written narratives from the CoSoWELL corpus (v 1.0), collected from over 1,000 U.S. and Canadian older adults (55+ y.o.) before and during the first year of the pandemic. We expected a decrease in the linguistic complexity of the narratives, given the oft-reported reduction in cognitive functioning associated with COVID-19. Contrary to this expectation, all measures of linguistic complexity showed a steady increase from the pre-pandemic level throughout the first year of the global lockdown. We discuss possible reasons for this boost in light of existing theories of cognition and offer a speculative link between the finding and reports of increased creativity during the pandemic.

Personality traits and cognitive reserve-High openness benefits cognition in the presence of age-related brain changes

Cognitive reserve explains differential susceptibility of cognitive performance to neuropathology. We investigated whether certain personality traits underlie cognitive reserve and are accordingly associated with better cognition and less cognitive decline in the presence of age-related brain changes. We included healthy adults aged 19-80 years for cross-sectional (N=399) and longitudinal (N=273, mean follow-up time=5 years, SD=0.7 years) analyses. Assessment of the BIG5 personality traits openness, conscientiousness, extraversion, agreeableness, and neuroticism was questionnaire-based. Each cognitive domain (perceptual speed, memory, fluid reasoning, vocabulary) was measured with up to six tasks. Cognitive domain-specific brain status variables were obtained by combining 77 structural brain measures into single scores using elastic net regularization. These brain status variables explained up to 43.1% of the variance in cognitive performance. We found that higher openness was associated with higher fluid reasoning and better vocabulary after controlling for brain status, age, and sex. Further, lower brain status was associated with a greater decline in perceptual speed only in individuals with low openness. We conclude that high openness benefits cognitive reserve.

The Indirect Effects of Proximal Stressors Associated with Posttraumatic Stress Symptoms Severity among Sexually Victimized Sexual Minority Women: A Cross-Sectional Path Analysis

Sexual minority women (SMW) are at risk for sexual victimization and stressors specifically related to their minority identity (e.g., discrimination). However, SMW experience posttraumatic stress disorder (PTSD) at rates beyond what may be explained by elevated risk for sexual victimization alone. This study aimed to examine the impact of stigma on PTSD symptoms among SMW survivors of sexual victimization. Results indicate that in relation to minority-specific distal stressors (i.e., heterosexist experiences), both minority-specific and general proximal stressors (i.e., internalized heterosexism, negative posttraumatic cognitions) indirectly affected PTSD symptom severity among this cross-sectional sample of sexually victimized SMW.

Plasma proteomic profile of abdominal obesity in older adults

OBJECTIVE

This study examines the plasma proteomic profile of abdominal obesity in older adults.

METHODS

The association of abdominal obesity (waist circumference [WC]) with 4265 plasma proteins identified using the SomaScan Assay was examined in 969 Ashkenazi Jewish participants (LonGenity cohort), aged 65 years and older (mean [SD] age 75.7 [6.7] years, 55.4% women), using regression models. Pathway analysis, as well as weighted correlation network analysis, was performed. WC was determined from the proteome using elastic net regression.

RESULTS

A total of 480 out of 4265 proteins were associated with WC in the linear regression model. Leptin (β [SE] = 12.363 [0.490]), inhibin β C chain (INHBC; β [SE] = 24.324 [1.448]), insulin-like growth factor-binding protein 2 (IGFBP-2; β [SE] = -12.782 [0.841]), heparan-sulfate 6-O-sulfotransferase 3 (H6ST3; β [SE] = -39.995 [2.729]), and matrix-remodeling-associated protein 8 (MXRA8; β [SE] = -27.101 [1.850]) were the top proteins associated with WC. Cell adhesion, extracellular matrix remodeling, and IGF transport pathways were the top enriched pathways associated with WC. WC signature determined from plasma proteins was highly correlated with measured WC (r = 0.80) and was associated with various metabolic and physical traits.

CONCLUSIONS

The study unveiled a multifaceted plasma proteomic profile of abdominal obesity in older adults, offering insights into its wide-ranging impact on the proteome. It also elucidated novel proteins, clusters of correlated proteins, and pathways that are intricately associated with abdominal obesity.

Modelling the factors affecting the spatiotemporal distribution of cabbage stem flea beetle (Psylliodes chrysocephala) larvae in winter oilseed rape (Brassica napus) in the UK

BACKGROUND

Cabbage stem flea beetle (CSFB; Psylliodes chrysocephala L.) management in oilseed rape (Brassica napus L.) has become an urgent issue in the absence of permitted and effective insecticides. Understanding the meteorological and management factors affecting their population dynamics has become critical to the development of pest management strategies.

RESULTS

The spatio-temporal changes in CSFB larval populations were assessed both in autumn and spring, in the UK from 2003 to 2017 (a period encompassing pre-and post-neonicotinoid insecticide restriction). After the neonicotinoid ban in 2013, the number of larvae both in autumn and spring increased 10-fold in the UK. When neonicotinoids were available, later sown crops contained fewer larvae than early sown crops, and bigger fields had fewer larvae than smaller fields, whereas after the ban, bigger fields tended to have more larvae than smaller fields. Wet and mild/hot Septembers were related with higher numbers of larvae when neonicotinoids were available and with lower larval numbers after the neonicotinoid ban. Low temperatures in December and January combined with high rainfall were related with high numbers of larvae in spring both before and after the neonicotinoid ban.

CONCLUSION

This study will help to produce decision support systems that allow future predictions of regional CSFB population changes and will help growers and consultants to adjust their management methods to reduce the risk of high infestations. © 2023 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Assessing the Negative Binomial-Lindley model for crash hotspot identification: Insights from Monte Carlo simulation analysis

Identifying hazardous crash sites (or hotspots) is a crucial step in highway safety management. The Negative Binomial (NB) model is the most common model used in safety analyses and evaluations - including hotspot identification. The NB model, however, is not without limitations. In fact, this model does not perform well when data are highly dispersed, include excess zero observations, or have a long tail. Recently, the Negative Binomial-Lindley (NB-L) model has been proposed as an alternative to the NB. The NB-L model overcomes several limitations related to the NB, such as addressing the issue of excess zero observations in highly dispersed data. However, it is not clear how the NB-L model performs regarding the hotspot identification. In this paper, an innovative Monte Carlo simulation protocol was designed to generate a wide range of simulated data characterized by different means, dispersions, and percentage of zeros. Next, the NB-L model was written as a Full-Bayes hierarchical model and compared with the Full-Bayes NB model for hotspot identification using extensive simulation scenarios. Most previous studies focused on statistical fit, and showed that the NB-L model fits the data better than the NB. In this research, however, we investigated the performance of the NB-L model in identifying the hazardous sites. We showed that there is a trade-off between the NB-L and NB when it comes to hotspot identification. Multiple performance metrics were used for the assessment. Among those, the results show that the NB-L model provides a better specificity in identifying hotspots, while the NB model provides a better sensitivity, especially for highly dispersed data. In other words, while the NB model performs better in identifying hazardous sites, the NB-L model performs better, when budget is limited, by not selecting non-hazardous sites as hazardous.

Development and external validation of a novel score for predicting postoperative 30‑day mortality in tumor craniotomy patients: A cross‑sectional diagnostic study

The identification of patients with craniotomy at high risk for postoperative 30-day mortality may contribute to achieving targeted delivery of interventions. The present study aimed to develop a personalized nomogram and scoring system for predicting the risk of postoperative 30-day mortality in such patients. In this retrospective cross-sectional study, 18,642 patients with craniotomy were stratified into a training cohort (n=7,800; year of surgery, 2012-2013) and an external validation cohort (n=10,842; year of surgery, 2014-2015). The least absolute shrinkage and selection operator (LASSO) model was used to select the most important variables among the candidate variables. Furthermore, a stepwise logistic regression model was established to screen out the risk factors based on the predictors chosen by the LASSO model. The model and a nomogram were constructed. The area under the receiver operating characteristic (ROC) curve (AUC) and calibration plot analysis were used to assess the model's discrimination ability and accuracy. The associated risk factors were categorized according to clinical cutoff points to create a scoring model for postoperative 30-day mortality. The total score was divided into four risk categories: Extremely high, high, intermediate and low risk. The postoperative 30-day mortality rates were 2.43 and 2.58% in the training and validation cohort, respectively. A simple nomogram and scoring system were developed for predicting the risk of postoperative 30-day mortality according to the white blood cell count; hematocrit and blood urea nitrogen levels; age range; functional health status; and incidence of disseminated cancer cells. The ROC AUC of the nomogram was 0.795 (95% CI: 0.764 to 0.826) in the training cohort and it was 0.738 (95% CI: 0.7091 to 0.7674) in the validation cohort. The calibration demonstrated a perfect fit between the predicted 30-day mortality risk and the observed 30-day mortality risk. Low, intermediate, high and extremely high risk statuses for 30-day mortality were associated with total scores of (-1.5 to -1), (-0.5 to 0.5), (1 to 2) and (2.5 to 9), respectively. A personalized nomogram and scoring system for predicting postoperative 30-day mortality in adult patients who underwent craniotomy were developed and validated, and individuals at high risk of 30-day mortality were able to be identified.

Assessing transmission attribution risk from simulated sequencing data in HIV molecular epidemiology

BACKGROUND

HIV molecular epidemiology (ME) is the analysis of sequence data together with individual-level clinical, demographic, and behavioral data to understand HIV epidemiology. The use of ME has raised concerns regarding identification of the putative source in direct transmission events. This could result in harm ranging from stigma to criminal prosecution in some jurisdictions. Here we assessed the risks of ME using simulated HIV genetic sequencing data.

METHODS

We simulated social networks of men-who-have-sex-with-men, calibrating the simulations to data from San Diego. We used these networks to simulate consensus and next-generation sequence (NGS) data to evaluate the risks of identifying direct transmissions using different HIV sequence lengths, and population sampling depths. To identify the source of transmissions, we calculated infector probability and used phyloscanner software for the analysis of consensus and NGS data, respectively.

RESULTS

Consensus sequence analyses showed that the risk of correctly inferring the source (direct transmission) within identified transmission pairs was very small and independent of sampling depth. Alternatively, NGS analyses showed that identification of the source of a transmission was very accurate, but only for 6.5% of inferred pairs. False positive transmissions were also observed, where one or more unobserved intermediaries were present when compared to the true network.

CONCLUSION

Source attribution using consensus sequences rarely infers direct transmission pairs with high confidence but is still useful for population studies. In contrast, source attribution using NGS data was much more accurate in identifying direct transmission pairs, but for only a small percentage of transmission pairs analyzed.

The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI

BACKGROUND

Single center MRI radiomics models are sensitive to data heterogeneity, limiting the diagnostic capabilities of current prostate cancer (PCa) radiomics models.

PURPOSE

To study the impact of image resampling on the diagnostic performance of radiomics in a multicenter prostate MRI setting.

STUDY TYPE

Retrospective.

POPULATION

Nine hundred thirty patients (nine centers, two vendors) with 737 eligible PCa lesions, randomly split into training (70%, N = 500), validation (10%, N = 89), and a held-out test set (20%, N = 148).

FIELD STRENGTH/SEQUENCE

1.5T and 3T scanners/T2-weighted imaging (T2W), diffusion-weighted imaging (DWI), and apparent diffusion coefficient maps.

ASSESSMENT

A total of 48 normalized radiomics datasets were created using various resampling methods, including different target resolutions (T2W: 0.35, 0.5, and 0.8 mm; DWI: 1.37, 2, and 2.5 mm), dimensionalities (2D/3D) and interpolation techniques (nearest neighbor, linear, Bspline and Blackman windowed-sinc). Each of the datasets was used to train a radiomics model to detect clinically relevant PCa (International Society of Urological Pathology grade ≥ 2). Baseline models were constructed using 2D and 3D datasets without image resampling. The resampling configurations with highest validation performance were evaluated in the test dataset and compared to the baseline models.

STATISTICAL TESTS

Area under the curve (AUC), DeLong test. The significance level used was 0.05.

RESULTS

The best 2D resampling model (T2W: Bspline and 0.5 mm resolution, DWI: nearest neighbor and 2 mm resolution) significantly outperformed the 2D baseline (AUC: 0.77 vs. 0.64). The best 3D resampling model (T2W: linear and 0.8 mm resolution, DWI: nearest neighbor and 2.5 mm resolution) significantly outperformed the 3D baseline (AUC: 0.79 vs. 0.67).

DATA CONCLUSION

Image resampling has a significant effect on the performance of multicenter radiomics artificial intelligence in prostate MRI. The recommended 2D resampling configuration is isotropic resampling with T2W at 0.5 mm (Bspline interpolation) and DWI at 2 mm (nearest neighbor interpolation). For the 3D radiomics, this work recommends isotropic resampling with T2W at 0.8 mm (linear interpolation) and DWI at 2.5 mm (nearest neighbor interpolation).

EVIDENCE LEVEL

3 TECHNICAL EFFICACY: Stage 2.

Gross Hematuria Does not Affect the Selection of Nephrectomy Types for Clinical Stage 1 Clear Cell Renal Cell Carcinoma: A Multicenter, Retrospective Cohort Study

PURPOSE

This study aimed to discuss the correlation between gross hematuria and postoperative upstaging (from T1 to T3a) in patients with cT1 clear cell renal cell carcinoma (ccRCC) and to compare oncologic outcomes of partial nephrectomy (PN) and radical nephrectomy (RN) in patients with gross hematuria.

METHODS

A total of 2145 patients who met the criteria were enrolled in the study (including 363 patients with gross hematuria). The least absolute selection and shrinkage operator logistic regression was used to evaluate the risk factor of postoperative pathological upstaging. The propensity score matching (PSM) and stable inverse probability of treatment weighting (IPTW) analysis were used to balance the confounding factors. The Kaplan-Meier analysis and multivariate Cox proportional risk regression model were used to assess the prognosis.

RESULTS

Gross hematuria was a risk factor of postoperative pathological upstaging (odds ratio [OR] = 3.96; 95% confidence interval [CI] 2.44-6.42; P < 0.001). After PSM and stable IPTW adjustment, the characteristics were similar in corresponding patients in the PN and RN groups. In the PSM cohort, PN did not have a statistically significant impact on recurrence-free survival (hazard ratio [HR] = 1.48; 95% CI 0.25-8.88; P = 0.67), metastasis-free survival (HR = 1.24; 95% CI 0.33-4.66; P = 0.75), and overall survival (HR = 1.46; 95% CI 0.31-6.73; P = 0.63) compared with RN. The results were confirmed in sensitivity analyses.

CONCLUSIONS

Although gross hematuria was associated with postoperative pathological upstaging in patients with cT1 ccRCC, PN should still be the preferred treatment for such patients.

Return-to-Manual Performance can be Predicted Before Automation Fails

OBJECTIVE

This study aimed to examine operator state variables (workload, fatigue, and trust in automation) that may predict return-to-manual (RTM) performance when automation fails in simulated air traffic control.

BACKGROUND

Prior research has largely focused on triggering adaptive automation based on reactive indicators of performance degradation or operator strain. A more direct and effective approach may be to proactively engage/disengage automation based on predicted operator RTM performance (conflict detection accuracy and response time), which requires analyses of within-person effects.

METHOD

Participants accepted and handed-off aircraft from their sector and were assisted by imperfect conflict detection/resolution automation. To avoid aircraft conflicts, participants were required to intervene when automation failed to detect a conflict. Participants periodically rated their workload, fatigue and trust in automation.

RESULTS

For participants with the same or higher average trust than the sample average, an increase in their trust (relative to their own average) slowed their subsequent RTM response time. For participants with lower average fatigue than the sample average, an increase in their fatigue (relative to own average) improved their subsequent RTM response time. There was no effect of workload on RTM performance.

CONCLUSIONS

RTM performance degraded as trust in automation increased relative to participants' own average, but only for individuals with average or high levels of trust.

APPLICATIONS

Study outcomes indicate a potential for future adaptive automation systems to detect vulnerable operator states in order to predict subsequent RTM performance decrements.

Quantile regression shrinkage and selection via the Lqsso

Quantile regression has recently received a considerable attention due to its remarkable development in enriching the variety of regression models. Many efforts have been made to blend different penalty and loss function to extend or develop novel regression models that are unique from different perspectives. Bearing in mind that the lasso quantile regression model ignores the randomness of the realizations in the penalty part, we propose a new penalty for the quantile regression models. Similar to the adaptive lasso quantile regression model, the proposed model simultaneously does estimation and variable selection tasks. We call the new model 'lqsso-QR', standing for the least quantile shrinkage and selection operator quantile regression. In this article, we present a sufficient and necessary condition for the variable selection of the lasso quantile regression to enjoy the consistent property. We show that the lqsso-QR follows oracle properties under some mild conditions. From computational perspective, we apply an efficient algorithm, originally developed for the lasso quantile regression. Using simulation studies, we elaborate on the superiority of the proposed model compared with other lasso-type penalties, especially regarding relative prediction error. Also, an application of our method to a real-life data; the rat eye data, is reported.

A Comparison of Hugging Frequency and Its Association with Momentary Mood Before and During COVID-19 Using Ecological Momentary Assessment

The COVID-19 pandemic has necessitated a drastic decrease in human social interactions including social touch. One of the most prevalent forms of touch is hugging. Hugging has been demonstrated to benefit both physical and mental well-being. In the present study, we used an ecological momentary assessment approach to assess the relationship between hugging and momentary mood in two independent cohorts sampled prior or during the pandemic. We found that the frequency of hugging was significantly reduced during the pandemic. Using multilevel modeling, we found a significant positive association between momentary mood and daily hugs. This effect was moderated by the cohort, as individuals during the pandemic showed a stronger positive association compared to the cohort sampled prior to the pandemic. While we have to stress that our results are correlational in nature, they potentially indicate that social touch is more beneficial in times of social distancing.

How Much Is Enough? The Relationship Between Prosocial Effort and Moral Character Judgments

The amount of effort required to bring about a prosocial outcome can vary from low-handing a stranger the wallet she just dropped-to high-spending days tracking down the owner of a lost wallet. The goal of the current research is to characterize the relationship between prosocial effort and moral character judgments. Does more prosocial effort always lead to rosier moral character judgments? Across four studies (N = 1,658), we find that moral character judgments increase with prosocial effort to a point and then plateau. We find evidence that this pattern is produced, in part, by descriptive and prescriptive norms: exceeding descriptive norms increases moral character judgments, but exceeding prescriptive norms has the opposite effect, which leads to a tapering off of moral character judgments at higher levels of effort.

Considerations and Determinants of Discharge Decisions among Prenatal Substance Exposed Infants

Limited research has examined a comprehensive set of predictors when evaluating discharge placement decisions for infants exposed to substances prenatally. Using a previously validated medical record data extraction tool, the current study examined prenatal substance exposure, infant intervention (i.e., pharmacologic, or non-pharmacologic), and demographic factors (e.g., race and ethnicity and rurality) as predictors of associations with discharge placement in a sample from a resource-poor state (N = 136; 69.9% Non-Hispanic White). Latent class analysis (LCA) was used to examine whether different classes emerged and how classes were differentially related to discharge placement decisions. Logistic regressions were used to determine whether each predictor was uniquely associated with placement decisions. Results of the LCA yielded a two-class solution comprised of (1) a Low Withdrawal Risk class, characterized by prenatal exposure to substances with low risk for neonatal abstinence syndrome (NAS) and non-pharmacologic intervention, and (2) a High Withdrawal Risk class, characterized by a high risk of NAS and pharmacologic intervention. Classes were not related to discharge placement decisions. Logistic regressions demonstrated that meth/amphetamine use during pregnancy was associated with greater odds of out of home placement above other substance types. Future research should replicate and continue examining the clinical utility of these classes.

The body of evidence of late-life depression: the complex relationship between depressive symptoms, movement, dyspnea and cognition

BACKGROUND

Physical symptoms play an important role in late-life depression and may contribute to residual symptomatology after antidepressant treatment. In this exploratory study, we examined the role of specific bodily dimensions including movement, respiratory functions, fear of falling, cognition, and physical weakness in older people with depression.

METHODS

Clinically stable older patients with major depression within a Psychiatric Consultation-Liaison program for Primary Care underwent comprehensive assessment of depressive symptoms, instrumental movement analysis, dyspnea, weakness, activity limitations, cognitive function, and fear of falling. Network analysis was performed to explore the unique adjusted associations between clinical dimensions.

RESULTS

Sadness was associated with worse turning and walking ability and movement transitions from walking to sitting, as well as with worse general cognitive abilities. Sadness was also connected with dyspnea, while neurovegetative depressive burden was connected with activity limitations.

DISCUSSION

Limitations of motor and cognitive function, dyspnea, and weakness may contribute to the persistence of residual symptoms of late-life depression.

Statistical plasmode simulations-Potentials, challenges and recommendations

Statistical data simulation is essential in the development of statistical models and methods as well as in their performance evaluation. To capture complex data structures, in particular for high-dimensional data, a variety of simulation approaches have been introduced including parametric and the so-called plasmode simulations. While there are concerns about the realism of parametrically simulated data, it is widely claimed that plasmodes come very close to reality with some aspects of the "truth" known. However, there are no explicit guidelines or state-of-the-art on how to perform plasmode data simulations. In the present paper, we first review existing literature and introduce the concept of statistical plasmode simulation. We then discuss advantages and challenges of statistical plasmodes and provide a step-wise procedure for their generation, including key steps to their implementation and reporting. Finally, we illustrate the concept of statistical plasmodes as well as the proposed plasmode generation procedure by means of a public real RNA data set on breast carcinoma patients.

Individual variation in life-history timing: synchronous presence, asynchronous events and phenological compensation in a wild mammal

Many animals and plants have species-typical annual cycles, but individuals vary in their timing of life-history events. Individual variation in fur replacement (moult) timing is poorly understood in mammals due to the challenge of repeated observations and longitudinal sampling. We examined factors that influence variation in moult duration and timing among elephant seals (Mirounga angustirostris). We quantified the onset and progression of fur loss in 1178 individuals. We found that an exceptionally rapid visible moult (7 days, the shortest of any mammals or birds), and a wide range of moult start dates (spanning 6-10× the event duration) facilitated high asynchrony across individuals (only 20% of individuals in the population moulting at the same time). Some of the variation was due to reproductive state, as reproductively mature females that skipped a breeding season moulted a week earlier than reproductive females. Moreover, individual variation in timing and duration within age-sex categories far outweighed (76-80%) variation among age-sex categories. Individuals arriving at the end of the moult season spent 50% less time on the beach, which allowed them to catch up in their annual cycles and reduce population-level variance during breeding. These findings underscore the importance of individual variation in annual cycles.

Bayesian Mechanistic Inference, Statistical Mechanics, and a New Era for Monte Carlo

On the one hand, much of computational chemistry is concerned with "bottom-up" calculations which elucidate observable behavior starting from exact or approximated physical laws, a paradigm exemplified by typical quantum mechanical calculations and molecular dynamics simulations. On the other hand, "top down" computations aiming to formulate mathematical models consistent with observed data, e.g., parametrizing force fields, binding or kinetic models, have been of interest for decades but recently have grown in sophistication with the use of Bayesian inference (BI). Standard BI provides an estimation of parameter values, uncertainties, and correlations among parameters. Used for "model selection," BI can also distinguish between model structures such as the presence or absence of individual states and transitions. Fortunately for physical scientists, BI can be formulated within a statistical mechanics framework, and indeed, BI has led to a resurgence of interest in Monte Carlo (MC) algorithms, many of which have been directly adapted from or inspired by physical strategies. Certain MC algorithms─notably procedures using an "infinite temperature" reference state─can be successful in a 5-20 parameter BI context which would be unworkable in molecular spaces of 103 coordinates and more. This Review provides a pedagogical introduction to BI and reviews key aspects of BI through a physical lens, setting the computations in terms of energy landscapes and free energy calculations and describing promising sampling algorithms. Statistical mechanics and basic probability theory also provide a reference for understanding intrinsic limitations of Bayesian inference with regard to model selection and the choice of priors.

Modeling children's moral development in postwar Taiwan through naturalistic observations preserved in historical texts

A core issue in the interdisciplinary study of human morality is its ontogeny in diverse cultures, but systematic, naturalistic data in specific cultural contexts are rare to find. This study conducts a novel analysis of 213 children's socio-moral behavior in a historical, non-Western, rural setting, based on a unique dataset of naturalistic observations from the first field research on Han Chinese children. Using multilevel multinomial modeling, we examined a range of proactive behaviors in 0-to-12-year-old children's peer cooperation and conflict in an entire community in postwar Taiwan. We modeled the effects of age, sex, kinship, and behavioral roles, and revealed complex interactions between these four variables in shaping children's moral development. We discovered linkages between coercive and non-coercive behaviors as children strategically negotiated leadership dynamics. We identified connections between prosocial and aggressive behaviors, illuminating the nuances of morality in real life. Our analysis also revealed gendered patterns and age-related trends that deviated from cultural norms and contradicted popular assumptions about Chinese family values. These findings highlight the importance of naturalistic observations in cultural contexts for understanding how we become moral persons. This re-analysis of historically significant fieldnotes also enriches the interdisciplinary study of child development across societies.

Identification of clinical characteristics biomarkers for rheumatoid arthritis through targeted DNA methylation sequencing

OBJECTIVES

Rheumatoid arthritis (RA) is a complex disease with a challenging diagnosis, especially in seronegative patients. The aim of this study is to investigate whether the methylation sites associated with the overall immune response in RA can assist in clinical diagnosis, using targeted methylation sequencing technology on peripheral venous blood samples.

METHODS

The study enrolled 241 RA patients, 30 osteoarthritis patients (OA), and 30 healthy volunteers control (HC). Fifty significant cytosine guanine (CG) sites between undifferentiated arthritis and RA were selected and analyzed using targeted DNA methylation sequencing. Logistic regression models were used to establish diagnostic models for different clinical features of RA, and six machine learning methods (logit model, random forest, support vector machine, adaboost, naive bayes, and learning vector quantization) were used to construct clinical diagnostic models for different subtypes of RA. Least absolute shrinkage and selection operator regression and detrended correspondence analysis were utilized to screen for important CGs. Spearman correlation was used to calculate the correlation coefficient.

RESULTS

The study identified 16 important CG sites, including tumor necrosis factort receptor associated factor 5 (TRAF5) (chr1:211500151), mothers against decapentaplegic homolog 3 (SMAD3) (chr15:67357339), tumor endothelial marker 1 (CD248) (chr11:66083766), lysosomal trafficking regulator (LYST) (chr1:235998714), PR domain zinc finger protein 16 (PRDM16) (chr1:3307069), A-kinase anchoring protein 10 (AKAP10) (chr17:19850460), G protein subunit gamma 7 (GNG7) (chr19:2546620), yes1 associated transcriptional regulator (YAP1) (chr11:101980632), PRDM16 (chr1:3163969), histone deacetylase complex subunit sin3a (SIN3A) (chr15:75747445), prenylated rab acceptor protein 2 (ARL6IP5) (chr3:69134502), mitogen-activated protein kinase kinase kinase 4 (MAP3K4) (chr6:161412392), wnt family member 7A (WNT7A) (chr3:13895991), inhibin subunit beta B (INHBB) (chr2:121107018), deoxyribonucleic acid replication helicase/nuclease 2 (DNA2) (chr10:70231628) and chromosome 14 open reading frame 180 (C14orf180) (chr14:105055171). Seven CG sites showed abnormal changes between the three groups (P < 0.05), and 16 CG sites were significantly correlated with common clinical indicators (P < 0.05). Diagnostic models constructed using different CG sites had an area under the receiver operating characteristic curve (AUC) range of 0.64-0.78 for high-level clinical indicators of high clinical value, with specificity ranging from 0.42 to 0.77 and sensitivity ranging from 0.57 to 0.88. The AUC range for low-level clinical indicators of high clinical value was 0.63-0.72, with specificity ranging from 0.48 to 0.74 and sensitivity ranging from 0.72 to 0.88. Diagnostic models constructed using different CG sites showed good overall diagnostic accuracy for the four subtypes of RA, with an accuracy range of 0.61-0.96, a balanced accuracy range of 0.46-0.94, and an AUC range of 0.46-0.94.

CONCLUSIONS

This study identified potential clinical diagnostic biomarkers for RA and provided novel insights into the diagnosis and subtyping of RA. The use of targeted deoxyribonucleic acid (DNA) methylation sequencing and machine learning methods for establishing diagnostic models for different clinical features and subtypes of RA is innovative and can improve the accuracy and efficiency of RA diagnosis.

A novel network-based method identifies a cuproplasia-related pan-cancer gene signature to predict patient outcome

Copper is a vital micronutrient involved in many biological processes and is an essential component of tumour cell growth and migration. Copper influences tumour growth through a process called cuproplasia, defined as abnormal copper-dependent cell-growth and proliferation. Copper-chelation therapy targeting this process has demonstrated efficacy in several clinical trials against cancer. While the molecular pathways associated with cuproplasia are partially known, genetic heterogeneity across different cancer types has limited the understanding of how cuproplasia impacts patient survival. Utilising RNA-sequencing data from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) datasets, we generated gene regulatory networks to identify the critical cuproplasia-related genes across 23 different cancer types. From this, we identified a novel 8-gene cuproplasia-related gene signature associated with pan-cancer survival, and a 6-gene prognostic risk score model in low grade glioma. These findings highlight the use of gene regulatory networks to identify cuproplasia-related gene signatures that could be used to generate risk score models. This can potentially identify patients who could benefit from copper-chelation therapy and identifies novel targeted therapeutic strategies.

Bayesian Regression Facilitates Quantitative Modeling of Cell Metabolism

This paper presents Maud, a command-line application that implements Bayesian statistical inference for kinetic models of biochemical metabolic reaction networks. Maud takes into account quantitative information from omics experiments and background knowledge as well as structural information about kinetic mechanisms, regulatory interactions, and enzyme knockouts. Our paper reviews the existing options in this area, presents a case study illustrating how Maud can be used to analyze a metabolic network, and explains the biological, statistical, and computational design decisions underpinning Maud.

Plasma Metabolome Predicts Aortic Stiffness and Future Risk of Coronary Artery Disease and Mortality After 23 Years of Follow-Up in the General Population

BACKGROUND

Increased aortic stiffness (arteriosclerosis) is associated with early vascular aging independent of age and sex. The underlying mechanisms of early vascular aging remain largely unexplored in the general population. We aimed to investigate the plasma metabolomic profile in aortic stiffness (vascular aging) and associated risk of incident cardiovascular disease and mortality.

METHODS AND RESULTS

We included 6865 individuals from 2 Swedish population-based cohorts. Untargeted plasma metabolomics was performed by liquid-chromatography mass spectrometry. Aortic stiffness was assessed directly by carotid-femoral pulse wave velocity (PWV) and indirectly by augmentation index (AIx@75). A least absolute shrinkage and selection operator (LASSO) regression model was created on plasma metabolites in order to predict aortic stiffness. Associations between metabolite-predicted aortic stiffness and risk of new-onset cardiovascular disease, cardiovascular mortality, and all-cause mortality were calculated. Metabolite-predicted aortic stiffness (PWV and AIx@75) was positively associated particularly with acylcarnitines, dimethylguanidino valeric acid, glutamate, and cystine. The plasma metabolome predicted aortic stiffness (PWV and AIx@75) with good accuracy (R2=0.27 and R2=0.39, respectively). Metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly correlated with age, sex, systolic blood pressure, body mass index, and low-density lipoprotein. After 23 years of follow-up, metabolite-predicted aortic stiffness (PWV and AIx@75) was significantly associated with increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality.

CONCLUSIONS

Aortic stiffness is associated particularly with altered metabolism of acylcarnitines, cystine, and dimethylguanidino valeric acid. These metabolic disturbances predict increased risk of new-onset coronary artery disease, cardiovascular mortality, and all-cause mortality after more than 23 years of follow-up in the general population.