Miracle-dz/UniFAR-FTUs huggingface dataset:
Electromembrane extraction and capillary electrophoresis with capacitively coupled contactless conductivity detection: Multi‐extraction capabilities to analyses trace metals from saline samples

Simultaneous electromembrane extraction (EME) of six trace metal cations (Cu2+, Zn2+, Co2+, Ni2+, Pb2+, Cd2+) from saline samples was investigated. CE with capacitively coupled contactless conductivity detection (C4D) was used to determine the metals in acceptor solutions due to its excellent compatibility with the minute volumes of acceptor solutions. Bis(2‐ethylhexyl)phosphate (DEHPA) was selected as a suitable nonselective modifier for EME transport of target metal cations. Both, the individual effect of each major inorganic cation (Na+, K+, Ca2+, Mg2+) and their synergistic effect on EME of the trace metal cations were evaluated. In both cases, a decrease in extraction efficiency was observed when major inorganic cations were present in the sample. This effect was more significant for Ca2+ and Mg2+. The system was optimized for simultaneous extractions of the six target metals from saline samples (50 mM Na+, 5 mM Mg2+, 1 mM K+, and 1 mM Ca2+) and following EME conditions were applied. Organic phase consisted of 1‐nonanol containing 1% (v/v) DEHPA, acceptor solution was 1 M acetic acid (HAc) and sample pH was adjusted to 5. Sample was stirred at 750 rpm and EMEs were carried out at extraction potential of 10 V for 20 min. The method presented a repeatability between 8 and 21.8% (n = 5), good linearity in 0.5–10 μM concentration range (R2 = 0.987‐0.999) and LOD better than 2.6 nM. Applicability of the EME–CE–C4D method to the analyses of metal cations in drinking water, seawater, and urine samples was also demonstrated.
Performance Reproduction and Prediction of Selected Dynamic Loop Scheduling Experiments

Scientific applications are complex, large, and often exhibit irregular and stochastic behavior. The use of efficient loop scheduling techniques, from static to fully dynamic, in computationally-intensive applications is crucial for improving their performance, often degraded by load imbalance, on highperformance computing (HPC) platforms. A number of dynamic loop scheduling (DLS) techniques have been proposed between the late 1980's and early 2000's, and efficiently used in scientific applications. In most cases, the computing systems on which they have been tested and validated are no longer available. This work is concerned with the minimization of the sources of uncertainty in the implementation of DLS techniques to avoid unnecessary influences on the performance of scientific applications. Therefore, it is important to ensure that the DLS techniques employed in scientific applications today adhere to their original design goals and specifications. The goal of this work is to attain and increase the trust in the implementation of DLS techniques in today's studies. To achieve this goal, the performance of a selection of scheduling experiments from the 1992 original work that introduced factoring is reproduced and predicted via both, simulative and native experimentation. The scientific challenge is the reproduction of the performance of the past experiments with incomplete information, such as the computing system characteristics and the implementation details. The experiments show that the simulation reproduces the performance achieved on the past computing platform and accurately predicts the performance achieved on the present computing platform. The performance reproduction and prediction confirm that the present implementation of the DLS techniques considered both, in simulation and natively, adheres to their original description. The results confirm the hypothesis that reproducing experiments of identical scheduling scenarios on past and modern hardware leads to an entirely different behavior from expected.
Distinct encoding of decision confidence in human medial prefrontal cortex

Significance Recent computational models propose that our sense of confidence in a choice reflects an estimate of the probability that the choice is correct. However, it has proven difficult to experimentally separate decision confidence from its component parts, such as our certainty about perceptual evidence or choice requirements. We have devised a task to dissociate these quantities and isolate a distinct encoding of decision confidence in the medial prefrontal cortex of the human brain. We show that activity in this area not only tracks expected performance on a task, but also is related to both within-subject and between-subject variation in a subjective sense of confidence. Our study illuminates why dysfunctional confidence often emerges following damage to the prefrontal cortex. Our confidence in a choice and the evidence pertaining to a choice appear to be inseparable. However, an emerging computational consensus holds that the brain should maintain separate estimates of these quantities for adaptive behavioral control. We have devised a psychophysical task to decouple confidence in a perceptual decision from both the reliability of sensory evidence and the relation of such evidence with respect to a choice boundary. Using human fMRI, we found that an area in the medial prefrontal cortex, the perigenual anterior cingulate cortex (pgACC), tracked expected performance, an aggregate signature of decision confidence, whereas neural areas previously proposed to encode decision confidence instead tracked sensory reliability (posterior parietal cortex and ventral striatum) or boundary distance (presupplementary motor area). Supporting that information encoded by pgACC is central to a subjective sense of decision confidence, we show that pgACC activity does not simply covary with expected performance, but is also linked to within-subject and between-subject variation in explicit confidence estimates. Our study is consistent with the proposal that the brain maintains choice-dependent and choice-independent estimates of certainty, and sheds light on why dysfunctional confidence often emerges following prefrontal lesions and/or degeneration.
Effect of Temperature Changes on Serum Protein Adsorption on Thermoresponsive Cell-Culture Surfaces Monitored by A Quartz Crystal Microbalance with Dissipation

Thermoresponsive cell-culture polystyrene (PS) surfaces that are grafted with poly(N-isopropylacrylamide) (PIPAAm) facilitate the cultivation of cells at 37 °C and the detachment of cultured cells as a sheet with an underlying extracellular matrix (ECM) by reducing the temperature. However, the ECM and cell detachment mechanisms are still unclear because the detachment of cells from thermoresponsive surfaces is governed by complex interactions among the cells/ECM/surface. To explore the dynamic behavior of serum protein adsorption/desorption, thermoresponsive surfaces that correspond to thermoresponsive tissue-culture PS dishes were formed on sensor chips for quartz crystal microbalance with dissipation (QCM-D) measurements. X-ray photoelectron spectroscopy (XPS) measurements and temperature-dependent frequency and dissipation shifts, Δf and ΔD, using QCM-D revealed that the thermoresponsive polymers were successfully grafted onto oxidized, thin PS films on the surfaces of the sensor chips. Increased amounts of adsorbed bovine serum albumin (BSA) and fibronectin (FN) were observed on the thermoresponsive polymer-grafted surfaces at 37 °C when compared with those at 20 °C because of enhanced hydrophobic interactions with the hydrophobic, thermoresponsive surface. While the calculated masses of adsorbed BSA and FN using QCM-D were 3–5 times more than those that were obtained from radiolabeling, the values were utilized for relative comparisons among the same substrate. More importantly, the thermoresponsive, dynamic behavior of serum protein adsorption/desorption was monitored using the QCM-D technique. Observations of this dynamic behavior revealed that the BSA and FN that were adsorbed at 37 °C remained on both surfaces after decreasing the temperature to 20 °C.
Language Expansion In Text-Based Games

Text-based games are suitable test-beds for designing agents that can learn by interaction with the environment in the form of natural language text. Very recently, deep reinforcement learning based agents have been successfully applied for playing text-based games. In this paper, we explore the possibility of designing a single agent to play several text-based games and of expanding the agent's vocabulary using the vocabulary of agents trained for multiple games. To this extent, we explore the application of recently proposed policy distillation method for video games to the text-based game setting. We also use text-based games as a test-bed to analyze and hence understand policy distillation approach in detail.
Community Preserving Network Embedding

Network embedding, aiming to learn the low-dimensional representations of nodes in networks, is of paramount importance in many real applications. One basic requirement of network embedding is to preserve the structure and inherent properties of the networks. While previous network embedding methods primarily preserve the microscopic structure, such as the first- and second-order proximities of nodes, the mesoscopic community structure, which is one of the most prominent feature of networks, is largely ignored. In this paper, we propose a novel Modularized Nonnegative Matrix Factorization (M-NMF) model to incorporate the community structure into network embedding. We exploit the consensus relationship between the representations of nodes and community structure, and then jointly optimize NMF based representation learning model and modularity based community detection model in a unified framework, which enables the learned representations of nodes to preserve both of the microscopic and community structures. We also provide efficient updating rules to infer the parameters of our model, together with the correctness and convergence guarantees. Extensive experimental results on a variety of real-world networks show the superior performance of the proposed method over the state-of-the-arts.
The Stability and Continuity of Maternally Reported and Observed Child Eating Behaviours and Feeding Practices across Early Childhood

Given that many eating behaviours and food preferences develop early in childhood and track across childhood, adolescence and into adulthood, interest has grown in the developmental trajectory of these behaviours. The aims of this study were twofold. First, to explore whether maternal reports of child eating behaviour and feeding practices are validated by independent observations of these constructs. Second, to explore the continuity and stability of both maternally reported and independently observed child eating behaviours and maternal feeding practices during early childhood. Sixty-five mothers completed measures of their child’s eating behaviour and their own feeding practices and mother–child dyads were observed during a family mealtime at approximately 3 and 4 years of age. Maternal reports of their child’s eating behaviours were validated by independent observations, however maternally reported feeding practices were not validated by observations of these behaviours. Maternally reported and independently observed child eating behaviours and parental feeding practices remained stable and showed continuity between 3 and 4 years of age, with the exception of child difficulty to feed and maternal pressure to eat which both significantly decreased over time. Findings provide an insight into the validity of maternal reports of fussy eating behaviour and parental feeding practices and the developmental trajectory of these behaviours across early childhood.
Nonlinear Distributional Gradient Temporal-Difference Learning

We devise a distributional variant of gradient temporal-difference (TD) learning. Distributional reinforcement learning has been demonstrated to outperform the regular one in the recent study \citep{bellemare2017distributional}. In the policy evaluation setting, we design two new algorithms called distributional GTD2 and distributional TDC using the Cram{\'e}r distance on the distributional version of the Bellman error objective function, which inherits advantages of both the nonlinear gradient TD algorithms and the distributional RL approach. In the control setting, we propose the distributional Greedy-GQ using the similar derivation. We prove the asymptotic almost-sure convergence of distributional GTD2 and TDC to a local optimal solution for general smooth function approximators, which includes neural networks that have been widely used in recent study to solve the real-life RL problems. In each step, the computational complexities of above three algorithms are linear w.r. t. \ the number of the parameters of the function approximator, thus can be implemented efficiently for neural networks.
Integrating hypertension phenotype and genotype with hybrid non‐negative matrix factorization

Motivation Hypertension is a heterogeneous syndrome in need of improved subtyping using phenotypic and genetic measurements with the goal of identifying subtypes of patients who share similar pathophysiologic mechanisms and may respond more uniformly to targeted treatments. Existing machine learning approaches often face challenges in integrating phenotype and genotype information and presenting to clinicians an interpretable model. We aim to provide informed patient stratification based on phenotype and genotype features. Results In this article, we present a hybrid non‐negative matrix factorization (HNMF) method to integrate phenotype and genotype information for patient stratification. HNMF simultaneously approximates the phenotypic and genetic feature matrices using different appropriate loss functions, and generates patient subtypes, phenotypic groups and genetic groups. Unlike previous methods, HNMF approximates phenotypic matrix under Frobenius loss, and genetic matrix under Kullback‐Leibler (KL) loss. We propose an alternating projected gradient method to solve the approximation problem. Simulation shows HNMF converges fast and accurately to the true factor matrices. On a real‐world clinical dataset, we used the patient factor matrix as features and examined the association of these features with indices of cardiac mechanics. We compared HNMF with six different models using phenotype or genotype features alone, with or without NMF, or using joint NMF with only one type of loss We also compared HNMF with 3 recently published methods for integrative clustering analysis, including iClusterBayes, Bayesian joint analysis and JIVE. HNMF significantly outperforms all comparison models. HNMF also reveals intuitive phenotype‐genotype interactions that characterize cardiac abnormalities. Availability and implementation Our code is publicly available on github at https://github.com/yuanluo/hnmf. Supplementary information Supplementary data are available at Bioinformatics online.
Syntia : Breaking State-ofthe-Art Binary Code Obfuscation via Program Synthesis

In modern businesses, code obfuscation has become a vital tool to protect, for example, intellectual property against competitors. In general, it attempts to impede program understanding by making the to-be-protected program more complex. In this paper, we will give an overview of contemporary (binary) code obfuscation techniques, including Mixed Boolean-Arithmetic and Virtual Machines. We further note a common theme in state-of-the-art deobfuscation techniques: They mostly use a mixed approach of symbolic execution and taint analysis; two techniques that require precise analysis of the underlying code. Also, these techniques require a non-trivial amount of domain knowledge. This limits the applicability of these techniques and hints at the necessity of finding alternative approaches to tackle the problem of code obfuscation. Consequently, we introduce program synthesis as a promising technique that is orthogonal to traditional deobfuscation techniques. As program synthesis can synthesize code of arbitrary code complexity, it is only limited by the complexity of the underlying code’s semantic and thus overcomes some of the limitations traditional approaches suffer from. Our prototype implementation, Syntia, is guided by Monte Carlo Tree Search (MCTS) and simplifies execution traces by dividing them into distinct trace windows whose semantics are then “learned” by the synthesis. To demonstrate the practical feasibility of our approach, we apply it to modern, commercial protection systems and emerging techniques such as Mixed Boolean-Arithmetic. Syntia automatically learns the semantics of 489 out of 500 random expressions obfuscated via Mixed Boolean-Arithmetic and achieves a success rate of more than 94% when synthesizing the semantics of arithmetic instruction handlers in two state-of-the-art virtualization-based protection systems, Themida and VMProtect. Finally, we discuss the role of program synthesis in the landscape of modern deobfuscation techniques.
Cancer risk associated with living at high altitude in Ecuadorian population from 2005 to 2014

Background and aims Cancer is a leading cause of death in Ecuador with high social and economic impact. This study aims to determinate the influence of living at a high altitude on the risk of developing or dying from cancer among the Ecuadorian population. Methods This is an ecological and epidemiological analysis of cancer mortality and prevalence rates, based on national data from the Ecuadorian National Statistics and Census Institute, corresponding to the period between 2005 and 2014. This study includes the analysis of various types of cancer: gastric, colorectal, hepatic/bile duct, breast, uterine/cervix, and lymphatic/hematopoietic, using rates of mortality and prevalence. Additionally, the association between the risk of getting or dying from cancer and living at high altitude was investigated. This comparison was made between the population living in Highlands, over 2000 meters above sea level, and low-lying regions. Results Living at high altitude was associated with a higher prevalence of cancer and also with a high mortality rate due to cancer. Risk of getting cancer was related to living at a higher altitude, as well as an increased risk of death by cancer: gastric (OR:1.204; p<0.001), colorectal (OR:1.421; p<0.001), hepatic/bile duct (OR:1.184; p<0.001), breast (OR:1.067; p=0.030), or lymphatic/hematopoietic neoplasms (OR:1.135; p<0.001). Conclusions Through an epidemiologic analysis, the association between developing or dying from cancer and living at high altitude was obtained. However, further researche is needed to clarify these findings, something that could have a substantial impact on cancer prevention.
SAT Encodings for Distance-Based Belief Merging Operators

We present SAT encoding schemes for distance-based belief merging operators relying on the (possibly weighted) drastic distance or the Hamming distance between interpretations, and using sum, GMax (leximax) or GMin (leximin) as aggregation function. In order to evaluate these encoding schemes, we generated benchmarks of a time-tabling problem and translated them into belief merging instances. Then, taking advantage of these schemes, we compiled the merged bases of the resulting instances into query-equivalent CNF formulae. Experiments have shown the benefits which can be gained by considering the SAT encoding schemes we pointed out. Especially, thanks to them, we succeeded in computing query-equivalent formulae for merging instances based on hundreds of variables, which are out of reach of previous implementations.
Influence of Baseline Muscle Strength and Size Measures on Training Adaptations in Resistance-trained Men

The influence of baseline strength or muscle size on adaptations to training is not well-understood. Comparisons between novice and advanced lifters, and between stronger and weaker experienced-lifters, have produced conflicting results. This study examined the effect of baseline muscle strength and size on subsequent adaptations in resistance-trained individuals following a traditional high-volume, short-rest resistance training protocol. Fourteen resistance-trained men (24.0±2.7 y; 90.1±11.7 kg; 169.9±29.0 cm) completed pre-training (PRE) ultrasound measurements of muscle cross-sectional area (CSA) in the rectus femoris (RF), vastus lateralis (VL), pectoralis major, and triceps brachii (TRI) prior to strength assessments (e.g., one-repetition maximum strength bench press and back-squat). Post-training (POST) assessments were completed following 8-wks (4 d·wk−1) of resistance training. Comparisons were made between stronger (STR) and weaker (WKR) participants, and between larger (LGR) and smaller (SMR) participants, based upon PRE-muscle strength and size, respectively. When groups were based on upper-body strength, repeated measures analysis of variance indicated a significant group × time interaction where greater improvements in bench press strength were observed in WKR (12.5±8.6%, p = 0.013) compared to STR (1.3±5.4%, p=0.546). Within this comparison, STR also possessed more resistance training experience than WKR (mean difference=3.1 y, p=0.002). No other differences in experience or adaptations to training were observed. These data suggest that following a short-duration training program (8-weeks), baseline size and strength have little impact on performance gains in resistance-trained individuals who possess similar years of experience. However, when training experience is different, baseline strength may affect adaptations.
Solving the Rubik's Cube Without Human Knowledge

A generally intelligent agent must be able to teach itself how to solve problems in complex domains with minimal human supervision. Recently, deep reinforcement learning algorithms combined with self-play have achieved superhuman proficiency in Go, Chess, and Shogi without human data or domain knowledge. In these environments, a reward is always received at the end of the game, however, for many combinatorial optimization environments, rewards are sparse and episodes are not guaranteed to terminate. We introduce Autodidactic Iteration: a novel reinforcement learning algorithm that is able to teach itself how to solve the Rubik's Cube with no human assistance. Our algorithm is able to solve 100% of randomly scrambled cubes while achieving a median solve length of 30 moves -- less than or equal to solvers that employ human domain knowledge.
Polyphenols in Parkinson’s Disease: A Systematic Review of In Vivo Studies

Parkinson’s disease (PD) is the second most common neurodegenerative disorder. However, therapeutic options treating only its symptoms are very disappointing. Therefore there is an ongoing search for compounds capable of tackling the multi-dimensional features of PD. Recently natural polyphenols have gained great interest as potential therapeutic agents. Herein, we have attempted to summarize results obtained in different animal models demonstrating their neuroprotective effects. The in vivo findings presented below are supported by human subject data and reports regarding the ability of polyphenols to cross the blood-brain barrier. The beneficial effects of polyphenols are demonstrated by the results of behavioral examinations, mainly related to motor and cognitive capabilities, histopathological and immunohistochemical examination concerning the protection of dopaminergic neurons, analyses of dopamine and the concentration of its metabolites, as well as mechanistic studies regarding the modulation of oxidative stress, neuroinflammation, cellular iron management, proteinopathy, and additionally the regulation of signaling pathways. Importantly, data about brain distribution of the metabolic derivatives of the reviewed polyphenols are crucial for the justification of their nutritional intake in neuroprotective intervention, as well as for the identification of potential targets for a novel therapeutic approach to Parkinson’s disease.