Miracle-dz/UniFAR-FTUs huggingface dataset:
Revealing variants in SARS-CoV-2 interaction domain of ACE2 and loss of function intolerance through analysis of >200,000 exomes

Our researchers took a look at a sequence of DNA known as the ACE2 gene. This gene is most well known for its role in regulating blood pressure. But in recent times, it’s drawn a lot of attention from the scientific community because it may also serve as a doorway of sorts, enabling viruses like SARS-CoV-2 to infect cells. Our researchers looked at the ACE2 gene in more than 200,000 people, comparing their exact DNA sequences to see where there are differences among people. Variation in the DNA sequence of a gene is common and is sometimes meaningless. But other times, small changes in the DNA sequence can alter the protein that is made from that gene. In this case the ACE2 gene makes the ACE2 protein, which is what the SARS-CoV-2 virus interacts with. We found a lot of variation between individuals and checked to see if that variation coincided with any traits (i.e., people with variant X tend to have high blood pressure more often than people without variant X). All of the traits we looked at were non-COVID-19-related traits, meaning we haven’t asked these people anything about COVID-19 yet (this is because these DNA sequences were collected before the pandemic). We found that there are a number of variations observed among people in a specific part of the ACE2 gene. These variations are expected to alter the shape or functionality of a specific part of the ACE2 protein: The part that interacts with the SARS-CoV-2 virus. We don’t yet know what the real-life significance of this variation is, but it’s possible that these variants decrease the protein’s ability to interact with the SARS-CoV-2 virus, thus decreasing the person’s likelihood of being infected. We can speculate that there will be a spectrum of vulnerability to COVID-19 among people, where some people are more vulnerable than others, and that variants in this part of the ACE2 gene may be one of the reasons. The research we presented here shines a light on this part of the ACE2 gene and may give future researchers a direction to go in as they try to figure out what makes people vulnerable to COVID-19 and similar viruses.
HydraPicker: Fully Automated Particle Picking in Cryo-EM by Utilizing Dataset Bias in Single Shot Detection

Particle picking in cryo-EM is a form of object detection for noisy, low contrast, and out-of-focus microscopy images, taken of different (unknown) structures. This paper presents a fully automated approach which, for the ﬁrst time, explicitly considers training on multiple structures, while simultaneously learning both specialized models for each structure used for training and a generic model that can be applied to unseen structures. The presented architecture is fully convolutional and divided into two parts: (i) a portion which shares its weights across all structures and (ii) N+1 parallel sets of sub-architectures, N of which are specialized to the structures used for training and a generic model whose weights are tied to the layers for the specialized models. Experiments reveal improvements in multiple use cases over the-state-of-art and present additional possibilities to practitioners.
Deep Learning Fusion of RGB and Depth Images for Pedestrian Detection

In this paper, we propose an effective method based on the Faster-RCNN structureto combine RGB and depth images for pedestrian detection. During the training stage,we generate a semantic segmentation map from the depth image and use it to refine theconvolutional features extracted from the RGB images. In addition, we acquire moreaccurate region proposals by exploring the perspective projection with the help of depthinformation. Experimental results demonstrate that our proposed method achieves thestate-of-the-art RGBD pedestrian detection performance on KITTI [12] datase
ANCONE: An Interactive System for Mining and Visualization of Students' Information in the Context of PLANEA 2015

Data mining has been widely used in different areas of knowledge, and education is no exception. Data mining uses computer models to analyze data and answer research questions to help in decision making. This article uses data from the PLANEA 2015 Mathematics in Middle school (last year of Middle school) test, which measures the academic achievement and provides a personal, family and school context, in order to find those characteristics that are related to the academic level of the tested students. In this article, an interactive visualization system was developed that allows observing interesting patterns and association rules by combining relevant attributes (variables) and the States. To reduce the analysis space, the Correlation-Based Feature Selection method was used to reduce categorical and numerical attributes. The results show a significant reduction (93%) in the number of attributes, with very little loss of information, when certain attributes are eliminated. Particularly, the 232 categorical attributes obtained from each student are reduced to only 18 attributes, which are correlated with the results of students in the PLANEA test. In addition, empirically it was discovered that choosing the mode from the labels of plausible values as the target class increases the accuracy in classifiers used to show the goodness of the reduction obtained. Some of the relevant attributes are the "AcademicAspiration", "FamilyResources", "MotherStudies" and "FatherStudies". From the 30 States with information, only 8 are in the Basic level, the other States are in the Below Basic level .
Practices of Using Blockchain Technology in ICT under the Digitalization of the World Economy

Pursuing the purpose of effective functioning in today's conditions, the business is forced to transform rapidly, to modernize at all levels. The world is changing, erasing the limits of its certainty. Companies need quality transformations and strategies that are effective in the face of rapid change towards "deep" digitization. Massive corporate management systems increasingly need the flexibility to keep pace with change. And companies with an innovative culture are more in need of creative tasks than implementing detailed regulations. In the post-industrial time of digital economy, issues related to the development of the information sphere, the media and communications, the usage of modern information systems to develop the economy and stabilize social development as a whole, come first. The basic principles of practical application of Block-chain are investigated in the work. The stages of development of Blockchain technology, the stages of development of Blockchain technologies by time, the application of distributed registry technology in Blockchain applications, the principles of construction and operation of Blockchain have been specified. The benefits of using NEM for business are substantiated and the components of Proxima X technology, protocols and service layers, on-line and off-line protocols, decentralized applications are exposed
Merge-SfM: Merging Partial Reconstructions

Recovering a 3D scene from unordered photo collections is a long-studied topic in computer vision. Existing reconstruction pipelines, both incremental and global, have already achieved remarkable results. This paper addresses the problem of fusing multiple existing partial 3D reconstructions, in particular ﬁnding the overlapping regions and transformations (7 DOF) between partial reconstructions. Unlike the previous methods which have to take the entire epipolar geometry (EG) graph as the input and reconstruct the scene, we propose an approach that reuses the existing reconstructed 3D models as input and merges them by utilizing all the internal information to avoid repeated work. This approach is divided into two steps. The ﬁrst is to ﬁnd overlapping areas between partial reconstructions based on Fisher similarity lists. Then, based on those overlaps, pairwise rotation between partial reconstructions is estimated by solving an (cid:96) 1 approximation optimization problem. After global rotation estimation, translation and scale between each pair of partial reconstructions are computed simultaneously in a global manner. In order to ﬁnd the optimal transformation path, the maximal spanning tree (MST) is constructed in the second stage. Our approach is evaluated on diverse challenging public datasets and compared to state-of-the-art Structure from Motion (SfM) methods. Experiments show that our merging approach achieves high computational efﬁciency while preserving similar reconstruction accuracy and robustness. In addition, our method has superior extensibility which can add partial 3D reconstructions gradually to extend an existing 3D scene.
Lifestyle associated factors and risk of urinary bladder cancer: A prospective cohort study from Norway

A number of lifestyle associated factors, such as high body mass index (BMI), low physical activity, and related metabolic disorders, are associated with increased risk of cancer at several sites. For urinary bladder cancer (BC), such studies show inconsistent results, which could result from inadequate adjustment for smoking and occupational exposure. In the population‐based Janus Cohort (n = 292 851), we investigated the independent and combined impact of BMI, physical activity, blood pressure, and blood lipids on the risk of BC, by thorough adjustment for smoking and potential occupational exposure. We used cox proportional hazard regression to estimate hazard ratios (HRs) with 95% confidence intervals (CIs) for the associations between the lifestyle associated factors and BC risk. The associations observed were dependent on smoking status and gender. Among men, diastolic blood pressure (DBP) (HR 1.07, 95% CI 1.02‐1.12) and systolic blood pressure (SBP) (HR 1.04, 95% CI 1.01‐1.07) were positively associated with BC risk. Stratification by smoking status revealed a positive association between DBP and BC risk in never smokers (HR 1.14, 95% CI 1.00‐1.30), while no association was seen for current and former smokers. A risk score, integrating information across the lifestyle factors was positively associated with BC risk in men (ptrend = 0.043). In women, physical activity was associated with a decreased BC risk, but only among never smokers (HR 0.65, 95% CI 0.45‐0.94). In conclusion, relations between lifestyle associated factors and BC risk were most evident in never smokers, suggesting that smoking dominates the relation in current smokers.
Dietary Patterns Based on Estimated Glomerular Filtration Rate and Kidney Function Decline in the General Population: The Lifelines Cohort Study

No specific dietary patterns have been established that are linked with loss of kidney function. We aimed to identify an estimated glomerular filtration rate-based dietary pattern (eGFR-DP) and to evaluate its association with eGFR decline and chronic kidney disease (CKD) incidence in the general population. We included 78,335 participants from the Lifelines cohort in the Northern Netherlands. All participants had an eGFR >60 mL/min/1.73 m2 at baseline and completed a second visit five years later. The eGFR-DP was constructed at baseline using a 110-item food frequency questionnaire by reduced rank regression, stratified by sex. Logistic regression was performed to evaluated the association between the eGFR-DP score and either a ≥20% eGFR decline or incident CKD. Among women, eGFR-DP were characterized by high consumption of egg, cheese, and legumes and low consumption of sweets, white meat, and commercially prepared dishes. In men, eGFR-DP were characterized by high consumption of cheese, bread, milk, fruits, vegetables, and beer and low consumption of white and red meat. A higher eGFR-DP score was associated with a lower risk of a ≥20% eGFR decline (OR 4th vs. 1st quartile, women: 0.79 [95% CI: 0.73–0.87]; men: 0.67 [0.59–0.76]). The association between the eGFR-DP score and CKD incidence was lost upon adjustment for baseline eGFR. Our results provide support for dietary interventions to prevent kidney function decline in the general population.
Management of Citrus sinensis peels for protection and treatment against gastric ulcer induced by ethanol in rats

Abstract Introduction: Stomach ulcer is one of the most prevalent disorders worldwide. The study was aimed to isolate and characterize the major polymethoxylated flavonoids in Citrus sinensis peels petroleum ether extract and investigate its protective and curative effect on gastric ulcer. Material and methods: Some spectral analyses were used for identification of the isolated compounds from the petroleum ether extract of Citrus sinensis peels. One oral dose (0.5 ml/100 g b.wt.) of absolute ethanol was orally given to rats after starvation for 24 h to induce gastric ulcer. To explore the protective and curative role of the plant extract, it was orally (250 mg/kg b.wt.) given for 1 week either before or post-ulcer induction. A reference drug, ranitidine (100 mg/kg b.wt.), was also evaluated. Stomach acidity, gastric volume, lesion counts, glutathione (GSH), superoxide dismutase (SOD), malondialdehyde (MDA), succinate dehydrogenase (SDH), lactate dehydrogenase (LDH), acid phosphatase (AP), interlukin-10 (IL-10) and prostaglandin E2 (PGE2) were estimated. Stomach histopathological features were monitored. Results: Nine polymethoxy flavonoids were identified from the extract. Treatment with C. sinensis peels extract recorded amelioration in all parameters. Conclusion: Citrus sinensis petroleum ether peels extract had protective and curative effects against gastric ulcer. Therefore, the extract recorded anti-secretory, anti-ulcerative, anti-inflammatory, and antioxidant effects. Its healing action exceeded its protective role due to its richness in polymethoxylated flavonoids
Soil Microbial Biogeography in a Changing World: Recent Advances and Future Perspectives

Soil microbial communities are fundamental to maintaining key soil processes associated with litter decomposition, nutrient cycling, and plant productivity and are thus integral to human well-being. Recent technological advances have exponentially increased our knowledge concerning the global ecological distributions of microbial communities across space and time and have provided evidence for their contribution to ecosystem functions. However, major knowledge gaps in soil biogeography remain to be addressed over the coming years as technology and research questions continue to evolve. ABSTRACT Soil microbial communities are fundamental to maintaining key soil processes associated with litter decomposition, nutrient cycling, and plant productivity and are thus integral to human well-being. Recent technological advances have exponentially increased our knowledge concerning the global ecological distributions of microbial communities across space and time and have provided evidence for their contribution to ecosystem functions. However, major knowledge gaps in soil biogeography remain to be addressed over the coming years as technology and research questions continue to evolve. In this minireview, we state recent advances and future directions in the study of soil microbial biogeography and discuss the need for a clearer concept of microbial species, projections of soil microbial distributions toward future global change scenarios, and the importance of embracing culture and isolation approaches to determine microbial functional profiles. This knowledge will be critical to better predict ecosystem functions in a changing world.
BPA disrupts 17-estradiol-mediated hepatic protection against ischemia/reperfusion injury in rat liver by upregulating the Ang II/AT1R signaling pathway

Bisphenol A (BPA), a xenoestrogen commonly used in plastics, may act as an endocrine disruptor, which indicates that BPA might be a public health risk. The present study aimed to investigate the effect of BPA on 17β-estradiol (E2)-mediated protection against liver ischemia/reperfusion (I/R) injury, and to identify the underlying mechanisms using a rat model. A total of 56 male Sprague Dawley rats were randomly divided into the following seven groups: i) Sham; ii) I/R; iii) Sham + BPA; iv) I/R + BPA; v) I/R + E2; vi) I/R + E2 + BPA; and vii) I/R + E2 + BPA + losartan [LOS; an angiotensin II (Ang II) type I receptor (ATIR) antagonist]. A rat model of hepatic I/R injury was established by inducing hepatic ischemia for 60 min followed by reperfusion for 24 h. When ischemia was induced, rats were treated with vehicle, E2, BPA or LOS. After 24 h of reperfusion, blood samples and hepatic tissues were collected for histopathological and biochemical examinations. The results suggested that 4 mg/kg BPA did not significantly alter the liver function, or Ang II and AT1R expression levels in the Sham and I/R groups. However, 4 mg/kg BPA inhibited E2-mediated hepatic protection by enhancing hepatic necrosis, and increasing the release of alanine transaminase, alkaline phosphatase and total bilirubin (P<0.05). Moreover, BPA increased serum and hepatic Ang II levels, as well as AT1R protein expression levels in the E2-treated rat model of liver I/R injury (P<0.05). LOS treatment reversed the negative effects of BPA on hepatic necrosis and liver serum marker levels, although it did not reverse BPA-mediated upregulation of serum and hepatic Ang II levels, or hepatic AT1R expression. Therefore, the present study suggested that BPA disrupted E2-mediated hepatic protection following I/R injury, but did not significantly affect healthy or I/R-injured livers; therefore, the mechanism underlying the effects of BPA may be associated with upregulation of the Ang II/AT1R signaling pathway.
Role of enhanced glucocorticoid receptor sensitivity in inflammation in PTSD: Insights from a computational model for circadian-neuroendocrine-immune interactions

Although glucocorticoid resistance contributes to increased inflammation, individuals with post-traumatic stress disorder (PTSD) exhibit increased glucocorticoid receptor (GR) sensitivity along with increased inflammation. It is not clear how inflammation co-exists with a hyper-responsive hypothalamic pituitary adrenal axis (HPA axis). To understand this better, we developed and analyzed an integrated mathematical model for the HPA axis and the immune system. We performed mathematical simulations for a dexamethasone suppression test and IC50-dexamethasone for cytokine suppression, by varying model parameters. The model analysis suggests that increasing the steepness of the dose response curve for GR activity may reduce anti-inflammatory effects of GRs at the ambient glucocorticoid levels thereby increasing pro-inflammatory response. The adaptive response of pro-inflammatory cytokine mediated stimulatory effects on the HPA-axis is reduced due to dominance of the GR-mediated negative feedback on the HPA-axis. To verify these hypotheses we analyzed the clinical data on neuro-endocrine variables and cytokines obtained from war-zone veterans with and without PTSD. We observed significant group differences for cortisol and ACTH suppression tests, pro-inflammatory cytokines TNFα and IL6, hs-CRP, promoter methylation of GR gene and IC50-Dex for lysozyme suppression. Causal inference modelling revealed significant associations between cortisol suppression and post-dex cortisol decline, promoter methylation of NR3C1-1F, IC50-Dex and pro-inflammatory cytokines. We noted significant mediation effects of NR3C1-1F promoter methylation on inflammatory cytokines through changes in GR sensitivity. Our findings suggest that increased GR sensitivity may contribute to increased inflammation, therefore, interventions to restore GR sensitivity may normalize inflammation in PTSD.
In Vitro Differentiation of Human Skin-Derived Cells into Functional Sensory Neurons-Like

Skin-derived precursor cells (SKPs) are neural crest stem cells that persist in certain adult tissues, particularly in the skin. They can generate a large type of cell in vitro, including neurons. SKPs were induced to differentiate into sensory neurons (SNs) by molecules that were previously shown to be important for the generation of SNs: purmorphamine, CHIR99021, BMP4, GDNF, BDNF, and NGF. We showed that the differentiation of SKPs induced the upregulation of neurogenins. At the end of the differentiation protocol, transcriptional analysis was performed on BRN3A and a marker of pain-sensing nerve cell PRDM12 genes: 1000 times higher for PRDM12 and 2500 times higher for BRN3A in differentiated cells than they were in undifferentiated SKPs. Using immunostaining, we showed that 65% and 80% of cells expressed peripheral neuron markers BRN3A and PERIPHERIN, respectively. Furthermore, differentiated cells expressed TRPV1, PAR2, TRPA1, substance P, CGRP, HR1. Using calcium imaging, we observed that a proportion of cells responded to histamine, SLIGKV (a specific agonist of PAR2), polygodial (a specific agonist of TRPA1), and capsaicin (a specific agonist of TRPV1). In conclusion, SKPs are able to differentiate directly into functional SNs. These differentiated cells will be very useful for further in vitro studies.
Bayesian accrual modeling and prediction in multicenter clinical trials with varying center activation times

Investigators who manage multicenter clinical trials need to pay careful attention to patterns of subject accrual, and the prediction of activation time for pending centers is potentially crucial for subject accrual prediction. We propose a Bayesian hierarchical model to predict subject accrual for multicenter clinical trials in which center activation times vary. We define center activation time as the time at which a center can begin enrolling patients in the trial. The difference in activation times between centers is assumed to follow an exponential distribution, and the model of subject accrual integrates prior information for the study with actual enrollment progress. We apply our proposed Bayesian multicenter accrual model to two multicenter clinical studies. The first is the PAIN‐CONTRoLS study, a multicenter clinical trial with a goal of activating 40 centers and enrolling 400 patients within 104 weeks. The second is the HOBIT trial, a multicenter clinical trial with a goal of activating 14 centers and enrolling 200 subjects within 36 months. In summary, the Bayesian multicenter accrual model provides a prediction of subject accrual while accounting for both center‐ and individual patient‐level variation.