Miracle-dz/UniFAR-FTUs huggingface dataset:
[Inhibition of TGF-β promotes functional recovery of spinal cord injury in mice by reducing fibronectin deposition].

OBJECTIVE To investigate the effect of transforming growth factor (TGF-β) inhibition on functional recovery of spinal cord injury in mice. METHODS Twelve mice were divided into treatment group, control group and sham-operated group (n=4). The mice in the treatment group were subjected to hemisection of the spinal cord and received intraperitoneal injection of TGF-β neutralizing antibody (1D11) 3 times a week (25 μL each time), and those in control group were injected with the vehicle antibody (13C4) following spinal cord hemisection. The sham-operated mice underwent sham operation to expose the spinal cord without hemisection. Four weeks later, the heart of the mice was perfused and 1-2 cm of the spinal cord spanning the injury site was harvested. Immunofluorescence staining of FSP1, fibronectin, and PGP9.5 was performed to assess fibroblast recruitment in the injury area, fibronectin deposition, and neurological recovery. For further verification of the results, we used a mouse model of spinal cord clamp injury to observe the survival of axons and distribution of astrocytes by detecting expressions of 5-HT and GFAP with immunofluorescence assay. RESULTS In the hemisection injury model, fibroblasts recruitment and fibronectin deposition in the injured area was significantly reduced and the neurological function was improved in 1D11 treatment group as compared with those in 13C4-treated group (P < 0.05). In the spinal cord clamp injury model, treatment with 1D11, as compared with the 13C4, resulted in significantly increased number of 5-HT-positive axons with extended axonal length and obviously increased the number of GFAP-positive astrocytes in the injured area (P < 0.05). CONCLUSION Inhibiting TGF-β after spinal cord injury can reduce the recruitment of fibroblasts and fibronectin deposition to promote recovery of neurological function and repair of the injured spinal cord in mice.
Predictive validity of A-level grades and teacher-predicted grades in UK medical school applicants: a retrospective analysis of administrative data in a time of COVID-19

Objectives To compare in UK medical students the predictive validity of attained A-level grades and teacher-predicted A levels for undergraduate and postgraduate outcomes. Teacher-predicted A-level grades are a plausible proxy for the teacher-estimated grades that replaced UK examinations in 2020 as a result of the COVID-19 pandemic. The study also models the likely future consequences for UK medical schools of replacing public A-level examination grades with teacher-predicted grades. Design Longitudinal observational study using UK Medical Education Database data. Setting UK medical education and training. Participants Dataset 1: 81 202 medical school applicants in 2010–2018 with predicted and attained A-level grades. Dataset 2: 22 150 18-year-old medical school applicants in 2010–2014 with predicted and attained A-level grades, of whom 12 600 had medical school assessment outcomes and 1340 had postgraduate outcomes available. Outcome measures Undergraduate and postgraduate medical examination results in relation to attained and teacher-predicted A-level results. Results Dataset 1: teacher-predicted grades were accurate for 48.8% of A levels, overpredicted in 44.7% of cases and underpredicted in 6.5% of cases. Dataset 2: undergraduate and postgraduate outcomes correlated significantly better with attained than with teacher-predicted A-level grades. Modelling suggests that using teacher-estimated grades instead of attained grades will mean that 2020 entrants are more likely to underattain compared with previous years, 13% more gaining the equivalent of the lowest performance decile and 16% fewer reaching the equivalent of the current top decile, with knock-on effects for postgraduate training. Conclusions The replacement of attained A-level examination grades with teacher-estimated grades as a result of the COVID-19 pandemic may result in 2020 medical school entrants having somewhat lower academic performance compared with previous years. Medical schools may need to consider additional teaching for entrants who are struggling or who might need extra support for missed aspects of A-level teaching.
Variation in State-Level Eviction Moratorium Protections and Mental Health Among US Adults During the COVID-19 Pandemic

Key Points Question Which eviction protections were associated with reduced mental distress among renters during the COVID-19 pandemic? Findings In this cohort study assessing data from 2317 adult respondents of the Understanding Coronavirus in America Survey, moratoriums that blocked landlords from giving notice or filing for evictions were associated with a relative reduction in mental distress of approximately 13%, whereas protections that blocked only court hearings, judgments, and enforcement did not reduce distress significantly. Meaning Findings indicate that only moratoriums that blocked eviction notice and filing were associated with reduced renter distress, suggesting that to protect renters’ mental health, interventions should aim to prevent evictions before filing.
Hyperacusis: Testing, Treatments and a Possible Mechanism

Hyperacusis is a well known phenomenon consisting of a collapse of loudness tolerance. The following generalisations can be made: (1) Hyperacusis is not heightened hearing sensitivity (hearing thresholds are not better than normal); (2) hyperacusis is often accompanied by tinnitus; (3) the severity of hyperacusis is usually inversely proportional to the pitch of the offending noise; (4) perhaps most important, overprotection of the ears is a natural reaction of hyperacusic patients but it must be avoided as it progressively exacerbates hyperacusis. A testing procedure, which involves determining loudness discomfort levels at a number if frequencies, will be described and typical data will be presented. Successful treatment for hyperacusis involves two components: First, training patients to use sound level meters to distinguish between truly damaging sound levels versus those which simply sound too loud; and second, instructing them in the use of a desensitisation program which involves listening to pink noise for several hours daily, starting at a low sound level and progressively increasing it over a period of several months. A new type of protective hearing device will also be described which allows the wearer to avoid loud sounds while preventing over-protection of the ears. When using the device, known as the Star 2001, hyperacusis patients are able to go out and about without fear of encountering loud sounds.The theoretical basis for hyperacusis is not yet known but a potential neural mechanism will be discussed.
Comparison of sound speed and attenuation measured in a sandy sediment to predictions based on the Biot theory of porous media

During the sediment acoustics experiment in 1999 (SAX99), several researchers measured sound speed and attenuation. Together, the measurements span the frequency range of about 125 Hz-400 kHz. The data are unique both for the frequency range spanned at a common location, and for the extensive environmental characterization that was carried out as part of SAX99. Environmental measurements were sufficient to determine or bound the values of almost all the sediment and pore water physical property input parameters of the Biot poroelastic model for sediment. However, the measurement uncertainties for some of the parameters result in significant uncertainties for Biot-model predictions. Here, measured sound-speed and attenuation results are compared to the frequency dependence predicted by Biot theory and a simpler "effective density" fluid model derived from Biot theory. Model/data comparisons are shown where the uncertainty in Biot predictions due to the measurement uncertainties for values of each input parameter are quantified. A final set of parameter values, for use in other modeling applications e.g., in modeling backscattering (Williams et al., 2002) are given, that optimize the fit of the Biot and effective density fluid models to the sound-speed dispersion and attenuation measured during SAX99. The results indicate that the variation of sound speed with frequency is fairly well modeled by Biot theory but the variation of attenuation with frequency deviates from Biot theory predictions for homogeneous sediment as frequency increases. This deviation may be due to scattering from volume heterogeneity. Another possibility for this deviation is shearing at grain contacts hypothesized by Buckingham; comparisons are also made with this model.
Deep Transfer Learning for Cross-Species Plant Disease Diagnosis Adapting Mixed Subdomains

A deep transfer learning framework adapting mixed subdomains is proposed for cross-species plant disease diagnosis. Most existing deep transfer learning studies focus on knowledge transfer between highly correlated domains. These methods may fail to deal with domains that are poorly correlated. In this study, mixed domain images were generated from source and target image groups for improving the correlation between the mixed domain (training dataset) and the target domain (testing dataset). A subdomain alignment mechanism is employed to transfer knowledge from the mixed domain to the target domain. The proposed framework captures the fine-grained information more effectively. Extensive experiments were conducted and prove that the proposed method produces a more effective result compared with existing deep transfer learning technologies for poorly related subdomains.
Metaheuristics for Mining Massive Datasets: A Comprehensive Study of PSO for Classification

The increasing importance of Meta-Heuristic Algorithms for Data Mining Problems has paved way for the emergence of a new era of computing algorithms that performs the functionalities of legacy algorithms using meta heuristic techniques. The occurrence of this change was due to the massive increase in the data that is used for analysis and the inability of the legacy techniques to either incorporate huge data, or the increase in the time consumed by these algorithms. This paper deals with probabilistic nature of PSO and to understand the error and how it helps in obtaining efficient solutions. A discussion on why PSO was selected as the base algorithm for our study, and its nuances are discussed in detail. The paper also deals with techniques to overcome the local optima and the scalability and parallelization options available in PSO. The discussion ends with metric wise comparisons of PSO with a variety of datasets and how PSO for classification provides better stability and accuracy on massive datasets rather than smaller ones justifying the study.
Genome-Wide Gene Expression Profiles Reveal Distinct Molecular Characteristics of the Goose Granulosa Cells

Granulosa cells (GCs) are decisive players in follicular development. In this study, the follicle tissues and GCs were isolated from the goose during the peak-laying period to perform hematoxylin-eosin staining and RNA-seq, respectively. Moreover, the dynamic mRNA and lncRNA expression profiles and mRNA-lncRNA network analysis were integrated to identify the important genes and lncRNAs. The morphological analysis showed that the size of the GCs did not significantly change, but the thickness of the granulosa layer cells differed significantly across the developmental stages. Subsequently, 14,286 mRNAs, 3,956 lncRNAs, and 1,329 TUCPs (transcripts with unknown coding potential) were detected in the GCs. We identified 37 common DEGs in the pre-hierarchical and hierarchical follicle stages, respectively, which might be critical for follicle development. Moreover, 3,089 significant time-course DEGs (Differentially expressed genes) and 13 core genes in 4 clusters were screened during goose GCs development. Finally, the network lncRNA G8399 with CADH5 and KLF2, and lncRNA G8399 with LARP6 and EOMES were found to be important for follicular development in GCs. Thus, the results would provide a rich resource for elucidating the reproductive biology of geese and accelerate the improvement of the egg-laying performance of geese.
Sensing-Based Analytics in Education: The Rise of Multimodal Data Enabled Learning Systems

The proliferation of sensing technology and the produced sensing-based analytics (SBA) has driven several fields in the development of tools and methods that have transformed their industries. The utilization of SBA fulfills the vision of integrating many sources of information, coming from different modalities (e.g., affective, cognitive, and embodiment), to strengthen learning systems’ capacity (e.g., adaptation, promote awareness, and reflection). The authors present a practical framework that outlines four phases that can enable learning systems to leverage on multimodal data coming from SBA. Moreover, the authors showcase the benefits of SBA through a case study and discuss how sensing integration can advance contemporary learning systems.
Tachyon: Design and Control of High Payload, Robust, and Dynamic Quadruped Robot with Series-Parallel Elastic Actuators

This paper introduces a quadruped robot, Tachyon, which aims to achieve high payload, robust, and dynamic locomotion on the various terrain with high energy efficiency. Thanks to a novel compact series-parallel elastic actuator (SPEA) on the upper link and a four-bar linkage design in the knee joint for constant vertical foot force, the 41-kg robot can carry more than 20 kg of payloads with dynamic walking. The combination of a robust horizontal CoM stabilizer and SPEA joint torque controller provides low impedance force controllability of the whole-body even when only the knee joint can accurately detect its joint torque. The major performance of Tachyon is demonstrated by carrying a 20-kg rice bag—half of its body weight—and climbing stairs with dynamic locomotion. When the robot climbs the stairs, the SPEA parallel spring improves energy efficiency by 16% and is also effective for various other gaits. The robustness of the robot is also shown by its high flexibility and fall avoidance capability when an unknown disturbance of 400 N or more is applied.
Magnetic Position Sensors Revisited

COBROS (Calibration and Operation Based on ROtational Symmetry) is a novel approach for position sensors that encompasses most existing architectures, but provides added values, such as stray field immunity, intrinsic error detection and a high degree of flexibility. With the latter, a given hardware is leveraged for multiple demands on the mere signal processing level. The method is based on systematically exploiting the information of concurrent probes of the position defining field.
Climbot-Ω: A Soft Robot with Novel Grippers and Rigid-compliantly Constrained Body for Climbing on Various Poles

Soft climbing robots have been attracting increasing attention in soft robotics community, and a lot of prototypes been proposed with basic climbing function implemented. Climbing on poles is a challenge with soft robots, and the capability of current pole-climbing soft robots needs to be improved in terms of adaptability to various poles and deformation controllability or constraining of the soft body. In this paper, a rigid-compliant coupling or constraint method is proposed in design of pole climbing robots. Specifically, a novel gripper with inner expanding bubbles and a rigid-compliant belt is presented and designed, featured with excellent shape/size-adaptability and grip-reliability. To constrain undesired deformation of the soft body, rigid-compliant belts are also adopted. Three rigid-compliant constrained actuators are connected to implement an Ω-shaped deformation of the body for climbing motion. The kinematic feature of the body is established, and the two main features of the inner expanding gripper are analyzed. A series of experiments, where the robot climbs on poles with different shapes and sizes and in different poses, have verified the effectiveness of the presented rigid-compliant constraint and the shape/size adaptability and grip-reliability of the novel inner expanding grippers.
Speech Recognition and Separation System using Deep Learning

Human voice is considered one of the most important features and speech helps humans to communicate with each other. Analysis of speech features is carried out to recognize and separate the target speech. Speech signals are continuous and generally contain overlap regions which make conventional methods like signal based matrices inefficient, thus there is a need to develop an advanced and efficient, architecture that can handle speech recognition and speech separation efficiently. This paper provides a brief view of the work carried out for the speech recognition and separation process with the help of deep learning using mel-frequency cepstral coefficients as a parameter. The speech recognition model is implemented using MFCC-DNN based approach and the speech separation model is based on DNN architecture. Various methods were used like MFCC extraction, DNN tuning, etc. to get better performance and higher accuracy than conventional methods like single channel speech separation, HMM etc.
Integrated Model of Aircraft Two-stage Synchronous Starter-Generator Considering Magnetic Saturation

The two-stage synchronous starter-generator (TSSG) system is a good candidate for the more-electric aircraft (MEA) integrated starter-generator (ISG) system due to the high reliability and good power generation quality. The accurate model of TSSG is necessary for the detailed analysis and advanced control research. However, separated modeling of brushless exciter (BE) or main machine (MM) cannot reflect the coupling relationship, so it is necessary to establish the integrated model of the TSSG including BE, rotating rectifier and MM. In this paper, two integrated models of TSSG based on double-side Voltage-Behind-Reactance (VBR) and state-space equations, respectively, are proposed considering magnetic saturation, and comparative simulation was carried out to verify these models.
The Usage of Kinematic Singularities to Produce Periodic High-Powered Locomotion

Legged robots primarily energize their center of mass through external contact during stance phase. This links their range of possible motions to actuator power limits applied during usually short periods of time. Enabling limb actuators to pump energy into the system during non-contact phases can greatly extend the energetic profile of possible motions. However, funneling this extra energy into useful dynamics is a problem of its own. In this paper, we propose designing limb mechanisms that purposefully integrate kinematic singularities into their configuration space to enable airborne energization and enhance gait periodicity before sensors and feedback control are incorporated. The result produces a mechanical reflex that unloads spring energy stored during flight phase into a useful push-off motion as triggered by the onset of stance phase. The implementation generates an "S" shaped curve into a specific slice of the configuration space, motivating the name S-curve. We compare our proposed approach to more conventional strategies and survey the design parameters that can be used to shape an S-curve. Ranges of useful S-curves are determined through a simulation study and a linkage mechanism capable of producing an S-curve is displayed.
The Study on High Building Structures Decentralized Vibration Control

An interesting result that has been observed here is that systems that have two-level hierarchical control can exhibit a special form of fault-tolerance. This concept is illustrated using an example of displacement control in a 4 floor building subject to seismic excitation. The two-level system performance, for both actuator malfunctions and model uncertainties is compared with a centralized robust control design using H-∞ optimization with respect to the exogenous seismic inputs.
Experimentations and Analysis on Indoor Positioning through Fusion with Inertial Sensors and Dynamically Calibrated Wi-Fi FTM Ranging

By fusing the relative inertial sensory data and Wi-Fi Fine Time Measurement (FTM) ranging which can be utilized to calculate absolute position through Extended Kalman Filter (EKF) fusion framework with an extra dynamical Wi-Fi FTM ranging calibration mechanism, the indoor positioning performance was evaluated. The result shows that the positioning error reaches down to 1.5 m in some certain environmental configurations. Analytical findings were retrieved from experimental setups that resemble various typical indoor scenarios. It shows promising result utilizing the widely applied inertial sensory algorithm such as Pedestrian Dead Reckoning (PDR), together with the Wi-Fi FTM capable access points.
Soft-CCD Algorithm for Inverse Kinematics of Soft Continuum Manipulators

To date, soft robots have been increasingly designed and analyzed, especially, Soft Continuum Manipulators (SCMs). Due to dexterous deformability, their Inverse Kinematics (IK) is still difficult to solve. Cyclic Coordinate Descent (CCD) algorithm is one of the classical optimization algorithms to solve IK of rigid manipulators with prismatic or rotational joints. However, it cannot be directly extrapolated to SCMs with configuration space parameters such as center arc, bending angle, and torsion angle. Here, we modified the CCD algorithm from a new view and proposed several tricks to set constraints. Numerical and experimental results show that the soft-CCD algorithm can quickly and accurately generate solutions for IK of SCMs. This study provides the necessary kinematic foundation for fault tolerance, obstacle avoidance, trajectory planning, and other further explorations of SCMs.
Computational Design of Reconfigurable Underactuated Linkages for Adaptive Grippers*

We present an optimization-based structural-parametric synthesis method for reconfigurable closed-chain underactuated linkages for robotic systems that physically interact with the environment with an emphasis on adaptive grasping. The key idea is to implement morphological computation concepts to keep both necessary trajectory-specific holonomic constraints and mechanism adaptivity using variable length links (VLL), while we evolve from a fully actuated to an underactuated system satisfying imposed design requirements. It allows to minimize the number of actuators, weight, and cost but keep high payload and endurance that are not reachable by tendon-driven designs. Despite the method is general enough, for clarity, we demonstrate its use on a number of finger mechanisms for adaptive grippers.
Understanding and Segmenting Human Demonstrations into Reusable Compliant Primitives

Hard coded robotic manipulation skills work well in known, predictable and repeatable situations. Human environments, however, are better described as dynamic, chaotic, uncertain or unstructured. Therefore, plans relying on preprogrammed trajectories are bound to fail in these settings. In order to increase robustness to uncertainty and avoid coding new skills from scratch, we can make flexible plans that execute existing autonomous primitives based on the sensed state of the environment. A key challenge of this approach is finding the sequence of primitives required to perform the desired task. This work uses a variation of a Hidden Markov Model (HMM) with an augmented particle filter to find the primitive sequence using only a reduced number of human demonstrations. The algorithm was tested on 40 demonstrations of two different manipulation tasks involving six primitives. It was seeded with a single manually labelled demonstration of each task and was able to automatically label the other 38 demonstration sequences with an average success of 81.5%. The results show improved convergence and a 9% increase in accuracy over other versions of the algorithm.