nils-herrmann/clean_scirepeval huggingface dataset:
Memantine Monotherapy for Alzheimer’s Disease: A Systematic Review and Meta-Analysis

We performed an updated meta-analysis of randomized placebo-controlled trials testing memantine monotherapy for patients with Alzheimer's disease (AD).



The meta-analysis included randomized controlled trials of memantine monotherapy for AD, omitting those in which patients were also administered a cholinesterase inhibitor. Cognitive function, activities of daily living, behavioral disturbances, global function, stage of dementia, drug discontinuation rate, and individual side effects were compared between memantine monotherapy and placebo groups. The primary outcomes were cognitive function and behavioral disturbances; the others were secondary outcomes.



Nine studies including 2433 patients that met the study's inclusion criteria were identified. Memantine monotherapy significantly improved cognitive function [standardized mean difference (SMD)=-0.27, 95% confidence interval (CI)=-0.39 to -0.14, p=0.0001], behavioral disturbances (SMD=-0.12, 95% CI=-0.22 to -0.01, p=0.03), activities of daily living (SMD=-0.09, 95% CI=-0.19 to -0.00, p=0.05), global function assessment (SMD=-0.18, 95% CI=-0.27 to -0.09, p=0.0001), and stage of dementia (SMD=-0.23, 95% CI=-0.33 to -0.12, p=0.0001) scores. Memantine was superior to placebo in terms of discontinuation because of inefficacy [risk ratio (RR)=0.36, 95% CI=0.17¬ to 0.74, p=0.006, number needed to harm (NNH)=non significant]. Moreover, memantine was associated with less agitation compared with placebo (RR=0.68, 95% CI=0.49 to 0.94, p=0.02, NNH=non significant). There were no significant differences in the rate of discontinuation because of all causes, all adverse events, and individual side effects other than agitation between the memantine monotherapy and placebo groups.



Memantine monotherapy improved cognition, behavior, activities of daily living, global function, and stage of dementia and was well-tolerated by AD patients. However, the effect size in terms of efficacy outcomes was small and thus there is limited evidence of clinical benefit.
A note on Beckett-Gray codes and the relationship of Gray codes to data structures

In this paper we introduce a natural mathematical structure derived from Samuel Beckett’s play “Quad”. We call this structure a binary Beckett-Gray code. We enumerate all codes for n ≤ 6 and give examples for n = 7, 8. Beckett-Gray codes can be realized as successive states of a queue data structure. We show that the binary reflected Gray code can be realized as successive states of two stack data structures.
Job satisfaction and motivation of health workers in public and private sectors: cross-sectional analysis from two Indian states

Ensuring health worker job satisfaction and motivation are important if health workers are to be retained and effectively deliver health services in many developing countries, whether they work in the public or private sector. The objectives of the paper are to identify important aspects of health worker satisfaction and motivation in two Indian states working in public and private sectors.



Cross-sectional surveys of 1916 public and private sector health workers in Andhra Pradesh and Uttar Pradesh, India, were conducted using a standardized instrument to identify health workers' satisfaction with key work factors related to motivation. Ratings were compared with how important health workers consider these factors.



There was high variability in the ratings for areas of satisfaction and motivation across the different practice settings, but there were also commonalities. Four groups of factors were identified, with those relating to job content and work environment viewed as the most important characteristics of the ideal job, and rated higher than a good income. In both states, public sector health workers rated "good employment benefits" as significantly more important than private sector workers, as well as a "superior who recognizes work". There were large differences in whether these factors were considered present on the job, particularly between public and private sector health workers in Uttar Pradesh, where the public sector fared consistently lower (P < 0.01). Discordance between what motivational factors health workers considered important and their perceptions of actual presence of these factors were also highest in Uttar Pradesh in the public sector, where all 17 items had greater discordance for public sector workers than for workers in the private sector (P < 0.001).



There are common areas of health worker motivation that should be considered by managers and policy makers, particularly the importance of non-financial motivators such as working environment and skill development opportunities. But managers also need to focus on the importance of locally assessing conditions and managing incentives to ensure health workers are motivated in their work.
Dynamics of biofilm formation by Listeria monocytogenes on stainless steel under mono-species and mixed-culture simulated fish processing conditions and chemical disinfection challenges.

The progressive ability of a six-strains L. monocytogenes cocktail to form biofilm on stainless steel (SS), under fish-processing simulated conditions, was investigated, together with the biocide tolerance of the developed sessile communities. To do this, the pathogenic bacteria were left to form biofilms on SS coupons incubated at 15°C, for up to 240h, in periodically renewable model fish juice substrate, prepared by aquatic extraction of sea bream flesh, under both mono-species and mixed-culture conditions. In the latter case, L. monocytogenes cells were left to produce biofilms together with either a five-strains cocktail of four Pseudomonas species (fragi, savastanoi, putida and fluorescens), or whole fish indigenous microflora. The biofilm populations of L. monocytogenes, Pseudomonas spp., Enterobacteriaceae, H2S producing and aerobic plate count (APC) bacteria, both before and after disinfection, were enumerated by selective agar plating, following their removal from surfaces through bead vortexing. Scanning electron microscopy was also applied to monitor biofilm formation dynamics and anti-biofilm biocidal actions. Results revealed the clear dominance of Pseudomonas spp. bacteria in all the mixed-culture sessile communities throughout the whole incubation period, with the in parallel sole presence of L. monocytogenes cells to further increase (ca. 10-fold) their sessile growth. With respect to L. monocytogenes and under mono-species conditions, its maximum biofilm population (ca. 6logCFU/cm2) was reached at 192h of incubation, whereas when solely Pseudomonas spp. cells were also present, its biofilm formation was either slightly hindered or favored, depending on the incubation day. However, when all the fish indigenous microflora was present, biofilm formation by the pathogen was greatly hampered and never exceeded 3logCFU/cm2, while under the same conditions, APC biofilm counts had already surpassed 7logCFU/cm2 by the end of the first 96h of incubation. All here tested disinfection treatments, composed of two common food industry biocides gradually applied for 15 to 30min, were insufficient against L. monocytogenes mono-species biofilm communities, with the resistance of the latter to significantly increase from the 3rd to 7th day of incubation. However, all these treatments resulted in no detectable L. monocytogenes cells upon their application against the mixed-culture sessile communities also containing the fish indigenous microflora, something probably associated with the low attached population level of these pathogenic cells before disinfection (<102CFU/cm2) under such mixed-culture conditions. Taken together, all these results expand our knowledge on both the population dynamics and resistance of L. monocytogenes biofilm cells under conditions resembling those encountered within the seafood industry and should be considered upon designing and applying effective anti-biofilm strategies.
3D-based Deep Convolutional Neural Network for action recognition with depth sequences

Traditional algorithms to design hand-crafted features for action recognition have been a hot research area in last decade. Compared to RGB video, depth sequence is more insensitive to lighting changes and more discriminative due to its capability to catch geometric information of object. Unlike many existing methods for action recognition which depend on well-designed features, this paper studies deep learning-based action recognition using depth sequences and the corresponding skeleton joint information. Firstly, we construct a 3Dbased Deep Convolutional Neural Network (3DCNN) to directly learn spatiotemporal features from raw depth sequences, then compute a joint based feature vector named JointVector for each sequence by taking into account the simple position and angle information between skeleton joints. Finally, support vector machine (SVM) classification results from 3DCNN learned features and JointVector are fused to take action recognition. Experimental results demonstrate that our method can learn feature representation which is time-invariant and viewpoint-invariant from depth sequences. The proposed method achieves comparable results to the state-of-the-art methods on the UTKinect-Action3D dataset and achieves superior performance in comparison to baseline methods on the MSR-Action3D dataset. We further investigate the generalization of the trained model by transferring the learned features from one dataset (MSREmail addresses: liuzhi@cqut.edu.cn (Zhi Liu), czhang10@ccny.cuny.edu (Chenyang Zhang), ytian@ccny.cuny.edu (Yingli Tian)  2016 Action3D) to another dataset (UTKinect-Action3D) without retraining and obtain very promising classification accuracy.
Hypomania after augmenting venlafaxine and olanzapine with sarcosine in a patient with schizophrenia: a case study

Glutamate is the main excitatory neurotransmitter in the central nervous system. Dysfunction of the glutamatergic system plays an important and well-established role in the pathogenesis of schizophrenia. Agents with glutamatergic properties such as N-methyl-D-aspartate receptor coagonists (ie, glycine, D-cycloserine) and glycine transporter type 1 inhibitors (eg, sarcosine, bitopertin) are investigated in schizophrenia with special focus on negative and cognitive symptomatology. In this article, we describe a case of a 34-year-old woman with diagnosis of schizophrenia with persistent moderate negative and cognitive symptoms, a participant of the Polish Sarcosine Study (PULSAR) treated with olanzapine (25 mg per day) and venlafaxine (75 mg per day). During ten weeks of sarcosine administration (2 g per day) the patient's activity and mood improved, but in the following 2 weeks, the patient reported decreased need for sleep, elevated mood, libido and general activity. We diagnosed drug-induced hypomania and recommended decreasing the daily dose of venlafaxine to 37.5 mg per day, which resulted in normalization of mood and activity in about 1 week. After this change, activity and mood remained stable and better than before adding sarcosine, and subsequent depressive symptoms were not noted. We describe here the second case report where sarcosine induced important affect changes when added to antidepressive and antipsychotic treatment, which supports the hypothesis of clinically important glutamate-serotonin interaction.
SVRG meets SAGA: k-SVRG - A Tale of Limited Memory

In recent years, many variance reduced algorithms for empirical risk minimization have been introduced. In contrast to vanilla SGD, these methods converge linearly on strong convex problems. To obtain the variance reduction, current methods either require frequent passes over the full data to recompute gradients—without making any progress during this time (like in SVRG), or they require memory of the same size as the input problem (like SAGA). In this work, we propose k-SVRG, an algorithm that interpolates between those two extremes: it makes best use of the available memory and in turn does avoid full passes over the data without making progress. We prove linear convergence of k-SVRG on strongly convex problems and convergence to stationary points on non-convex problems. Numerical experiments show the effectiveness of our method.
Trajectory clustering via deep representation learning

Trajectory clustering, which aims at discovering groups of similar trajectories, has long been considered as a corner stone task for revealing movement patterns as well as facilitating higher-level applications like location prediction. While a plethora of trajectory clustering techniques have been proposed, they often rely on spatiotemporal similarity measures that are not space- and time-invariant. As a result, they cannot detect trajectory clusters where the within-cluster similarity occurs in different regions and time periods. In this paper, we revisit the trajectory clustering problem by learning quality low-dimensional representations of the trajectories. We first use a sliding window to extract a set of moving behavior features that capture space- and time-invariant characteristics of the trajectories. With the feature extraction module, we transform each trajectory into a feature sequence to describe object movements, and further employ a sequence to sequence autoencoder to learn fixed-length deep representations. The learnt representations robustly encode the movement characteristics of the objects and thus lead to space- and time-invariant clusters. We evaluate the proposed method on both synthetic and real data, and observe significant performance improvements over existing methods.
Automotive functional safety = safety + security

Standard approaches to functional safety as described in the automotive functional safety standard ISO 26262 are focused on reducing the risk of hazards due to random hardware faults or systematic failures during design (e.g. software bugs). However, as vehicle systems become increasingly complex and ever more connected to the internet of things, a third source of hazard must be considered, that of intentional manipulation of the electrical/electronic control systems either via direct physical contact or via the systems' open interfaces. This article describes how the process prescribed by the ISO 26262 can be extended with methods from the domain of embedded security to protect the systems against this third source of hazard.
Sexual Masochism Disorder with Asphyxiophilia: A Deadly yet Underrecognized Disease

DSM-5 distinguishes between paraphilias and paraphilic disorders. Paraphilias are defined as atypical, yet not necessarily disordered, sexual practices. Paraphilic disorders are instead diseases, which include distress, impairment in functioning, or entail risk of harm one's self or others. Hence, DSM-5 new approach to paraphilias demedicalizes and destigmatizes unusual sexual behaviors, provided they are not distressing or detrimental to self or others. Asphyxiophilia, a dangerous and potentially deadly form of sexual masochism involving sexual arousal by oxygen deprivation, are clearly described as disorders. Although autoerotic asphyxia has been associated with estimated mortality rates ranging from 250 to 1000 deaths per year in the United States, in Italy, knowledge on this condition is very poor. Episodes of death caused by autoerotic asphyxia seem to be underestimated because it often can be confounded with suicide cases, particularly in the Italian context where family members of the victim often try to disguise autoerotic behaviors of the victims. The current paper provides a review on sexual masochism disorder with asphyxiophilia and discusses one specific case as an example to examine those conditions that may or may not influence the likelihood that death from autoerotic asphyxia be erroneously reported as suicide or accidental injury.
Primo Vascular System: A Unique Biological System Shifting a Medical Paradigm.

The primo vascular system has a specific anatomical and immunohistochemical signature that sets it apart from the arteriovenous and lymphatic systems. With immune and endocrine functions, the primo vascular system has been found to play a large role in biological processes, including tissue regeneration, inflammation, and cancer metastases. Although scientifically confirmed in 2002, the original discovery was made in the early 1960s by Bong-Han Kim, a North Korean scientist. It would take nearly 40 years after that discovery for scientists to revisit Kim's research to confirm the early findings. The presence of primo vessels in and around blood and lymph vessels, nerves, viscera, and fascia, as well as in the brain and spinal cord, reveals a common link that could potentially open novel possibilities of integration with cranial, lymphatic, visceral, and fascial approaches in manual medicine.
Efficient & Effective Selective Query Rewriting with Efficiency Predictions

To enhance effectiveness, a user's query can be rewritten internally by the search engine in many ways, for example by applying proximity, or by expanding the query with related terms. However, approaches that benefit effectiveness often have a negative impact on efficiency, which has impacts upon the user satisfaction, if the query is excessively slow. In this paper, we propose a novel framework for using the predicted execution time of various query rewritings to select between alternatives on a per-query basis, in a manner that ensures both effectiveness and efficiency. In particular, we propose the prediction of the execution time of ephemeral (e.g., proximity) posting lists generated from uni-gram inverted index posting lists, which are used in establishing the permissible query rewriting alternatives that may execute in the allowed time. Experiments examining both the effectiveness and efficiency of the proposed approach demonstrate that a 49% decrease in mean response time (and 62% decrease in 95th-percentile response time) can be attained without significantly hindering the effectiveness of the search engine.
Anomaly Detection for Portfolio Risk Management An evaluation of econometric and machine learning based approaches to detecting anomalous behaviour in portfolio risk measures

Financial institutions manage numerous portfolios whose risk must be managed continuously, and the large amounts of data that has to be processed renders this a considerable effort. As such, a system that autonomously detects anomalies in the risk measures of financial portfolios, would be of great value. To this end, the two econometric models ARMA-GARCH and EWMA, and the two machine learning based algorithms LSTM and HTM, were evaluated for the task of performing unsupervised anomaly detection on the streaming time series of portfolio risk measures. Three datasets of returns and Value-at-Risk series were synthesized and one dataset of real-world Value-at-Risk series had labels handcrafted for the experiments in this thesis. The results revealed that the LSTM has great potential in this domain, due to an ability to adapt to different types of time series and for being effective at finding a wide range of anomalies. However, the EWMA had the benefit of being faster and more interpretable, but lacked the ability to capture anomalous trends. The ARMA-GARCH was found to have difficulties in finding a good fit to the time series of risk measures, resulting in poor performance, and the HTM was outperformed by the other algorithms in every regard, due to an inability to learn the autoregressive behaviour of the time series.
Calibrated, Registered Images of an Extended Urban Area

We describe a dataset of several thousand calibrated, time-stamped, geo-referenced, high dynamic range color images, acquired under uncontrolled, variable illumination conditions in an outdoor region spanning several hundred meters. The image data is grouped into several regions which have little mutual inter-visibility. For each group, the calibration data is globally consistent on average to roughly five centimeters and 0 1°, or about four pixels of epipolar registration. All image, feature and calibration data is available for interactive inspection and downloading at http://city.lcs.mit.edu/data. Calibrated imagery is of fundamental interest in a variety of applications. We have made this data available in the belief that researchers in computer graphics, computer vision, photogrammetry and digital cartography will find it of value as a test set for their own image registration algorithms, as a calibrated image set for applications such as image-based rendering, metric 3D reconstruction, and appearance recovery, and as input for existing GIS applications.
Advanced Glycation End Products and Diabetic Complications

During long standing hyperglycaemic state in diabetes mellitus, glucose forms covalent adducts with the plasma proteins through a non-enzymatic process known as glycation. Protein glycation and formation of advanced glycation end products (AGEs) play an important role in the pathogenesis of diabetic complications like retinopathy, nephropathy, neuropathy, cardiomyopathy along with some other diseases such as rheumatoid arthritis, osteoporosis and aging. Glycation of proteins interferes with their normal functions by disrupting molecular conformation, altering enzymatic activity, and interfering with receptor functioning. AGEs form intra- and extracellular cross linking not only with proteins, but with some other endogenous key molecules including lipids and nucleic acids to contribute in the development of diabetic complications. Recent studies suggest that AGEs interact with plasma membrane localized receptors for AGEs (RAGE) to alter intracellular signaling, gene expression, release of pro-inflammatory molecules and free radicals. The present review discusses the glycation of plasma proteins such as albumin, fibrinogen, globulins and collagen to form different types of AGEs. Furthermore, the role of AGEs in the pathogenesis of diabetic complications including retinopathy, cataract, neuropathy, nephropathy and cardiomyopathy is also discussed.
Addressing Path Dependence and Incorporating Sample Weights in the Nonlinear Blinder-Oaxaca Decomposition Technique for Logit , Probit and Other Nonlinear Models

The Blinder-Oaxaca decomposition technique is widely used to identify and quantify the separate contributions of differences in measurable characteristics to group differences in an outcome of interest. The use of a linear probability model and the standard BlinderOaxaca decomposition, however, can provide misleading estimates when the dependent variable is binary, especially when group differences are very large for an influential explanatory variable. A simulation method of performing a nonlinear decomposition that uses estimates from a logit, probit or other nonlinear model was first developed in a Journal of Labor Economics article (Fairlie 1999). This nonlinear decomposition technique has been used in nearly a thousand subsequent studies published in a wide range of fields and disciplines. In this paper, I address concerns over path dependence in using the nonlinear decomposition technique. I also present a straightforward method of incorporating sample weights in the technique. I thank Eric Aldrich and Ben Jann for comments and suggestions, and Brandon Heck for research assistance.