Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Penetrance of Hereditary Hemochromatosis-Related End-Organ Damage of C282Y Homozygosity, A Newfoundland Experience

INTRODUCTION: Hereditary hemochromatosis is an autosomal recessive disorder of iron absorption, leading to organ dysfunction. C282Y gene homozygosity is implicated in 80%–95% of cases of hereditary hemochromatosis. The clinical penetrance of this genotype remains unclear. The purpose of the study was to better describe the clinical penetrance and disease progression of C282Y homozygotes. METHODS: This is a retrospective study of all individuals in Newfoundland and Labrador, Canada, homozygous for the C282Y mutation from 1999 to 2009. Using electronic health records, laboratory values, phlebotomy status, radiologic reports, and clinic records were recorded up to November 2017. Iron overload status was classified via the HealthIron study. SPSS Version 19.0 (IBM Corporation) was used for descriptive statistics. Predictors of disease penetrance were assessed with logistic regression; a Student t test was used for continuous variables, and χ2 tests were used for categorical variables. RESULTS: Between 1999 and 2009, 360 individuals tested positive for C282Y/C282Y. The mean age of diagnosis was 49.1 years. Three hundred six individuals had adequate follow-up for analysis (mean 11.6 years). End-organ damage was observed in 18.3%, with 5.8% developing liver disease. End-organ damage was more frequently observed in men 24.3% vs 10.5% (P < 0.05). Clinical penetrance in postmenopausal women approached that of men 18.3%. DISCUSSION: This is the largest reported cohort of C282Y homozygotes, followed for an extended duration of time in North America. The findings reflect outcomes in routine clinical practice and suggest that C282Y homozygosity uncommonly causes end-organ damage and liver disease.
Words are the Window to the Soul: Language-based User Representations for Fake News Detection

Cognitive and social traits of individuals are reflected in language use. Moreover, individuals who are prone to spread fake news online often share common traits. Building on these ideas, we introduce a model that creates representations of individuals on social media based only on the language they produce, and use them to detect fake news. We show that language-based user representations are beneficial for this task. We also present an extended analysis of the language of fake news spreaders, showing that its main features are mostly domain independent and consistent across two English datasets. Finally, we exploit the relation between language use and connections in the social graph to assess the presence of the Echo Chamber effect in our data.
Effect of Denosumab on Glucose Homeostasis in Postmenopausal Women with Breast Cancer Treated with Aromatase Inhibitors: A Pilot Study

Background Aromatase inhibitors in women with breast cancer have been associated with cancer treatment-induced bone loss (CTIBL), increased fracture risk, and impairment of glucose metabolism. Denosumab (Dmab), a monoclonal antibody against RANKL, which is a key regulator of the osteoclast activity, is effective as an antiresorptive agent in the treatment of CTIBL. Since RANKL/RANK pathway may contribute to the pathogenesis of glucometabolic disorders, it has been suggested that Dmab may improve glucose homeostasis. Our pilot study evaluated the effect of a single administration of 60 mg Dmab on glucose metabolism in a cohort of women with breast cancer treated with aromatase inhibitors. Methods Fifteen postmenopausal nondiabetic women were prospectively enrolled. Oral glucose tolerance test (OGTT) and metabolic parameters, including FGF21, were assessed at baseline and one month after Dmab injection. Midterm glucose control was evaluated by measuring glycated haemoglobin (HbA1c) levels 5 months after Dmab. Results Parameters of glucose metabolism were not different one month after Dmab but circulating FGF21 levels significantly decreased (128.5 ± 46.8 versus 100.2 ± 48.8 pg/mL; p=0.016). Considering patients with insulin resistance at baseline (HOMA-IR > 2.5 and Matsuda Index < 2.5; n = 5), reduced mean fasting insulin levels (16.3 ± 4.9 versus 13.5 ± 3.5 mcU/mL; p=0.029) and increased insulin sensitivity index QUICKI (0.317 ± 0.013 versus 0.327 ± 0.009; p=0.025) were found. Nonetheless, HbA1c increased 5 months after Dmab (36.0 ± 2.3 versus 39.6 ± 3.1 mmol/mol; p=0.01). Conclusions Although RANKL blockade induced a short-term positive effect on insulin sensitivity, particularly in insulin-resistant patients, a benefit on long-term glucose metabolism was not evident. In conclusion, Dmab is safe for glucose metabolism in aromatase inhibitor-treated women with breast cancer.
Study of the Mechanism Underlying the Antihypertensive Effects of Eucommia ulmoides and Tribulus terrestris Based on an Analysis of the Intestinal Microbiota and Metabonomics

The combination of Eucommia ulmoides and Tribulus terrestris (ET) has been widely utilized in clinical practice for thousands of years, but the mechanism underlying its efficacy has not been elucidated to date. This study attempted to investigate the role played by the intestinal microbiota and fecal metabolism in the response of elderly spontaneous hypertensive rats (SHRs) to ET administration as a treatment for hypertension. Fourteen male spontaneously hypertensive rats (SHRs, 18 months old) were randomly divided into an ET group and an SHR group, and 7 Wistar-Kyoto (WKY) rats of the same age were employed as the control group. The ET group was intragastrically administered 1.0 g/kg/d ET for 42 days, and SHRs and WKY rats were administered an equal amount of normal saline intragastrically. The intestinal microbiota and fecal metabolism were analyzed by 16S rRNA sequencing and the GC-MS (gas chromatography-mass spectrometry)/MS assay. ET treatment decreased blood pressure steadily, improved the colonic tissue morphology, and changed the structure and composition of the imbalanced microbiota in SHRs. Specifically, ET treatment increased the abundance of Eubacterium, which might be one of the target microbes for ET, and had a negative correlation with the levels of α-tocopherol, chenodeoxycholic acid, and deoxycholic acid according to the Spearman correlation analysis. The change in the intestinal microbiota affected the fecal metabolic pattern of SHRs. Eight potential biomarkers were determined to be primarily enriched in ABC transporters, phenylalanine metabolism, central carbon metabolism in cancer, purine metabolism, and protein digestion and absorption. The correlation analysis demonstrated that the abundance of Eubacterium and the decreased levels of α-tocopherol, chenodeoxycholic acid, and deoxycholic acid in the ET group were highly correlated. Our results suggest that ET has a good antihypertensive effect, which may be driven by the intestinal microbiota and their beneficial metabolites. The results of this study may help to elucidate the antihypertensive mechanism of ET.
Reinforced Medical Report Generation with X-Linear Attention and Repetition Penalty

To reduce doctors' workload, deep-learning-based automatic medical report generation has recently attracted more and more research efforts, where attention mechanisms and reinforcement learning are integrated with the classic encoder-decoder architecture to enhance the performance of deep models. However, these state-of-the-art solutions mainly suffer from two shortcomings: (i) their attention mechanisms cannot utilize high-order feature interactions, and (ii) due to the use of TF-IDF-based reward functions, these methods are fragile with generating repeated terms. Therefore, in this work, we propose a reinforced medical report generation solution with x-linear attention and repetition penalty mechanisms (ReMRG-XR) to overcome these problems. Specifically, x-linear attention modules are used to explore high-order feature interactions and achieve multi-modal reasoning, while repetition penalty is used to apply penalties to repeated terms during the model's training process. Extensive experimental studies have been conducted on two public datasets, and the results show that ReMRG-XR greatly outperforms the state-of-the-art baselines in terms of all metrics.
Solutions of Heun's general equation and elliptic Darboux equation

New solutions for the elliptic Darboux equation are obtained as particular cases of solutions constructed for Heun's general equation. We consider two groups of power series expansions and two new groups of expansions in series of Gauss hypergeometric functions. The convergence of one group in power series is determined by means of ratio tests for infinite series, while the other groups are designed to solve problems which admit finite‐series solutions. Actually, we envisage periodic quasi‐exactly solvable potentials for which the stationary one‐dimensional Schrödinger equation is reduced to the Darboux equation. In general, finite‐ and infinite‐series solutions are obtained from power series expansions for Heun's equation. However, we show that the Schrödinger equation admits additional finite‐series expansions in terms of hypergeometric functions for a family of associated Lamé potentials used in band theory of solids.For each finite‐series solution, we find as well four infinite‐series expansions which are bounded and convergent for all values of the independent variable.
Reinfection of Severe Acute Respiratory Syndrome Coronavirus 2 in an Immunocompromised Patient: A Case Report

Note Potential conflicts of interest. P. L. W. reports personal fees from Gilead, Pfizer, F2G, and MSD and meeting sponsorship from Dynamiker, Bruker, and Launch outside the submitted work. R. D. reports expert opinion fees from MSD and an educational grant from Gilead Sciences outside the submitted work. M. B. reports personal fees from Gilead and meeting sponsorship from AbbVie outside the submitted work. All remaining authors: No reported conflicts of interest. All authors have submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest. Conflicts that the editors consider relevant to the content of the manuscript have been disclosed.
PLAS: Latent Action Space for Offline Reinforcement Learning

The goal of offline reinforcement learning is to learn a policy from a fixed dataset, without further interactions with the environment. This setting will be an increasingly more important paradigm for real-world applications of reinforcement learning such as robotics, in which data collection is slow and potentially dangerous. Existing off-policy algorithms have limited performance on static datasets due to extrapolation errors from out-of-distribution actions. This leads to the challenge of constraining the policy to select actions within the support of the dataset during training. We propose to simply learn the Policy in the Latent Action Space (PLAS) such that this requirement is naturally satisfied. We evaluate our method on continuous control benchmarks in simulation and a deformable object manipulation task with a physical robot. We demonstrate that our method provides competitive performance consistently across various continuous control tasks and different types of datasets, outperforming existing offline reinforcement learning methods with explicit constraints. Videos and code are available at this https URL.
Fast photometric variability of very low mass stars in IC 348: detection of superflare in an M dwarf

We present here optical I-band photometric variability study down to $\simeq$ 19 mag of a young ($\sim$2-3 Myr) star-forming region IC 348 in the Perseus molecular cloud. We aim to explore the fast rotation (in the time-scales of hours) in Very Low Mass stars (VLMs) including Brown Dwarfs (BDs). From a sample of 177 light-curves using our new I-band observations, we detect new photometric variability in 22 young M-dwarfs including 6 BDs, which are bonafide members in IC 348 and well-characterized in the spectral type of M-dwarfs. Out of 22 variables, 11 M dwarfs including one BD show hour-scale periodic variability in the period range 3.5 - 11 hours and rest are aperiodic in nature. Interestingly, an optical flare is detected in a young M2.75 dwarf in one night data on 20 December 2016. From the flare light curve, we estimate the emitted flared energy of 1.48 $\times$ 10$^{35}$ ergs. The observed flared energy with an uncertainty of tens of per cent is close to the super-flare range ($\sim$ 10$^{34}$ ergs), which is rarely observed in active M dwarfs.
Computational imperfections in human visual search

Numerous studies have reported that human behavior on perceptual inference tasks – such as cue combination and visual search – is well accounted for by optimal models. However, others have argued that optimal models are often overly flexible and, therefore, lack explanatory power. In addition, it has been suggested that inference performed by neural systems is inherently noisy, which would preclude optimality in many perception tasks. Here, we reconsider human performance on visual search by devising an approach that strongly reduces model flexibility and tests for suboptimalities due to imprecisions in neural inference. Subjects performed a target detection task in which targets and distractors were ellipses with orientations drawn from Gaussian distributions with different means. We controlled the level of sensory uncertainty through stimulus presentation time (short vs. unlimited) and the elongation of the ellipses (low vs. high). Moreover, we created four levels of external uncertainty by varying the amount of overlap between the target and distractor distributions. Since sensory noise was negligible in the conditions with unlimited display time, we were able to estimate deviations from optimality without having to fit free parameters. In conditions with short display time, we limited the flexibility of the optimal model by using a separate task to estimate sensory noise levels. We found clear evidence for suboptimalities in all tested conditions. Moreover, we estimate that the performance loss due to computational imperfections was of comparable magnitude to the loss due to sensory noise. Our results provide support for the proposal that neural inference is inherently imprecise and challenge previous claims of optimality in perception. Author summary The main task of perceptual systems is to create truthful representations of the world. They do so by using sensory information that is often astonishingly imprecise due to measurement errors in our senses. Consequently, it is often impossible to be 100% correct all the time on tasks that involve perception, such as judging whether a visual target is present in a cluttered scene. Observers are typically defined as optimal if they perform as well as theoretically possible given the sensory imprecisions. Numerous studies have reported that humans are optimal observers in perception-based tasks, but the validity of these findings has recently been questioned for two different reasons. First, it has been argued that a lot of the evidence is based on studies that used overly flexible models. Second, there are indications that inference performed by brains is inherently imprecise, due to limitations in the neural systems performing the inference. In this study, we reconsider optimality in perception by devising a research method that makes several improvements over previous studies. We apply this method to a visual search task and find clear indications of suboptimalities. Our findings imply that the perceptual systems may indeed not be as perfect as previously thought.
Real-Time Polyp Detection, Localisation and Segmentation in Colonoscopy Using Deep Learning

Computer-aided detection, localisation, and segmentation methods can help improve colonoscopy procedures. Even though many methods have been built to tackle automatic detection and segmentation of polyps, benchmarking of state-of-the-art methods still remains an open problem. This is due to the increasing number of researched computer-vision methods that can be applied to polyp datasets. Benchmarking of novel methods can provide a direction to the development of automated polyp detection and segmentation tasks. Furthermore, it ensures that the produced results in the community are reproducible and provide a fair comparison of developed methods. In this paper, we benchmark several recent state-of-the-art methods using Kvasir-SEG, an open-access dataset of colonoscopy images, for polyp detection, localisation, and segmentation evaluating both method accuracy and speed. Whilst, most methods in literature have competitive performance over accuracy, we show that YOLOv4 with a Darknet53 backbone and cross-stage-partial connections achieved a better trade-off between an average precision of 0.8513 and mean IoU of 0.8025, and the fastest speed of 48 frames per second for the detection and localisation task. Likewise, UNet with a ResNet34 backbone achieved the highest dice coefficient of 0.8757 and the best average speed of 35 frames per second for the segmentation task. Our comprehensive comparison with various state-of-the-art methods reveal the importance of benchmarking the deep learning methods for automated real-time polyp identification and delineations that can potentially transform current clinical practices and minimise miss-detection rates.
Downregulation of miR-200a Protects Mouse Leydig Cells Against Triptolide by Triggering Autophagy

Background MicroRNAs play important roles in testicular development and spermatogenesis. Previous research has indicated that the level of miR-200a was significantly upregulated in patients with different spermatogenic impairments. However, the mechanism by which miR-200a regulated spermatogenic impairments remains unclear. Methods Leydig cells were treated with triptolide (TP) to mimic spermatogenic impairments. CCK-8 and flow cytometry were used to detect the proliferation and apoptosis in Leydig cells, respectively. In addition, Western blot assay was used to examine ATG7, ATG5, p62 protein levels in MLTC-1 cells. Results TP dose-dependently upregulated the expression of miR-200a in MLTC-1 cells. In addition, TP inhibited the proliferation of MLTC-1 cells via inducing apoptosis and oxidative stress; however, these phenomena were notably reversed by miR-200a antagomir. Furthermore, luciferase reporter assay identified that ATG7 was the direct binding target of miR-200a. TP treatment markedly inhibited the activation of autophagy in MLTC-1 cells via inhibition of ATG7. Conversely, downregulation of miR-200a significantly induced autophagy in TP-treated MLTC-1 cells by activation of ATG7. Meanwhile, the cell protective effects of miR-200a against TP were reversed by autophagy inhibitor 3MA, indicating that autophagy plays an important role. Conclusion These results indicated that downregulation of miR-200a could protect MLTC-1 cells against TP by inducing autophagy. Therefore, miR-200a might serve as a new therapeutic target for the treatment of male hypogonadism.
Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation

Scene flow is the task of estimating 3D motion vectors to individual points of a dynamic 3D scene. Motion vectors have shown to be beneficial for downstream tasks such as action classification and collision avoidance. However, data collected via LiDAR sensors and stereo cameras are computation and labor intensive to precisely annotate for scene flow. We address this annotation bottleneck on two ends. We propose a 3D scene flow benchmark and a novel self-supervised setup for training flow models. The benchmark consists of datasets designed to study individual aspects of flow estimation in progressive order of complexity, from a single object in motion to real-world scenes. Furthermore, we introduce Adversarial Metric Learning for self-supervised flow estimation. The flow model is fed with sequences of point clouds to perform flow estimation. A second model learns a latent metric to distinguish between the points translated by the flow estimations and the target point cloud. This latent metric is learned via a Multi-Scale Triplet loss, which uses intermediary feature vectors for the loss calculation. We use our proposed benchmark to draw insights about the performance of the baselines and of different models when trained using our setup. We find that our setup is able to keep motion coherence and preserve local geometries, which many self-supervised baselines fail to grasp. Dealing with occlusions, on the other hand, is still an open challenge.
Molecular Mechanics-Driven Graph Neural Network with Multiplex Graph for Molecular Structures

The prediction of physicochemical properties from molecular structures is a crucial task for artificial intelligence aided molecular design. A growing number of Graph Neural Networks (GNNs) have been proposed to address this challenge. These models improve their expressive power by incorporating auxiliary information in molecules while inevitably increase their computational complexity. In this work, we aim to design a GNN which is both powerful and efficient for molecule structures. To achieve such goal, we propose a molecular mechanics-driven approach by first representing each molecule as a two-layer multiplex graph, where one layer contains only local connections that mainly capture the covalent interactions and another layer contains global connections that can simulate non-covalent interactions. Then for each layer, a corresponding message passing module is proposed to balance the trade-off of expression power and computational complexity. Based on these two modules, we build Multiplex Molecular Graph Neural Network (MXMNet). When validated by the QM9 dataset for small molecules and PDBBind dataset for large protein-ligand complexes, MXMNet achieves superior results to the existing state-of-the-art models under restricted resources.
Cellular Mechanisms Triggered by the Cotreatment of Resveratrol and Doxorubicin in Breast Cancer: A Translational In Vitro–In Silico Model

Doxorubicin (Doxo) is the most effective chemotherapeutic agent for the treatment of breast cancer. However, resistance to Doxo is common. Adjuvant compounds capable of modulating mechanisms involved in Doxo resistance may potentiate the effectiveness of the drug. Resveratrol (Rsv) has been tested as an adjuvant in mammary malignancies. However, the cellular and molecular mechanisms underlying the effects of cotreatment with Doxo and Rsv in breast cancer are poorly understood. Here, we combined in vitro and in silico analysis to characterize these mechanisms. In vitro, we employed a clinically relevant experimental design consisting of acute (24 h) treatment followed by 15 days of analysis. Acute Rsv potentiated the long-lasting effect of Doxo through the induction of apoptosis and senescence. Cells that survived to the cotreatment triggered high levels of autophagy. Autophagy inhibition during its peak of activation but not concomitant with Doxo+Rsv increased the long-term toxicity of the cotreatment. To uncover key proteins potentially associated with in vitro effects, an in silico multistep strategy was implemented. Chemical-protein networks were predicted based on constitutive gene expression of MCF7 cells and interatomic data from breast cancer. Topological analysis, KM survival analysis, and a quantitative model based on the connectivity between apoptosis, senescence, and autophagy were performed. We found seven putative genes predicted to be modulated by Rsv in the context of Doxo treatment: CCND1, CDH1, ESR1, HSP90AA1, MAPK3, PTPN11, and RPS6KB1. Six out of these seven genes have been experimentally proven to be modulated by Rsv in cancer cells, with 4 of the 6 genes in MCF7 cells. In conclusion, acute Rsv potentiated the long-term toxicity of Doxo in breast cancer potentially through the modulation of genes and mechanisms involved in Doxo resistance. Rational autophagy inhibition potentiated the effects of Rsv+Doxo, a strategy that should be further tested in animal models.