A Composite Ranking of Risk Factors for COVID-19 Time-To-Event Data from a Turkish Cohort

Having a complete and reliable list of risk factors from routine laboratory blood test for COVID-19 disease severity and mortality is important for patient care and hospital management. It is common to use meta-analysis to combine analysis results from different studies to make it more reproducible. In this paper, we propose to run multiple analyses on the same set of data to produce a more robust list of risk factors. With our time-to-event survival data, the standard survival analysis were extended in three directions. The first is to extend from tests and corresponding p-values to machine learning and their prediction performance. The second is to extend from single-variable to multiple-variable analysis. The third is to expand from analyzing time-to-decease data with death as the event of interest to analyzing time-to-hospital-release data to treat early recover as a meaningful event as well. Our extension of the type of analyses leads to ten ranking lists. We conclude that 20 out of 30 factors are deemed to be reliably associated to faster-death or faster-recovery. Considering correlation among factors and evidenced by stepwise variable selection in random survival forest, 10~15 factors seem to be able to achieve the optimal prognosis performance. Our final list of risk factors contains calcium, white blood cell and neutrophils count, urea and creatine, d-dimer, red cell distribution widths, age, ferritin, glucose, lactate dehydrogenase, lymphocyte, basophils, anemia related factors (hemoglobin, hematocrit, mean corpuscular hemoglobin concentration), sodium, potassium, eosinophils, and aspartate aminotransferase.

Bearing fault diagnosis using a speed-adaptive network based on vibro-speed data fusion and majority voting

Fault diagnosis of rolling bearings is key to maintain and repair modern rotating machinery. Rolling bearings are usually working in non-stationary conditions with time-varying loads and speeds. Existing diagnosis methods based on vibration signals only don’ t have the ability to adapt to rotational speed. And when the load changes, the accuracy rate of them will be obviously reduced. A method is put forward which fuses multi-modal sensor signals to fit speed information. Firstly, the features are extracted from raw vibration signals and instantaneous rotating speed signals, and fused by 1D-CNN-based networks. Secondly, to improve the robustness of the model when the load changes, a majority voting mechanism is proposed in the diagnosis stage. Lastly, Multiple variable speed samples of four bearings under three loads are obtained to evaluate the performance of the proposed method by analyzing the loss function, accuracy rate and F1 score under different variable speed samples. It is empirically found that the proposed method achieves higher diagnostic accuracy and speed-adaptive ability than the algorithms based on vibration signal only. Moreover, A couple of ablation studies are also conducted to investigate the inner mechanism of the proposed speed-adaptive network.

Hypomineralized Primary Teeth in Preterm Low Birth Weight Children and Its Association with Molar Incisor Hypomineralization—A 3-Year-Prospective Study

The present prospective study was conducted to assess the prevalence of enamel hypomineralization (EH) in primary dentition among preterm low birth weight (PT-LBW) children, incidence of molar incisor hypomineralization (MIH) in the same cohorts, and to determine associations between PT-LBW, hypomineralization in primary second molars, and MIH. A total of 287 PTLBW study subjects and 290 control full-term normal birth weight subjects were followed up for 36 months. Enamel defects were recorded at baseline. The same cohorts were examined after 3 years for MIH using the European Academy of Paediatric Dentistry (EAPD) criteria. Multiple variable logistic regression models were developed. A total of 279 children (48.4%) presented with EH in primary dentition and 207 (35.9%) children presented with MIH. Children with primary second molar hypomineralization had 2.13 (R2 = 0.19, 95% CI = 0.98–4.19, p = 0.005) times higher frequency of MIH. Children with PT-LBW had 3.02 times (R2 = 0.31, 95% CI = 1.01–5.94, p = 0.005) higher frequency of MIH incidence after adjusting for childhood infection, prenatal history, and presence of hypomineralized primary second molars. To conclude, the present study showed significant association between PT-LBW, hypomineralized second primary molars, and incidence of MIH.

Two-Stage Modulation Study for DAB Converter

The dual active bridge (DAB) is a dc/dc converter frequently employed in multiple applications due to its advantages of isolation, power flexibility, and input and output voltage defined by design. Different multi-phase modulations have been reported to increase DAB efficiency and reduce components stress but, certainly, the control and implementation complexity increases with every displacement angle added because of the multiple variable measurements. This paper presents two-stage modulations based on the single phase-shift (SPS), which allows maintaining simplicity while improving the efficiency of the DAB converter depending on the design conditions. The paper also gives a direction for selecting the proper modulation to achieve the best efficiency for each situation. The different two-stage modulations are described, analyzed, and experimentally tested.

Linear regression equations to predict β-lactam, macrolide, lincosamide and fluoroquinolone minimum inhibitory concentrations from molecular antimicrobial resistance determinants in Streptococcus pneumoniae

Antimicrobial resistance in Streptococcus pneumoniae represents a threat to public health and monitoring the dissemination of resistant strains is essential to guiding health policy. Multiple-variable linear regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to antimicrobial minimum inhibitory concentration (MIC) for penicillin, ceftriaxone, erythromycin, clarithromycin, clindamycin, levofloxacin, and trimethoprim/sulfamethoxazole. Training data sets consisting of Canadian S. pneumoniae isolated from 1995 to 2019 were used to generate multiple-variable linear regression equations for each antimicrobial. The regression equations were then applied to validation data sets of Canadian (n=439) and USA (n=607 and n=747) isolates. The MIC for β-lactam antimicrobials were fully explained by amino acid substitutions in motif regions of the penicillin binding proteins PBP1a, PPB2b, and PBP2x. Accuracy of predicted MICs within one doubling dilution to phenotypically determined MICs for penicillin was 97.4%, ceftriaxone 98.2%; erythromycin 94.8%; clarithromycin 96.6%; clindamycin 98.2%; levofloxacin 100%; and trimethoprim/sulfamethoxazole 98.8%; with an overall sensitivity of 95.8% and specificity of 98.0%. Accuracy of predicted MICs to the phenotypically determined MICs was similar to phenotype-only MIC comparison studies. The ability to acquire detailed antimicrobial resistance information directly from molecular determinants will facilitate the transition from routine phenotypic testing to whole genome sequencing analysis and can fill the surveillance gap in an era of increased reliance on nucleic acid assay diagnostics to better monitor the dynamics of S. pneumoniae .

Multiple Variable Social Distancing Model in Reducing COVID 19 Spreading: A Long Term Social Distancing Model in Developing Nations Like Bangladesh

Covid-19 pandemic requires strong and inclusive social distancing policies for the people. Governments around the world have taken policy steps and citizen were expected to behave accordingly. However, Countries like Bangladesh faced enormous difficulties and challenges to make people understand and obey them. There is variety of groups in the society among whom a few were respectful and some were negligent towards those social distancing policies. This study tried to find what makes them obey or disobey the prescribed pandemic behavior. This research found people over-confident, religiously blind, economically bound, poorly educated, doubtful to their government, impatient about the time frame of the lockdown and social distancing rules and so on. Additionally, sometimes government policies were proved to be incompatible with the rising situation.   Primary hypothesis of this study is, ``willingness of social distancing is a dependent variable where over-confidence, education, poverty, religion, public trusts are independent variables. Taking general policies like other natural disaster or calamities may not be as effective as it was before.’’ This pandemic is an event which needs an in-depth research, wider policy analysis and firm implementation. Here is a suggestion called `multiple variable social distancing model' which will study about the variables first and guide to a wider policy structure afterwards.

In a Neotropical Periurban Park, Fruit Consumption by Birds Seems to Be a Random Process

Frugivory interactions between birds and fruit-bearing plants are shaped by the abundance of its interacting species, their temporal overlap, the matching of their morphologies, as well as fruit and seed characteristics. Our study evaluates the role of seven factors of fruits and plants in determining the frequency of whole-fruit consumption by birds. We studied the frugivory network of a Neotropical periurban park in Xalapa, Veracruz, Mexico, and quantified relative abundance and phenology of birds and fruit, as well as fruit morphology, chromatic and achromatic contrast, and nutritional content. Using a maximum likelihood approach, we compared the observed interaction network with 62 single- and multiple-variable probabilistic models. Our network is composed of 11 plants and 17 birds involved in 81 frugivory interactions. This network is nested, modular, and relatively specialized. However, the frequency of pairwise interactions is not explained by the variables examined in our probabilistic models and found the null model has the best performance. This indicates that no single predictor or combination of them is better at explaining the observed frequency of pairwise interactions than the null model. The subsequent four top-ranking models, with ΔAIC values < 100, are single-variable ones: carbohydrate content, lipid content, chromatic contrast, and morphology. Two- and three-variable models show the poorest fit to observed data. The lack of a deterministic pattern does not support any of our predictions nor neutral- or niche-based processes shaping the observed pattern of fruit consumption in our interaction network. It may also mean that fruit consumption by birds in this periurban park is a random process. Although our study failed to find a pattern, our work exemplifies how investigations done in urban settings, poor in species and interactions, can help us understand the role of disturbance in the organization of frugivory networks and the processes governing their structure.

Autosomal dominant polycystic kidney disease: A case report

Autosomal dominant polycystic kidney disease (ADPKD) is an inherited condition that causes small fluid-filled sacs called cysts to develop in the kidneys. ADPKD affects 1 in 500–1000 people. We report the case of a 70-year-old male patient with complaints of difficulty in passing urine, lower abdominal pain, fever with chills, and hematuria. The ultrasonography report of the abdomen and pelvis showed bilaterally mildly enlarged kidneys with multiple variable simple cysts slightly distorting the normal renal architecture and mildly increased residual visualized cortical echoes concerning adult polycystic kidney changes. The therapeutic regimen was targeted to treat the symptoms of the patient. In India, there is a need for epidemiological studies on ADPKD since insignificant data are present that could rationally reflect its prevalence and incidence, for which this case report could be utilized.

Transgender Polyvictimization in the U.S. Transgender Survey

Relative to cisgender people, transgender individuals not only are at an elevated risk of experiencing at least one form of physical or sexual violence, but also at an increased risk of polyvictimization: that is, exposure to multiple types of violence over the life course. Given that polyvictimization increases vulnerability to adverse mental and physical health sequelae, there is a pressing need to identify which sociodemographic subgroups of transgender people are at greater risk of polyvictimization. Understanding these risk profiles will have important implications for developing transgender-specific models for violence prevention, screening, and intervention. Responding to this need in the literature, the present article offers secondary data analyses of the 2015 U.S. Transgender Survey, the largest study to-date of transgender people in the United States ( N = 27,715 transgender adults), with participants selected from all 50 states and Washington, D.C., as well as several U.S. territories and overseas military bases. Multiple variable regressions examine sociodemographic predictors of five distinct forms of violence, as well as how many of these five violence types were experienced. The examined sociodemographic predictors included gender, sexual orientation, race-ethnicity, citizenship status, ever been homeless, has a disability, transgender outness, gender visual conformity, household income, and age. The five assessed violence victimization types included intimate partner violence, nonpartner sexual assault, antitransgender family violence, antitransgender physical violence during Kindergarten through 12th grade, and past-year antitransgender physical violence. With some exceptions, results indicate that more marginalized segments of transgender communities have a greater likelihood of experiencing polyvictimization. Recommendations are discussed for future research and service provision.