Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparing Machine Learning to Regression Methods for Mortality Prediction Using Veterans Affairs Electronic Health Record Clinical Data

Background: It is unclear whether machine learning methods yield more accurate electronic health record (EHR) prediction models compared with traditional regression methods. Objective: The objective of this study was to compare machine learning and traditional regression models for 10-year mortality prediction using EHR data. Design: This was a cohort study. Setting: Veterans Affairs (VA) EHR data. Participants: Veterans age above 50 with a primary care visit in 2005, divided into separate training and testing cohorts (n= 124,360 each). Measurements and Analytic Methods: The primary outcome was 10-year all-cause mortality. We considered 924 potential predictors across a wide range of EHR data elements including demographics (3), vital signs (9), medication classes (399), disease diagnoses (293), laboratory results (71), and health care utilization (149). We compared discrimination (c-statistics), calibration metrics, and diagnostic test characteristics (sensitivity, specificity, and positive and negative predictive values) of machine learning and regression models. Results: Our cohort mean age (SD) was 68.2 (10.5), 93.9% were male; 39.4% died within 10 years. Models yielded testing cohort c-statistics between 0.827 and 0.837. Utilizing all 924 predictors, the Gradient Boosting model yielded the highest c-statistic [0.837, 95% confidence interval (CI): 0.835–0.839]. The full (unselected) logistic regression model had the highest c-statistic of regression models (0.833, 95% CI: 0.830–0.835) but showed evidence of overfitting. The discrimination of the stepwise selection logistic model (101 predictors) was similar (0.832, 95% CI: 0.830–0.834) with minimal overfitting. All models were well-calibrated and had similar diagnostic test characteristics. Limitation: Our results should be confirmed in non-VA EHRs. Conclusion: The differences in c-statistic between the best machine learning model (924-predictor Gradient Boosting) and 101-predictor stepwise logistic models for 10-year mortality prediction were modest, suggesting stepwise regression methods continue to be a reasonable method for VA EHR mortality prediction model development.
Natural compounds based chemotherapeutic against Chagas disease and leishmaniasis: mitochondrion as a strategic target

Over the past years, natural products have been explored in order to find biological active substances to treat various diseases. Regarding their potential action against parasites such as trypanosomatids, specially Trypanosoma cruzi and Leishmania spp. , much advance has been achieved. Extracts and purified molecules of several species from genera Piper, Tanacetum, Porophyllum, and Copaifera have been widely investigated by our research group and exhibited interesting antitrypanosomal and antileishmanial activities. These natural compounds affected different structures in parasites, and we believe that the mitochondrion is a strategic target to induce parasite death. Considering that these trypanosomatids have a unique mitochondrion, this cellular target has been extensively studied aiming to find more selective drugs, since the current treatment of these neglected tropical diseases has some challenges such as high toxicity and prolonged treatment time. Here, we summarise some results obtained with natural products from our research group and we further highlighted some strategies that must be considered to finally develop an effective chemotherapeutic agent against these parasites.
An Intervention Framework for Addressing Stigma on College Campuses

Abstract The ongoing COVID-19 pandemic will only exacerbate the rising mental health concerns among college students. However, stigma toward such concerns continues to hinder mental health care utilization among the students, requiring urgent evidence that can help guide college campuses in implementing effective antistigma interventions. We propose and provide evidence for an intervention based on findings from a 3-year-long antistigma intervention that was implemented on a Southeastern college campus in the United States. Unique random samples of college students, totaling N = 1727 across 3 years, were recruited as participants. Each year, participants completed a preintervention and postintervention survey comprising of questions related to demographics, stigma, and mental health care knowledge. Findings indicate that the stakeholder-led intervention decreased personal stigma and increased mental health care knowledge among students who were exposed to the intervention. Further research is needed to assess feasibility and efficacy of the proposed intervention framework on other campuses.
Targeting radioresistance and replication fork stability in prostate cancer

The bromodomain and extraterminal (BET) family of chromatin reader proteins bind to acetylated histones and regulate gene expression. The development of BET inhibitors (BETi) has expanded our knowledge of BET protein function beyond transcriptional regulation and has ushered several prostate cancer (PCa) clinical trials. However, BETi as a single agent is not associated with antitumor activity in patients with castration-resistant prostate cancer (CRPC). We hypothesized novel combinatorial strategies are likely to enhance the efficacy of BETi. By using PCa patient-derived explants and xenograft models, we show that BETi treatment enhanced the efficacy of radiation therapy (RT) and overcame radioresistance. Mechanistically, BETi potentiated the activity of RT by blocking DNA repair. We also report a synergistic relationship between BETi and topoisomerase I (TOP1) inhibitors (TOP1i). We show that the BETi OTX015 synergized with the new class of synthetic noncamptothecin TOP1i, LMP400 (indotecan), to block tumor growth in aggressive CRPC xenograft models. Mechanistically, BETi potentiated the antitumor activity of TOP1i by disrupting replication fork stability. Longitudinal analysis of patient tumors indicated that TOP1 transcript abundance increased as patients progressed from hormone-sensitive prostate cancer to CRPC. TOP1 was highly expressed in metastatic CRPC, and its expression correlated with the expression of BET family genes. These studies open new avenues for the rational combinatorial treatment of aggressive PCa.
Effect of environmental temperature on semen quality and seminal plasma metabolites of Mediterranean buffalo bulls

Abstract High-quality semen with high viability is critical to improving the in-vitro fertilization efficiency. This study aimed to understand the effect of ambient temperature and humidity on semen quality and seminal plasma biochemical parameters of Mediterranean buffalo in March and July. The metabolites of seminal plasma in two seasons were detected using the UPLC-MS/MS method. The results showed that temperature and humidity index (THI) in March were 66.86 ± 2.98, and 82.94 ± 3.52 in July. Compared with in March, breath frequency, rectal temperature, and heat shock protein 70 expressions of seminal plasma were significantly increased in July (p < 0.05), motility of sperm was dramatically reduced, and sperm deformity rate was significantly increased (p < 0.05). Fructose, acid phosphatase and α-glucosidase in seminal plasma were significantly increased (p < 0.05) in July, while testosterone level was significantly reduced (p < 0.05). Six different metabolites were found in the two groups, which involved in three metabolic pathways, the tricarboxylic acid cycle, glycerophospholipid, glyoxylic acid and dicarboxylic acid. The above results indicate that the increased ambient temperature has obvious side effects on the semen quality of Mediterranean buffalo, and the compromised quality is associated with the change of metabolites related to male hormone secretion, energy metabolism and fatty acid oxidation.
Lay-it-out: Interactive Design of Layout-Sensitive Grammars

Layout-sensitive grammars have been adopted in many modern programming languages. However, tool support for this kind of grammars still remains limited and immature. In this paper, we present Lay-it-out, an interactive framework for layout-sensitive grammar design. Beginning with a user-defined ambiguous grammar, our framework refines it by synthesizing layout constraints through user interaction. For ease of interaction, a shortest nonempty ambiguous sentence (if exists) is automatically generated by our bounded ambiguity checker via SMT solving. The soundness and completeness of our SMT encoding are mechanized in the Coq proof assistant. Case studies on real grammars, including a full grammar , demonstrate the practicality and scalability of our approach.
Endocannabinoid signaling in brain diseases: Emerging relevance of glial cells

The discovery of cannabinoid receptors as the primary molecular targets of psychotropic cannabinoid Δ9‐tetrahydrocannabinol (Δ9‐THC) in late 1980s paved the way for investigations on the effects of cannabis‐based therapeutics in brain pathology. Ever since, a wealth of results obtained from studies on human tissue samples and animal models have highlighted a promising therapeutic potential of cannabinoids and endocannabinoids in a variety of neurological disorders. However, clinical success has been limited and major questions concerning endocannabinoid signaling need to be satisfactorily addressed, particularly with regard to their role as modulators of glial cells in neurodegenerative diseases. Indeed, recent studies have brought into the limelight diverse, often unexpected functions of astrocytes, oligodendrocytes, and microglia in brain injury and disease, thus providing scientific basis for targeting glial cells to treat brain disorders. This Review summarizes the current knowledge on the molecular and cellular hallmarks of endocannabinoid signaling in glial cells and its clinical relevance in neurodegenerative and chronic inflammatory disorders.
How Deep is Your Art: An Experimental Study on the Limits of Artistic Understanding in a Single-Task, Single-Modality Neural Network

Computational modeling of artwork meaning is complex and difficult. This is because art interpretation is multidimensional and highly subjective. This paper experimentally investigated the degree to which a state-of-the-art Deep Convolutional Neural Network (DCNN), a popular Machine Learning approach, can correctly distinguish modern conceptual art work into the galleries devised by art curators. Two hypotheses were proposed to state that the DCNN model uses Exhibited Properties for classification, like shape and color, but not Non-Exhibited Properties, such as historical context and artist intention. The two hypotheses were experimentally validated using a methodology designed for this purpose. VGG-11 DCNN pre-trained on ImageNet dataset and discriminatively fine-tuned was trained on handcrafted datasets designed from real-world conceptual photography galleries. Experimental results supported the two hypotheses showing that the DCNN model ignores Non-Exhibited Properties and uses only Exhibited Properties for artwork classification. This work points to current DCNN limitations, which should be addressed by future DNN models.
Learning (Local) Surrogate Loss Functions for Predict-Then-Optimize Problems

Decision-Focused Learning (DFL) is a paradigm for tailoring a predictive model to a downstream optimisation task that uses its predictions, so that it can perform better on that speciﬁc task . The main technical challenge associated with DFL is that it requires being able to differentiate through argmin operations to work. However, these arg min optimisations are often piecewise constant and, as a result, naively differentiating through them would provide uninformative gradients. Past work has largely focused on getting around this issue by handcrafting task-speciﬁc surrogates to the original optimisation problem that provide informative gradients when differentiated through. However, ﬁnding these surrogates can be challenging and the need to handcraft surrogates for each new task limits the usability of DFL. In addition, even after applying these relaxation techniques, there are no guarantees that the resulting surrogates are convex and, as a result, training a predictive model on them may lead to said model getting stuck in local minimas. In this paper, we provide an approach to learn faithful task-speciﬁc surrogates which (a) only requires access to a black-box oracle that can solve the optimisation problem and is thus generalizable , and (b) can be convex by construction and so can be easily optimized over. To the best of our knowledge, this is the ﬁrst work on using learning to ﬁnd good surrogates for DFL. We evaluate our approach on a budget allocation problem from the literature and ﬁnd that our approach outperforms even the hand-crafted (non-convex) surrogate loss proposed by the original paper. Taking a step back, we hope that the generality and simplicity of our approach will help lower the barrier
Identification and validation of a seven‐gene prognostic marker in colon cancer based on single‐cell transcriptome analysis

Abstract Colon cancer (CC) is one of the most commonly diagnosed tumours worldwide. Single‐cell RNA sequencing (scRNA‐seq) can accurately reflect the heterogeneity within and between tumour cells and identify important genes associated with cancer development and growth. In this study, scRNA‐seq was used to identify reliable prognostic biomarkers in CC. ScRNA‐seq data of CC before and after 5‐fluorouracil treatment were first downloaded from the Gene Expression Omnibus database. The data were pre‐processed, and dimensionality reduction was performed using principal component analysis and t‐distributed stochastic neighbour embedding algorithms. Additionally, the transcriptome data, somatic variant data, and clinical reports of patients with CC were obtained from The Cancer Genome Atlas database. Seven key genes were identified using Cox regression analysis and the least absolute shrinkage and selection operator method to establish signatures associated with CC prognoses. The identified signatures were validated on independent datasets, and somatic mutations and potential oncogenic pathways were further explored. Based on these features, gene signatures, and other clinical variables, a more effective predictive model nomogram for patients with CC was constructed, and a decision curve analysis was performed to assess the utility of the nomogram. A prognostic signature consisting of seven prognostic‐related genes, including CAV2, EREG, NGFRAP1, WBSCR22, SPINT2, CCDC28A, and BCL10, was constructed and validated. The proficiency and credibility of the signature were verified in both internal and external datasets, and the results showed that the seven‐gene signature could effectively predict the prognosis of patients with CC under various clinical conditions. A nomogram was then constructed based on features such as the RiskScore, patients' age, neoplasm stage, and tumor (T), nodes (N), and metastases (M) classification, and the nomogram had good clinical utility. Higher RiskScores were associated with a higher tumour mutational burden, which was confirmed to be a prognostic risk factor. Gene set enrichment analysis showed that high‐score groups were enriched in ‘cytoplasmic DNA sensing’, ‘Extracellular matrix receptor interactions’, and ‘focal adhesion’, and low‐score groups were enriched in ‘natural killer cell‐mediated cytotoxicity’, and ‘T‐cell receptor signalling pathways’, among other pathways. A robust seven‐gene marker for CC was identified based on scRNA‐seq data and was validated in multiple independent cohort studies. These findings provide a new potential marker to predict the prognosis of patients with CC.
Invertible minor assignment and complexity of set-valued functions

We introduce an algebraic model based on the expansion of the determinant of two matrices to provide a criterion for the additivity of Z d -valued set functions. Each individual term of the expansion is deformed through a monomial factor in d indeterminates with exponents deﬁned by the set function. A family of sparse polynomials is derived from the coupling of Grassmann-Plücker relations for the two matrices, and its factorisation properties in the ring of Laurent polynomials are studied. In this work, we focus on matrices satisfying a given generic condition to prove that such a deformation returns a determinantal expansion if and only if it is induced by a diagonal matrix of units in C ( t ) acting as a kernel into the initial determinant expansion, which guarantees the additivity of the set function. This result is applied to explore complexity reduction for set functions, with special attention to permutations of families of subsets. ,
Predicting Cardiovascular and Cerebrovascular Events Based on Instantaneous High-Order Singular Entropy and Deep Belief Network

Automatically predicting cardiovascular and cerebrovascular events (CCEs) is a key technology that can prevent deaths and disabilities. Herein, we propose predicting CCE occurrences based on heart rate variability (HRV) analysis and a deep belief network (DBN). The proposed prediction algorithm uses eight novel HRV signal features, which are calculated based on the following steps. First, the instantaneous amplitude (IA), instantaneous frequency (IF), and instantaneous phase (IP) are calculated for the HRV signals. Second, the high-order cumulant is estimated for the HRV and its IA, IF, and IP. Third, a high-order singular entropy is calculated to measure the fluctuation in signals. Fourth, eight novel features are obtained and processed using a DBN classifier designed for CCE prediction. The DBN classification method, with the novel HRV features, outperformed existing methods in terms of accuracy. Thus, the scheme proposed herein provided a novel direction for predicting CCEs.
DELTA: Dynamically Optimizing GPU Memory beyond Tensor Recomputation

The further development of deep neural networks is hampered by the limited GPU memory resource. Therefore, the optimization of GPU memory resources is highly demanded. Swapping and recomputation are commonly applied to make better use of GPU memory in deep learning. However, as an emerging domain, several challenges remain:1)The efficiency of recomputation is limited for both static and dynamic methods. 2)Swapping requires offloading parameters manually, which incurs a great time cost. 3) There is no such dynamic and fine-grained method that involves tensor swapping together with tensor recomputation nowadays. To remedy the above issues, we propose a novel scheduler manager named DELTA(Dynamic tEnsor offLoad and recompuTAtion). To the best of our knowledge, we are the first to make a reasonable dynamic runtime scheduler on the combination of tensor swapping and tensor recomputation without user oversight. In DELTA, we propose a filter algorithm to select the optimal tensors to be released out of GPU memory and present a director algorithm to select a proper action for each of these tensors. Furthermore, prefetching and overlapping are deliberately considered to overcome the time cost caused by swapping and recomputing tensors. Experimental results show that DELTA not only saves 40%-70% of GPU memory, surpassing the state-of-the-art method to a great extent but also gets comparable convergence results as the baseline with acceptable time delay. Also, DELTA gains 2.04$\times$ maximum batchsize when training ResNet-50 and 2.25$\times$ when training ResNet-101 compared with the baseline. Besides, comparisons between the swapping cost and recomputation cost in our experiments demonstrate the importance of making a reasonable dynamic scheduler on tensor swapping and tensor recomputation, which refutes the arguments in some related work that swapping should be the first and best choice.
Investigating the Properties of Neural Network Representations in Reinforcement Learning

In this paper we investigate the properties of representations learned by deep reinforcement learning systems. Much of the early work on representations for reinforcement learning focused on designing fixed-basis architectures to achieve properties thought to be desirable, such as orthogonality and sparsity. In contrast, the idea behind deep reinforcement learning methods is that the agent designer should not encode representational properties, but rather that the data stream should determine the properties of the representation -- good representations emerge under appropriate training schemes. In this paper we bring these two perspectives together, empirically investigating the properties of representations that support transfer in reinforcement learning. We introduce and measure six representational properties over more than 25 thousand agent-task settings. We consider Deep Q-learning agents with different auxiliary losses in a pixel-based navigation environment, with source and transfer tasks corresponding to different goal locations. We develop a method to better understand why some representations work better for transfer, through a systematic approach varying task similarity and measuring and correlating representation properties with transfer performance. We demonstrate the generality of the methodology by investigating representations learned by a Rainbow agent that successfully transfer across games modes in Atari 2600.
Vakyansh: ASR Toolkit for Low Resource Indic languages

We present Vakyansh, an end to end toolkit for Speech Recognition in Indic languages. India is home to almost 121 languages and around 125 crore speakers. Yet most of the languages are low resource in terms of data and pretrained models. Through Vakyansh, we introduce automatic data pipelines for data creation, model training, model evaluation and deployment. We create 14,000 hours of speech data in 23 Indic languages and train wav2vec 2.0 based pretrained models. These pretrained models are then finetuned to create state of the art speech recognition models for 18 Indic languages which are followed by language models and punctuation restoration models. We open source all these resources with a mission that this will inspire the speech community to develop speech first applications using our ASR models in Indic languages.
Non-autoregressive Translation with Dependency-Aware Decoder

Non-autoregressive translation (NAT) models suffer from inferior translation quality due to removal of dependency on previous target to-kens from inputs to the decoder. In this paper, we propose a novel and general approach to enhance the target dependency within the NAT decoder from two perspectives: decoder input and decoder self-attention . First, we transform the initial decoder input from the source language space to the target language space through a novel attentive transformation process. The transformation reassem-bles the decoder input based on target token embeddings and conditions the ﬁnal output on the target-side information. Second, before NAT training, we introduce an effective forward-backward pre-training phase, implemented with different triangle attention masks. This pre-training phase enables the model to gradually learn bidirectional dependencies for the ﬁnal NAT decoding process. Experimental results demonstrate that the proposed approaches consistently improve highly competitive NAT models on four WMT translation directions by up to 1.88 BLEU score, while over-all maintaining inference latency comparable to other fully NAT models. 1