Miracle-dz/UniFAR-FTUs huggingface dataset:
Developmental Differences in Filtering Auditory and Visual Distractors During Visual Selective Attention

The current experiment examined changes in visual selective attention in young children, older children, young adults, and older adults while participants were instructed to ignore auditory and visual distractors. The aims of the study were to: (a) determine if the Perceptual Load Hypothesis (PLH) (distraction greater under low perceptual load) could predict which irrelevant stimuli would disrupt visual selective attention, and (b) if auditory to visual shifts found in modality dominance research could be extended to selective attention tasks. Overall, distractibility decreased with age, with incompatible distractors having larger costs in young and older children than adults. In regard to accuracy, visual distractibility did not differ across age nor load, whereas, auditory interference was more pronounced early in development and correlated with age. Auditory and visual distractors also slowed down responses in young and older children more than adults. Finally, the PLH did not predict performance. Rather, children often showed the opposite pattern, with visual distractors having a greater cost in the high load condition (older children) and auditory distractors having a greater cost in the high load condition (young children). These findings are consistent with research examining the development of modality dominance and shed light on changes in multisensory processing and selective attention across the lifespan.
Using a cross-contextual reciprocal learning approach in a multisite implementation research project to improve self-management for type 2 diabetes

This paper reports on the use of reciprocal learning for identifying, adopting and adapting a type 2 diabetes self-management support intervention in a multisite implementation trial conducted in a rural setting in a low-income country (Uganda), a periurban township in a middle-income country (South Africa) and socioeconomically disadvantaged suburbs in a high-income country (Sweden). The learning process was guided by a framework for knowledge translation and structured into three learning cycles, allowing for a balance between evidence, stakeholder interaction and contextual adaptation. Key factors included commitment, common goals, leadership and partnerships. Synergistic outcomes were the cocreation of knowledge, interventions and implementation methods, including reverse innovations such as adaption of community-linked models of care. Contextualisation was achieved by cross-site exchanges and local stakeholder interaction to balance intervention fidelity with local adaptation. Interdisciplinary and cross-site collaboration resulted in the establishment of learning networks. Limitations of reciprocal learning relate to the complexity of the process with unpredictable outcomes and the limited generalisability of results.
Network Coding with Random Packet-Index Assignment for Data Collection Networks

This paper considers data collection using a network of uncoordinated, heterogeneous, and possibly mobile devices. Using medium and short-range radio technologies, multi-hop communication is required to deliver data to some sink. While numerous techniques from managed networks can be adapted, one of the most efficient (from the energy and spectrum use perspective) is network coding (NC). NC is well suited to networks with mobility and unreliability, however, practical NC requires a precise identification of individual packets that have been mixed together. In a purely decentralized system, this requires either conveying identifiers in headers along with coded information as in COPE, or integrating a more complex protocol in order to efficiently identify the sources (participants) and their payloads. A novel solution, Network Coding with Random Packet Index Assignment (NeCoRPIA), is presented where packet indices in NC headers are selected in a decentralized way, by choosing them randomly. Traditional network decoding can be applied when all original packets have different indices. When this is not the case, \emph{i.e.}, in case of collisions of indices, a specific decoding algorithm is proposed. A theoretical analysis of its performance in terms of complexity and decoding error probability is described. Simulation results match well the theoretical results. Comparisons of NeCoRPIA header lengths with those of a COPE-based NC protocol are also provided.
Lumbar stabilisation exercises versus back endurance-resistance exercise training in athletes with chronic low back pain: protocol of a randomised controlled trial

Background Chronic low back pain (CLBP) is an important disorder in athletes that may negatively affect their performance in competitions. The literature usually recommends physiotherapy based on exercises for back pain management in athletes. Recent evidence suggests that interventions based on lumbar muscle stabilisation exercises (LMSE) and back endurance-resistance exercises (BERE) may improve back pain and function performance. However, it is still unclear which type of exercise is more effective for the treatment of CLBP in athletes. Objective To compare the efficacy of LMSE versus BERE in athletes with CLBP. Design The study is a 2-arm, prospectively registered, randomised controlled trial. Setting The physical therapy clinical and biomechanics laboratory of the UNOPAR University. Participants 32 male athletes with CLBP, age between 18 and 40 years old, recruited from the local community. Intervention An 8-week intervention programme will be carried out with LMSE s versus BERE. Measurements Trunk neuromuscular patterns during balance tasks (unipodal and over a ball) using electromyography and force platform parameters, pain, disability, fear and avoidance will be assessed by a blinded assessor at baseline and at follow-up after 8 weeks of intervention period. Limitations The absence of blinding intervention and the exclusion of female athletes, seated sports and swimmers will affect the internal and external validity of the study. Conclusions The results of this study will elucidate which of these two interventions promote better results in trunk neuromuscular pattern, back pain and function in male athletes with CLBP.
Young cohorts of Russians drink less: age–period–cohort modelling of alcohol use prevalence 1994–2016

BACKGROUND AND AIMS Young Russians have been drinking less alcohol, and fewer strong spirits in particular, in recent years. This study aimed to disentangle age, period and birth cohort effects for the first time in Russia to improve our understanding of these trends. DESIGN Age, period and cohort analysis of annual nationally representative repeated cross-sectional surveys [Russian Longitudinal Monitoring Survey- Higher School of Economics (RLMS-HSE)] using separate logistic models for each gender. SETTING Russia 1994-2016. PARTICIPANTS A total of 195 234 respondents aged 14-85 years. MEASUREMENTS Age (14 groups: 14-17 to 76+ years), period (21 years: 1994-2016) and birth cohorts (17 groups: 1920-24 to 2000-02). Outcome measures were 30-day overall and beverage-specific alcohol use prevalence accounting for vodka, moonshine, beer and wine. Controls were per capita income, education, marital status, ethnicity, residence type and regional climate. FINDINGS Controlling for age and period effects, the most recent cohorts had lower rates of participation than older cohorts. Findings were valid for females born in 1995-2002 (P = 0.000) and males born in 1990-94 (P = 0.002) and 1995-2002 (P = 0.000). The period effects were strong in 1994-2003 due to intensive substitution of beer in place of vodka. Period effects were also important in determining a decline of prevalence in 2008-15 due to restrictive alcohol policy. Age effects showed an inverse U-shaped trend in both genders, except for moonshine and wine. Overall, drinking profiles were beverage-specific. Models indicated diverse beverage-specific effects of income, ethnicity, education, marital status and residence on the prevalence of alcohol use. CONCLUSION The recent downward trend in alcohol use in Russia appears to be attributable to reduced participation rates among younger cohorts born after 1990.
The Boosted DC Algorithm for nonsmooth functions

The Boosted Difference of Convex functions Algorithm (BDCA) was recently proposed for minimizing smooth difference of convex (DC) functions. BDCA accelerates the convergence of the classical Difference of Convex functions Algorithm (DCA) thanks to an additional line search step. The purpose of this paper is twofold. Firstly, to show that this scheme can be generalized and successfully applied to certain types of nonsmooth DC functions, namely, those that can be expressed as the difference of a smooth function and a possibly nonsmooth one. Secondly, to show that there is complete freedom in the choice of the trial step size for the line search, which is something that can further improve its performance. We prove that any limit point of the BDCA iterative sequence is a critical point of the problem under consideration, and that the corresponding objective value is monotonically decreasing and convergent. The global convergence and convergent rate of the iterations are obtained under the Kurdyka-Lojasiewicz property. Applications and numerical experiments for two problems in data science are presented, demonstrating that BDCA outperforms DCA. Specifically, for the Minimum Sum-of-Squares Clustering problem, BDCA was on average sixteen times faster than DCA, and for the Multidimensional Scaling problem, BDCA was three times faster than DCA.
“ Role of Homoeopathic Medicines in Cases of Polycystic Ovarian Disease Assessed by Modern Diagnostic Parameters ”

50 well-diagnosed cases of PCOD have been short listed for this study from OPD record of Gaurang Clinic and Centre for Homoeopathic Research. In all cases, pre and post treatment pelvic ultrasound was done apart from detailed hormone analysis i.e. LH, FSH, Oestrogen, Progesterone, Prolactin, and Testosterone in a few cases. It was repeated in cases in which any one of them was found to be abnormal. Out of 50 patients treated with homoeopathic medicines, complete resolution of PCOD was achieved in 22 (44.00%), significant improvement in 6 (12.00%), 18 (36.00%) maintained status quo while 4 (8.00%) did not improve. Calcarea carbonica was the medicine of choice in maximum number of patients (24) but maximum response was obtained with Lycopodium. The statistical analysis of the data showed reduction in size of ovary, number and size of cysts.
Selective Influences of Maximum Dynamic Strength and Bar-Power Output on Team Sports Performance: A Comprehensive Study of Four Different Disciplines

This study examined the selective influences of one-repetition maximum (1RM) values [assessed in the half-squat (HS)] and bar-power production [assessed in both HS and jump squat (JS) exercises] on the physical performance of male and female team sport athletes from four different sports. Three-hundred and three elite players (31 Olympians) from four different disciplines (47 male soccer players, 58 female soccer players, 28 male handball players, 58 female handball players, 49 male rugby players, and 63 male futsal players) participated in this study. The physical tests were performed over 2 consecutive days for soccer and rugby players, and in 1 day for the remaining athletes. On the first day, rugby and soccer athletes performed squat jumps (SJ), countermovement jumps (CMJ), and HS 1RM. On the second day, they executed HS and JS tests (to assess the maximum bar-power output) and the linear and change-of-direction (COD) speed tests. For the other players, the sequence of the measurements was the same; however, they did not perform the HS exercise. Athletes were separated, using a median split analysis, into two distinct groups, according to their bar-power output in both JS and HS exercises and their performance in HS 1RM. The magnitude-based inferences method was used to examine the differences between “higher” and “lower” performance groups. Overall, the bar-power outputs were better connected to improved acceleration, speed, and jump performance than the 1RM measures. From these findings, it is possible to infer that players able to produce higher bar-power outputs are likely to sprint faster and jump higher. Therefore, coaches involved in team sports are strongly encouraged to use the bar-power method to evaluate the athletic performance of their players.
SIGNet: Semantic Instance Aided Unsupervised 3D Geometry Perception

Unsupervised learning for geometric perception (depth, optical flow, etc.) is of great interest to autonomous systems. Recent works on unsupervised learning have made considerable progress on perceiving geometry; however, they usually ignore the coherence of objects and perform poorly under scenarios with dark and noisy environments. In contrast, supervised learning algorithms, which are robust, require large labeled geometric dataset. This paper introduces SIGNet, a novel framework that provides robust geometry perception without requiring geometrically informative labels. Specifically, SIGNet integrates semantic information to make depth and flow predictions consistent with objects and robust to low lighting conditions. SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error). In particular, SIGNet improves the dynamic object class performance by 39% in depth prediction and 29% in flow prediction. Our code will be made available at https://github.com/mengyuest/SIGNet
The Importance of Sampling inMeta-Reinforcement Learning

We interpret meta-reinforcement learning as the problem of learning how to quickly find a good sampling distribution in a new environment. This interpretation leads to the development of two new meta-reinforcement learning algorithms: E-MAML and E-$\text{RL}^2$. Results are presented on a new environment we call `Krazy World': a difficult high-dimensional gridworld which is designed to highlight the importance of correctly differentiating through sampling distributions in meta-reinforcement learning. Further results are presented on a set of maze environments. We show E-MAML and E-$\text{RL}^2$ deliver better performance than baseline algorithms on both tasks.
The Distribution and Outcomes of the 21-Gene Recurrence Score in T1-T2N0 Estrogen Receptor-Positive Breast Cancer With Different Histologic Subtypes

Background: The clinical value of 21-gene recurrence score (RS) in various breast cancer histologic subtypes is not well established. Aims: To assess the distribution and outcomes of the 21-gene RS among various T1-T2N0 estrogen receptor-positive breast cancer histologic subtypes. Methods: Using the Surveillance, Epidemiology and End Results database, we investigated the distribution and outcomes of the 21-gene RS among various breast cancer histologic subtypes between 2004 and 2015. The histologic subtypes with 200 or more cases were further analyzed. Results: We identified 83,665 patients including eight histologic subtypes. The most common subtype was invasive ductal carcinoma not otherwise specified (IDC NOS) (77.9%), followed by lobular carcinoma NOS, mixed infiltrating duct and lobular carcinoma (IDC-L), mucinous adenocarcinoma, tubular adenocarcinoma, micropapillary ductal carcinoma, cribriform carcinoma NOS, and intraductal papillary adenocarcinoma with invasion with 10.8, 7.7, 2.1, 0.6, 0.3, 0.2, and 0.2%, respectively. The 5-years breast cancer specific survival (BCSS) was 98.8, 98.8, 98.9, 99.6, 100, 100, 100, and 100%, respectively (P = 0.011). Patients with IDC NOS (8.9%), micropapillary ductal carcinoma (8.8%), and intraductal papillary adenocarcinoma with invasion (8.2%) had significantly higher percentage of high-risk RS compared to other histologic subtypes (1.0–3.8%) (P < 0.001). The mean RS was higher in IDC NOS, lobular carcinoma NOS, and IDC-L compared to other subtypes. In multivariate analysis, 21-gene RS was the independent prognostic factor in patients with IDC NOS (P < 0.001), lobular carcinoma NOS (P < 0.001), and IDC-L (P < 0.001), patients with a higher RS was associated with poor BCSS. Conclusion: Our results demonstrate that there is a significant difference in distribution of 21-gene RS in T1-T2N0 estrogen receptor-positive breast cancer with different histologic subtypes. Long-term studies with larger series are needed to confirm the role of the 21-gene RS array in prognosis assessment and chemotherapy decision-making in special histologic subtypes with favorable prognosis.
Thermodynamics and Stability of Rhabdophanes, Hydrated Rare Earth Phosphates REPO4 · n H2O

Rare earth phosphates comprise a large family of compounds proposed as possible nuclear waste disposal forms. We report structural and thermodynamic properties of a series of rare earth rhabdophanes and monazites. The water content of the rhabdophanes, including both adsorbed and structural water, decreases linearly with increase in ionic radius of the rare earth. The energetics of the transformation of rhabdophane to monazite plus water and the enthalpy of formation of rhabdophane from the constituent oxides was determined by high temperature drop solution calorimetry. The former varies linearly with the ionic radius of the lanthanide, except for cerium. By combining the enthalpy of formation determined by high temperature drop solution calorimetry and the free energy of formation determined previously by solubility experiments, a complete set of thermodynamic data was derived for the rhabdophanes. They are thermodynamically metastable with respect to the corresponding monazites plus water at all temperatures under ambient pressure conditions. This conclusion strengthens the case for monazites being an excellent nuclear waste form.
An effective method to reduce residual lithium compounds on Ni-rich Li [ Ni 0 . 6 Co 0 . 2 Mn 0 . 2 ] O 2 active material using phosphoric acid derived Li 3 PO 4 nanolayer

Ni-rich Li[Ni0.6Co0. 2Mn0.2]O2 surface is modified with H3PO4. After the coating at 80oC, the products are heated further at a moderate temperature of 500oC in air, where the added H3PO4 transforms to Li3PO4 compounds after reacting with residual LiOH and Li2CO3 on the surface. A thin and uniform smooth nanolayer (<10 nm) is observed on the surface of Li[Ni0.6Co0. 2Mn0.2]O2 as confirmed by transmission electron microscopy (TEM). Time-of-flight secondary ion mass spectroscopic (ToF-SIMS) data exhibit the presence of LiP+, LiPO+, and Li2PO2+ fragments, indicating the formation of Li3PO4 coating layer on the surface of the Li[Ni0.6Co0. 2Mn0.2]O2. As a result, the amounts of residual lithium compounds, such as LiOH and Li2CO3, are significantly reduced through the formation of Li3PO4 from the starting H3PO4. As a result, the Li3PO4-coated Li[Ni0.6Co0. 2Mn0.2]O2 exhibits noticeable improvement in capacity retention and rate capability due to the reduction of residual LiOH and Li2CO3 compounds. Further investigation of the extensively cycled electrodes by XRD, TEM, and ToF-SIMS demonstrate that the Li3PO4 coating layers have multi-functions: absorption of water in the electrolyte that lowers HF level, HF scavenging, and protection of the active materials from deteriorating side reactions with the electrolyte during extensive cycling, enabling high capacity retention over 1000 cycles.
Multivariate Deep Learning Classification of Alzheimer’s Disease Based on Hierarchical Partner Matching Independent Component Analysis

Machine learning and pattern recognition have been widely investigated in order to look for the biomarkers of Alzheimer’s disease (AD). However, most existing methods extract features by seed-based correlation, which not only requires prior information but also ignores the relationship between resting state functional magnetic resonance imaging (rs-fMRI) voxels. In this study, we proposed a deep learning classification framework with multivariate data-driven based feature extraction for automatic diagnosis of AD. Specifically, a three-level hierarchical partner matching independent components analysis (3LHPM-ICA) approach was proposed first in order to address the issues in spatial individual ICA, including the uncertainty of the numbers of components, the randomness of initial values, and the correspondence of ICs of multiple subjects, resulting in stable and reliable ICs which were applied as the intrinsic brain functional connectivity (FC) features. Second, Granger causality (GC) was utilized to infer directional interaction between the ICs that were identified by the 3LHPM-ICA method and extract the effective connectivity features. Finally, a deep learning classification framework was developed to distinguish AD from controls by fusing the functional and effective connectivities. A resting state fMRI dataset containing 34 AD patients and 34 normal controls (NCs) was applied to the multivariate deep learning platform, leading to a classification accuracy of 95.59%, with a sensitivity of 97.06% and a specificity of 94.12% with leave-one-out cross validation (LOOCV). The experimental results demonstrated that the measures of neural connectivities of ICA and GC followed by deep learning classification represented the most powerful methods of distinguishing AD clinical data from NCs, and these aberrant brain connectivities might serve as robust brain biomarkers for AD. This approach also allows for expansion of the methodology to classify other psychiatric disorders.
Variable Fall Climate Influences Nutrient Resorption and Reserve Storage in Young Peach Trees

A delay of leaf senescence resulting from variable fall climate may allow for additional nutrient resorption, and storage within reserve organs. Autumn leaves and reserve organs (<1 year shoots, >1 year shoots, stem above and below the graft union, the tap root, and fine roots) during dormancy of young peach trees were evaluated following warmer fall temperatures and limited soil moisture on two cultivars (‘Scarletprince’ and ‘Autumnprince’ both on GuardianTM rootstock) over two seasons. Four treatments were established for the two cultivars: (1) well-watered trees (100% ETc needs) in ambient outdoor temperatures; (2) water deficient trees (50% ETc needs) in ambient outdoor temperatures; (3) well-watered trees grown within a greenhouse; and (4) water deficient trees within a greenhouse. The greenhouse environment was on average 5°C warmer than the ambient outdoor temperature. Senescence was delayed on greenhouse-grown trees both years with leaf number and area similar in the greenhouse and outdoor environments prior to senescence. Across leaf samples, leaf nitrogen and phosphorus concentrations were lower within delayed senescence tree leaves while potassium was lower in leaves experiencing normal senescence. During dormancy, multiple reserve organs showed higher nitrogen, phosphorus, and potassium in trees with delayed senescence than normal senescence and similar increases were observed in water-deficient trees compared to well-watered trees. Phosphorus and potassium concentrations were also higher in multiple reserve organs within ‘Autumnprince’ trees compared to ‘Scarletprince’ trees. This study suggests variable climate conditions of increased temperatures or reduced soil moisture during autumn resulting in delayed senescence influence the process of nutrient resorption and increase nutrient storage within reserve organs.