Miracle-dz/UniFAR-FTUs huggingface dataset:
The Association of Individual and Regional Socioeconomic Status on Initial Peritonitis and Outcomes in Peritoneal Dialysis Patients: A Propensity Score-Matched Cohort Study

♦ Background: Research indicates that the socioeconomic status (SES) of individuals and the area where they live are related to initial peritonitis and outcomes in peritoneal dialysis (PD). We conducted a retrospective, multi-center cohort study in China to examine these associations. ♦ Methods: Data on 2,171 PD patients were collected from 7 centers, including baseline demographic, socioeconomic, and laboratory data. We explored the potential risk factors for initial peritonitis and outcomes using univariate Cox regression and unadjusted binary logistic regression. Then, we used propensity score matching to balance statistically significant risk factors for initial peritonitis and outcomes, and Kaplan-Meier survival analysis to compare differences in peritonitis-free rates between different groups of participants after matching. ♦ Results: A total of 563 (25.9%) initial episodes of peritonitis occurred during the study period. The Kaplan-Meier peritonitis-free rate curve showed high-income patients had a significantly lower risk than low-income patients (p = 0.007) after matching for age, hemoglobin, albumin, and regional SES and PD center. The risk of treatment failure was significantly lower in the high-income than the low-income group after matching for the organism causing peritonitis and PD center: odds ratio (OR) = 0.27 (0.09 – 0.80, p = 0.018). Regional SES and education were not associated with initial peritonitis and outcomes. ♦ Conclusions: Our study demonstrates low individual income is a risk factor for the initial onset of peritonitis and treatment failure after initial peritonitis.
Risk Factors for Shoulder Pain in a Cohort of French Workers: A Structural Equation Model

Shoulder pain is common in the working population and causes loss of productivity, high economic costs, and long absences. Simultaneous evaluation of the complex relationships between work organization (e.g., work pace, application of quality standards), psychosocial and physical risk factors, stress, and shoulder pain is rare. The aim of this study was to explore the direct and indirect relationships between workplace risk factors, perceived stress, and occurrence of shoulder pain in workers of the Cohorte des Salariés Ligériens study. A total of 3,710 workers in a French region were randomly selected for inclusion between 2002 and 2005. They completed a self-administered questionnaire about musculoskeletal symptoms, individual factors, and exposure to work constraints. In 2007, they responded to a follow-up questionnaire. The study sample comprised 1,400 workers free of shoulder pain at baseline. Structural equation models were used. For both sexes, exposure to factors related to work organization had an effect on physical and psychosocial risk factors. Psychological demand was the only psychosocial constraint that increased perceived stress. Shoulder pain was influenced directly by physical risk factors for both sexes and by perceived stress for men. In view of their distal action, work organization is an important target for strategies to prevent shoulder pain in the working population.
Mitochondrial DNA variation in Eisenia n. nordenskioldi (Lumbricidae) in Europe and Southern Urals

Abstract There are many peregrine European earthworm species that are found in Siberia. In contrast, it is generally considered that the only Siberian species, E. n. nordenskioldi, was capable to disperse in the reverse direction, from Siberia into Europe. We studied genetic diversity of E. n. nordenskioldi in Southern Urals and Eastern Europe using the mitochondrial cox1 gene. We found that E. n. nordenskioldi from that region represents a new genetic lineage distinct from the previously known populations of this species from Siberia. Molecular clock estimates suggest that this newly found lineage separated from the rest of the species in Lower Pleistocene. Within the studied sample, we detected two geographically restricted groups, which also diverged long before the Holocene, one found in the East European Plain and the other restricted to the Urals. Those two groups were found in sympatry in only one population. Therefore, our results do not support the traditional viewpoint, suggesting that E. n. nordenskioldi is definitely not a recent invader in Europe.
Ant colony optimization for component assignment problems in circular consecutive-k-out-of-n systems

The Circular Consecutive-k-out-of-n (Cir/Con/k/n) system consists of n components arranged in a circular sequence and each component has different reliability. The purpose of optimization is to obtain an optimal assignment which can make the Cir/Con/k/n system most reliable. As the number of components n increases, the computation time for the optimization of Cir/Con/k/n system will increase considerably. In this paper, we propose applying the Ant Colony Optimization (ACO) algorithm to obtain quasi optimal assignments for such problems. First of all, we describe the basic principles and the operation procedures of ACO through pseudo code. After that, a large number of simulation experiments on different types of systems and components are carried out to verify the effectiveness of the ACO. The results of simulation experiments demonstrate the advantage of ACO in the optimization of Cir/Con/k/n system.
Softmax discriminant classifier for detection of risk levels in alcoholic EEG signals

In various societies, alcoholic beverages are widely used. A good and healthy lifestyle can be leaded if the consumption of alcohol is minimal and occasional. However, with the over intake of alcohol, it disturbs the decision making process of the person and it leads to severe mental, physical and emotional health problems. The alcohol detection level is important to determine the person's capability to do a particular job. In this paper, Softmax Discriminant Classifier (SDC) is employed to detect the risk level of a single chronic alcoholic patient from Electroencephalography (EEG) Signals. Results show that an average Perfect Classification rate of 72.70%, an average Performance Index of 62.00% along with an average classification accuracy of about 86.35% is obtained.
Systematic review and meta-analysis of the association between maternal smoking in pregnancy and childhood overweight and obesity

Background By 2020, it is predicted that 60 million children worldwide will be overweight. Maternal smoking in pregnancy has been suggested as a contributing factor. Our objective was to systematically review studies on this, thereby expanding the evidence base for this association. Methods Systematic review with meta-analysis, Prospero Registration number CRD42012002859. We searched PubMed, Embase, Global Health, Web of Science and the Grey literature. We included prevalence, cohort and cross-sectional studies involving full-term, singleton pregnancies. Published and unpublished studies through to 1 January 2015 in all languages, demonstrating an objective overweight outcome up until 18 years of age and data presented as an OR, were included. Quality assessment was undertaken using an adaption of the Newcastle-Ottawa scale. Statistical analysis was performed using Review Manager V.5. 3. Findings The meta-analysis included 39 studies of 236 687 children from Europe, Australia, North America and South America and Asia. Maternal smoking in pregnancy ranged from 5.5% to 38.7%, with the prevalence of overweight from 6.3% to 32.1% and obesity from 2.6% to 17%. Pooled adjusted ORs demonstrated an elevated odds of maternal smoking in pregnancy for childhood overweight (OR 1.37, 95% CI 1.28 to 1.46, I2 45%) and childhood obesity (OR 1.55, 95% CI 1.40 to 1.73, I2 24%). Interpretation Our results demonstrate an association between maternal prenatal smoking and childhood overweight. This contributes to the growing evidence for the aetiology of childhood overweight, providing important information for policymakers and health professionals alike in planning cessation programmes or antismoking interventions for pregnant female smokers.
Synergistic Effects of Copper Sites on Apparent Stability of Multicopper Oxidase, Fet3p

Saccharomyces cerevisiae Fet3p is a multicopper oxidase that contains three cupredoxin-like domains and four copper ions located in three distinct metal sites (T1 in domain 3; T2 and the binuclear T3 at the interface between domains 1 and 3). To probe the role of the copper sites in Fet3p thermodynamic stability, we performed urea-induced unfolding experiments with holo-, apo- and three partially-metallated (T1, T2 and T1/T2 sites depleted of copper) forms of Fet3p. Using a combination of spectroscopic probes (circular dichroism, fluorescence intensity and maximum, 8-anilinonaphthalene-1-sulfonic acid (ANS) emission, oxidase activity and blue color), we reveal that all forms of Fet3p unfold in a four-state reaction with two partially-folded intermediates. Using phase diagrams, it emerged that Fet3p with all copper sites filled had a significantly higher stability as compared to the combined contributions of the individual copper sites. Hence, there is long-range inter-domain communication between distal copper sites that contribute to overall Fet3p stability.
Mobile Robot Global Localization Using Particle Swarm Optimization with a 2D Range Scan

This paper presents a novel approach based on the particle swarm optimization (PSO) for globally localizing a mobile robot with a single laser scan, under the assumption that the initial pose of the robot is unknown. The environment map is first converted with a signed fitness function that encodes the distance to the nearest obstacle from a given location. Using the end-point model of a laser beam, captured sensor data are associated with the world model without data association or feature extraction. The PSO is then performed to explore the pose space to search for the correct robot pose iteratively, in which the potential solutions are optimized by scan matching technique to get more accurate pose estimation. The proposed approach performs better than the popular particle filter based approach with regard to convergence speed, estimation precision and computational cost. Experiment results based on public domain dataset demonstrate the effectiveness of proposed algorithm.
An FPGA-accelerated high-throughput data optimization system for high-speed transfer via wide area network

In this paper, we propose an FPGA-accelerated data optimization system for high-speed data transfer via Wide Area Network (WAN). To maintain high quality in cloud services, high-speed transfer among data centers via WAN is important. To accelerate transfers via WAN, data optimization techniques are used to reduce the size of the data transmitted to the WAN. Compression and deduplication are widely-used data optimization techniques; data sent is shrunk before transmission and restored upon receipt. However, the computational time of these techniques is so great that it is difficult to achieve high performance using only a CPU. To solve this problem, we developed an efficient data optimization system using both CPU and FPGA. We designed a dedicated accelerator whose architecture is suitable for each processing characteristic of data optimization. We also proposed an efficient data flow inside the FPGA and communication method between the CPU and the FPGA. Thanks to the appropriate pipelining and parallelization, and the data flow, the measured throughput of our developed accelerator achieves over 41 Gbps. With this accelerator, we can realize a data transfer system whose throughput is 40 Gbps end-to-end: as far as we know the fastest performance for a WAN optimization system.
Conditional Deletion of Atoh1 Reveals Distinct Critical Periods for Survival and Function of Hair Cells in the Organ of Corti

Atonal homolog1 (Atoh1) encodes a basic helix–loop–helix protein that is the first transcription factor to be expressed in differentiating hair cells. Previous work suggests that expression of Atoh1 in prosensory precursors is necessary for the differentiation and survival of hair cells, but it is not clear whether Atoh1 is required exclusively for these processes, or whether it regulates other functions later during hair cell maturation. We used EGFP-tagged Atoh1 knock-in mice to demonstrate for the first time that Atoh1 protein is expressed in hair cell precursors several days before the appearance of differentiated markers, but not in the broad pattern expected of a proneural gene. We conditionally deleted Atoh1 at different points in hair cell development and observe a rapid onset of hair cell defects, suggesting that the Atoh1 protein is unstable in differentiating hair cells and is necessary through an extended phase of their differentiation. Conditional deletion of Atoh1 reveals multiple functions in hair cell survival, maturation of stereociliary bundles, and auditory function. We show the presence of distinct critical periods for Atoh1 in each of these functions, suggesting that Atoh1 may be directly regulating many aspects of hair cell function. Finally, we show that the supporting cell death that accompanies loss of Atoh1 in hair cells is likely caused by the abortive trans-differentiation of supporting cells into hair cells. Together our data suggest that Atoh1 regulates multiple aspects of hair cell development and function.
Speech emotion recognition using convolutional long short-term memory neural network and support vector machines

In this paper, we propose a speech emotion recognition technique using convolutional long short-term memory (LSTM) recurrent neural network (ConvLSTM-RNN) as a phoneme-based feature extractor from raw input speech signal. In the proposed technique, ConvLSTM-RNN outputs phoneme- based emotion probabilities to every frame of an input utterance. Then these probabilities are converted into statistical features of the input utterance and used for the input features of support vector machines (SVMs) or linear discriminant analysis (LDA) system to classify the utterance-level emotions. To assess the effectiveness of the proposed technique, we conducted experiments in the classification of four emotions (anger, happiness, sadness, and neutral) on IEMOCAP database. The result showed that the proposed technique with either of SVM or LDA classifier outperforms the conventional ConvLSTM-based one.
Body composition assessment in the infant

Body composition assessment provides a sharper picture of the human biological response to genetic and environmental influences than measures of body size and weight. Infant body composition is particularly important as a marker of fetal adaptation and developmental programming of subsequent health and disease, but until recently, the range of options for measuring infant body composition was relatively narrow. The purpose of this Toolkit: Methods in Human Biology review is to provide a comprehensive overview of methods of body composition methods currently used in infants 0 to 2 years of age, including anthropometric prediction equations, air displacement plethysmography (ADP), dual energy X‐ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), isotope dilution, and magnetic resonance imaging (MRI). Information on the reliability, validity, and accuracy of the methods is provided. Unique aspects of infant physiology and behavior create challenges for body composition assessment, but this review provides guidance on suitable testing approaches and environments that may aid researchers in this important area of investigation. Am. J. Hum. Biol. 26:291–304, 2014. © 2013 Wiley Periodicals, Inc.
Surgery for Velopharyngeal Dysfunction

Objective: This systematic review sought to evaluate the consensus in the literature regarding the surgical management of VPD and to determine whether a particular procedure results in superior speech outcome or less morbidity Design: A systematic review was carried out according to PRISMA-P guidelines. Systematic review software was used to facilitate 3-stage screening and data extraction by 2 reviewers. Setting: University teaching hospital. Patients, Participants: Studies that reported perceptual speech assessment or obstructive sleep apnea (OSA) in patients who had undergone surgery for VPD were included in the review. Interventions: Four categories of surgery for VPD were examined—pharyngeal flap, sphincter pharyngoplasty, palatoplasty, and posterior pharyngeal wall augmentation. Main outcome measures: Perceptual speech assessment, need for further surgery, and occurrence of OSA were the outcomes of interest. Results: Eighty-three relevant studies were identified, comprising data on 4011 patients. Pharyngeal flap was the most common procedure (64% of patients). Overall, 70.7% of patients attained normal resonance and 65.3% attained normal nasal emission. There was no notable difference in speech outcomes, need for further surgery, or occurrence of OSA across the 4 categories of surgery examined. Heterogeneous groups of patients were reported upon and a variety of perceptual speech assessment scales were used. Conclusions: There is a lack of consensus in the literature to guide procedure selection for patients with VPD. The development of a standardized minimum data set to record postoperative speech, OSA, and patient-reported outcomes is required.
On the application of improved symplectic integrators in Hamiltonian Monte Carlo

ABSTRACT We explore the construction of new symplectic numerical integration schemes to be used in Hamiltonian Monte Carlo and study their efficiency. Integration schemes from Blanes et al., and a new scheme are considered as candidates to the commonly used leapfrog method. All integration schemes are tested within the framework of the No-U-Turn sampler (NUTS), both for a logistic regression model and a student t-model. The results show that the leapfrog method is inferior to all the new methods both in terms of asymptotic expected acceptance probability for a model problem and the efficient sample size per computing time for the realistic models.
Playing the game of snake with limited knowledge: Unsupervised neuro-controllers trained using particle swarm optimization

Methods in the domain of artificial intelligence (AI) have been applied to develop agents capable of playing a variety of games. The single-player variant of Snake is a well-known and popular video game that requires a player to navigate a line-based representation of a snake through a two-dimensional playing area, while avoiding collisions with the walls of the playing area and the body of the snake itself. A score and the snake length are increased whenever the snake is moved through items representing food. The game thus becomes more challenging as the score increases. The application of AI techniques to playing the game of Snake has not been very well explored. This paper proposes a novel technique that uses particle swarm optimization for the unsupervised training of neuro-controllers in order to play the game of Snake. The proposed technique assumes nothing about effective game playing strategies, and thus works with limited knowledge. Sensory input is also minimal. Due to the lack of similar AI-based approaches for playing Snake, the proposed technique is empirically compared against three hand-designed Snake playing agents in terms of several performance measures. The performance of the proposed technique demonstrates the feasibility of the approach, and suggests that future research into AI-based controllers for Snake will be fruitful.
On the Links between Probabilistic Graphical Models and Submodular Optimisation. (Liens entre modèles graphiques probabilistes et optimisation sous-modulaire)

A probabilistic graphical model encodes conditional independences among random variables, which is related to factorisable distributions. Moreover, the entropy of a probability distribution on a set of discrete random variables is always bounded by the entropy of its factorisable counterpart. This is due to the submodularity of entropy on the set of discrete random variables. Submodular functions are also generalisation of matroid rank function; therefore, linear functions may be optimised on the associated polytopes exactly using a greedy algorithm. In this manuscript, we exploit these links between the structures of graphical models and submodular functions: we use greedy algorithms to optimise linear functions on the polytopes related to graphic and hypergraphic matroids for learning the structures of graphical models, while we use inference algorithms on graphs to optimise submodular functions. The irst main contribution of the thesis aims at approximating a probabilistic distribution with a factorisable tractable distribution under the maximum likelihood framework. Since the tractability of exact inference is exponential in the treewidth of the decomposable graph, our goal is to learn bounded treewidth decomposable graphs, which is known to be NP-hard. We pose this as a combinatorial optimisation problem and provide convex relaxations based on graphic and hypergraphic matroids. This leads to an approximate solution with good empirical performance. In the second main contribution, we use the fact that the entropy of a probability distribution is always bounded by the entropy of its factorisable counterpart mainly as a consequence of submodularity. This property of entropy is generalised to all submodular functions and bounds based on graphical models are proposed. We refer to them as graph-based bounds. An algorithm is developped to maximise submodular functions, which is NP-hard, by maximising the graph-based bound using variational inference algorithms on graphs. The third main contribution of the thesis deals with minimising submodular functions that can be written as sum of “simple” submodular functions. It is broadly subdivided into two parts. The irst part deals with reviewing algorithms that minimise sum of “simple” submodular functions using minimisation oracles of the “simple” functions. Here, we speciically deal with cut functions in large scale problems and the minimisation oracles of the “simple” functions are graph inference algorithms. The second part proposes algorithms to minimise sum of general submodular functions using the structure of the polytopes related to individual “simple” submodular functions.