Miracle-dz/UniFAR-FTUs huggingface dataset:
Fluoropolymer: A Review on Its Emulsion Preparation and Wettability to Solid-Liquid Interface

In the preparation of a superamphiphobic surface, the most basic method is to reduce the surface free energy of the interface. The C—F bond has a very low surface free energy, which can significantly change the wettability of the solid–liquid interface and make it a hydrophobic or oleophobic, or even superamphiphobic surface. Based on the analysis of a large number of research articles, the preparation and application progress in fluoropolymer emulsion were summarized. After that, some corresponding thoughts were put forward combined with our professional characteristics. According to recent research, the status of the fluoropolymer emulsion preparation system was analyzed. In addition, all related aspects of fluoropolymer emulsion were systematically classified in varying degrees. Furthermore, the interaction between fluoropolymer structure and properties, especially the interaction with nanomaterials, was also explored. The aim of this review is to try to attract more scholars’ attention to fluorocarbon interfacial materials. It is expected that it will make a certain theoretical and practical significance in the preparation and application of fluoropolymer.
Lymphocyte‐predominant lesional inflammatory infiltrates of the skin are associated with mucosal‐dominant phenotype in pemphigus

Pemphigus is a potentially life‐threatening autoimmune blistering disease. To date, studies assessing the association of histopathology with clinical phenotype are lacking. We sought to evaluate the main histopathologic findings and, also, the potential links between cutaneous inflammatory infiltrates and clinical characteristics in pemphigus.
Gauge wheel of superfluid /sup 4/He

The assumption is explored that the equilibrium order parameter of a superfluid rotates with a rotating container. Since rotations and changes of phase are then intimately related in inhomogeneous configurations, effects like the /sup 3/He-A ''gauge wheel'' of Liu and Cross should arise in any superfluid. Such effects may be difficult to observe, but it is shown that their existence in superfluid /sup 4/He is directly implied by nothing more than the conventional equations of two-fluid hydrodynamics.
Acute Benefits of Acidified Milk Drinks with 10-g and 15-g Protein on Shifting and Updating Performances in Young Adults: A Randomized Controlled Trial

Background: Accumulating evidence has shown that protein-rich milk drinks can rapidly improve cognitive performance. However, the optimum doses of milk protein that are needed to improve cognitive function remain to be investigated. Objective: This study aimed to determine whether acidified milk drinks with 10-g and 15-g milk protein have acute benefits on key cognitive functions in healthy young adults. Design: In this double-blinded randomized control trial, 66 young adults were randomly assigned to one of three groups (0-g, 10-g, and 15-g milk protein groups). Key cognitive functions (processing speed, inhibition, shifting, updating, and working memory capacity) were assessed before and 15 and 60 min after the drink intake. Results: We found that the shifting performance improved at 15 min after intake of the acidified 10-g and 15-g milk protein drinks compared to intake of the 0-g milk protein drink, and this acute effect of the acidified 15-g milk protein drink lasted for 60 min. In addition, updating performance improved at 60 min after intake of the acidified 10-g and 15-g milk protein drinks compared to intake of the 0-g milk protein drink. Conclusion: Our findings suggest that the acidified 10-g and 15-g milk protein drinks have an acute benefit on shifting and updating performance in healthy young adults.
Cascaded Segmentation U-Net for Quality Evaluation of Scraping Workpiece

In the terms of industry, the hand-scraping method is a key technology for achieving high precision in machine tools, and the quality of scraping workpieces directly affects the accuracy and service life of the machine tool. However, most of the quality evaluation of the scraping workpieces is carried out by the scraping worker’s subjective judgment, which results in differences in the quality of the scraping workpieces and is time-consuming. Hence, in this research, an edge-cloud computing system was developed to obtain the relevant parameters, which are the percentage of point (POP) and the peak point per square inch (PPI), for evaluating the quality of scraping workpieces. On the cloud computing server-side, a novel network called cascaded segmentation U-Net is proposed to high-quality segment the height of points (HOP) (around 40 μm height) in favor of small datasets training and then carries out a post-processing algorithm that automatically calculates POP and PPI. This research emphasizes the architecture of the network itself instead. The design of the components of our network is based on the basic idea of identity function, which not only solves the problem of the misjudgment of the oil ditch and the residual pigment but also allows the network to be end-to-end trained effectively. At the head of the network, a cascaded multi-stage pixel-wise classification is designed for obtaining more accurate HOP borders. Furthermore, the “Cross-dimension Compression” stage is used to fuse high-dimensional semantic feature maps across the depth of the feature maps into low-dimensional feature maps, producing decipherable content for final pixel-wise classification. Our system can achieve an error rate of 3.7% and 0.9 points for POP and PPI. The novel network achieves an Intersection over Union (IoU) of 90.2%.
Broken replica symmetry and metastable states in spin glasses

The average number (NS(f)) of uncorrelated metastable states of free energy kBTf per spin in the Sherrington-Kirkpatrick model of an Ising spin glass is shown to equal the extremum with respect to n of exp(nf N)(Zn), where (Zn) is the bond-averaged nth power of the partition function calculated within the two-group replica symmetry breaking scheme.
Predicting dropout from psychological treatment using different machine learning algorithms, resampling methods, and sample sizes

Abstract Objective: The occurrence of dropout from psychological interventions is associated with poor treatment outcome and high health, societal and economic costs. Recently, machine learning (ML) algorithms have been tested in psychotherapy outcome research. Dropout predictions are usually limited by imbalanced datasets and the size of the sample. This paper aims to improve dropout prediction by comparing ML algorithms, sample sizes and resampling methods. Method: Twenty ML algorithms were examined in twelve subsamples (drawn from a sample of N = 49,602) using four resampling methods in comparison to the absence of resampling and to each other. Prediction accuracy was evaluated in an independent holdout dataset using the F1-Measure. Results: Resampling methods improved the performance of ML algorithms and down-sampling can be recommended, as it was the fastest method and as accurate as the other methods. For the highest mean F1-Score of . 51 a minimum sample size of N = 300 was necessary. No specific algorithm or algorithm group can be recommended. Conclusion: Resampling methods could improve the accuracy of predicting dropout in psychological interventions. Down-sampling is recommended as it is the least computationally taxing method. The training sample should contain at least 300 cases.
COVID-19 Clusters in Belgian Nursing Homes: Impact of Facility Characteristics and Vaccination on Cluster Occurrence, Duration and Severity

COVID-19 severely affected nursing home residents from March 2020 onwards in Belgium. This study aimed to model the impact of vaccination and facility characteristics on cluster occurrence, duration and severity in this setting. Possible clusters were identified between June 2020 and January 2022, based on the Belgian COVID-19 surveillance in nursing homes. Median attack rates (AR) among residents and staff, case hospitalization rates (CHR) and case fatality rates (CFR) were calculated. A negative binomial model was used to identify the association between nursing home characteristics and the number of cases, hospital admissions and deaths and the duration of the cluster. A total of 2239 clusters were detected in more than 80% of nursing homes. Most of these (62%) occurred before the start of COVID-19 vaccination (end of December 2020). After vaccination, the number of clusters, the AR among residents and staff, the CHR and the CFR dropped. Previous cluster(s) and vaccination decreased the number of cases, hospital admissions and deaths among residents. Previous cluster experience and having started vaccination were protective factors. We recommend continued implementation of targeted interventions such as vaccination, large-scale screening and immediate implementation of additional infection prevention and control measures.
Predicting protein flexibility with AlphaFold

AlphaFold2 has revolutionized protein structure prediction from amino‐acid sequence. In addition to protein structures, high‐resolution dynamics information about various protein regions is important for understanding protein function. Although AlphaFold2 has neither been designed nor trained to predict protein dynamics, it is shown here how the information returned by AlphaFold2 can be used to predict dynamic protein regions at the individual residue level. The approach, which is termed cdsAF2, uses the 3D protein structure returned by AlphaFold2 to predict backbone NMR NH S2 order parameters using a local contact model that takes into account the contacts made by each peptide plane along the backbone with its environment. By combining for each residue AlphaFold2's pLDDT confidence score for the structure prediction accuracy with the predicted S2 value using the local contact model, an estimator is obtained that semi‐quantitatively captures many of the dynamics features observed in experimental backbone NMR NH S2 order parameter profiles. The method is demonstrated for a set nine proteins of different sizes and variable amounts of dynamics and disorder.
A new model for the interaction of auroral electrons with the atmosphere: Spectral degradation, backscatter, optical emission, and ionization

A new computational model has been created to describe the interaction of auroral electrons with the atmosphere. For electrons of energy greater than 500 eV, continuous energy losses and small angle deflections are combined in a Fokker-Planck diffusion equation that computes energy spectrums over all pitch angles throughout the atmosphere. These fluxes are then used to determine the rates of secondary electron and degraded (E < 500 eV) primary electron production at all heights. This information is used to compute upward and downward hemispherical fluxes in the energy range 0–500 eV, taking into account discrete energy losses, large angle scattering, and particle transport along magnetic field lines. The model has been used to compute energy spectrums, ionization rates, backscatter ratios, and optical emissions associated with different incident electron spectrums. For monoenergetic electrons of energy 2 keV and above the results obtained agree well with the work of Rees (1969) and Rees and Maeda (1973). At lower energies the effects of transport and elastic collisions become progressively more important, and the present results differ significantly both from the Rees and Maeda results and from those obtained from the ideas of energy degradation. Finally, spectrums typical of the nighttime auroral oval and daytime polar cusp are used to obtain the altitude dependent fluxes, ionization rates, and optical emissions.
Edge Detection and Segmentation of Multiple Contours from CT Scan Images

AbstractAn image consists of a number of objects. Some applications require us to identify particular objects and isolate them from the image. This target is achieved by image segmentation. Segmentation of multiple objects is a challenging area of scientific research. The objective of image segmentation is to identify the desired objects, distinguish and separate them from the image consisting of several other objects and background. In this paper, we have used edge detection as a tool to carry out image segmentation. The process involves several steps like image preprocessing, Edge detection based on Canny Edge Detection method, boundary tracing using Moore neighborhood tracing algorithm and use of point in polygon algorithm for image segmentation. We have proposed a novel algorithm to segment multiple objects from an image. The method produces good results with appreciable accuracy.
Humoral Immune Response Profile of COVID-19 Reveals Severity and Variant-Specific Epitopes: Lessons from SARS-CoV-2 Peptide Microarray

The amaranthine scale of the COVID-19 pandemic and unpredictable disease severity is of grave concern. Serological diagnostic aids are an excellent choice for clinicians for rapid and easy prognosis of the disease. To this end, we studied the humoral immune response to SARS-CoV-2 infection to map immunogenic regions in the SARS-CoV-2 proteome at amino acid resolution using a high-density SARS-CoV-2 proteome peptide microarray. The microarray has 4932 overlapping peptides printed in duplicates spanning the entire SARS-CoV-2 proteome. We found 204 and 676 immunogenic peptides against IgA and IgG, corresponding to 137 and 412 IgA and IgG epitopes, respectively. Of these, 6 and 307 epitopes could discriminate between disease severity. The emergence of variants has added to the complexity of the disease. Using the mutation panel available, we could detect 5 and 10 immunogenic peptides against IgA and IgG with mutations belonging to SAR-CoV-2 variants. The study revealed severity-based epitopes that could be presented as potential prognostic serological markers. Further, the mutant epitope immunogenicity could indicate the putative use of these markers for diagnosing variants responsible for the infection.
Appendage‐restricted gene induction using a heated agarose gel for studying regeneration in metamorphosed Xenopus laevis and Pleurodeles waltl

Amphibians and fish often regenerate lost parts of their appendages (tail, limb, and fin) after amputation. Limb regeneration in adult amphibians provides an excellent model for appendage (limb) regeneration through 3D morphogenesis along the proximodistal, dorsoventral, and anteroposterior axes in mammals, because the limb is a homologous organ among amphibians and mammals. However, manipulating gene expression in specific appendages of adult amphibians remains difficult; this in turn hinders elucidation of the molecular mechanisms underlying appendage regeneration. To address this problem, we devised a system for appendage‐specific gene induction using a simplified protocol named the “agarose‐embedded heat shock (AeHS) method” involving the combination of a heat‐shock‐inducible system and insertion of an appendage in a temperature‐controlled agarose gel. Gene expression was then induced specifically and ubiquitously in the regenerating limbs of metamorphosed amphibians, including a frog (Xenopus laevis) and newt (Pleurodeles waltl). We also induced gene expression in the regenerating tail of a metamorphosed P. waltl newt using the same method. This method can be applied to adult amphibians with large body sizes. Furthermore, this method enables simultaneous induction of gene expression in multiple individuals; further, the data are obtained in a reproducible manner, enabling the analysis of gene functions in limb and tail regeneration. Therefore, this method will facilitate elucidation of the molecular mechanisms underlying appendage regeneration in amphibians, which can support the development of regenerative therapies for organs, such as the limbs and spinal cord.
Elastomer-Embedded Multiplexed Optical Fiber Sensor System for Multiplane Shape Reconstruction

This paper presents the development and application of a multiplexed intensity variation-based sensor system for multiplane shape reconstruction. The sensor is based on a polymer optical fiber (POF) with sequential lateral sections coupled with a flexible light-emitting diode (LED) belt. The optical source modulation enables the development of 30 independent sensors using one photodetector, where the sensor system is embedded in polydimethylsiloxane (PDMS) resin in two configurations. Configuration 1 is a continuous PDMS layer applied in the interface between the flexible LED belt and the POF, whereas Configuration 2 comprises a 20 mm length PDMS layer only on each lateral section and LED region. The finite element method (FEM) is employed for the strain distribution evaluation in different conditions, including the strain distribution on the sensor system subjected to momentums in roll, pitch and yaw conditions. The experimental results of pressure application at 30 regions for each configuration indicated a higher sensitivity of Configuration 1 (83.58 a.u./kPa) when compared with Configuration 2 (40.06 a.u./kPa). However, Configuration 2 presented the smallest cross-sensitivity between sequential sensors (0.94 a.u./kPa against 45.5 a.u./kPa of Configuration 1). Then, the possibility of real-time loading condition monitoring and shape reconstruction is evaluated using Configuration 1 subjected to momentums in roll, pitch and yaw, as well as mechanical waves applied on the sensor structure. The strain distribution on the sensor presented the same pattern as the one obtained in the simulations, and the real-time response of each sensor was obtained for each case. In addition, the possibility of real-time loading condition estimation is analyzed using the k-means algorithm (an unsupervised machine learning approach) for the clusterization of data regarding the loading condition. The comparison between the predicted results and the real ones shows a 90.55% success rate. Thus, the proposed sensor device is a feasible alternative for integrated sensing in movement analysis, structural health monitoring submitted to dynamic loading and robotics for the assessment of the robot structure.
Design and Validation of a Low-Level Controller for Hierarchically Controlled Exoskeletons

In this work, a generalized low-level controller is presented for sensor collection, motor input, and networking with a high-level controller. In hierarchically controlled exoskeletal systems, which utilize series elastic actuators (SEAs), the hardware for sensor collection and motor command is separated from the computationally expensive high-level controller algorithm. The low-level controller is a hardware device that must collect sensor feedback, condition and filter the measurements, send actuator inputs, and network with the high-level controller at a real-time rate. This research outlines the hardware of two printed circuit board (PCB) designs for collecting and conditioning sensor feedback from two SEA subsystems and an inertial measurement unit (IMU). The SEAs have a joint and motor encoder, motor current, and force sensor feedback that can be measured using the proposed generalized low-level controller presented in this work. In addition, the high and low-level networking approach is discussed in detail, with a full breakdown of the data storage within a communication frame during the run-time operation. The challenges of device synchronization and updates rates of high and low-level controllers are also discussed. Further, the low-level controller was validated using a pendulum test bed, complete with full sensor feedback, including IMU results for two open-loop scenarios. Moreover, this work can be extended to other hierarchically controlled robotic systems that utilize SEA subsystems, such as humanoid robots, assistive rehabilitation robots, training simulators, and robotic-assisted surgical devices. The hardware and software designs presented in this work are available open source to enable researchers with a direct solution for data acquisition and the control of low-level devices in a robotic system.
An Image-Based Quantized Compressive Sensing Scheme Using Zadoff–Chu Measurement Matrix

In this paper, a complex-valued Zadoff–Chu measurement matrix is proposed and used in an image-based quantized compressive sensing (CS) scheme. The results of theoretical analysis and simulations show that the reconstruction performance generated by the proposed Zadoff–Chu measurement matrix is better than that obtained by commonly used real-valued measurement matrices. We also applied block compressive sensing (BCS) to reduce the computational complexity of CS and analyzed the effect of block size on the reconstruction performance of the method. The results of simulations revealed that an appropriate choice of block size can not only reduce the computational complexity but also improve the accuracy of reconstruction. Moreover, we studied the effect of quantization on the reconstruction performance of image-based BCS through simulations, and the results showed that analog-to-digital converters with medium resolutions are sufficient to implement quantization and achieve comparable reconstruction performance to that obtained at high resolutions, based on which an image-based BCS framework with low power consumption can thus be developed.
Use of Hu-PBL Mice to Study Pathogenesis of Human-Restricted Viruses

Different humanized mouse models have been developed to study human diseases such as autoimmune illnesses, cancer and viral infections. These models are based on the use of immunodeficient mouse strains that are transplanted with human tissues or human immune cells. Among the latter, mice transplanted with hematopoietic stem cells have been widely used to study human infectious diseases. However, mouse models built upon the transplantation of donor-specific mature immune cells are still under development, especially in the field of viral infections. These models can retain the unique immune memory of the donor, making them suitable for the study of correlates of protection upon natural infection or vaccination. Here, we will review some of these models and how they have been applied to virology research. Moreover, the future applications and the potential of these models to design therapies against human viral infections are discussed.
Cytological and Molecular Mechanism of Low Pollen Grain Viability in a Germplasm Line of Double Lotus

Self-fertilization rate is an essential index of lotus reproductive system development, and pollen activity is a key factor affecting lotus seed setting rate. Based on cytology and molecular biology, this study addresses the main reasons for the low self-set rate of double lotus. It takes two different double lotus breeds into consideration, namely ‘Sijingganshan’ with a low self-crossing rate and ‘Jinfurong’ with a high self-crossing rate. Cytological analysis results showed that the pollen abortion caused by excessive degradation of tapetum during the single phase was the root cause for the low self-mating rate of double lotus. Subsequent transcriptome analysis revealed that the gene NnPTC1 related to programmed tapetum cell death was significantly differentially expressed during the critical period of abortion, which further verified the specific expression of NnPTC1 in anthers. It was found that the expression level of NnPTC1 in ‘Sijingganshan’ at the mononuclear stage of its microspore development was significantly higher than that of ‘Jinfurong’ at the same stage. The overexpression of NnPTC1 resulted in the premature degradation of the tapetum and significantly decreased seed setting rate. These results indicated that the NnPTC1 gene regulated the pollen abortion of double lotus. The mechanism causing a low seed setting rate for double lotus was preliminarily revealed, which provided a theoretical basis for cultivating lotus varieties with both flower and seed.