Miracle-dz/UniFAR-FTUs huggingface dataset:
A new, fast method to search for morphological convergence with shape data

Morphological convergence is an intensely studied macroevolutionary phenomenon. It refers to the morphological resemblance between phylogenetically distant taxa. Currently available methods to explore evolutionary convergence either: rely on the analysis of the phenotypic resemblance between sister clades as compared to their ancestor, fit different evolutionary regimes to different parts of the tree to see whether the same regime explains phenotypic evolution in phylogenetically distant clades, or assess deviations from the congruence between phylogenetic and phenotypic distances. We introduce a new test for morphological convergence working directly with non-ultrametric (i.e. paleontological) as well as ultrametric phylogenies and multivariate data. The method (developed as the function search.conv within the R package RRphylo) tests whether unrelated clades are morphologically more similar to each other than expected by their phylogenetic distance. It additionally permits using known phenotypes as the most recent common ancestors of clades, taking full advantage of fossil information. We assessed the power of search.conv and the incidence of false positives by means of simulations, and then applied it to three well-known and long-discussed cases of (purported) morphological convergence: the evolution of grazing adaptation in the mandible of ungulates with high-crowned molars, the evolution of mandibular shape in sabertooth cats, and the evolution of discrete ecomorphs among anoles of Caribbean islands. The search.conv method was found to be powerful, correctly identifying simulated cases of convergent morphological evolution in 95% of the cases. Type I error rate is as low as 4–6%. We found search.conv is some three orders of magnitude faster than a competing method for testing convergence.
Expanded eligibility for HIV testing increases HIV diagnoses—A cross-sectional study in seven health facilities in western Kenya

Homa Bay, Siaya, and Kisumu counties in western Kenya have the highest estimated HIV prevalence (16.3–21.0%) in the country, and struggle to meet program targets for HIV testing services (HTS). The Kenya Ministry of Health (MOH) recommends annual HIV testing for the general population. We assessed the degree to which reducing the interval for retesting to less than 12 months increased diagnosis of HIV in outpatient departments (OPD) in western Kenya. We conducted a retrospective analysis of routinely collected program data from seven high-volume (>800 monthlyOPD visits) health facilities in March–December, 2017. Data from persons ≥15 years of age seeking medical care (patients) in the OPD and non-care-seekers (non-patients) accompanying patients to the OPD were included. Outcomes were meeting MOH (routine) criteria versus criteria for a reduced retesting interval (RRI) of <12 months, and HIV test result. STATA version 14.2 was used to calculate frequencies and proportions, and to test for differences using bivariate analysis. During the 9-month period, 119,950 clients were screened for HIV testing eligibility, of whom 79% (94,766) were eligible and 97% (92,153) received a test. Among 92,153 clients tested, the median age was 28 years, 57% were female and 40% (36,728) were non-patients. Overall, 20% (18,120) of clients tested met routine eligibility criteria: 4% (3,972) had never been tested, 10% (9,316) reported a negative HIV test in the past >12 months, and 5% (4,832) met other criteria. The remaining 80% (74,033) met criteria for a RRI of < 12 months. In total 1.3% (1,185) of clients had a positive test. Although the percent yield was over 2-fold higher among those meeting routine criteria (2.4% vs. 1.0%; p<0.001), 63% (750) of all HIV infections were found among clients tested less than 12 months ago, the majority (81%) of whom reported having a negative test in the past 3–12 months. Non-patients accounted for 45% (539) of all HIV-positive persons identified. Percent yield was higher among non-patients as compared to patients (1.5% vs. 1.2%; p-value = <0.001) overall and across eligibility criteria and age categories. The majority of HIV diagnoses in the OPD occurred among clients reporting a negative HIV test in the past 12 months, clients ineligible for testing under the current MOH guidelines. Nearly half of all HIV-positive individuals identified in the OPD were non-patients. Our findings suggest that in the setting of a generalized HIV epidemic, retesting persons reporting an HIV-negative test in the past 3–12 months, and routine testing of non-patients accessing the OPD are key strategies for timely diagnosis of persons living with HIV.
Effects of Pair Versus Individual Housing on Performance, Health, and Behavior of Dairy Calves

Simple Summary In modern dairy farming systems, calves are often housed in individual pens or hutches, which results in less social interaction with their peers during the milk-feeding period. The aim of this study was to evaluate the effects of pair versus individual housing on performance, health, and behavior of dairy calves from the milk-feeding period to the first week after mixing. Results showed that pair versus individual housing had no effects on body weight, starter intake or average daily gain during the milk-feeding period, while pair housing increased the growth performance of calves during weaning and postweaning periods, and the beneficial effects of pair housing on growth faded after calves were mixed and moved to group housing. Paired calves showed higher diarrhea frequency only in week three. The behavior of calves was altered at different periods, including increased time spent in feeding, chewing and ruminating, and decreased self-grooming time, and a drop of non-nutritive manipulation for all calves after they were mixed and moved to group housing. We also found less social contact may lead to more non-nutritive manipulation. Abstract The aim of this study was to evaluate the effects of pair versus individual housing on performance, health, and behavior of dairy calves. Thirty female Holstein dairy calves were assigned to individual (n = 10) or pair housing (n = 10 pairs). The results showed that both treatments had a similar starter intake and average daily gain (ADG) during the preweaning period. During weaning and postweaning periods, paired calves had a higher starter intake, and the ADG of paired calves continued to increase but calves housed individually experienced a growth check. Paired calves showed higher diarrhea frequency only in week three. The results on behavior showed that feeding, chewing and ruminating time increased, and self-grooming time decreased with age during weaning and postweaning periods, and paired calves spent less time feeding, standing and self-grooming but more time lying during this time. After mixing, feeding, and chewing and ruminating time continued to rise, and self-grooming time continued to decline for both treatments. All calves spent less time standing and non-nutritive manipulation after mixing, and previously individually housed calves tended to increase non-nutritive manipulation. These results showed that pair housing improved growth during weaning and postweaning periods and that calves altered their behavior at different phases. Less social contact may lead to more non-nutritive manipulation.
Effect of exercise training on the FNDC5/BDNF pathway in spontaneously hypertensive rats

Increased sympathetic activity contributes to the development of cardiovascular diseases such as hypertension. Exercise training lowers sympathetic activity and is beneficial for the prevention and treatment of hypertension and associated cognitive impairment. Increased BDNF expression in skeletal muscle, heart, and brain may contribute to these actions of exercise, but the mechanisms by which this occurs are unknown. We postulated that hypertension is associated with decreased hippocampal BDNF, which can be restored by exercise‐mediated upregulation of fibronectin type‐II domain‐containing 5 (FNDC5). Spontaneously hypertensive rats (SHR) and normotensive Wistar–Kyoto rats (WKY) were subjected to 5 weeks of motorized treadmill training. BDNF and FNDC5 expressions were measured in the left ventricle (LV), quadriceps, soleus muscle, and brain areas. Exercise training reduced blood pressure (BP) in both strains. BDNF and FNDC5 protein in the LV were increased in SHR, but exercise increased only BDNF protein in both strains. BDNF mRNA, but not protein, was increased in the quadriceps of SHR, and BDNF mRNA and protein were decreased by exercise in both groups. FNDC5 protein was higher in SHR in both the quadriceps and soleus muscle, whereas exercise increased FNDC5 protein only in the quadriceps in both strains. BDNF mRNA was lower in the dentate gyrus (DG) of SHR, which was normalized by exercise. BDNF mRNA expression in the DG negatively correlated with BP. No differences in FNDC5 expression were observed in the brain, suggesting that enhanced BDNF signaling may contribute to the cardiovascular and neurological benefits of exercise training, and these processes involve peripheral, but not central, FNDC5.
Variation in Tick Load Among Bird Body Parts: Implications for Studying the Role of Birds in the Ecology and Epidemiology of Tick-Borne Diseases

Abstract Wild birds play important roles in the maintenance and dispersal of tick populations and tick-borne pathogens, yet in field studies of tick-borne disease ecology and epidemiology there is limited standardization of how birds are searched for ticks. We conducted a qualitative literature review of 100 field studies where birds were searched for ticks to characterize which parts of a bird's anatomy are typically sampled. To increase understanding of potential biases associated with different sampling approaches, we described variation in tick loads among bird body parts using field-collected data from 459 wild-caught birds that were searched across the entire body. The literature review illustrated a lack of clarity and consistency in tick-searching protocols: 57% of studies did not explicitly report whether entire birds or only particular body parts were searched, 34% reported concentrating searches on certain body parts (most frequently the head only), and only 9% explicitly reported searching the entire bird. Based on field-collected data, only 22% of ticks were found on the head, indicating that studies focusing on the head likely miss a large proportion of ticks. We provide tentative evidence that feeding locations may vary among tick species; 89% of Amblyomma americanum, 73% of Ambloyomma maculatum, and 56% of Haemaphysalis leporispalustris were on body parts other than the head. Our findings indicate a need for clear reporting and increased standardization of tick searching methodologies, including sampling the entire bird body, to provide an unbiased understanding of the role of birds in the maintenance and emergence of tick-borne pathogens.
Preparation and Characterization of Bio-Based PLA/PBAT and Cinnamon Essential Oil Polymer Fibers and Life-Cycle Assessment from Hydrolytic Degradation

Nowadays, the need to reduce the dependence on fuel products and to achieve a sustainable development is of special importance due to environmental concerns. Therefore, new alternatives must be sought. In this work, extruded fibers from poly (lactic acid) (PLA) and poly (butylene adipate-co-terephthalate) (PBAT) added with cinnamon essential oil (CEO) were prepared and characterized, and the hydrolytic degradation was assessed. A two-phase system was observed with spherical particles of PBAT embedded in the PLA matrix. The thermal analysis showed partial miscibility between PLA and PBAT. Mechanically, Young’s modulus decreased and the elongation at break increased with the incorporation of PBAT and CEO into the blends. The variation in weight loss for the fibers was below 5% during the period of hydrolytic degradation studied with the most important changes at 37 °C and pH 8.50. From microscopy, the formation of cracks in the fiber surface was evidenced, especially for PLA fibers in alkaline medium at 37 °C. This study shows the importance of the variables that influence the performance of polyester-cinnamon essential oil-based fibers in agro-industrial applications for horticultural product preservation.
CFTR transmembrane segments are impaired in their conformational adaptability by a pathogenic loop mutation and dynamically stabilized by Lumacaftor

The cystic fibrosis transmembrane conductance regulator (CFTR) is an ion channel protein that is defective in individuals with cystic fibrosis (CF). To advance the rational design of CF therapies, it is important to elucidate how mutational defects in CFTR lead to its impairment and how pharmacological compounds interact with and alter CFTR. Here, using a helical-hairpin construct derived from CFTR's transmembrane (TM) helices 3 and 4 (TM3/4) and their intervening loop, we investigated the structural effects of a patient-derived CF-phenotypic mutation, E217G, located in the loop region of CFTR's membrane-spanning domain. Employing a single-molecule FRET assay to probe the folding status of reconstituted hairpins in lipid bilayers, we found that the E217G hairpin exhibits an altered adaptive packing behavior stemming from an additional GXXXG helix–helix interaction motif created in the mutant hairpin. This observation suggested that the misfolding and functional defects caused by the E217G mutation arise from an impaired conformational adaptability of TM helical segments in CFTR. The addition of the small-molecule corrector Lumacaftor exerts a helix stabilization effect not only on the E217G mutant hairpin, but also on WT TM3/4 and other mutations in the hairpin. This finding suggests a general mode of action for Lumacaftor through which this corrector efficiently improves maturation of various CFTR mutants.
Strong and weak lensing of Gravitational Waves: a semi-analytical approach

In this paper we study gravitational lensing of gravitational wave events. The main point of the present work is to introduce a semi-analytic approach so that each ingredient can be varied and tested individually. Our analytic model for the source population is motivated by a numerical study and we compare semi-analytical and numerical results. We determine the expected magnification for events seen at a given observed luminosity distance. We find that while the probability of significant magnification of the observed LIGO-Virgo events is very small, the probability distribution of the magnification has a significant tail to high $\mu$ such that e.g. the variance of the magnification is very large and even diverges in the geometric optics approximation. For the 10 binary black hole mergers observed by LIGO-Virgo in the O1+O2 observation campaigns, the probability that one of them has been magnified with magnification of 5 or bigger is $\mathcal{P}_{\text{obs}}(>5)\sim 0.01$ while the probability of magnification by 50 or bigger is $\mathcal{P}_{\text{obs}}(>50)\sim 0.005$.
Split Learning for collaborative deep learning in healthcare

Shortage of labeled data has been holding the surge of deep learning in healthcare back, as sample sizes are often small, patient information cannot be shared openly, and multi-center collaborative studies are a burden to set up. Distributed machine learning methods promise to mitigate these problems. We argue for a split learning based approach and apply this distributed learning method for the first time in the medical field to compare performance against (1) centrally hosted and (2) non collaborative configurations for a range of participants. Two medical deep learning tasks are used to compare split learning to conventional single and multi center approaches: a binary classification problem of a data set of 9000 fundus photos, and multi-label classification problem of a data set of 156,535 chest X-rays. The several distributed learning setups are compared for a range of 1-50 distributed participants. Performance of the split learning configuration remained constant for any number of clients compared to a single center study, showing a marked difference compared to the non collaborative configuration after 2 clients (p < 0.001) for both sets. Our results affirm the benefits of collaborative training of deep neural networks in health care. Our work proves the significant benefit of distributed learning in healthcare, and paves the way for future real-world implementations.
Spectrum Allocation in Wireless Networks for Crowd Labelling

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), while tremendous labels are required for AI model training via supervised learning. To tackle this challenge, a novel framework of wireless crowd labelling is proposed that downloads data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. The integration of the rate-distortion theory and the principle of repetition labelling gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Aiming at maximizing the labelling throughput, this work focuses on optimizing the joint annotator-and-spectrum allocation (JASA). To develop an efficient solution approach, an optimal sequential annotator-clustering scheme is derived. Thereby, the optimal JASA policy can be found by an efficient tree search.
The Application of Machine Learning Techniques for Predicting Results in Team Sport: A Review

Over the past two decades, Machine Learning (ML) techniques have been increasingly utilized for the purpose of predicting outcomes in sport. In this paper, we provide a review of studies that have used ML for predicting results in team sport, covering studies from 1996 to 2019. We sought to answer five key research questions while extensively surveying papers in this field. This paper offers insights into which ML algorithms have tended to be used in this field, as well as those that are beginning to emerge with successful outcomes. Our research highlights defining characteristics of successful studies and identifies robust strategies for evaluating accuracy results in this application domain. Our study considers accuracies that have been achieved across different sports and explores the notion that outcomes of some team sports could be inherently more difficult to predict than others. Finally, our study uncovers common themes of future research directions across all surveyed papers, looking for gaps and opportunities, while proposing recommendations for future researchers in this domain.
Extending Multi-Object Tracking systems to better exploit appearance and 3D information

Tracking multiple objects in real time is essential for a variety of real-world applications, with self-driving industry being at the foremost. This work involves exploiting temporally varying appearance and motion information for tracking. Siamese networks have recently become highly successful at appearance based single object tracking and Recurrent Neural Networks have started dominating both motion and appearance based tracking. Our work focuses on combining Siamese networks and RNNs to exploit appearance and motion information respectively to build a joint system capable of real time multi-object tracking. We further explore heuristics based constraints for tracking in the Birds Eye View Space for efficiently exploiting 3D information as a constrained optimization problem for track prediction.
X-ray emissions from magnetic polar regions of neutron stars

Structures of X-ray emitting magnetic polar regions on neutron stars in X-ray pulsars are studied in a range of the accretion rate, 10$^{17}$ g s$^{-1} \sim 10^{18}$ g s$^{-1}$. It is shown that a thin but tall, radiation energy dominated, X-ray emitting polar cone appears at each of the polar regions. The height of the polar cone is several times as large as the neutron star radius. The energy gain due to the gravity of the neutron star in the polar cone exceeds the energy loss due to photon diffusion in the azimuthal direction of the cone, and a significant amount of energy is advected to the neutron star surface. Then, the radiation energy carried with the flow should become so large for the radiation pressure to overcome the magnetic pressure at the bottom of the cone. As a result, the matter should expand in the tangential direction along the neutron star surface, dragging the magnetic lines of force, and form a mound-like structure. The advected energy to the bottom of the cone should finally be radiated away from the surface of the polar mound and the matter should be settled on the neutron star surface there. From such configurations, we can expect an X-ray spectrum composed of a multi-color blackbody spectrum from the polar cone region and a quasi-single blackbody spectrum from the polar mound region. These spectral properties agree with observations. A combination of a fairly sharp pencil beam and a broad fan beam is expected from the polar cone region, while a broad pencil beam is expected from the polar mound region. With these X-ray beam properties, basic patterns of pulse profiles of X-ray pulsars can be explained too.
Unsupervised Representation Learning by Predicting Random Distances

Deep neural networks have gained great success in a broad range of tasks due to its remarkable capability to learn semantically rich features from high-dimensional data. However, they often require large-scale labelled data to successfully learn such features, which significantly hinders their adaption in unsupervised learning tasks, such as anomaly detection and clustering, and limits their applications to critical domains where obtaining massive labelled data is prohibitively expensive. To enable unsupervised learning on those domains, in this work we propose to learn features without using any labelled data by training neural networks to predict data distances in a randomly projected space. Random mapping is a theoretically proven approach to obtain approximately preserved distances. To well predict these distances, the representation learner is optimised to learn genuine class structures that are implicitly embedded in the randomly projected space. Empirical results on 19 real-world datasets show that our learned representations substantially outperform a few state-of-the-art methods for both anomaly detection and clustering tasks. Code is available at: \url{https://git.io/RDP}
Large-scale 6D Object Pose Estimation Dataset for Industrial Bin-Picking

In this paper, we introduce a new public dataset for 6D object pose estimation and instance segmentation for industrial bin-picking. The dataset comprises both synthetic and real-world scenes. For both, point clouds, depth images, and annotations comprising the 6D pose (position and orientation), a visibility score, and a segmentation mask for each object are provided. Along with the raw data, a method for precisely annotating real-world scenes is proposed.To the best of our knowledge, this is the first public dataset for 6D object pose estimation and instance segmentation for bin-picking containing sufficiently annotated data for learning-based approaches. Furthermore, it is one of the largest public datasets for object pose estimation in general. The dataset is publicly available at http://www.bin-picking.ai/en/ dataset.html.