Long-term statistical assessment of meteorological indicators and COVID-19 outbreak in hot and arid climate, Bahrain

The COVID-19 pandemic has significantly impacted the global lifestyle, and the spreading of the virus is unprecedented. This study is aimed at assessing the association between the meteorological indicators such as air temperature (°C), relative humidity (%), wind speed (w/s), solar radiation, and PM_2.5 with the COVID-19 infected cases in the hot, arid climate of Bahrain. Kendall and Spearman rank correlation coefficients and quantile on quantile regression were used as main econometric analysis to determine the degree of the relationship between related variables. The dataset analysis was performed from 05 April 2020, to 10 January 2021. The empirical findings indicate that the air temperature, humidity, solar radiation, wind speed indicators, and PM2.5 have a significant association with the COVID-19 newly infected cases. The current study findings allow us to suggest that Bahrain's relatively successful response to neighboring GULF economies can be attributed to the successful environmental reforms and significant upgrades to the health care facilities. We further report that a long-term empirical analysis between meteorological factors and respiratory illness threats will provide useful policy measures against future outbreaks.

Correlation between phenotypic virulence traits and antibiotic resistance in Pseudomonas aeruginosa clinical isolates

Pseudomonas aeruginosa is a ubiquitous pathogen capable of infecting virtually all tissues and its one of the standout amongst the most hazardous microorganisms of high morbidity and mortality rates especially in debilitated patients with few successful antibiotic choices available. This pathogen regulating most virulence traits by that so-called quorum sensing (QS), a cell to cell communication system. the present study was intended to phenotypically evaluate the activity of specific virulence traits (including swarming and swimming motility, protease, pyocyanin, and biofilm production) in Pseudomonas aeruginosa clinical isolates and assess the statistical correlation between these traits and antibiotic resistance. One hundred and thirteen bacterial isolates were obtained from different clinical samples and identified as P. aeruginosa, among them, 73.4% have the ability to forming biofilm with different degrees; 59.2% were able to produce pyocyanin pigment while all isolates having the ability to make swarming and swimming motility and able to produce protease enzyme with different degrees. The isolates that produce the higher levels of the virulence traits were identified by both biochemical using Vitek2 automated system and genetically via 16s rRNA gene analysis. The statistical analysis results indicate that a positive significant correlation was found between biofilm formation and other studied virulence traits except for protease (r = 0.584: 0.324, P < 0.05) while a non-significant correlation was found between biofilm formation and protease activity (r = 0.105, P ˃ 0.05). Swimming and swarming motility have a positive significant correlation with other studied virulence traits (r = 0.613: 0.297, P < 0.05) except for protease. Pyocyanin pigment production have a positive significant correlation with other studied virulence traits (r = 0.33: 0.297, P < 0.05) except for protease. on the other hand, negative significant correlations were found between biofilm formation, swimming; and swarming motility, Pyocyanin pigment production, and the susceptibility of antibiotics (r = -0.512: -0.281, P < 0.05). Detection of such correlations in P. aeruginosa is useful for study the behavior of this pathogen and may be provide a new target for the treatment of MDR infections.

Safety effectiveness and performance of lane support systems for driving assistance and automation - Experimental test and logistic regression for rare events

As of 2022, lane-keeping assistance systems and other selected safety technologies will become mandatory in new European vehicles to increase safety for passengers, pedestrians and cyclists. Lane support systems (LSS) are based on advanced computer vision technologies and they are expected to give safety benefits in reducing Ran of Road and head-on crashes comparable to already consolidated physical countermeasures like rumble strips. Anyway, despite the assumed technology readiness, there is still much uncertainty regarding the needs of vision systems for "reading" the road and limited results are still available from in field testing. In such framework the paper presents an experimental test of LSS performance carried out in two lane rural roads with different geometric alignments and sections characterized by variable maintenance conditions for pavement and markings. LSS faults were detected in 2.6 % of the sections, running the roads in day light and dry pavement conditions. Logit models were developed to better understand road characteristics and conditions that can affect the system performance. The Firth penalized maximum-likelihood method was applied to estimate the logistic regression coefficients and standard errors to account for the rareness of the events. Results show that luminance coefficient of marking in diffuse lighting conditions (Qd) and horizontal curvature radius (1/R) are the main predictors of system fault. Based on the case study and test conditions, other marking characteristics, longitudinal cracking, verge width and running speed resulted not significant in explaining the probability of fault. Thresholds values for Qd and 1/R are suggested and remarks on road maintenance and design standards presented.

[Correlation: not all correlation entails causality]

The concept of correlation entails having a couple of observations (X and Y), that is to say, the value that Y acquires for a determined value of X; the correlation makes it possible to examine the trend of two variables to be grouped together. We know that, with increasing age, blood pressure figures also increase, therefore, if we want to answer a research question like "what is the connection between age and blood pressure?" the relevant statistical test is a correlation test. This test makes it possible to quantify the magnitude of the correlation between two variables, but it is also helpful for predicting values. If these variables had a perfect correlation, the value of the variable Y could be deduced by knowing the value of X. Because of these advantages, the correlation is one of the most frequently used tests in the clinical setting since, in addition to measuring the direction and magnitude of the association of two variables, it is one of the foundations for prediction models, such as linear regression model, logistic regression model and Cox proportional hazards model.

Comparative in vitro/in situ approaches to three biomarker responses of Myriophyllum alterniflorum exposed to metal stress

Surface water pollution by trace metal elements constitutes problems for both public and terrestrial/aquatic ecosystem health. Myriophyllum alterniflorum (alternate watermilfoil), an aquatic macrophyte known for bioaccumulating this type of pollutant, is an attractive species for plant biomonitoring within the scope of environmental research. The two metal elements copper (Cu) and cadmium (Cd) are considered in the present study. Cu is essential for plant development at low concentrations, while very high Cu concentrations are detrimental or even lethal to most plants. On the other hand, Cd is usually toxic even at low concentrations since it adversely affects the physiological plant functions. In order to check whether watermilfoil could be used for the in situ biomonitoring of Cu or Cd pollution in rivers, the plant biomarker sensitivity is first tested during long-term in vitro assays. Three markers specific to oxidative stress (glucose-6-phosphate dehydrogenase, malondialdehyde and α-tocopherol) are evaluated by varying the pollutant concentration levels. Given the absence of effective correlations between Cu and all biomarkers, the response profiles actually reveal a dependency between Cd concentration and malondialdehyde or α-tocopherol biomarkers. Conversely, preliminary in situ assays performed at 14 different localities demonstrate some clear correlations between all biomarkers and Cu, whereas the scarcity of Cd-contaminated rivers prevents using the statistical data. Consequently, the three indicated biomarkers appear to be effective for purposes of metal exposure analyses; moreover, the in situ approach, although preliminary, proves to be paramount in developing water biomonitoring bases.

Statistically correlating NMR spectra and LC-MS data to facilitate the identification of individual metabolites in metabolomics mixtures

NMR and LC-MS are two powerful techniques for metabolomics studies. In NMR spectra and LC-MS data collected on a series of metabolite mixtures, signals of the same individual metabolite are quantitatively correlated, based on the fact that NMR and LC-MS signals are derived from the same metabolite covary. Deconvoluting NMR spectra and LC-MS data of the mixtures through this kind of statistical correlation, NMR and LC-MS spectra of individual metabolites can be obtained as if the specific metabolite is virtually isolated from the mixture. Integrating NMR and LC-MS spectra, more abundant and orthogonal information on the same compound can significantly facilitate the identification of individual metabolites in the mixture. This strategy was demonstrated by deconvoluting 1D ¹³C, DEPT, HSQC, TOCSY, and LC-MS spectra acquired on 10 mixtures consisting of 6 typical metabolites with varying concentration. Based on statistical correlation analysis, NMR and LC-MS signals of individual metabolites in the mixtures can be extracted as if their spectra are acquired on the purified metabolite, which notably facilitates structure identification. Statistically correlating NMR spectra and LC-MS data (CoNaM) may represent a novel approach to identification of individual compounds in a mixture. The success of this strategy on the synthetic metabolite mixtures encourages application of the proposed strategy of CoNaM to biological samples (such as serum and cell extracts) in metabolomics studies to facilitate identification of potential biomarkers.

Relationship between the curve of Spee and craniofacial variables: A regression analysis

INTRODUCTION

The aim of this regression analysis was to identify the determining factors, which impact the curve of Spee during its genesis, its therapeutic reconstruction, and its stability, within a continuously evolving craniofacial morphology throughout life.

MATERIAL AND METHODS

We selected a total of 107 patients, according to the inclusion criteria. A morphological and functional clinical examination was performed for each patient: plaster models, tracing of the curve of Spee, crowding, Angle's classification, overjet and overbite were thus recorded. Then, we made a cephalometric analysis based on the standardized lateral cephalograms. In the sagittal dimension, we measured the values of angles ANB, SNA, SNB, SND, I/i; and the following distances: AoBo, I/NA, i/NB, SE and SL. In the vertical dimension, we measured the values of angles FMA, GoGn/SN, the occlusal plane, and the following distances: SAr, ArD, Ar/Con, Con/Gn, GoPo, HFP, HFA and IF. The statistical analysis was performed using the SPSS software with a significance level of 0.05.

RESULTS

Our sample including 107 subjects was composed of 77 female patients (71.3%) and 30 male patients (27.8%) 7 hypodivergent patients (6.5%), 56 hyperdivergent patients (52.3%) and 44 normodivergent patients (41.1%). Patients' mean age was 19.35±5.95 years. The hypodivergent patients presented more pronounced curves of Spee compared to the normodivergent and the hyperdivergent populations; patients in skeletal Class I presented less pronounced curves of Spee compared to patients in skeletal Class II and Class III. These differences were non significant (P>0.05). The curve of Spee was positively and moderately correlated with Angle's classification, overjet, overbite, sellion-articulare distance, and breathing type (P<0.05). We found no correlation between age, gender and the other parameters included in the study with the curve of Spee (P>0.05). Seventy five percent (75%) of the hyperdivergent patients with an oral breathing presented an overbite of 3mm, which is quite excessive given the characteristics often admitted for this typology; this parameter could explain the overbite observed in the hyperdivergent population included in this study. For the multivariate analysis, the overbite and the sellion-articulare distance remained independently related to the curve of Spee according to the breathing type, Angle's classification, and overjet. This regression model explains 21.4% of the changes in the curve of Spee.

Relationships between facial divergence and DMD parameters

INTRODUCTION

The aim of this study was to examine the relationship between facial divergence and the parameters of dentomaxillary discrepancy (DMD), in particular crowding, the curve of Spee and the position of the incisors in the sagittal dimension.

MATERIAL AND METHODS

A total of 90 young adult patients was selected from among the Moroccan orthodontic population attending the dentofacial orthopedic department and satisfying the following inclusion criteria: complete permanent dentition and a skeletal class I pattern with no previous orthodontic treatment, no crossbite, no periodontal disease, no mandibular asymmetry and no condylodiscal disunion. On cephalometric tracings, measurements were made of angles FMA, Go-Gn/SN, Occ/SN in the vertical direction, and of the values I/NA et i/NB in the sagittal direction. The curve of Spee and dental crowding were assessed using the one-way ANOVA test and the Bonferroni post-hoc test. Correlation analysis was performed between divergence and the different variables measured, using SPSS software with a 0.05 significance threshold.

RESULTS

Patients recruited for the study had a mean age of 19.8±0.5 and were distributed as follows: 28 normodivergent, 31 hypodivergent and 31 hyperdivergent, 42 females and 48 males. Comparison showed that hypodivergent subjects had less crowding than hypo- or normodivergent individuals (P<0.05). Hypodivergent subjects had a more pronounced curve of Spee than the other two groups. This difference was not significant (P>0.05). Hyperdivergent subjects presented more labioversion and vestibular positioning of the incisors compared with the hypodivergent (P<0.05) and normodivergent (P<0.05) groups. Correlation analysis showed that crowding and the incisor positions in millimeters and in degrees were positively correlated to a moderate extent with facial divergence (r=0.3, r=0.5, r=0.4; P<0.05), while the curve of Spee was not (P>0.05). No correlation was found between age or sex and the DMD parameters (P>0.05).

Streptococcus suis sorption on agricultural soils: role of soil physico-chemical properties

Understanding pathogen sorption on natural soil particles is crucial to protect public health from soilborne and waterborne diseases. Sorption of pathogen Streptococcus suis on 10 agricultural soils was examined, and its correlations with soil physico-chemical properties were also elucidated. S. suis sorption isotherms conformed to the linear equation, with partition coefficients (Ks) ranging from 12.7 mL g(-1) to 100.1 mL g(-1). Bacteria were observed to sorb on the external surfaces of soil aggregates by scanning electron microscopy. Using Pearson correlation and linear regression analysis, solution pH was found to have significant negative correlations with Ks. Stepwise multiple regression and path analysis revealed that pH and cation exchange capacity (CEC) were the main factors influencing sorption behaviors. The obtained overall model (Ks=389.6-45.9×pH-1.3×CEC, R(2)=0.943, P<0.001) can accurately predict Ks values. However, the variability in Ks was less dependent on soil organic matter, specific surface area, soil texture and zeta potential, probably due to the internal-surface shielding phenomenon of soil aggregates. Additionally, the sorption trends cannot be interpreted by interaction energy barriers calculated using the Derjaguin-Landau-Verwey-Overbeek (DLVO) theory, suggesting the limits of DLVO theory in describing pathogen sorption on natural soils. Our results also indicated soil pH and CEC should be preferentially considered when modeling S. suis sorption process.