Miracle-dz/UniFAR-FTUs huggingface dataset:
A Multivariate Poisson Deep Learning Model for Genomic Prediction of Count Data

The paradigm called genomic selection (GS) is a revolutionary way of developing new plants and animals. This is a predictive methodology, since it uses learning methods to perform its task. Unfortunately, there is no universal model that can be used for all types of predictions; for this reason, specific methodologies are required for each type of output (response variables). Since there is a lack of efficient methodologies for multivariate count data outcomes, in this paper, a multivariate Poisson deep neural network (MPDN) model is proposed for the genomic prediction of various count outcomes simultaneously. The MPDN model uses the minus log-likelihood of a Poisson distribution as a loss function, in hidden layers for capturing nonlinear patterns using the rectified linear unit (RELU) activation function and, in the output layer, the exponential activation function was used for producing outputs on the same scale of counts. The proposed MPDN model was compared to conventional generalized Poisson regression models and univariate Poisson deep learning models in two experimental data sets of count data. We found that the proposed MPDL outperformed univariate Poisson deep neural network models, but did not outperform, in terms of prediction, the univariate generalized Poisson regression models. All deep learning models were implemented in Tensorflow as back-end and Keras as front-end, which allows implementing these models on moderate and large data sets, which is a significant advantage over previous GS models for multivariate count data.
Immune Organs and Immune Cells on a Chip: An Overview of Biomedical Applications

Understanding the immune system is of great importance for the development of drugs and the design of medical implants. Traditionally, two-dimensional static cultures have been used to investigate the immune system in vitro, while animal models have been used to study the immune system’s function and behavior in vivo. However, these conventional models do not fully emulate the complexity of the human immune system or the human in vivo microenvironment. Consequently, many promising preclinical findings have not been reproduced in human clinical trials. Organ-on-a-chip platforms can provide a solution to bridge this gap by offering human micro-(patho)physiological systems in which the immune system can be studied. This review provides an overview of the existing immune-organs-on-a-chip platforms, with a special emphasis on interorgan communication. In addition, future challenges to develop a comprehensive immune system-on-chip model are discussed.
Enhancing Mouth-Based Emotion Recognition Using Transfer Learning

This work concludes the first study on mouth-based emotion recognition while adopting a transfer learning approach. Transfer learning results are paramount for mouth-based emotion emotion recognition, because few datasets are available, and most of them include emotional expressions simulated by actors, instead of adopting real-world categorisation. Using transfer learning, we can use fewer training data than training a whole network from scratch, and thus more efficiently fine-tune the network with emotional data and improve the convolutional neural network’s performance accuracy in the desired domain. The proposed approach aims at improving emotion recognition dynamically, taking into account not only new scenarios but also modified situations to the initial training phase, because the image of the mouth can be available even when the whole face is visible only in an unfavourable perspective. Typical applications include automated supervision of bedridden critical patients in a healthcare management environment, and portable applications supporting disabled users having difficulties in seeing or recognising facial emotions. This achievement takes advantage of previous preliminary works on mouth-based emotion recognition using deep-learning, and has the further benefit of having been tested and compared to a set of other networks using an extensive dataset for face-based emotion recognition, well known in the literature. The accuracy of mouth-based emotion recognition was also compared to the corresponding full-face emotion recognition; we found that the loss in accuracy is mostly compensated by consistent performance in the visual emotion recognition domain. We can, therefore, state that our method proves the importance of mouth detection in the complex process of emotion recognition.
High Thermoelectric Performance in Two-Dimensional Janus Monolayer Material WS-X (X = Se and Te)

In the present work, Janus monolayers WSSe and WSTe are investigated by combining first-principles calculations and semiclassical Boltzmann transport theory. Janus WSSe and WSTe monolayers show a direct band gap of 1.72 and 1.84 eV at K-points, respectively. These layered materials have an extraordinary Seebeck coefficient and electrical conductivity. This combination of high Seebeck coefficient and high electrical conductivity leads to a significantly large power factor. In addition, the lattice thermal conductivity in the Janus monolayer is found to be relatively very low as compared to the WS2 monolayer. This leads to a high figure of merit (ZT) value of 2.56 at higher temperatures for the Janus WSTe monolayer. We propose that the Janus WSTe monolayer could be used as a potential thermoelectric material due to its high thermoelectric performance. The result suggests that the Janus monolayer is a better candidate for excellent thermoelectric conversion.
Archaeal roots of intramembrane aspartyl protease siblings signal peptide peptidase and presenilin

Signal peptides help newly synthesized proteins reach the cell membrane or be secreted. As part of a biological process key to immune response and surveillance in humans, and associated with diseases, for example, Alzheimer, remnant signal peptides and other transmembrane segments are proteolyzed by the intramembrane aspartyl protease (IAP) enzyme family. Here, we identified IAP orthologs throughout the tree of life. In addition to eukaryotes, IAPs are encoded in metabolically diverse archaea from a wide range of environments. We found three distinct clades of archaeal IAPs: (a) Euryarchaeota (eg, halophilic Halobacteriales, methanogenic Methanosarcinales and Methanomicrobiales, marine Poseidoniales, acidophilic Thermoplasmatales, hyperthermophilic Archaeoglobus spp.), (b) DPANN, and (c) Bathyarchaeota, Crenarchaeota, and Asgard. IAPs were also present in bacterial genomes from uncultivated members of Candidate Phylum Radiation, perhaps due to horizontal gene transfer from DPANN archaeal lineages. Sequence analysis of the catalytic motif YD…GXGD (where X is any amino acid) in IAPs from archaea and bacteria reveals WD in Lokiarchaeota and many residue types in the X position. Gene neighborhood analysis in halophilic archaea shows IAP genes near corrinoid transporters (btuCDF genes). In marine Euryarchaeota, a putative BtuF‐like domain is found in N‐terminus of the IAP gene, suggesting a role for these IAPs in metal ion cofactor or other nutrient scavenging. Interestingly, eukaryotic IAP family members appear to have evolved either from Euryarchaeota or from Asgard archaea. Taken together, our phylogenetic and bioinformatics analysis should prompt experiments to probe the biological roles of IAPs in prokaryotic secretomes.
Protein Tyrosine Kinase 6 Promotes Oncogenic Signaling at Cell Plasma Membrane in Prostate Cancer

Protein tyrosine kinase 6 (PTK6) is an intracellular Src-related tyrosine kinase that is localized to different cellular compartments due to the lack of an N-terminal myristoylation consensus sequence. In normal tissue, PTK6 is expressed in differentiated epithelial cells, and involved in promoting differentiation and mediating apoptosis in response to apoptotic stimuli. Elevated expression of PTK6 is detected in different types of human cancer including breast, colon, head and neck, melanoma and ovarian cancer, where it promotes cancer cell proliferation, migration and survival through different signaling pathways. In prostate cancer, we discovered elevated expression, aberrant localization and activation of PTK6. High levels of PTK6 predict low survival and high recurrence for human prostate cancer patients. As human prostate cancer progresses to advanced stages, PTK6 is translocated from nucleus to cytoplasm. This is also observed in a murine prostate cancer model. Activation of PTK6 is detected in both human and mouse prostate tumor samples. Interestingly, the pool of active PTK6, which is phosphorylated at tyrosine residue 342, is primarily associated with the cell plasma membrane. To investigate the role of PTK6 in prostate cancer, I identified several novel PTK6 substrates. I showed that PTK6 directly phosphorylates AKT, focal adhesion kinase (FAK) and p130 Crk-associated substrate (p130CAS) independent of Src family kinases. PTK6 positively regulates AKT activity by phosphorylating AKT at tyrosine residues 315 and 326. PTK6 also plays a critical role in promoting cell survival against anoikis by phosphorylating FAK and activating AKT survival signaling. In addition, PTK6 phosphorylates p130CAS and activates Erk5 to promote peripheral adhesion complex formation and cell migration. PTK6 is also involved in promoting epithelial-mesenchymal transition (EMT) of prostate cancer cells. In sum, I demonstrated that PTK6 promotes proliferation, migration, anoikis resistance, EMT, and invasion of prostate cancer cells through activating its substrates AKT, FAK and p130CAS. This study suggested that elevated PTK6 expression, translocation of PTK6 from nucleus to cytoplasm, and activation of PTK6 at plasma membrane in human prostate cancer might all contribute to the access and phosphorylation of these substrates and activation of their signaling pathways, therefore promoting prostate cancer progression.
Is Endoscopic Discectomy the Next Gold Standard in the Management of Lumbar Disc Disease? Systematic Review and Superiority Analysis

Study Design: Systematic review and meta-analysis. Objectives: We performed this meta-analysis to evaluate whether endoscopic discectomy (ED) shows superiority compared with the current gold standard of microdiscectomy (MD) in management of lumbar disc disease. Materials and Methods: We conducted independent and duplicate electronic database search including PubMed, Embase, and Cochrane Library from 1990 till April 2020 for studies comparing ED and MD in the management of lumbar disc disease. Analysis was performed in R platform using OpenMeta[Analyst] software. Results: We included 27 studies, including 11 randomized controlled trials (RCTs), 7 nonrandomized prospective, and 9 retrospective studies involving 4018 patients in the meta-analysis. We stratified the results based on the study design. Considering the heterogeneity in some results between study designs, we weighed our conclusion essentially based on results of RCTs. On analyzing the RCTs, superiority was established at 95% confidence interval for ED compared with MD in terms of functional outcomes like Oswestry Disability Index (ODI) score (P = .008), duration of surgery (P = .023), and length of hospital stay (P < .001) although significant heterogeneity was noted. Similarly, noninferiority to MD was established by ED in other outcomes like visual analogue scale score for back pain (P = .860) and leg pain (P = .495), MacNab classification (P = .097), recurrences (P = .993), reoperations (P = .740), and return-to-work period (P = .748). Conclusion: Our meta-analysis established the superiority of endoscopic discectomy in outcome measures like ODI score, duration of surgery, overall complications, length of hospital stay and noninferiority in other measures analyzed. With recent advances in the field of ED, the procedure has the potential to take over the place of MD as the gold standard of care in management of lumbar disc disease.
Factors Associated with Body Size Perception and Body Image (Dis)Satisfaction in the Elderly: Results of the ELSA-Brasil Study

The study aimed to assess the association between body image perception and (dis)satisfaction and sociodemographic and behavioral factors in the elderly, using multinomial logistic regression. Data were analyzed for 1686 women and 1499 men participating in the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil). Men with less schooling and women with lower per capita income showed higher odds of underestimating their body size. Former smokers of both sexes showed higher odds of overestimating their body size; lower schooling and lower per capita income decreased these odds. Increasing age, lower per capita income, and smoking increased the odds of dissatisfaction due to thinness in men, and married marital status decreased these odds. In women, low per capita income, weekly consumption of vegetables, and smoking increased the odds of such dissatisfaction. Factors that increased the odds of dissatisfaction due to excess weight in both sexes were primary or secondary schooling and former smoking. In women, low per capita income, weak physical activity, weekly consumption of vegetables, and excessive alcohol intake also increased the odds of such dissatisfaction. The results suggest that improved living conditions and the adoption of healthy behaviors can help reduce misperceived negative body image among elderly.
Dietary Iron Overload Differentially Modulates Chemically-Induced Liver Injury in Rats

Hepatic iron overload is well known as an important risk factor for progression of liver diseases; however, it is unknown whether it can alter the susceptibility to drug-induced hepatotoxicity. Here we investigate the pathological roles of iron overload in two single-dose models of chemically-induced liver injury. Rats were fed a high-iron (Fe) or standard diet (Cont) for four weeks and were then administered with allyl alcohol (AA) or carbon tetrachloride (CCl4). Twenty-four hours after administration mild mononuclear cell infiltration was seen in the periportal/portal area (Zone 1) in Cont-AA group, whereas extensive hepatocellular necrosis was seen in Fe-AA group. Centrilobular (Zone 3) hepatocellular necrosis was prominent in Cont-CCl4 group, which was attenuated in Fe-CCl4 group. Hepatic lipid peroxidation and hepatocellular DNA damage increased in Fe-AA group compared with Cont-AA group. Hepatic caspase-3 cleavage increased in Cont-CCl4 group, which was suppressed in Fe-CCl4 group. Our results showed that dietary iron overload exacerbates AA-induced Zone-1 liver injury via enhanced oxidative stress while it attenuates CCl4-induced Zone-3 liver injury, partly via the suppression of apoptosis pathway. This study suggested that susceptibility to drugs or chemical compounds can be differentially altered in iron-overloaded livers.
Effects of Ultrasonic-Assisted Extraction on the Yield and the Antioxidative Potential of Bergenia emeiensis Triterpenes

This study was the first designed to evaluate the extraction and antioxidant ability of triterpenes from Bergenia emeiensis rhizomes. The yield of triterpenes from B. emeiensis was mainly affected by the concentration of ethanol, followed by the extraction time, solvent to sample ratio, and the power of ultrasound. Thus, the response surface method was applied to investigate the interaction between the two factors and to optimize the extraction process. The optimal extraction conditions were 210 W, 75% ethanol, 40 min and 25 mL/g with a maximum yield of 229.37 ± 7.16 mg UAE/g. Moreover, the antioxidant ability of triterpenes from B. emeiensis (TBE) was evaluated by determining the scavenging capacity on free radicals and the protection on CHO cells and Caenorhabditis elegans against oxidative stress. The results showed the triterpenes could clear 2,2-Diphenyl-1-picryl-hydrazyl (DPPH) radicals well and had a strong reducing power. In addition, the survival of CHO cells was higher than that of the control group as a result of reducing the reactive oxygen species (ROS) level and promoting the activities of antioxidant enzymes. In addition, TBE could also enhance the survival of C. elegans under H2O2 conditions. Therefore, triterpenes from B. emeiensis could be developed into a beneficial potential for antioxidants.
Strategy or symptom: Semantic clustering and risk of Alzheimer’s disease-related impairment

ABSTRACT Alzheimer’s disease (AD) is the most common form of dementia, impacting global cognitive performance, including episodic memory. Semantic clustering is a learning strategy involving grouping words of similar meaning and can improve episodic memory performance, e.g., list learning. As the APOE ε4 allele is the most validated genetic risk factor for AD, we predicted that its presence would be associated with poorer list learning performance, and we hypothesized that semantic clustering moderates or mediates this association. The sample comprised 699 healthy older adults participating in the CHARIOT PRO Main Study, 169 of whom were APOE ε4 carriers. Participants’ ability to form groups of related stimuli (assessed via a categorization task, CAT), and their use of semantic clustering during list learning, were investigated using the Neuropsychological Assessment Battery (NAB). CAT scores predicted the use of semantic clustering in, and performance on, the list learning task. CAT scores were not significantly lower in APOE ε4 carriers, suggesting that the ability to categorize was preserved. However, APOE ε4 carriers made less use of semantic clustering in list learning. Semantic clustering use partially mediated the relationship between CAT scores and list learning performance, and, in women only, moderated the impact of APOE ε4 on list learning performance. The results suggest that better categorization ability is associated with greater use of mnemonic strategies and better performance on memory tasks regardless of genetic risk, but that APOE ε4 carriers make less use of such strategies. Furthermore, female APOE ε4 carriers may benefit more than their non-carriers from using semantic clustering to aid list learning. Thus, semantic clustering may be a contributing factor of their “cognitive reserve”, compensating for potential deficits in episodic memory.
Effectiveness of surgical and non-surgical interventions for managing diabetic shoulder pain: a systematic review

Abstract Purpose This systematic review evaluated and compared the effectiveness of non-surgical and surgical interventions for managing shoulder pain in patients with diabetes. Methods PubMed, Scopus, CINAHL, EMBASE, Sport Discus, and Cochrane library were searched for studies published in the last 20 years. Randomized clinical trials (RCTs) and cohort studies that assessed shoulder pain in patients with diabetes and implemented one or a combination of non-surgical and surgical interventions were eligible for inclusion. The quality of the included studies was assessed using the Structured Effectiveness Quality Evaluation Scale (SEQES) tool. Data extracted from the eligible studies included study design, patient characteristics, duration of symptoms, type of interventions, outcome measures used to assess pain, follow-up intervals, and research findings. Results A narrative synthesis with effect sizes (ES) or between-group differences was conducted. A total of 25 (14 non-surgical and 11 surgical) studies met the inclusion criteria. Six studies addressed physiotherapeutic interventions (three RCTs and three cohorts – ES = 0.07–1.3), three studies assessed the effect of steroid injections (two RCTs and one cohort – ES = 0.2–0.4), two cohorts addressed arthrographic capsular distension (between-group difference of 1.1 on Visual Analogue Scale), two cohorts addressed MUA, and one RCT addressed suprascapular nerve block (ES = 1–6). For the surgical studies, six cohorts addressed arthroscopic capsular release (ES = 0.2), three cohorts addressed arthroscopic rotator cuff repair (ES = 0.05–0.5), and one cohort addressed arthroplasty (ES = 0.3). Conclusion Moderate- to very-low-quality evidence suggests large effects of physiotherapy modalities plus exercise and suprascapular nerve block, and trivial to small effects for surgical interventions for improving shoulder pain in patients with diabetes. Future well-designed studies are needed to provide accurate estimates of the true effects of these interventions on improving shoulder pain in patients with diabetes. Implications For Rehabilitation Shoulder pain may lead to disability in patients with diabetes. We recommend the use of physiotherapy interventions to reduce shoulder pain. Corticosteroid injections are recommended for short-term shoulder pain relief.
Reporting of demographic data and representativeness in machine learning models using electronic health records

OBJECTIVE The development of machine learning (ML) algorithms to address a variety of issues faced in clinical practice has increased rapidly. However, questions have arisen regarding biases in their development that can affect their applicability in specific populations. We sought to evaluate whether studies developing ML models from electronic health record (EHR) data report sufficient demographic data on the study populations to demonstrate representativeness and reproducibility. MATERIALS AND METHODS We searched PubMed for articles applying ML models to improve clinical decision-making using EHR data. We limited our search to papers published between 2015 and 2019. RESULTS Across the 164 studies reviewed, demographic variables were inconsistently reported and/or included as model inputs. Race/ethnicity was not reported in 64%; gender and age were not reported in 24% and 21% of studies, respectively. Socioeconomic status of the population was not reported in 92% of studies. Studies that mentioned these variables often did not report if they were included as model inputs. Few models (12%) were validated using external populations. Few studies (17%) open-sourced their code. Populations in the ML studies include higher proportions of White and Black yet fewer Hispanic subjects compared to the general US population. DISCUSSION The demographic characteristics of study populations are poorly reported in the ML literature based on EHR data. Demographic representativeness in training data and model transparency is necessary to ensure that ML models are deployed in an equitable and reproducible manner. Wider adoption of reporting guidelines is warranted to improve representativeness and reproducibility.
Identification of Potential COX-2 Inhibitors for the Treatment of Inflammatory Diseases Using Molecular Modeling Approaches

Non-steroidal anti-inflammatory drugs are inhibitors of cyclooxygenase-2 (COX-2) that were developed in order to avoid the side effects of non-selective inhibitors of COX-1. Thus, the present study aims to identify new selective chemical entities for the COX-2 enzyme via molecular modeling approaches. The best pharmacophore model was used to identify compounds within the ZINC database. The molecular properties were determined and selected with Pearson’s correlation for the construction of quantitative structure–activity relationship (QSAR) models to predict the biological activities of the compounds obtained with virtual screening. The pharmacokinetic/toxicological profiles of the compounds were determined, as well as the binding modes through molecular docking compared to commercial compounds (rofecoxib and celecoxib). The QSAR analysis showed a fit with R = 0.9617, R2 = 0.9250, standard error of estimate (SEE) = 0.2238, and F = 46.2739, with the tetra-parametric regression model. After the analysis, only three promising inhibitors were selected, Z-964, Z-627, and Z-814, with their predicted pIC50 (−log IC50) values, Z-814 = 7.9484, Z-627 = 9.3458, and Z-964 = 9.5272. All candidates inhibitors complied with Lipinski’s rule of five, which predicts a good oral availability and can be used in in vitro and in vivo tests in the zebrafish model in order to confirm the obtained in silico data.