Miracle-dz/UniFAR-FTUs huggingface dataset:
Pain Interference, Psychopathology, and General Medical Conditions Among Black and White Adults in the US General Population

Objectives: To assess how race might moderate relationships between pain interference and psychopathology, and general medical conditions among a nationally representative sample of black and white adults. Methods: Chi-square tests and multivariable logistic regression analyses were performed on data from the National Epidemiologic Survey on Alcohol and Related Conditions on 32,474 adult respondents (25% black, 75% white), who were categorized according to 1 of 3 levels of pain interference (ie, no/low, moderate, or severe). Results: Pain interference was associated with race in bivariate analyses (P < .001); relative to white respondents, black respondents had lower rates of no/low pain interference (78.9% vs 80.3%), lower rates of moderate pain interference (6.8% vs 7.8%), and higher rates of severe pain interference (14.3% vs 11.9%). Pain interference was associated with past-year axis I psychiatric disorders in both black and white respondents (P < 0.001) with the largest odds typically observed in association with moderate or severe pain interference. A stronger relationship was observed in black as compared with white respondents between severe pain interference and any axis I disorder (odds ratio [OR] 1.28, P < 0.05) and alcohol abuse or dependence (OR 1.90, P < 0.05), and between moderate pain interference and tachycardia (OR 1.69, P < 0.05). In contrast, a weaker relationship was observed in black as compared with white respondents between moderate pain interference and any general medical condition (OR 0.70, P < 0.05). Conclusions: These findings underscore the complexity of race-related disparities in health and suggest the importance of further study of the possible mechanisms underlying the apparent differences between black and white groups in the relationships between pain interference, psychopathology, and general medical conditions.
Malaria Incidence Rates from Time Series of 2-Wave Panel Surveys

Methodology to estimate malaria incidence rates from a commonly occurring form of interval-censored longitudinal parasitological data—specifically, 2-wave panel data—was first proposed 40 years ago based on the theory of continuous-time homogeneous Markov Chains. Assumptions of the methodology were suitable for settings with high malaria transmission in the absence of control measures, but are violated in areas experiencing fast decline or that have achieved very low transmission. No further developments that can accommodate such violations have been put forth since then. We extend previous work and propose a new methodology to estimate malaria incidence rates from 2-wave panel data, utilizing the class of 2-component mixtures of continuous-time Markov chains, representing two sub-populations with distinct behavior/attitude towards malaria prevention and treatment. Model identification, or even partial identification, requires context-specific a priori constraints on parameters. The method can be applied to scenarios of any transmission intensity. We provide an application utilizing data from Dar es Salaam, an area that experienced steady decline in malaria over almost five years after a larviciding intervention. We conducted sensitivity analysis to account for possible sampling variation in input data and model assumptions/parameters, and we considered differences in estimates due to submicroscopic infections. Results showed that, assuming defensible a priori constraints on model parameters, most of the uncertainty in the estimated incidence rates was due to sampling variation, not to partial identifiability of the mixture model for the case at hand. Differences between microscopy- and PCR-based rates depend on the transmission intensity. Leveraging on a method to estimate incidence rates from 2-wave panel data under any transmission intensity, and from the increasing availability of such data, there is an opportunity to foster further methodological developments, particularly focused on partial identifiability and the diversity of a priori parameter constraints associated with different human-ecosystem interfaces. As a consequence there can be more nuanced planning and evaluation of malaria control programs than heretofore.
Detecting hidden diversification shifts in models of trait-dependent speciation and extinction

The distribution of diversity can vary considerably from clade to clade. Attempts to understand these patterns often employ state-dependent speciation and extinction models to determine whether the evolution of a particular novel trait has increased speciation rates and/or decreased their extinction rates. It is still unclear, however, whether these models are uncovering important drivers of diversification, or whether they are simply pointing to more complex patterns involving many unmeasured and co-distributed factors. Here we describe an extension to the popular state-dependent speciation and extinction models that specifically accounts for the presence of unmeasured factors that could impact diversification rates estimated for the states of any observed trait, addressing at least one major criticism of BiSSE methods. Specifically, our model, which we refer to as HiSSE (Hidden-State Speciation and Extinction), assumes that related to each observed state in the model are “hidden” states that exhibit potentially distinct diversification dynamics and transition rates than the observed states in isolation. We also demonstrate how our model can be used as character-independent diversification (CID) models that allow for a complex diversification process that is independent of the evolution of a character. Under rigorous simulation tests and when applied to empirical data, we find that HiSSE performs reasonably well, and can at least detect net diversification rate differences between observed and hidden states and detect when diversification rate differences do not correlate with the observed states. We discuss the remaining issues with state-dependent speciation and extinction models in general, and the important ways in which HiSSE provides a more nuanced understanding of trait-dependent diversification.
Physical and Morphological Structure of Chicken Feathers (Keratin Biofiber) in Natural, Chemically and Thermally Modified Forms

Because of the constant challenge to preserve the environment and the search for new materials, a comparative study was carried out using keratin fiber, a fibrous protein, found in the chicken feathers. Five different samples of the feather were analyzed by Scanning Electron Microscopy (SEM) and X-ray diffractometer (XRD). First in their natural form Keratin Fiber (KF); the second treated with sodium hydroxide (KFNaOH); the third and fourth samples were semi carbonized at 220℃ in an oven without atmospheric control for 24 hours (samples obtained: Clear brown (SCFC) and Dark brown (SCFD)); and the fifth sample was carbonized by pyrolysis Carbonized Feathers (CF). The SEM result shows that the KF has a hollow structure, with knots and hooks. The KFNaOH structure presented rougher than that of the KF, but lost their hooks. The SCFC and SCFD presented brittle structures, but preserved the hollow structure of KF; however, it was only noticeable to a magnification of 3000 times. On the other hand, the CF, was shiny, black, and showed a higher amount of porosity with open micro-pores and micro-tubes, preserved the hollow structure of KF than any other samples studied, and also presented well-defined closed micro-tubes. From the XRD analysis of the KF, CF, KFNaOH, SCFC and SCFD, presented semi-crystalline structures, with the following indices of crystallinity, 20.09%, 18.93%, 17.97%, 15.02% and 14.31%, respectively. The CF presented smaller size crystallites, in between the micro- particulates, around 27 nm and the KFNaOH with larger size around 74 nm. From this study it was concluded that micro-porous carbon material from chicken feathers (KF) could be efficiently obtained through pyrolysis.
Does Transcranial Direct Current Stimulation Combined with Peripheral Electrical Stimulation Have an Additive Effect in the Control of Hip Joint Osteonecrosis Pain Associated with Sickle Cell Disease? A Protocol for a One-Session Double Blind, Block-Randomized Clinical Trial

Chronic pain in Sickle Cell Disease (SCD) is probably related to maladaptive plasticity of brain areas involved in nociceptive processing. Transcranial Direct Current Stimulation (tDCS) and Peripheral Electrical Stimulation (PES) can modulate cortical excitability and help to control chronic pain. Studies have shown that combined use of tDCS and PES has additive effects. However, to date, no study investigated additive effects of these neuromodulatory techniques on chronic pain in patients with SCD. This protocol describes a study aiming to assess whether combined use of tDCS and PES more effectively alleviate pain in patients with SCD compared to single use of each technique. The study consists of a one-session double blind, block-randomized clinical trial (NCT02813629) in which 128 participants with SCD and femoral osteonecrosis will be enrolled. Stepwise procedures will occur on two independent days. On day 1, participants will be screened for eligibility criteria. On day 2, data collection will occur in four stages: sample characterization, baseline assessment, intervention, and post-intervention assessment. These procedures will last ~5 h. Participants will be divided into two groups according to homozygous for S allele (HbSS) (n = 64) and heterozygous for S and C alleles (HbSC) (n = 64) genotypes. Participants in each group will be randomly assigned, equally, to one of the following interventions: (1) active tDCS + active PES; (2) active tDCS + sham PES; (3) sham tDCS + active PES; and (4) sham tDCS + sham PES. Active tDCS intervention will consist of 20 min 2 mA anodic stimulation over the primary motor cortex contralateral to the most painful hip. Active PES intervention will consist of 30 min sensory electrical stimulation at 100 Hz over the most painful hip. The main study outcome will be pain intensity, measured by a Visual Analogue Scale. In addition, electroencephalographic power density, cortical maps of the gluteus maximus muscle elicited by Transcranial Magnetic Stimulation (TMS), serum levels of Brain-derived Neurotrophic Factor (BDNF), and Tumor Necrosis Factor (TNF) will be assessed as secondary outcomes. Data will be analyzed using ANOVA of repeated measures, controlling for confounding variables.
Hyperelastic Materials Characterization by Planar Tension Tests and Full-field Strain Measurement

Several techniques for full-field strain measurement are nowadays available and reliable, and the topics of research should be to find well suited methods and procedures to use the great amount of data in an appropriate and sharp way. In this terms, one of the best and effective techniques developed in recent years is the Virtual Field Method (VFM). In this paper a Digital Image Correlation technique is applied to acquire strain fields on flat rubber specimens with different aspect ratios. They are loaded with a standard traction machine and the different types of geometry generate heterogeneous tensions distributions (from uniaxial up to more and more biaxial). A recursive numerical procedure is proposed in order to find the best fit material dependent parameters of a particular hyperelastic model. Global load data, deformation data of the surface of the specimen and stress distribution data obtained starting from a guess set of constitutive law parameters are used to calculate the terms and coefficients of the VFM equations. Two independent virtual fields are involved in the procedure to use as many as possible available data. After a recursive process where the global error defined from VFM equations is minimized and combining together data from all specimen geometries, it is possible to obtain a set of material parameters able to reproduce a heterogeneous planar tensional state.
Are the benefits of autonomy satisfaction and the costs of autonomy frustration dependent on individuals' autonomy strength?

OBJECTIVE From a self-determination theory perspective, individuals are assumed to benefit and suffer from, respectively, the satisfaction and frustration of the psychological need for autonomy, even if they score low on autonomy strength. Yet, previous studies on need strength are scarce, operationalized need strength differently, and produced inconsistent findings. METHOD In two studies among 224 South African adults (Mage = 24.13, SD = 4.25; 54.0% male) and 156 Belgian prisoners (Mage = 38.60, SD = 11.68; 88.5% male), we investigated the moderating role of autonomy valuation and desire in the relations of autonomy satisfaction and frustration with a variety of well-being and ill-being indicators. RESULTS Study 1 provided some evidence for the moderating role of mostly explicit autonomy desire (rather than explicit autonomy valuation). In Study 2, neither explicit nor implicit autonomy desire played a consistent moderating role. CONCLUSIONS Overall, these findings are congruent with a moderate (albeit not with a strong) interpretation of the universality claim made within self-determination theory, provide initial evidence for a differentiation between deficit-based and growth-oriented interpersonal differences in need strength, and indicate that the potential moderating role of need strength deserves continued attention before any firm conclusions can be drawn.
Submodularity-inspired Data Selection for Goal-oriented Chatbot Training based on Sentence Embeddings

Spoken language understanding (SLU) systems, such as goal-oriented chatbots or personal assistants, rely on an initial natural language understanding (NLU) module to determine the intent and to extract the relevant information from the user queries they take as input. SLU systems usually help users to solve problems in relatively narrow domains and require a large amount of in-domain training data. This leads to significant data availability issues that inhibit the development of successful systems. To alleviate this problem, we propose a technique of data selection in the low-data regime that enables us to train with fewer labeled sentences, thus smaller labelling costs. We propose a submodularity-inspired data ranking function, the ratio-penalty marginal gain, for selecting data points to label based only on the information extracted from the textual embedding space. We show that the distances in the embedding space are a viable source of information that can be used for data selection. Our method outperforms two known active learning techniques and enables cost-efficient training of the NLU unit. Moreover, our proposed selection technique does not need the model to be retrained in between the selection steps, making it time efficient as well.
Fairness-Aware Loan Recommendation for Microfinance Services

Up to date, more than 15 billion US dollars have been invested in microfinance that benefited more than 160 million people in developing countries. The Kiva organization is one of the successful examples that use a decentralized matching process to match lenders and borrowers. Interested lenders from around the world can look for cases among thousands of applicants they found promising to lend the money to. But how can loan borrowers and lenders be successfully matched up in a microfinance platform like Kiva? We argue that a sophisticate recommender not only pairs up loan lenders and borrowers in accordance to their preferences, but should also help to diversify the distribution of donations to reduce the inequality of loans is highly demanded, as altruism, like any resource, can be congestible. In this paper, we propose a fairness-aware recommendation system based on one-class collaborative-filtering techniques for charity and micro-loan platform such as Kiva.org. Our experiments on real dataset indicates that the proposed method can largely improve the loan distribution fairness while retaining the accuracy of recommendations.
Search of latent periodicity in amino acid sequences by means of genetic algorithm and dynamic programming

Abstract The aim of this study was to show that amino acid sequences have a latent periodicity with insertions and deletions of amino acids in unknown positions of the analyzed sequence. Genetic algorithm, dynamic programming and random weight matrices were used to develop a new mathematical algorithm for latent periodicity search. A multiple alignment of periods was calculated with help of the direct optimization of the position-weight matrix without using pairwise alignments. The developed algorithm was applied to analyze amino acid sequences of a small number of proteins. This study showed the presence of latent periodicity with insertions and deletions in the amino acid sequences of such proteins, for which the presence of latent periodicity was not previously known. The origin of latent periodicity with insertions and deletions is discussed.
A QRD-M/Kalman filter-based detection and channel estimation algorithm for MIMO-OFDM systems

The use of multiple transmit/receive antennas forming a multiple-input multiple-output (MIMO) system can significantly enhance channel capacity. This paper considers a V-BLAST-type combination of orthogonal frequency-division multiplexing (OFDM) with MIMO (MIMO-OFDM) for enhanced spectral efficiency and multiuser downlink throughput. A new joint data detection and channel estimation algorithm for MIMO-OFDM is proposed which combines the QRD-M algorithm and Kalman filter. The individual channels between antenna elements are tracked using a Kalman filter, and the QRD-M algorithm uses a limited tree search to approximate the maximum-likelihood detector. A closed-form symbol-error rate, conditioned on a static channel realization, is presented for the M=1 case with QPSK modulation. An adaptive complexity QRD-M algorithm (AC-QRD-M) is also considered which assigns different values of M to each subcarrier according to its estimated received power. A rule for choosing M using subcarrier powers is obtained using a kernel density estimate combined with the Lloyd-Max algorithm.
Dietary intake of nutrients involved in one‐carbon metabolism and risk of urothelial cell carcinoma: A prospective cohort study

Nutrients involved in one‐carbon metabolism may play a role in carcinogenesis through DNA replication, repair and methylation mechanisms. Most studies on urothelial cell carcinoma (UCC) have focused on folate. We sought to examine the association between B‐group vitamins and methionine intake and UCC risk, overall and by subtype, and to test whether these associations are different for population subgroups whose nutritional status may be compromised. We followed participants in the Melbourne Collaborative Cohort Study (N = 41,513) for over 20 years and observed 500 UCC cases (89% originating in the bladder; superficial: 279, invasive: 221). Energy‐adjusted dietary intakes of B vitamins (B1, B2, B3, B5, B6, B8, B9 and B12) and methionine were estimated from a 121‐item food frequency questionnaire administered at baseline (1990–1994), using the residuals method. We used Cox regression models to compute hazard ratios (HRs) of UCC risk per standard deviation (SD) of log‐transformed nutrient intakes and 95% confidence intervals, adjusted for potential confounders. We investigated associations by tumor subtype, and tested interactions with sex, country of birth, smoking and alcohol drinking. The risk of UCC appeared not to be associated with intake of B‐group vitamins or methionine, and findings were consistent across tumor subtypes and across demographic and lifestyle characteristics of the participants. A potential interaction between vitamin B1 and alcohol drinking was observed (all participants: HR per 1 SD = 0.99 (0.91–1.09), never drinkers: HR = 0.81 (0.69–0.97), p‐interaction = 0.02), which needs to be confirmed by other studies. Our findings do not indicate that dietary intake of nutrients involved in one‐carbon metabolism are associated with UCC risk.
Adaptive channel quality estimation method for enhanced time slotted channel hopping on wireless sensor networks

As IoT emerges, energy consumption becomes important. ETSCH introduces whitelisting high-quality channels in TSCH and channel list for Enhanced Beacons, to improve reliability under dynamic interference. However, it is difficult to extend ETSCH to multi-hop network because energy consumption of energy-detecting nodes is larger than that of other nodes. In this paper, we propose Adaptive Channel Quality Estimation (A-CQE), which controls frequency of energy detections depending on Interference Dynamicity that are proposed to determine whether interference is dynamic or not. Experiments show ETSCH with A-CQE improves on average 10.1% in terms of energy consumption without lowering reliability compared with ETSCH.
Towards an open science platform for the evaluation of data fusion

Combining the results of different search engines in order to improve upon their performance has been the subject of many research papers. This has become known as the “Data Fusion” task, and has great promise in dealing with the vast quantity of unstructured textual data that is a feature of many Big Data scenarios. However, no universally-accepted evaluation methodology has emerged in the community. This makes it difficult to make meaningful comparisons between the various proposed techniques from reading the literature alone. Variations in the datasets, metrics, and baseline results have all contributed to this difficulty. This paper argues that a more unified approach is required, and that a centralised software platform should be developed to aid researchers in making comparisons between their algorithms and others. The desirable qualities of such a system have been identified and proposed, and an early prototype has been developed. Re-implementing algorithms published by other researchers is a great burden on those proposing new techniques. The prototype system has the potential to greatly reduce this burden and thus encourage more comparable results being generated and published more easily.
Optimizations for Multiple Collective Sources in Delivery Systems

In this paper we investigate a new subset of delivery problems where the destinations are all to be delivered from one or more sources so that the total distance is minimized. For example, food is delivered for the customers who place orders from one or more restaurants. For one source, we propose and compare three greedy algorithms namely nearest neighbor first (NNF), polar angle sweep (PAS), and distance sweep (DS). For multiple sources, each destination is from a specific source, thus requiring that a destination must be visited after its source. We give an optimization algorithm called "collect all then distribute" (CATD). We conducted comprehensive experiments based on various synthesized data sets and compared the accuracy and runtime complexity of the proposed algorithms. Our conclusion is that the NNF and CATD algorithms have clear advantages over other alternatives.