Miracle-dz/UniFAR-FTUs huggingface dataset:
Facing different predators: adaptiveness of behavioral and morphological traits under predation

Abstract Predation is thought to be one of the main structuring forces in animal communities. However, selective predation is often measured on isolated traits in response to a single predatory species, but only rarely are selective forces on several traits quantified or even compared between different predators naturally occurring in the same system. In the present study, we therefore measured behavioral and morphological traits in young-of-the-year Eurasian perch Perca fluviatilis and compared their selective values in response to the 2 most common predators, adult perch and pike Esox lucius. Using mixed effects models and model averaging to analyze our data, we quantified and compared the selectivity of the 2 predators on the different morphological and behavioral traits. We found that selection on the behavioral traits was higher than on morphological traits and perch predators preyed overall more selectively than pike predators. Pike tended to positively select shallow bodied and nonvigilant individuals (i.e. individuals not performing predator inspection). In contrast, perch predators selected mainly for bolder juvenile perch (i.e. individuals spending more time in the open, more active), which was most important. Our results are to the best of our knowledge the first that analyzed behavioral and morphological adaptations of juvenile perch facing 2 different predation strategies. We found that relative specific predation intensity for the divergent traits differed between the predators, providing some additional ideas why juvenile perch display such a high degree of phenotypic plasticity.
Physical activity promotion by health practitioners: a distance-learning training component to improve knowledge and counseling

Aim To report an evaluation of health professionals’ participation in a distance-learning physical activity training course developed in a low socio-economic region of São Paulo city, Brazil. Background In countries with public universal health systems, physical activity promotion in primary health care settings can reap results, particularly given that such interventions have the potential to reach a large percentage of the population. However, few studies proposed physical activity training for health professionals in low- and middle-income countries. Brazil is a continental country and has the Unified Health System which incorporates family health teams in over 85% of Brazilian cities. Methods The physical activity training was part of the fifth module of an educational intervention throughout a distance-learning course focusing on health professionals at M’Boi Mirim district in São Paulo city. The training totaled 3 h and had five themes of physical activity: (1) concepts, definitions benefits; (2) evaluation; (3) recommendation; (4) interventions; (5) physical activity counseling. The opinion of health professionals was evaluated after training by two open questions. Findings Out of 106 professionals who took part of the course, only 22.6% (n=24) had accessed the fifth module. These professionals were predominantly female (79.2%), nurses (66.7%) and aged 30 years or older. Responses highlighted the course approach focused on physical activity for improving patient’s quality of life and well-being, disease prevention and health improvements. Regarding the themes for improvement, the health professionals identified that there was a need to experience physical activity classes first-hand, and the need to link physical activity counseling to the local venues that provide structured physical activity programs. We recommend that further training courses can be conducted based on this model for health professionals to promote physical activity to the community in Brazil.
Development of a Proximity Labeling System to Map the Chlamydia trachomatis Inclusion Membrane

Chlamydia grows within a membrane-bound vacuole termed an inclusion. The cellular processes that support the biogenesis and integrity of this pathogen-specified parasitic organelle are not understood. Chlamydia secretes integral membrane proteins called Incs that insert into the chlamydial inclusion membrane (IM). Incs contain at least two hydrophobic transmembrane domains flanked by termini, which vary in size and are exposed to the host cytosol. In addition, Incs are temporally expressed during the chlamydial developmental cycle. Data examining Inc function are limited because of (i) the difficulty in working with hydrophobic proteins and (ii) the inherent fragility of the IM. We hypothesize that Incs function collaboratively to maintain the integrity of the chlamydial inclusion with small Incs organizing the IM and larger Incs interfacing with host cell machinery. To study this hypothesis, we have adapted a proximity-labeling strategy using APEX2, a mutant soybean ascorbate peroxidase that biotinylates interacting and proximal proteins within minutes in the presence of H2O2 and its exogenous substrate, biotin-phenol. We successfully expressed, from an inducible background, APEX2 alone, or fusion proteins of IncATM (TM = transmembrane domain only), IncA, and IncF with APEX2 in Chlamydia trachomatis serovar L2. IncF-APEX2, IncATM-APEX2, and IncA-APEX2 localized to the IM whereas APEX2, lacking a secretion signal, remained associated with the bacteria. We determined the impact of overexpression on inclusion diameter, plasmid stability, and Golgi-derived sphingomyelin acquisition. While there was an overall impact of inducing construct expression, IncF-APEX2 overexpression most negatively impacted these measurements. Importantly, Inc-APEX2 expression in the presence of biotin-phenol resulted in biotinylation of the IM. These data suggest that Inc expression is regulated to control optimal IM biogenesis. We subsequently defined lysis conditions that solubilized known Incs and were compatible with pulldown conditions. Importantly, we have created powerful tools to allow direct examination of the dynamic composition of the IM, which will provide novel insights into key interactions that promote chlamydial growth and development within the inclusion.
Path selection and rate allocation in self-backhauled mmWave networks

We investigate the problem of multi-hop scheduling in self-backhauled millimeter wave (mmWave) networks. Owing to the high path loss and blockage of mmWave links, multi-hop paths/routes between the macro base station and the intended users via full-duplex small cells need to be carefully selected. This paper addresses the fundamental question: “how to select the best paths and how to allocate rates over these paths subject to latency constraints? ” To answer this question, we propose a new system design, which factors in mmWave-specific channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint and network stability. The studied problem is decoupled into: (i) a path/route selection and (ii) rate allocation, whereby learning the best paths is done by means of a reinforcement learning algorithm, and the rate allocation is solved by applying the successive convex approximation method. Via numerical results, our approach ensures reliable communication with a guaranteed probability of 99.9999%, and reduces latency by 50.64% and 92.9% as compared to baselines.
Hepatic, lipid and genetic factors associated with obesity: crosstalk with alcohol dependence?

Abstract Objectives: Alcohol dependence represents a leading cause of mortality and morbidity. Understanding the variables that contribute to this diagnosis and its severity is critical. An overlap between factors that may predispose people to become obese and those that may increase the risk of alcohol dependence may exist. However, data in the literature are not conclusive. Therefore, this study aimed to identify the association between alcohol dependence and obesity-related factors, including biochemical and genetic factors. Methods: In a case–control study with 829 participants, factors involved with metabolism and obesity were assessed, including biochemical lipid and liver markers, and the fat mass and obesity-associated (FTO) single nucleotide polymorphism (SNP) rs8050136. Results: Increased triglycerides, having one or two minor A alleles for rs8050136 and being a smoker were associated with increased risk of alcohol dependence, while increased low-density lipoprotein cholesterol was associated with decreased risk. In addition, having abnormal gamma-glutamyl transferase and being female were factors associated with an increased severity of alcohol dependence. Conclusions: Our preliminary findings suggest a link between alcohol dependence and obesity-related biochemical and genetic factors. Future studies are needed to better understand if these factors may play a predictive role and/or may act as biomarkers for treatment response.
Proposal of a power saving network for rice fields using LoRa

We developed a field management system with a field server that helps decrease farmers workload. It will be also enable the transfer of knowledge to the next generation of farmers in Japan. In order to reduce the burden of agricultural work, less frequent maintenance of the field server is desired. In this paper, we explain construction method of a local wireless network to cut the power consumption of the field servers by LoRa. As experimental results, we confirmed that transmission and reception of data between the master unit and the field server performed normally. The power consumption of the field server was 75.36 mW per day, and it is theoretically possible to operate for 995 consecutive days. Continuous operation of 995 days is sufficient operation time for the monitoring of rice cultivation work.
A Generic Self-Evolving Neuro-Fuzzy Controller Based High-Performance Hexacopter Altitude Control System

Nowadays, the application of fully autonomous system like rotary wing unamnned air vehicles (UAVs) are increasing sharply. Due to the complex nonlinear dynamics a huge research interest is witnessed in developing learning machine based intelligent, self-organizing evolving controller for these vehicles notably to address the system's dynamic characteristics. In this work, such an evolving controller namely Generic-controller (Gcontroller) is proposed to control the altitude of a rotary wing UAV namely hexacopter. This controller can work with very minor expert domain knowledge. The evolving architecture of this controller is based on an advanced incremental learning algorithm namely Generic Evolving Neuro-Fuzzy Inference System (GENEFIS). The controller does not require any offline training, since it starts operating from scratch with an empty set of fuzzy rules, and then add or delete rules on demand. The adaptation laws for the consequent paramters are derived from the sliding model control (SMC) theory. The Lyapunov theory is used to guarantee the stability of the proposed controller. In addition, an auxiliary robustifying control term is implemented to obtain an uniform asymptotic convergence of tracking error to zero. Finally, the G-controller's performance evaluation is observed through the altitude tracking of an UAV namely hexacopter for various trajectories.
Achieving high angular resolution via view synthesis: Quality assessment of 3D content on super multiview lightfield display

Super multiview (SMV) lightfield displays provide glasses- free immersive 3D experience via continuous motion parallax. Such displays require input views acquired at high angular resolution, which could be economically and technologically prohibitive. To achieve high view density, synthesizing absent views from the captured ones has recently been suggested. Clearly, a critical proportion of the views should be unsynthesized in order to ensure acceptable quality of the 3D content. In order to determine the said proportion we subjectively assessed the degradation in perceptual 3D quality vis-à- vis the proportion of synthesized views. The qualitative analysis of the subjective scores ascertained that the quality of 3D perception is mainly affected by discontinuities in motion parallax introduced by view synthesis, along with 2D quality of the constituent views. The subjective scores collected in the process exhibited a three-regime behaviour, corresponding to small, moderate and large proportion of synthesized views. For a high 3D quality experience, we recommend operation in the first regime, albeit its high sensitivity to the proportion of synthesized views. The second regime, less susceptible to the said proportion, can be considered when a moderate perceptual quality is acceptable. The third regime, exhibiting monotonous decrease in perceptual quality, is not recommended.
89Zr-DFO-AMG102 Immuno-PET to Determine Local Hepatocyte Growth Factor Protein Levels in Tumors for Enhanced Patient Selection

The hepatocyte growth factor (HGF) binding antibody rilotumumab (AMG102) was modified for use as a 89Zr-based immuno-PET imaging agent to noninvasively determine the local levels of HGF protein in tumors. Because recent clinical trials of HGF-targeting therapies have been largely unsuccessful in several different cancers (e.g., gastric, brain, lung), we have synthesized and validated 89Zr-DFO-AMG102 as a companion diagnostic for improved identification and selection of patients having high local levels of HGF in tumors. To date, patient selection has not been performed using the local levels of HGF protein in tumors. Methods: The chelator p-SCN-Bn-DFO was conjugated to AMG102, radiolabeling with 89Zr was performed in high radiochemical yields and purity (>99%), and binding affinity of the modified antibody was confirmed using an enzyme-linked immunosorbent assay (ELISA)–type binding assay. PET imaging, biodistribution, autoradiography and immunohistochemistry, and ex vivo HGF ELISA experiments were performed on murine xenografts of U87MG (HGF-positive, MET-positive) and MKN45 (HGF-negative, MET-positive) and 4 patient-derived xenografts (MET-positive, HGF unknown). Results: Tumor uptake of 89Zr-DFO-AMG102 at 120 h after injection in U87MG xenografts (HGF-positive) was high (36.8 ± 7.8 percentage injected dose per gram [%ID/g]), whereas uptake in MKN45 xenografts (HGF-negative) was 5.0 ± 1.3 %ID/g and a control of nonspecific human IgG 89Zr-DFO-IgG in U87MG tumors was 11.5 ± 3.3 %ID/g, demonstrating selective uptake in HGF-positive tumors. Similar experiments performed in 4 different gastric cancer patient-derived xenograft models showed low uptake of 89Zr-DFO-AMG102 (∼4–7 %ID/g), which corresponded with low HGF levels in these tumors (ex vivo ELISA). Autoradiography, immunohistochemical staining, and HGF ELISA assays confirmed that elevated levels of HGF protein were present only in U87MG tumors and that 89Zr-DFO-AMG102 uptake was closely correlated with HGF protein levels in tumors. Conclusion: The new immuno-PET imaging agent 89Zr-DFO-AMG102 was successfully synthesized, radiolabeled, and validated in vitro and in vivo to selectively accumulate in tumors with high local levels of HGF protein. These results suggest that 89Zr-DFO-AMG102 would be a valuable companion diagnostic tool for the noninvasive selection of patients with elevated local concentrations of HGF in tumors for planning any HGF-targeted therapy, with the potential to improve clinical outcomes.
Voluntary phantom hand and finger movements in transhumerai amputees could be used to naturally control polydigital prostheses

An arm amputation is extremely invalidating since many of our daily tasks require bi-manual and precise control of hand movements. Perfect hand prostheses should therefore offer a natural, intuitive and cognitively simple control over their numerous biomimetic active degrees of freedom. While efficient polydigital prostheses are commercially available, their control remains complex to master and offers limited possibilities, especially for high amputation levels. In this pilot study, we demonstrate the possibility for upper-arm amputees to intuitively control a polydigital hand prosthesis by using surface myoelectric activities of residual limb muscles (sEMG) associated with phantom limb movements, even if these residual arm muscles on which the phantom activity is measured were not naturally associated with hand movements before amputation. Using pattern recognition methods, three arm amputees were able, without training, to initiate 5–8 movements of a robotic hand (including individual finger movements) by simply mobilizing their phantom limb while the robotic hand was mimicking the action in real time. This innovative control approach could offer to numerous upper-limb amputees an access to recent biomimetic prostheses with multiple controllable joints, without requiring surgery or complex training; and might deeply change the way the phantom limb is apprehended by both patients and clinicians.
Evolution of Cooperation in Evolutionary Robotics: the Tradeoff between Evolvability and Efficiency

In this paper, we investigate the benefits and drawbacks of different approaches for solving a cooperative foraging task with two robots. We compare a classical clonal approach with an additional approach which favors the evolution of heterogeneous behaviors according to two defining criteria: the evolvability of the cooperative solution and the efficiency of the coordination behaviors evolved. Our results reveal a tradeoff between evolvability and efficiency: the clonal approach evolves cooperation with a higher probability than a non-clonal approach, but heterogeneous behaviors evolved with the non-clonal approach systematically show better fitness scores. We then propose to overcome this tradeoff and improve on both of these criteria for each approach. To this end, we investigate the use of incremental evolution to transfer coordination behaviors evolved in a simpler task. We show that this leads to a significant increase in evolvability for the non-clonal approach, while the clonal approach does not benefit from any gain in terms of efficiency.
Sparse Random Feature Algorithm as Coordinate Descent in Hilbert Space

In this paper, we propose a Sparse Random Features algorithm, which learns a sparse non-linear predictor by minimizing an l1-regularized objective function over the Hilbert Space induced from a kernel function. By interpreting the algorithm as Randomized Coordinate Descent in an infinite-dimensional space, we show the proposed approach converges to a solution within ∊-precision of that using an exact kernel method, by drawing O(1/∊) random features, in contrast to the O(1/∊2) convergence achieved by current Monte-Carlo analyses of Random Features. In our experiments, the Sparse Random Feature algorithm obtains a sparse solution that requires less memory and prediction time, while maintaining comparable performance on regression and classification tasks. Moreover, as an approximate solver for the infinite-dimensional l1-regularized problem, the randomized approach also enjoys better convergence guarantees than a Boosting approach in the setting where the greedy Boosting step cannot be performed exactly.
Outpatient treatment of acute bacterial skin and skin structure infections (ABSSSI) with tedizolid phosphate and linezolid in patients in the United States

Background: Acute bacterial skin and skin structure infections (ABSSSI) are a frequent cause of hospital admissions in the United States. Safe and effective outpatient treatments may lower ABSSSI-associated health care costs by reducing unnecessary hospital admissions. Using data from 2 phase 3 trials (ESTABLISH-1, NCT01170221; ESTABLISH-2, NCT01421511), this post-hoc analysis explored the efficacy and safety of tedizolid in an outpatient setting. Methods: Subgroup analysis was performed on US outpatients (defined as patients who were not in hospital at the time of treatment initiation) with ABSSSI caused by presumed or proven gram-positive pathogens. Patients were randomly assigned to receive tedizolid phosphate 200 mg once daily for 6 days (n = 403) or linezolid 600 mg twice daily for 10 days (n = 410). The primary end point was early clinical response (48–72 hours after the start of treatment). Secondary end points included investigator-assessed clinical response at end of therapy (EOT) and post-therapy evaluation (PTE; 7–14 days after therapy). Additional assessments included the patient-reported level of pain using a visual analog scale (VAS) and the per-pathogen favorable microbiological response rate at the PTE visit. Compliance with treatment and safety outcomes was also recorded. Results: Early clinical response was similar between treatment groups (tedizolid, 82.4%; linezolid, 79.0%), as was investigator-assessed clinical response at EOT (tedizolid, 87.1%; linezolid, 86.1%) and PTE (tedizolid, 83.1%; linezolid, 83.7%). Mean changes from baseline to days 10 to 13 in VAS scores were identical between treatment groups (tedizolid, –51.9 mm; linezolid, –51.9 mm). Microbiological eradication rates were generally similar in both treatment groups for all key pathogens. Patients in both groups had favorable response at PTE. More tedizolid-treated patients (89.3%) than linezolid-treated patients (77.3%) were compliant with treatment. The most frequently reported drug-related treatment-emergent adverse events were nausea (tedizolid, 10.7%; linezolid, 13.8%), diarrhea (tedizolid, 4.5%; linezolid, 5.9%), and headache (tedizolid, 5.5%; linezolid, 4.4%). Treatment discontinuation rates were low for both treatment groups (tedizolid, 0.7%; linezolid, 1.0%). Conclusion: Short-course therapy with tedizolid can successfully treat patients with ABSSSI caused by presumed or proven gram-positive pathogens in an outpatient setting.
WorldTree: A Corpus of Explanation Graphs for Elementary Science Questions supporting Multi-hop Inference

Developing methods of automated inference that are able to provide users with compelling human-readable justifications for why the answer to a question is correct is critical for domains such as science and medicine, where user trust and detecting costly errors are limiting factors to adoption. One of the central barriers to training question answering models on explainable inference tasks is the lack of gold explanations to serve as training data. In this paper we present a corpus of explanations for standardized science exams, a recent challenge task for question answering. We manually construct a corpus of detailed explanations for nearly all publicly available standardized elementary science question (approximately 1,680 3rd through 5th grade questions) and represent these as "explanation graphs" -- sets of lexically overlapping sentences that describe how to arrive at the correct answer to a question through a combination of domain and world knowledge. We also provide an explanation-centered tablestore, a collection of semi-structured tables that contain the knowledge to construct these elementary science explanations. Together, these two knowledge resources map out a substantial portion of the knowledge required for answering and explaining elementary science exams, and provide both structured and free-text training data for the explainable inference task.