Miracle-dz/UniFAR-FTUs huggingface dataset:
Expanding the family of extracellular chaperones: Identification of human plasma proteins with chaperone activity

Proteostasis, the balance of protein synthesis, folding and degradation, is essential to maintain cellular function and viability, and the many known intracellular chaperones are recognized as playing key roles in sustaining life. In contrast, the identity of constitutively secreted extracellular chaperones (ECs) and their physiological roles in extracellular proteostasis is less completely understood. We designed and implemented a novel strategy, based on the well‐known propensity of chaperones to bind to regions of hydrophobicity exposed on misfolding proteins, to discover new ECs present in human blood. We used a destabilized protein that misfolds at 37°C as “bait” to bind to potential ECs in human serum and captured the complexes formed on magnetic beads. Proteins eluted from the beads were identified by mass spectrometry and a group of seven abundant serum proteins was selected for in vitro analysis of chaperone activity. Five of these proteins were shown to specifically inhibit protein aggregation. Vitronectin and plasminogen activator‐3 inhibited both the in vitro aggregation of the Alzheimer's β peptide (Aβ1–42) to form fibrillar amyloid, and the aggregation of citrate synthase (CS) to form unstructured (amorphous) aggregates. In contrast, prothrombin, C1r, and C1s inhibited the aggregation of Aβ1–42 but did not inhibit CS aggregation. This study thus identified five novel and abundant putative ECs which may play important roles in the maintenance of extracellular proteostasis, and which apparently have differing abilities to inhibit the amorphous and amyloid‐forming protein aggregation pathways.
Genomic impact of stress-induced transposable element mobility in Arabidopsis

Abstract Transposable elements (TEs) have long been known to be major contributors to plant evolution, adaptation and crop domestication. Stress-induced TE mobilization is of particular interest because it may result in novel gene regulatory pathways responding to stresses and thereby contribute to stress adaptation. Here, we investigated the genomic impacts of stress induced TE mobilization in wild type Arabidopsis plants. We find that the heat-stress responsive ONSEN TE displays an insertion site preference that is associated with specific chromatin states, especially those rich in H2A.Z histone variant and H3K27me3 histone mark. In order to better understand how novel ONSEN insertions affect the plant's response to heat stress, we carried out an in-depth transcriptomic analysis. We find that in addition to simple gene knockouts, ONSEN can produce a plethora of gene expression changes such as: constitutive activation of gene expression, alternative splicing, acquisition of heat-responsiveness, exonisation and genesis of novel non-coding and antisense RNAs. This report shows how the mobilization of a single TE-family can lead to a rapid rise of its copy number increasing the host's genome size and contribute to a broad range of transcriptomic novelty on which natural selection can then act.
CSCD2: an integrated interactional database of cancer-specific circular RNAs

Abstract The significant function of circRNAs in cancer was recognized in recent work, so a well-organized resource is required for characterizing the interactions between circRNAs and other functional molecules (such as microRNA and RNA-binding protein) in cancer. We previously developed cancer-specific circRNA database (CSCD), a comprehensive database for cancer-specific circRNAs, which is widely used in circRNA research. Here, we updated CSCD to CSCD2 (http://geneyun.net/CSCD2 or http://gb.whu.edu.cn/CSCD2), which includes significantly more cancer-specific circRNAs identified from a large number of human cancer and normal tissues/cell lines. CSCD2 contains >1000 samples (825 tissues and 288 cell lines) and identifies a large number of circRNAs: 1 013 461 cancer-specific circRNAs, 1 533 704 circRNAs from only normal samples and 354 422 circRNAs from both cancer and normal samples. In addition, CSCD2 predicts potential miRNA–circRNA and RBP–circRNA interactions using binding motifs from >200 RBPs and 2000 microRNAs. Furthermore, the potential full-length and open reading frame sequence of these circRNAs were also predicted. Collectively, CSCD2 provides a significantly enhanced resource for exploring the function and regulation of circRNAs in cancer.
Reactive extrusion of bio-based polymer blends and composites – Current trends and future developments

Reactive extrusion is a cost-effective and environmentally-friendly method to produce new materials with enhanced performance properties. At present, reactive extrusion allows in-situ polymerization, modification/functionalization of polymers or chemical bonding of two (or more) immiscible phases, which can be carried out on commonly used extrusion lines. Although reactive extrusion has been known for many years, its application for processing of bio-based polymer blends and composites is a relatively new direction of scientific research. This work presents a literature review on recent advances in the processing of bio-based polymer blends and composites via reactive extrusion. We described compatibilization mechanisms for different types of biodegradable polymeric materials based on: (i) aliphatic polyesters, (ii) aliphatic polyesters/starch and (iii) aliphatic polyester/natural rubber systems. A special attention was focused on conventional and dynamic cross-linking of bio-based polymer blends and composites as an effective way to prepare new materials with unique properties e.g. biodegradable thermoplastic elastomers or shape-memory materials. Advantages and limitations affecting future trends in development of biodegradable polymer blends and composites reactive extrusion are also discussed.
TCCGAN: A Stacked Generative Adversarial Network for Clinical Tongue Images Color Correction

Tongue diagnosis has become an important part of Stomatology. However, the light source environment, camera equipment, and camera settings will impact the acquired tongue image’s quality in the actual scene. To solve the problem of color constancy in tongue image acquisition, we propose the TCCGAN network to correct the tongue image’s color. We first present a differentiable weighted histogram network for color feature extraction, which is used in a new upsample module called the mixed feature attention upsample module to assist image generation. Then, a stacked network is built to generate tongue images from coarse to fine. Finally, we analyze the limitations of the traditional loss function and propose a new loss function. The experimental results show that the image quality generated by our method is better than other methods, and the accuracy of the downstream diagnosis and classification task is significantly improved.
A Virtual GPU as Developer-Friendly OpenMP Offload Target

While parallel programming is hard, programming accelerators has always been even more complicated. One fundamental reason is the lack of mature tooling that can be used to inspect a program that executes on two different architectures. As GPU software stacks of different vendors provide vastly different experience for developers, it is clear that the gold standard for debugging is still host (CPU) execution with its myriad of mature tooling options. In this work we present a virtual GPU (VGPU) OpenMP offloading target that allows to emulate a GPU execution environment on the host. In contrast to classical “host offloading”, the VGPU target reuses the same execution model, compilation paths, and runtimes as a physical GPU. While this execution mode is not able to perform as good as host-specific compilation, runtimes, and execution, it provides the developor with a more accurate stand-in for GPU offloading that is still amendable to existing host tooling.
Analysis on Nursing Care Activity Related Stress Level for Reduction of Caregiving Workload

In Japan, the demand for nursing homes is increasing due to the rapid aging of the population, while the shortage of caregivers has become a serious problem. This problem has been recognized as a social issue because it leads to an increase in the workload per caregiver. In response, we have been developing a platform that can easily collect nursing care activities in an effort to reduce the workload. In the process, we thought that the mental state (i.e., stress) of caregivers, which changes due to their care activities, might dramatically affect their work efficiency. The objective of this study is to obtain new knowledge for reducing the workload of caregivers by visualizing and analyzing his/her stress. In this paper, we ask caregivers to wear a heart-rate sensor, and measure objective stress indicators such as R-R interval (RRI) and low frequency (LF) /high frequency (HF) ratio obtained from each sensor, as well as subjective stress indicators obtained from questionnaires administered “before work,” “during lunch breaks,” and “after work. ” To be specific, we analyzed and visualized the changes in stress associated with care activities based on psychological indicators of caregivers collected in an actual nursing home. As a result, we found that tended to increase during certain care activities and there were some relationships between those activities and stress indicators.
Explaining the Performance of Supervised and Semi-Supervised Methods for Automated Sparse Matrix Format Selection

The performance of sparse matrix-vector multiplication kernels (SpMV) depends on the sparse matrix storage format and the architecture and the memory hierarchy of the target processor. Many sparse matrix storage formats along with corresponding SpMV algorithms have been proposed for improved SpMV performance. Given a sparse matrix and a target architecture, supervised Machine Learning techniques automate selecting the best formats. However, existing supervised approaches suffer from several drawbacks. They depend on large representative datasets and are expensive to train. In addition, retraining to incorporate new classes of matrices or different processor architectures is just as costly since new training data must be generated by benchmarking many instances. Furthermore, it is hard to understand the results of many supervised systems. We propose using semi-supervised machine learning techniques for format selection. We highlight the challenges in using the K-Means clustering for the sparse format selection problem and show how to adapt the algorithm to improve its performance. An empirical evaluation of our technique shows that the performance of our proposed semi-supervised learning approach is competitive with supervised methods, in addition to providing flexibility and explainability.
eReuse poster: the circular economy of digital devices

eReuse is an initiative and action-research project that since 2015 has iterated over research and activism to understand, develop, apply to communities, evaluate and scale-up the circular economy of digital devices. The work, in partnership with diverse organisations, has contributed to the transition towards a circular economy of digital devices that contributes effectively to sustainable development.
Transparent Resource Elasticity for Task-Based Cluster Environments with Work Stealing

Resource elasticity allows to dynamically change the resources of running jobs, which may significantly improve the throughput on supercomputers. Elasticity requires support from both job schedulers and user applications. Whereas the adaptation of traditional programs requires additional programmer effort, task-based programs can be made elastic in a transparent way. In this paper, we propose a corresponding technique for implementation in a runtime system. We refer to a work stealing-based runtime for clusters, which uses the lifeline scheme for victim selection, combines inter-node work stealing with intra-node work sharing, and handles dynamic independent tasks, i.e., tasks that may spawn child tasks but do not otherwise cooperate. We experimentally assess the elasticity overhead of our scheme and find that adding/releasing up to 64 nodes takes less than 0.5 seconds. This value is determined with the help of a new formula that estimates the overhead-free running time of work-stealing programs with a changing number of workers. Using this result, we then quantify the gain of deploying elastic jobs. For that, we simulate the execution of job sets that contain some percentage of elastic jobs on two hypothetical supercomputers. We use an existing elastic job scheduler, which we concretize, e.g. by a new heuristic to determine the minimum, maximum, and preferred number of nodes for a job. Results show that the makespan can be reduced by up to 20% if most jobs are elastic.
A Q-learning based SPS Resource Scheduling Algorithm for Reliable C-V2X Communication

In 5G networks, the Ultra Reliable Low Latency Communication (URLLC) scenario is studied specifically to support the Cellular-Vehicle-to-Everything (C-V2X) communication. In Mode 4 of the Third Generation Partnership Project (3GPP), the vehicles are enabled to transmit the messages in the decentralized mode without a central network controlling the radio resources by using the Semi-Persistent Scheduling (SPS) algorithm. In SPS, the channel sensing information is used for the resource reservation process by the vehicle itself in order to achieve a fast access to the vehicle network. However, the conventional SPS with a fixed reservation process would lead to a high collision probability of the packets in the case of high traffic density. Based on the study of impacts of SPS with reservation probabilities to both collision ratio and delay through simulations, a Q-learning based SPS (Q-SPS) algorithm is proposed in order to make an intelligent decision on the reservation probability with a reward feedback corresponding to the dynamic change of C-V2X network environment. The simulation results show that the update delay can be reduced by 32% while the packet reception rate is maintained at 95%, when compared with the conventional SPS algorithm in high traffic density.
Adapting SYCL’s SIMT Programming Paradigm for Accelerators via Program Reconstruction

We present an IR-to-IR Converter that is capable of converting from LLVM IR to Halide IR and MLIR’s Affine Dialect to support generation of high performance SYCL kernel code [10] targeting accelerators with explicit memory hierarchy design. The converter performs program reconstruction to raise abstraction of the IR. Once the IR is raised to the level of Halide IR, AKG [2] can be leveraged to generate performant DaVinci code [2]. Alternatively, when the IR is raised to MLIR’s Affine Dialect, existing MLIR Affine passes with minor modifications can be readily used. Subsequently, the IR is lowered back to LLVM IR through progressive lowering. LLVM’s LLC is used to create the final binary for both cases. We extend upon SYCL’s buffer, accessor and parallel_for abstractions to facilitate the IR raising process.
In Vivo Study of The Oestrogenic Activity of Milk

Abstract Introduction Milk has been suggested to be a possible source of oestrogenically active compounds. In order to assess the health risk for milk consumers and ensure the safety of this staple part of the human diet, it is important to study the effect of xenooestrogen mixtures present in milk. This investigation used the available in vivo model to learn to what extent such compounds may be endocrine disruptors. Material and Methods The recommended immature golden hamster uterotrophic bioassay was chosen. A total of 132 animals were divided into nine groups of experimental animals and positive and negative control groups, each of 12 animals. The experimental females received ad libitum either one of five samples of raw cow’s milk from individual animals or one of four samples of pasteurised or ultra-high temperature treated cow’s milk as retail products. After 7 days, the animals were sacrificed and necropsied. Uterine weight increases were measured as the endpoint of oestrogenic activity in milk. Results The milk samples from individual cows and the retail milk samples did not show oestrogenic activity. However, in three groups, decreased uterine weights were observed. Conclusion Considering that milk supplies are beneficial to health, contamination in this food should be avoided. There is a need for further animal experiments and epidemiological studies are warranted to evaluate any causative role of milk in human endocrinological disorders.
Catalytic defense against fungal pathogens using nanozymes

Abstract Fungal infections are still a major challenge for clinics, resulting from the resistance of drug-resistant fungi and the toxicity of antifungal drugs. Defense against fungal invasions via enzymatic catalysis has been found in nature. The use of nanozymes, as artificial enzyme mimics, may be a promising strategy to induce fungal death due to their advantages such as tunable catalytic activity, high stability, low cost, and easy preparation. Here, the importance of natural enzymes in the defense against fungi is outlined. The progress in antifungal performance and potential application of nanozymes and the related antifungal mechanisms are also summarized. Finally, the perspective and challenges in this field for future study, pointing out that nanozyme-based catalytic therapy represents a promising alternative strategy for antifungal treatment, are highlighted.
The Political Incorporation of Labor in Turkey: Tracing the Origins of a Nationalist Path

Abstract This study makes an important contribution to the literature on labor incorporation in developing areas based on existing historiography and archival material from Turkey. Specifically, we argue that the political incorporation of labor during the early period of state building is strongly influenced by elite preferences over who constitutes the nation. In doing so, we address a neglected dimension by putting the emphasis on ethnoreligious politics: the founders of modern Turkey pushed for a homogenizing program that prioritized Muslim-Turks over other minority groups, eventually paving the way to the state-led incorporation of labor. This is different from the experience of most Latin American countries that the existing literature draws on. Our findings make an important contribution to theoretical debates by highlighting the subtle link between nation-building and the pathways of labor incorporation in developing contexts.
Effect of space creativity and social climate on business model innovation in Do-it-Yourself laboratories: the mediating role of opportunity discovery in the case of Singapore

ABSTRACT In the recent decade, Do-it-Yourself laboratories (DIY labs) have proliferated in many cities across developing and developed economies. Although these labs play an important role in the innovation ecosystems, there is limited empirical research on if and how the creativity engendered in DIY labs affects innovation outcome. Despite the frequent association of DIY lab climate of sharing and cooperation with innovation, the link between them has been understudied. There is a lack of framework explaining the mechanism that entrepreneurial members employ at DIY labs to optimise their innovation outcome. The purpose of our research is to study the link between DIY lab characteristics (space creativity and social climate) and innovation outcome, particularly business model innovation (BMI). We also investigate if opportunity discovery (OD) is the key process underlying the link. Based on our empirical study of 164 start-up projects incubated at seven DIY labs in Singapore, we demonstrate that the space creativity of DIY labs has a significantly positive relationship with BMI and that the OD process partially mediates the relationship. Our findings also establish that while the social climate of DIY labs has no direct effect on BMI, the OD process fully mediates the link between them.
Activation of Asphalt to Prepare High Surface Area Carbon Material

Abstract High surface area porous carbon material was prepared via a method of activating asphalt with pretreatment. The crystalline, textural structure, morphology, and surface functionalities of the samples were characterized by X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), diffuse reflectance FTIR spectroscopy (DRIFTS), and Brunauer-Emmett-Teller (BET) techniques. When the asphalt was pretreated at 400 °C for 3 h under Ar flow, and then activated using KOH as activating agent at 800 °C for 3 h under Ar flow, the prepared porous carbon material has an amorphous nature and possesses a high surface area of 2460 m2/g, a narrow pore size distribution of 3 ∼ 5 nm, and a mean pore diameter of 3.8 nm.
Evaluation of Antifatigue Effects of 20(S)-Ginsenoside Rg3 in Forced Swimming Mice

The main objective of this study was to investigate the antifatigue effects of 20(S)-ginsenoside Rg3 using forced swimming test in mice. Male Kunming mice were randomly divided into four groups; a control and three 20(S)-ginsenoside Rg3-treated groups. The control group was given distilled water whereas the treated groups were given different doses of 20(S)-ginsenoside Rg3 (10, 20 and 40 mg/kg). Treatments were administered by gavage once a day lasting for 28 days. After 28 days, forced swimming tests were performed and fatigue related biochemical parameters were examined. The results showed that 20(S)-ginsenoside Rg3 significantly (P<0.05) prolong exhaustive exercise time, significantly decrease (p<0.05) the blood lactic acid and serum urea nitrogen levels, significantly increase (p<0.05) the levels of glycogen, superoxide dismutase, glutathione peroxidase and catalase in liver and muscle, as well as significantly (p<0.05) reduce malondialdehyde levels in liver and muscle. These findings suggested that 20(S)-ginsenoside Rg3 has antifatigue effects, and its antifatigue mechanisms could be related to the fact that 20(S)-ginsenoside Rg3 can activate energy metabolism, eliminate the accumulation of metabolites, and protect exercise-induced oxidative stress.
Intelligent optimization of dynamic traffic light control via diverse optimization priorities

Given the distribution difference of the vehicle flow in different urban areas, coordinating the optimization priorities of crossroads in the dynamic control of traffic lights is vital. To reduce traffic congestion at crossroads, in this study, an intelligent diverse optimization priority method (IDOPM) was developed for dynamic traffic light control at crossroads, where diverse optimization priorities can be flexibly and efficiently assigned to different crossroads. The IDOPM mainly consists of a dynamic state constructor and an optimization priority assigner. The dynamic state constructor controls the transformation of the signal combinations of traffic lights. The signal combinations of traffic lights at crossroads are abstracted as cells to be controlled by formulated rules. By designing duration particles and enhancing particles, the optimization priority assigner reconstructs the quantum particle swarm algorithm to assign crossroads with different optimization priorities. The results obtained by comparison with state‐of‐the‐art methods via extensive experiments confirmed the outstanding optimization performance of the proposed IDOPM in dynamic traffic light control.