Miracle-dz/UniFAR-FTUs huggingface dataset:
Decoding Categories from Human Brain Activity in the Human Visual Cortex Using the Triplet Network

Decoding visual stimuli from functional magnetic resonance imaging (fMRI) is of great significance for understanding the neural mechanism of the visual information processing in the human brain. How to extract effective information from massive voxel data in the brain to predict the brain state is a problem worth discussing in fMRI. However, the inherent characteristics of small quantity and high dimensionality in fMRI data limited the performance of brain decoding. As an effective way to acquire visual information, people usually compare with the prior knowledge learned when recognizing objects, and does not need to have a complete understanding of visual information. In this paper, we proposed a new visual classification model to decode the stimulus categories from the visual information of the brain based on the triplet network. The triplet network is a model framework with a comparison mechanism similar to that of human visual recognition objects, contains three-branches weight sharing subnetworks, which are composed of fully connected networks in our model. Our results showed that the decoding accuracy is 57.5±1.86% and 44.17±1.31% for S1 and S2, respectively. S1 was about 6% higher than the best traditional machine learning classifier SVM, while S2 was nearly 3.5% higher than SVM. Our results fully confirmed the validity of comparing the differences between samples in fMRI data with small quantity.
A Review on Adaptive Classifiers for BCI Classification

A Brain-Computer Interface (BCI) aims at providing a way for controlling external devices through the utilization of brain signals. One of the challenges in electroencephalography (EEG)-based BCI is to adjust the brain signal decoder to detect a user's intention as accurately and efficiently as possible, as EEG signals are non-stationary. Therefore, adaptive classification, an approach to adapt to the changes of the EEG signals, would be effective in overcoming this problem. This paper provides a review of the representative adaptive classifiers used in BCI, and it can be divided into four categories: adaptive linear discriminant analysis, adaptive support vector machine, adaptive Bayesian classifiers and adaptive Riemannian Geometry-based classifiers. Besides, the pros and cons of these adaptive classification algorithms are further described.
Empirical web accessibility evaluation for blind web users

The project intends to produce a novel, accurate and holistic system that assesses the accessibility of an entire website based on a sample of webpages. The project also strives to bring statistical rigour to the sampling and clustering of webpages. By orchestrating pre-existing evaluative tools, alongside developer testing, we can produce a combined accessibility assessment. A major benefit to the accessibility field is the effective, large-scale evaluation of large websites. The approach strives to improve automated testing coverage by combining evaluative tools, and minimise inaccuracy through effective clustering and sampling.
Evaluating Gather and Scatter Performance on CPUs and GPUs

This paper describes a new benchmark tool, Spatter, for assessing memory system architectures in the context of a specific category of indexed accesses known as gather and scatter. These types of operations are increasingly used to express sparse and irregular data access patterns, and they have widespread utility in many modern HPC applications including scientific simulations, data mining and analysis computations, and graph processing. However, many traditional benchmarking tools like STREAM, STRIDE, and GUPS focus on characterizing only uniform stride or fully random accesses despite evidence that modern applications use varied sets of more complex access patterns. Spatter is an open-source benchmark that provides a tunable and configurable framework to benchmark a variety of indexed access patterns, including variations of gather / scatter that are seen in HPC mini-apps evaluated in this work. The design of Spatter includes backends for OpenMP and CUDA, and experiments show how it can be used to evaluate 1) uniform access patterns for CPU and GPU, 2) prefetching regimes for gather / scatter, 3) compiler implementations of vectorization for gather / scatter, and 4) trace-driven “proxy patterns” that reflect the patterns found in multiple applications. The results from Spatter experiments show, for instance, that GPUs typically outperform CPUs for these operations in absolute bandwidth but not fraction of peak bandwidth, and that Spatter can better represent the performance of some cache-dependent mini-apps than traditional STREAM bandwidth measurements.
Intra-regional differences in the social perception of allophonic variation: The evaluation of [tʃ] and [ʃ] in Huelva and Lepe (Western Andalucía)

Abstract This study examines the social perceptions of the traditional Andalusian feature [ʃ] and the Castilian feature [tʃ] in the city of Huelva and the town of Lepe in Western Andalucía, Spain. A matched-guise experiment was created by digitally manipulating spontaneous speech from twelve Western Andalusian speakers, varying only in word-medial syllable-initial [tʃ] and [ʃ] for in disyllabic words. Based on 221 listeners from Huelva and Lepe, mixed effects linear regression models indicate that listeners evaluated speakers with [tʃ] guises as being of higher status, more cosmopolitan, and less friendly than speakers with [ʃ] guises. These findings interacted with speaker and listener gender, listener educational level, and listener origin. The implications are twofold: the traditional Andalusian feature is evaluated as less overtly prestigious than the supra-local Castilian feature; and, that two nearby communities of the same dialect variety may share similar language attitudes, but demonstrate nuanced differences in attitudes due to their unique historical and socioeconomic developments.
Self-Supervised Visual Odometry with Ego-Motion Sampling

In recent years, deep learning-based methods for monocular visual odometry have made good progress and now demonstrate state-of-the-art results on the well-known KITTI benchmark. However, collecting ground truth camera poses for training deep visual odometry models requires special equipment and thus might be difficult and expensive. To overcome this limitation, there have been proposed a number of unsupervised methods that exploit geometric relations between depth and motion. However, there is still a large gap in accuracy between unsupervised and supervised methods. In this work, we propose a simple method for generating self-supervision for visual odometry. During training, it requires dense depth maps and an approximate motion distribution of a target platform (e.g. a car or a robot). For each input frame, we sample camera motion from the given distribution, then using a depth map we compute an optical flow that corresponds to the sampled camera motion. Then, this generated optical flow serves as an input to a visual odometry model, while the sampled camera motion serves as a ground truth output. Experiments on KITTI demonstrate that a deep visual odometry method trained in the proposed self-supervised manner outperforms unsupervised visual odometry methods, thus reducing the gap between the methods that do not require supervision and fully supervised methods. The source code is available on GitHub.
PreFAM: Understanding the Impact of Prefetching in Fabric-Attached Memory Architectures

With many recent advances in interconnect technologies and memory interfaces, disaggregated memory systems are approaching industrial adoption. For instance, the recent Gen-Z consortium focuses on a new memory semantic protocol that enables fabric-attached memories (FAM), where the memory and other compute units can be directly attached to fabric interconnects. Decoupling of memory from compute units becomes a feasible option as the rate of data transfer increases due to the emergence of novel interconnect technologies, such as Silicon Photonic Interconnects. Disaggregated memories not only enable more efficient use of capacity (minimizes under-utilization) they also allow easy integration of evolving technologies. Additionally, they simplify the programming model at the same time allowing efficient sharing of data. However, the latency of accessing the data in these Fabric Attached disaggregated Memories (FAMs) is dependent on the latency imposed by the fabric interfaces. To reduce memory access latency and to improve the performance of FAM systems, in this paper, we explore techniques to prefetch data from FAMs to the local memory present in the node (PreFAM). We realize that since the memory access latency is high in FAMs, prefetching a cache block (64 bytes) from FAM can be inefficient, since the possibility of issuing demand requests before the completion of prefetch requests, to the same FAM locations, is high. Hence, we explore predicting and prefetching FAM blocks at a distance; prefetching blocks which are going to be accessed in future but not immediately. We show that, with prefetching, the performance of FAM architectures increases by 38.84%, while memory access latency is improved by 39.6%, with only 17.65% increase in the number of accesses to the FAM, on average. Further, by prefetching at a distance we show a performance improvement of 72.23%.
A Frequency-constrained Spectrum Difference Mapping Framework for Decoding Brain Activity from Functional Magnetic Resonance Imaging Data

Many studies have shown that spontaneous low-frequency oscillation is an intrinsic attribute of human brain activity based on the resting-state blood oxygen level-dependent (BOLD) functional Magnetic Resonance Imaging (fMRI) technology. Amplitude of low-frequency fluctuations (ALFF) is an effective way to capture the low-frequency fluctuation and has a hugely wide range of applications in mental disorders, neurological diseases, occupational neuroplasticity, etc. Such approaches, however, needs further improvement in two problems: low sensitivity to low-frequency signals; noise signal interference. Based on this, this paper proposes a frequency-constrained spectrum difference mapping framework (SDMF). A frequency domain is transformed through fast Fourier transform (FFT) and divided by designating Frequency(low), Frequency(mid), and Frequency(high). Then, spectrum difference value (SDV) is calculated between the two regions as the characterization value of brain activity state. Through our experimental results, we propose that SDMF can achieve the noise reduction effect, and it is the same as the region of the spontaneous active state in the traditional ALFF. In our method, it also showed that SDMF with different metrics has achieved the suppression of the Temporal, Lingual, and enhancement of the Occipital region. All in all, SDMF is a basic framework to analyze the band constraints, and the traditional ALFF can also be included as a special mode.
A CRISPR/Cas9‐based method for targeted DNA methylation enables cancer initiation in B lymphocytes

Targeted DNA methylation is important for understanding transcriptional modulation and epigenetic diseases. Although CRISPR‐Cas9 has potential for this purpose, it has not yet been successfully used to efficiently introduce DNA methylation and induce epigenetic diseases. We herein developed a new system that enables the replacement of an unmethylated promoter with a methylated promoter through microhomology‐mediated end joining‐based knock‐in. We successfully introduced an approximately 100% DNA methylation ratio at the cancer‐associated gene SP3 in HEK293 cells. Moreover, engineered SP3 promoter hypermethylation led to transcriptional suppression in human B lymphocytes and induced B‐cell lymphoma. Our system provides a promising framework for targeted DNA methylation and cancer initiation through epimutations.
Walk this way, fly that way: Goniozus jacintae attunes flight and foraging behaviour to leafroller host instar

Parasitoids exhibit distinct behaviours while foraging for their herbivorous hosts. Some are specialised with respect to the host stage they can utilise and even the age of individuals within a single stage. Observing the behavioural response of parasitoids to hosts of varying age may provide more practical understanding of potential biocontrol agents. A wind tunnel experiment was conducted to test for host‐stage dependent foraging behaviour in Goniozus jacintae Farrugia (Hymenoptera: Bethylidae), a common but understudied parasitoid of the light brown apple moth (LBAM), Epiphyas postvittana (Walker) (Lepidoptera: Tortricidae), which is a pest of grapevine, Vitis vinifera L. (Vitaceae), and other crops in Australia. Goniozus jacintae was already known to parasitise instars 3‐6 of LBAM, but most readily parasitises the later instars. Later instars are larger and have a higher rate of feeding, producing more faeces and silk deposits than earlier instars, and are likely to also produce more volatile cues that may be detected by parasitoids. We found that the timing, frequency, and duration of foraging behaviours of G. jacintae varied significantly among host instars. Flight duration was shortest when females were exposed to leaves infested by fifth instars. The profile of behaviours exhibited was different during pre‐ and post‐flight foraging, with the newly described slow walking behaviour only exhibited in close proximity to a potential host. These results contribute to the understanding of bethylid foraging, a relatively unexplored aspect of bethylid reproductive behaviour, and towards enhancing the efficacy of utilising G. jacintae in the control of leafroller pests.
Epigenetic Lens to Visualize the Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) Infection in COVID-19 Pandemic

In <20 years, we have witnessed three different epidemics with coronaviruses, SARS-CoV, MERS-CoV, and SARS-CoV-2 in human populations, causing widespread mortality. SARS-CoV-2, through its rapid global spread, has led to the pandemic that we call COVID-19. As of February 1, 2021, the global infections linked to SARS-CoV-2 stand at 103,503,340, with 2,236,960 deaths, and 75,108,099 recoveries. This review attempts to highlight host-pathogen interaction with particular emphasis on the role of epigenetic machinery in regulating the disease. Although researchers, since the start of the pandemic, have been intensely engaged in diverse areas to understand the mechanisms involved in SARS-CoV-2 infection to find answers that can bring about innovative ways to swiftly treat and prevent disease progression, this review provides an overview on how the host epigenetics is modulated and subverted by SARS-CoV-2 to enter the host cells and drive immunopathogenesis. Epigenetics is the study that combines genetic and non-genetic factors controlling phenotypic variation, which are primarily a consequence of external and environmental stimuli. These stimuli alter the activity of a gene without impinging on the DNA code. In viral-host interactions, DNA/RNA methylation, non-coding RNAs, chromatin remodeling, and histone modifications are known to regulate and modulate host gene expression patterns. Viruses such as Coronaviruses (an RNA virus) show intrinsic association with these processes. They have evolved the ability to tamper with host epigenetic machinery to interfere with immune sensing pathways to evade host immune response, thereby enhancing its replication and pathogenesis post-entry. These epigenetic alterations allow the virus to weaken the host's immune response to successfully spread infection. How this occurs, and what epigenetic mechanisms are altered is poorly understood both for coronaviruses and other respiratory RNA viruses. The review highlights several cutting-edge aspects of epigenetic work primarily pertinent to SARS-CoV-2, which has been published between 2019 and 2020 to showcase the current knowledge both in terms of success and failures and take lessons that will assist us in understanding the disease to develop better treatments suited to kill SARS-CoV-2.
Low Heart Rate Variability Predicts Stroke and Other Complications in the First Six Postoperative Months After a Hip Fracture Operation

Background: One-year mortality after hip fractures is underestimated and is reported as 25%. An improved risk stratifying could contribute to a better follow up of these patients. Heart Rate Variability (HRV) is an easy point-of-care investigation and is been used in cardiology, endocrinology, and perioperative care. This observational study intended to explore relevant associations between HRV parameters and 6-months mortality and morbidity after a hip fracture. Methods: One hundred and sixty-five patients admitted to two hospitals were included, and short-time HRV measurements (5 min, and 10 min at the two hospitals, respectively) were obtained. Mortality data were gathered by means of the Norwegian central address register. Patients, close relatives of patients, and in some cases their general physicians or nursery home physicians were interviewed 6 months postoperatively regarding the incidence of pneumonia, cardiac events, or stroke. Results: One and hundred fifty-seven (95.2%) patients were followed up after 6 months post-surgery. Twenty-one (13%) died during this period. Twenty patients (13%) developed pneumonia, eight (5 %) stroke, and four (2%) myocardial infarction. No HRV parameter was associated with 6-month general mortality. However, patients who developed stroke had significantly lower High Frequency Power (HF, p < 0.001) and lower Very Low Frequency Power (VLF, p = 0.003) at inclusion compared to patients without complications. Patients who developed pneumonia had at the inclusion lower root mean square of successive differences (RMSSD, p = 0.044). Patients with a history of coronary heart disease (n = 41) showed a mortality of 7%. Mortality in this group was associated with standard deviation of beat-to-beat intervals (SDNN, p = 0.006), Total Power (TP, p = 0.009), HF (p = 0.026), and Low Frequency Power (LF, p = 0.012). Beta-blocker intake was associated with lower heart rate, but not with differences in HRV parameters. Conclusion: In this exploratory study, we present for the first-time significant associations between different preoperative HRV parameters and stroke, myocardial infarction, and pneumonia during a 6-month period after hip fracture. HRV might be a simple and effective tool to identify patients at risk that would warrant better follow-up.
3D Tissue Models as an Effective Tool for Studying Viruses and Vaccine Development

The recent SARS-CoV-2 outbreak has researchers working tirelessly to understand the virus' pathogenesis and develop an effective vaccine. The urgent need for rapid development and deployment of such a vaccine has illustrated the limitations of current practices, and it has highlighted the need for alternative models for early screening of such technologies. Traditional 2D cell culture does not accurately capture the effects of a physiologically relevant environment as they fail to promote appropriate cell-cell and cell-environment interactions. This inability to capture the intricacies of the in vivo microenvironment prevents 2D cell cultures from demonstrating the necessary properties of native tissues required for the standard infection mechanisms of the virus, thus contributing the high failure rate of drug discovery and vaccine development. 3D cell culture models can bridge the gap between conventional cell culture and in vivo models. Methods such as 3D bioprinting, spheroids, organoids, organ-on-chip platform, and rotating wall vessel bioreactors offer ways to produce physiologically relevant models by mimicking in vivo microarchitecture, chemical gradients, cell–cell interactions and cell–environment interactions. The field of viral biology currently uses 3D cell culture models to understand the interactions between viruses and host cells, which is crucial knowledge for vaccine development. In this review, we discuss how 3D cell culture models have been used to investigate disease pathologies for coronaviruses and other viruses such as Zika Virus, Hepatitis, and Influenza, and how they may apply to drug discovery and vaccine development.
miR-135a Reduces Osteosarcoma Pulmonary Metastasis by Targeting Both BMI1 and KLF4

Because of the modest response rate after surgery and chemotherapy, treatment of osteosarcoma (OS) remains challenging due to tumor recurrence and metastasis. miR-135a has been reported to act as an anticarcinogenic regulator of several cancers. However, its expression and function in osteosarcoma remain largely unknown. Here, we reported that abridged miR-135a expression in OS cells and tissues, and its expression is inversely correlated with the expression of BMI1 and KLF4, which are described as oncogenes in several cancers. Ectopic expression of miR-135a inhibited cell invasion and expression of BMI1 and KLF4 in OS cells. In vivo investigation confirmed that miR-135a acts as a tumor suppressor in OS to inhibit tumor growth and lung metastasis in xenograft nude mice. BMI1 and KLF4 were revealed to be direct targets of miR-135a, and miR-135a had a similar effect as the combination of si-BMI1 and si-KLF4 on inhibiting tumor progression and the expression of BMI1 and KLF4 in vivo. Altogether, our results demonstrate that the targeting of BMI1/KLF4 with miR-135a may provide an applicable strategy for exploring novel therapeutic approaches for OS.
A Requirement of Protein Geranylgeranylation for Chemokine Receptor Signaling and Th17 Cell Function in an Animal Model of Multiple Sclerosis

Precisely controlled lymphocyte migration is critically required for immune surveillance and successful immune responses. Lymphocyte migration is strictly regulated by chemokines and chemokine receptors. Here we show that protein geranylgeranylation, a form of post-translational protein lipid modification, is required for chemokine receptor-proximal signaling. Mature thymocytes deficient for protein geranylgeranylation are impaired for thymus egress. Circulating mature T cells lacking protein geranylgeranylation fail to home to secondary lymphoid organs or to transmigrate in response to chemokines in vitro. Mechanistically, protein geranylgeranylation modifies the γ-subunits of the heterotrimeric small GTPases that are essential for chemokine receptor signaling. In addition, protein geranylgeranylation also promotes the differentiation of IL-17-producing T helper cells while inhibiting the differentiation of Foxp3+ regulatory T cells. Finally, mice with T cell lineage-specific deficiency of protein geranylgeranylation are resistant to experimental autoimmune encephalomyelitis induction. This study elucidated a critical role of protein geranylgeranylation in regulating T lymphocyte migration and function.
Response: Commentary: Deformation and Fault Propagation at the Lateral Termination of a Subduction Zone: The Alfeo Fault System in the Calabrian Arc, Southern Italy

Argnani (2020) raised concerns about our interpretation of the Alfeo Fault System (AFS) as a lithospheric tear bounding the Calabrian Arc (Maesano et al., 2020). Some of these concerns arise from elements overlooked by Argnani (2020); others are marginally related to our work; none of them implies possible changes in our results in the absence of newer data. We briefly discuss these issues in the following.