Miracle-dz/UniFAR-FTUs huggingface dataset:
Physician Practice Patterns and Barriers to Counselling on Physical Activity in Solid Organ Transplant Recipients

Introduction Many solid organ transplant (SOT) recipients fail to meet the recommendations for population-based physical activity (PA) levels. “Physician recommendation” has previously been reported by SOT recipients as a key facilitator to being more physically active. The purpose of this study was to determine the proportion of Canadian SOT physicians providing PA counselling and identify barriers to including such counselling as part of the SOT recipients’ routine care. Material and Methods We conducted a cross-sectional, web-based survey study to evaluate physicians’ PA counselling practices, including the prevalence and barriers to such practice. Investigators developed a 21-item questionnaire which was composed of 6 subsections: A) Pre-questionnaire eligibility (1 item); B) Consent (1 item); C) Demographics (6 items); D) Knowledge about physical activity (6 items); E) Physical activity counselling and potential barriers (5 items); and F) Future considerations (2 items). A survey link was sent to a convenience sample of transplant physicians who are members of the Canadian Society of Transplantation. Results and Discussion Thirty-four physicians (13.6%) participated in the survey. The majority of the respondents worked with kidney (65%; n=22), liver (32%; n=11), or pancreas (29%; n=10) organ groups. While 97% (n=33) of the participants reported providing PA counselling to their transplant patients, only 27% provided PA counselling to all their patients and only 18% (n=6) responded they were very confident in PA counselling. The main barriers reported by SOT physicians to PA counselling were lack of time (n=19; 56%) and exercise guidelines (n=18, 53%), followed by lack of confidence (n=8; 23%), research evidence (n=7; 20%) and knowledge about exercise (n=7; 20%). Conclusions Increasing educational opportunities for physicians to learn about PA and exercise, developing specific PA guidelines for SOT as well as establishing a referral system to exercise specialists might improve the frequency and quality of PA counselling post-transplant.
Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

We introduce the Kronecker factored online Laplace approximation for overcoming catastrophic forgetting in neural networks. The method is grounded in a Bayesian online learning framework, where we recursively approximate the posterior after every task with a Gaussian, leading to a quadratic penalty on changes to the weights. The Laplace approximation requires calculating the Hessian around a mode, which is typically intractable for modern architectures. In order to make our method scalable, we leverage recent block-diagonal Kronecker factored approximations to the curvature. Our algorithm achieves over 90% test accuracy across a sequence of 50 instantiations of the permuted MNIST dataset, substantially outperforming related methods for overcoming catastrophic forgetting.
Parallel Asynchronous Stochastic Variance Reduction for Nonconvex Optimization

Nowadays, asynchronous parallel algorithms have received much attention in the optimization field due to the crucial demands for modern large-scale optimization problems. However, most asynchronous algorithms focus on convex problems. Analysis on nonconvex problems is lacking. For the Asynchronous Stochastic Descent (ASGD) algorithm, the best result from (Lian et al., 2015) can only achieve an asymptotic O(\frac{1}{\epsilon^2}) rate (convergence to the stationary points) on nonconvex problems. In this paper, we study Stochastic Variance Reduced Gradient (SVRG) in the asynchronous setting. We propose the Asynchronous Stochastic Variance Reduced Gradient (ASVRG) algorithm for nonconvex finite-sum problems. We develop two schemes for ASVRG, depending on whether the parameters are updated as an atom or not. We prove that both of the two schemes can achieve linear speed up (a non-asymptotic O(\frac{n^\frac{2}{3}}{\epsilon}) rate to the stationary points) for nonconvex problems when the delay parameter \tau\leq n^{\frac{1}{3}}, where n is the number of training samples. We also establish a non-asymptotic O(\frac{n^\frac{2}{3}\tau^\frac{1}{3}}{\epsilon}) rate (convergence to the stationary points) for our algorithm without assumptions on \tau. This further demonstrates that even with asynchronous updating, SVRG has less number of Incremental First-order Oracles (IFOs) compared with Stochastic Gradient Descent and Gradient Descent. We also experiment on a shared memory multi-core system to demonstrate the efficiency of our algorithm.
Relationship between Franking Credits and the Market Risk Premium

In a dividend imputation tax system, equity investors have three potential sources of return: dividends, capital gains and franking (tax) credits. However, the standard procedures for estimating the market risk premium (MRP) for use in the capital asset pricing model, ignore the value of franking credits. Officer (1994) notes that if franking credits do affect the corporate cost of capital, their value must be added to the standard estimates of MRP. In the present paper, we explicitly derive the relationship between the value of franking credits (gamma) and the MRP. We show that the standard parameter estimates that have been adopted in practice (especially by Australian regulators) violate this deterministic mathematical relationship. We also show how information on dividend yields and effective tax rates bounds the values that can be reasonably used for gamma and the MRP. We make recommendations for how estimates of the MRP should be adjusted to reflect the value of franking credits in an internally consistent manner.
Ascites-Derived Extracellular microRNAs as Potential Biomarkers for Ovarian Cancer

Ovarian cancer as the most fatal gynecological malignancy is often manifested by excessive fluid accumulation known as ascites or effusion. Ascites-derived microRNAs (miRNAs) may be closely associated with ovarian cancer progression. However, our knowledge of their roles, altered expression, and clinical outcomes remained limited. In this study, large-scale expression profiling of 754 human miRNAs was performed using real-time quantitative polymerase chain reaction and 384-well TaqMan array human miRNA A and B cards to identify differentially expressed miRNAs between extracellular fraction of the ascitic fluid associated with high-grade serous ovarian carcinomas and control plasma. Of the 754 miRNAs, 153 were significantly differentially expressed relative to the controls. Expression of 7 individual miRNAs (miR-200a, miR-200b, miR-200c, miR-141, miR-429, miR-1290, and miR-30a-5p) was further validated in extended sample sets, including serous, endometrioid, and mucinous subtypes. All miR-200 family members and miR-1290 were conspicuously overexpressed, while miR-30a-5p was only weakly overexpressed. The ability of miRNAs expression to discriminate the pathological samples from the controls was strong. Receiver operating characteristic curve analyses found area under the curve (AUC) values of 1.000 for miR-200a, miR-200c, miR-141, miR-429, and miR-1290 and of AUC 0.996 and 0.885 for miR-200b and miR-30a-5p, respectively. Preliminary survival analyses indicated low expression level of miR-200b as significantly related to longer overall survival (hazard ratio [HR]: 0.25, mean survival 44 months), while high expression level was related to poor overall survival (HR: 4.04, mean survival 24 months). Our findings suggested that ascites-derived miRNAs should be further explored and evaluated as potential diagnostic and prognostic biomarkers for ovarian cancer.
Brain Stimulation in Alzheimer's Disease

Brain stimulation techniques can modulate cognitive functions in many neuropsychiatric diseases. Pilot studies have shown promising effects of brain stimulations on Alzheimer's disease (AD). Brain stimulations can be categorized into non-invasive brain stimulation (NIBS) and invasive brain stimulation (IBS). IBS includes deep brain stimulation (DBS), and invasive vagus nerve stimulation (VNS), whereas NIBS includes transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), electroconvulsive treatment (ECT), magnetic seizure therapy (MST), cranial electrostimulation (CES), and non-invasive VNS. We reviewed the cutting-edge research on these brain stimulation techniques and discussed their therapeutic effects on AD. Both IBS and NIBS may have potential to be developed as novel treatments for AD; however, mixed findings may result from different study designs, patients selection, population, or samples sizes. Therefore, the efficacy of NIBS and IBS in AD remains uncertain, and needs to be further investigated. Moreover, more standardized study designs with larger sample sizes and longitudinal follow-up are warranted for establishing a structural guide for future studies and clinical application.
Controlled-Deactivation CB1 Receptor Ligands as a Novel Strategy to Lower Intraocular Pressure

Nearly half a century has passed since the demonstration that cannabis and its chief psychoactive component Δ9-THC lowers intraocular pressure (IOP). Elevated IOP remains the chief hallmark and therapeutic target for glaucoma, a condition that places millions at risk of blindness. It is likely that Δ9-THC exerts much of its IOP-lowering effects via the activation of CB1 cannabinoid receptors. However, the initial promise of CB1 as a target for treating glaucoma has not thus far translated into a credible therapeutic strategy. We have recently shown that blocking monoacylglycerol lipase (MAGL), an enzyme that breaks the endocannabinoid 2-arachidonoyl glycerol (2-AG), substantially lowers IOP. Another strategy is to develop cannabinoid CB1 receptor agonists that are optimized for topical application to the eye. Recently we have reported on a controlled-deactivation approach where the “soft” drug concept of enzymatic deactivation was combined with a “depot effect” that is commonly observed with Δ9-THC and other lipophilic cannabinoids. This approach allowed us to develop novel cannabinoids with a predictable duration of action and is particularly attractive for the design of CB1 activators for ophthalmic use with limited or no psychoactive effects. We have tested a novel class of compounds using a combination of electrophysiology in autaptic hippocampal neurons, a well-characterized model of endogenous cannabinoid signaling, and measurements of IOP in a mouse model. We now report that AM7410 is a reasonably potent and efficacious agonist at CB1 in neurons and that it substantially (30%) lowers IOP for as long as 5 h after a single topical treatment. This effect is absent in CB1 knockout mice. Our results indicate that the direct targeting of CB1 receptors with controlled-deactivation ligands is a viable approach to lower IOP in a murine model and merits further study in other model systems.
Effect of multimorbidity on survival of patients diagnosed with heart failure: a retrospective cohort study in Singapore

Objective Multimorbidity in patients with heart failure (HF) results in poor prognosis and is an increasing public health concern. We aim to examine the effect of multimorbidity focusing on type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) on all-cause and cardiovascular disease (CVD)-specific mortality among patients diagnosed with HF in Singapore. Design Retrospective cohort study. Setting Primary and tertiary care in three (out of six) Regional Health Systems in Singapore. Participants Patients diagnosed with HF between 2003 and 2016 from three restructured hospitals and nine primary care polyclinics were included in this retrospective cohort study. Primary outcomes All-cause mortality and CVD-specific mortality. Results A total of 34 460 patients diagnosed with HF from 2003 to 2016 were included in this study and were followed up until 31 December 2016. The median follow-up time was 2.1 years. Comorbidities prior to HF diagnosis were considered. Patients were categorised as (1) HF only, (2) T2DM+HF, (3) CKD+HF and (4) T2DM+CKD+HF. Cox regression model was used to determine the effect of multimorbidity on (1) all-cause mortality and (2) CVD-specific mortality. Adjusting for demographics, other comorbidities, baseline treatment and duration of T2DM prior to HF diagnosis, ‘T2DM+CKD+HF’ patients had a 56% higher risk of all-cause mortality (HR: 1.56, 95% CI 1.48 to 1.63) and a 44% higher risk of CVD-specific mortality (HR: 1.44, 95% CI 1.32 to 1.56) compared with patients diagnosed with HF only. Conclusion All-cause and CVD-specific mortality risks increased with increasing multimorbidity. This study highlights the need for a new model of care that focuses on holistic patient management rather than disease management alone to improve survival among patients with HF with multimorbidity.
Human herpesvirus-6 pneumonitis in a patient with follicular lymphoma following immunochemotherapy with rituximab

Primary infection with human herpesvirus-6 (HHV-6) commonly occurs at an early age in children, most often at 3 years of age, and is associated with childhood diseases, such as exanthema subitum, hepatitis, febrile convulsions, or encephalitis. However, the virus occasionally reactivates from its latent state in immunosuppressed adults, especially post-transplant, resulting in serious disseminated, sometimes life-threatening end-organ complications. Herein, we report a case of a 68-year-old man with relapsed follicular lymphoma who developed HHV-6 pneumonitis. Eighteen months after achieving second complete remission by salvage immunochemotherapy with rituximab, the patient was complicated by pneumonia, with chest computed tomography finding showing disseminated nodular shadows with ground-glass opacity in both lungs. While empiric antibiotic and antifungal therapies did not improve the pneumonia, polymerase chain reaction–based viral screening tests on his bronchoalveolar lavage fluid detected the presence of HHV-6 DNA, and ganciclovir treatment quickly resolved the pneumonia, indicating that he suffered from HHV-6 pneumonitis. He had no other HHV-6–related end-organ damage, such as encephalitis. This case suggests that, although extremely rare, HHV-6 reactivation should be considered as one of the candidate pathogens for pulmonary complications of uncertain etiology in patients who have been treated with intensive immunosuppressive chemotherapy, even without hematopoietic stem cell transplantation. Furthermore, polymerase chain reaction–based viral screening testing on bronchoalveolar lavage fluid is a powerful diagnostic tool for pneumonitis due to viral reactivation, including HHV-6 reactivation.
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

The most prevalent notions of fairness in machine learning are statistical definitions: they fix a small collection of pre-defined groups, and then ask for parity of some statistic of the classifier across these groups. Constraints of this form are susceptible to intentional or inadvertent "fairness gerrymandering", in which a classifier appears to be fair on each individual group, but badly violates the fairness constraint on one or more structured subgroups defined over the protected attributes. We propose instead to demand statistical notions of fairness across exponentially (or infinitely) many subgroups, defined by a structured class of functions over the protected attributes. This interpolates between statistical definitions of fairness and recently proposed individual notions of fairness, but raises several computational challenges. It is no longer clear how to audit a fixed classifier to see if it satisfies such a strong definition of fairness. We prove that the computational problem of auditing subgroup fairness for both equality of false positive rates and statistical parity is equivalent to the problem of weak agnostic learning, which means it is computationally hard in the worst case, even for simple structured subclasses. We then derive two algorithms that provably converge to the best fair classifier, given access to oracles which can solve the agnostic learning problem. The algorithms are based on a formulation of subgroup fairness as a two-player zero-sum game between a Learner and an Auditor. Our first algorithm provably converges in a polynomial number of steps. Our second algorithm enjoys only provably asymptotic convergence, but has the merit of simplicity and faster per-step computation. We implement the simpler algorithm using linear regression as a heuristic oracle, and show that we can effectively both audit and learn fair classifiers on real datasets.
Improving Word Embeddings with Convolutional Feature Learning and Subword Information

We present a novel approach to learning word embeddings by exploring subword information (character n-gram, root/affix and inflections) and capturing the structural information of their context with convolutional feature learning. Specifically, we introduce a convolutional neural network architecture that allows us to measure structural information of context words and incorporate subword features conveying semantic, syntactic and morphological information related to the words. To assess the effectiveness of our model, we conduct extensive experiments on the standard word similarity and word analogy tasks. We showed improvements over existing state-of-the-art methods for learning word embeddings, including skipgram, GloVe, char n-gram and DSSM.
MS/MS-Guided Isolation of Clarinoside, a New Anti-Inflammatory Pentalogin Derivative

Re-investigation of the chemical composition of the annual plant Mitracarpus scaber Zucc. led to the identification of clarinoside, a new pentalogin derivative containing a rare quinovose moiety, and the known compound harounoside. While the planar structure was fully determined using tandem mass spectrometry (MS) and quantum mechanics (QM) calculations, the tridimensional structure was unravelled after isolation and NMR analysis. The absolute configuration was assigned by comparison of experimental and theoretical synchrotron radiation circular dichroism spectra. Both compounds were tested for anti-inflammatory activity, and compound 1 showed the ability to inhibit the production of interleukin-8 (Il-8) with an IC50 value of 9.17 μM.
Physiology underlies the assembly of ecological communities

Significance Ecology aims to understand the distribution of species and the processes that assemble communities. One common strategy is to use species traits to predict interaction with the abiotic and biotic environment, thus gaining an understanding of ecological communities. Here, we show that considering causal relationships among physiological and behavioral traits can help elucidate patterns of species distribution and community assembly in larval dragonflies across ponds differing in predation risk. Beyond a conceptual understanding of trait causality, we also show a striking ecological pattern: the activity of two biomolecules accounts for >80% of the variation in community composition. Together, our conceptual framework and demonstration of a strong ecological pattern suggest that causal relationships are key to advancing trait-based community ecology. Trait-based community ecology promises an understanding of the factors that determine species abundances and distributions across habitats. However, ecologists are often faced with large suites of potentially important traits, making generalizations across ecosystems and species difficult or even impossible. Here, we hypothesize that key traits structuring ecological communities may be causally dependent on common physiological mechanisms and that elucidating these mechanisms can help us understand the distributions of traits and species across habitats. We test this hypothesis by investigating putatively causal relationships between physiological and behavioral traits at the species and community levels in larvae of 17 species of dragonfly that co-occur at the landscape scale but segregate among lakes. We use tools borrowed from phenotypic selection analyses to show that physiological traits underlie activity rate, which has opposing effects on foraging and predator avoidance behaviors. The effect of activity on these behaviors ultimately shapes species distributions and community composition in habitats with either large-bodied fish or invertebrates as top predators. Remarkably, despite the inherent complexity of ecological communities, the expression of just two biomolecules accounts for a high proportion of the variation in behavioral traits and hence, dragonfly community composition between habitats. We suggest that causal relationships among traits can drive species distributions and community assembly.
Density-Adaptive Kernel based Re-Ranking for Person Re-Identification

Person Re-Identification (ReID) refers to the task of verifying the identity of a pedestrian observed from nonoverlapping surveillance cameras views. Recently, it has been validated that re-ranking could bring extra performance improvements in person ReID. However, the current re-ranking approaches either require feedbacks from users or suffer from burdensome computation cost. In this paper, we propose to exploit a density-adaptive kernel technique to perform efficient and effective re-ranking for person ReID. Specifically, we present two simple yet effective re-ranking methods, termed inverse Density-Adaptive Kernel based Re-ranking (inv-DAKR) and bidirectional Density-Adaptive Kernel based Re-ranking (bi-DAKR), which are based on a smooth kernel function with a density-adaptive parameter. Experiments on six benchmark data sets confirm that our proposals are effective and efficient.
Document Modeling with Hierarchical Deep Learning Approach for Sentiment Classification

Sentiment analysis has recently been considered as most active research field in NLP domain. Deep learning is a growing trend of machine learning due to its automatic learning capability with impressive results across different NLP task. In this paper a model is proposed to analyze the deep sentiment representation based on CNN and LSTM (modified version of RNN) network. We aim to improve the performance of traditional machine learning method by merging them with deep learning techniques to tackle the challenge of sentiment prediction of massive amount of unsupervised product review dataset. We make our model first learn to sentence representation with CNN. Next, the semantics of sentences are encoded with LSTM network for document representation. We conduct experiments on two review datasets based on movie review with evaluation metric 'accuracy'. The result shows that proposed model outperformed traditional machine learning as well as baseline neural network model
Dynamically Unfolding Recurrent Restorer: A Moving Endpoint Control Method for Image Restoration

In this paper, we propose a new control framework called the moving endpoint control to restore images corrupted by different degradation levels in one model. The proposed control problem contains a restoration dynamics which is modeled by an RNN. The moving endpoint, which is essentially the terminal time of the associated dynamics, is determined by a policy network. We call the proposed model the dynamically unfolding recurrent restorer (DURR). Numerical experiments show that DURR is able to achieve state-of-the-art performances on blind image denoising and JPEG image deblocking. Furthermore, DURR can well generalize to images with higher degradation levels that are not included in the training stage.