Miracle-dz/UniFAR-FTUs huggingface dataset:
Downregulation of GNAI3 Promotes the Pathogenesis of Methionine/Choline-Deficient Diet-Induced Nonalcoholic Fatty Liver Disease

Background/Aims The pathogenesis of nonalcoholic fatty liver disease (NAFLD) has not be fully elucidated, and the lack of therapeutic strategies for NAFLD is an urgent health problem. Guanine nucleotide binding protein, alpha inhibiting activity polypeptide 3 (GNAI3) participates in several biological processes, but its relationship with lipid metabolism and NAFLD has not yet been reported. We aimed to determine the function of GNAI3 in the development of NAFLD. Methods Mice were fed a methionine and choline-deficient diet to induce NAFLD. An NAFLD model in HepG2 cells was induced by free fatty acid treatment. GNAI3 levels in HepG2 cells were downregulated by shRNA. Protein levels of related proteins were evaluated by Western blotting, and mRNA levels were determined by quantitative reverse transcription polymerase chain reaction. Hematoxylin and eosin and Oil Red O staining were used to observe histological changes in liver tissue. Results The dysregulated hepatic lipid metabolism in the NAFLD mouse model was enhanced by GNAI3 knockout, which also provoked worse liver damage. In the NAFLD model in HepG2 cells, the downregulation of GNAI3 promoted cellular lipid accumulation and enhanced the changes in lipid metabolic enzyme levels. Conclusions This study demonstrates that GNAI3 participates in the development of NAFLD in both cellular and mouse models. The data indicate that GNAI3 is a potential new target for the treatment of NAFLD in humans.
The utility of C-reactive protein and procalcitonin for sepsis diagnosis in critically burned patients: A preliminary study

Objective To assess the utility of C-reactive protein (CRP) and procalcitonin (PCT) as biomarkers of infection in patients with severe burn injury. Methods The present study included severe burn injury patients consecutively admitted to the Virgen del Rocío University Hospital (Andalucia, Spain) intensive care unit during a 12-month period. The variables of interest were: age, sex, mechanism of injury, percentage of burned body surface area, the Abbreviated Burn Severity Index (ABSI) and the absence/presence of sepsis. The authors analyzed serum levels of CRP and PCT at admission and every 48 h thereafter until intensive care unit discharge or death. Each determination was considered to be a sample or unit of analysis. Results A total of 157 determinations were analyzed from 17 severe burn injury patients. Fifty-four samples were considered to be septic, 25 of which corresponded to the first day of a new onset of sepsis. The mean duration of these symptoms was four days (interquartile range two to five days). Significant differences were found in the distributions of CRP and PCT values between sepsis and no-sepsis samples. Analysis of the changes in these biomarkers over time showed that PCT increase (ΔPCT) differentiated these diagnoses, whereas CRP increase (ΔCRP) did not. ROC curve analysis revealed that ΔPCT could predict positive sepsis samples (area under the curve 0.75 [95% CI 0.58 to 0.90]; P=0.003). Conclusion These preliminary results showed that PCT had a better discriminatory capacity than CRP for identifying infectious processes in patients with severe burn injury. A larger sample size would be needed to confirm these results.
Comparison Detector: Convolutional Neural Networks for Cervical Cell Detection

Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell segmentation and discriminative hand-crafted features extraction. Recently there are emerging deep learning-based methods which train convolutional neural networks (CNN) to classify image patches, but they are computationally expensive. In this paper we propose to exploit contemporary object detection methods for cervical cancer detection. To deal with the limited size of training samples, we develop the comparison classifier into the state-of-the-art two-stage object detection method based on the comparison with the reference images of each category. In addition, we propose to learn the reference images of the background from the data instead of manually choosing them by some heuristic rules. This architecture, called the Comparison detector, shows significant improvement for small size dataset, achieving a mean Average Precision (mAP) 26.3% and an Average Recall (AR) 35.7%, both improving about 20 points compared to baseline model. Moreover, Comparison detector achieves same mAP performance as the current state-of-the-art model when training on the medium size dataset, and improves AR by 4 points. Our method is promising for the development of automation-assisted cervical cancer screening systems.
TensorFlow Doing HPC

TensorFlow is a popular emerging open-source programming framework supporting the execution of distributed applications on heterogeneous hardware. While TensorFlow has been initially designed for developing Machine Learning (ML) applications, in fact TensorFlow aims at supporting the development of a much broader range of application kinds that are outside the ML domain and can possibly include HPC applications. However, very few experiments have been conducted to evaluate TensorFlow performance when running HPC workloads on supercomputers. This work addresses this lack by designing four traditional HPC benchmark applications: STREAM, matrix-matrix multiply, Conjugate Gradient (CG) solver and Fast Fourier Transform (FFT). We analyze their performance on two supercomputers with accelerators and evaluate the potential of TensorFlow for developing HPC applications. Our tests show that TensorFlow can fully take advantage of high performance networks and accelerators on supercomputers. Running our TensorFlow STREAM benchmark, we obtain over 50% of theoretical communication bandwidth on our testing platform. We find an approximately 2×, 1.7× and 1.8× performance improvement when increasing the number of GPUs from two to four in the matrix-matrix multiply, CG and FFT applications respectively. All our performance results demonstrate that TensorFlow has high potential of emerging also as HPC programming framework for heterogeneous supercomputers.
Deep Griffin–Lim Iteration

This paper presents a novel phase reconstruction method (only from a given amplitude spectrogram) by combining a signal-processing-based approach and a deep neural network (DNN). To retrieve a time-domain signal from its amplitude spectrogram, the corresponding phase is required. One of the popular phase reconstruction methods is the Griffin–Lim algorithm (GLA), which is based on the redundancy of the short-time Fourier transform. However, GLA often involves many iterations and produces low-quality signals owing to the lack of prior knowledge of the target signal. In order to address these issues, in this study, we propose an architecture which stacks a sub-block including two GLA-inspired fixed layers and a DNN. The number of stacked sub-blocks is adjustable, and we can trade the performance and computational load based on requirements of applications. The effectiveness of the proposed method is investigated by reconstructing phases from amplitude spectrograms of speeches.
Sphere Decoding for Millimeter Wave Massive MIMO

Massive MIMO can significantly increase the data rate of a wireless communications system. The requirement for even greater data transmission rates in 5G systems has prompted the consideration of the largely unused millimeter wave (mmWave) band. To mitigate the high path loss at mmW frequencies and the poor scattering nature of the mmW channel (fewer paths exist), this paper proposes the use of a uniform planar array (UPA) hybrid beamforming technique with large antenna arrays and the sphere decoding algorithm to improve the performance of mmWave massive MIMO systems. When perfect Channel State Information (CSI) is available at the base Station (BS) and mobile Station (MS), computer simulations have shown that a gain of 10 dB or more can be achieved by using the Sphere Decoding algorithm compared to a system with Zero-forcing (ZF) or minimum mean- square error (MMSE) hybrid beamforming. Moreover, the complexity of the SD algorithm can be reduced by efficiently selecting the radius.
Accelerating Innovation: Complexity, Regulation, and Temporality

This Perspective paper explores recent moves seen in many countries toward accelerating the speed at which biomedical innovation can be delivered to the clinic. It discusses the drivers behind this and the rationale for it, illustrating this briefly in the field of regenerative medicine. It argues that the process reconfigures present/future relations, especially in terms of the relationship between different forms of evidence and risk calculations. The regulatory/innovation relationship is, as a result, being rewritten. Paradoxically, the moves toward acceleration are less to do with the arrival of a more streamlined evaluation system that selects for scientifically robust technologies ready for “the market. ” In contrast, it reflects the growing complexity of innovation itself: whereas Latour (1987) had argued that “science-in the-making” was backstage in contrast to “ready-made science,” the former is now very visible. This in turn has generated two other parallel processes—“regulation-in-the-making” and “risk-in-the-making. ” Such shifts can be seen in the field of regenerative medicine. The paper asks how best to engage with the move toward acceleration and thereby the future oversight of innovation.
Handling Sign Language Data: The Impact of Modality

Natural languages come in two different modalities. The impact of modality on the grammatical structure and linguistic theory has been discussed at great length in the last 20 years. By contrast, the impact of modality on linguistic data elicitation and collection, corpus studies, and experimental (psycholinguistic) studies is still underinvestigated. In this article, we address specific challenges that arise in judgment data elicitation and experimental studies of sign languages. These challenges are related to the socio-linguistic status of the Deaf community and the larger variability across signers within the same community, to the social status of sign languages, to properties of the visual-gestural modality and its interface with gesture, to methodological aspects of handling sign language data, and to specific linguistic features of sign languages. While some of these challenges also pertain to (some varieties of) spoken languages, other challenges are more modality-specific. The special combination of the challenges discussed in this article seems to be a specific facet empirical research on sign languages is faced with. In addition, we discuss the complementarity of theoretical approaches and experimental studies and show how the interaction of both approaches contributes to a better understanding of sign languages in particular and linguistic structures in general.
Control Over Skies: Survivability, Coverage, and Mobility Laws for Hierarchical Aerial Base Stations

Aerial base stations (ABSs) have gained significant importance in the next generation of wireless networks for accommodating mobile ground users and flash crowds with high convenience and quality. However, to achieve an efficient ABS network, many factors pertaining to ABS flight, governing laws and information transmissions must be studied. In this article, multidrone communications are studied in three major aspects, survivability, coverage, and mobility laws, which optimize the multitier ABS network to avoid issues related to intercell interference, deficient energy, frequent handovers, and lifetime. The article includes simulation results of hierarchical ABS allocations for handling a set of users over a defined geographical area. Several open issues and challenges are presented to provide deep insights into the ABS network management and its utility framework.
A new prospective on the role of melatonin in diabetes and its complications

Abstract Melatonin is a hormone secreted by the pineal gland under the control of the circadian rhythm, and is released in the dark and suppressed during the day. In the past decades, melatonin has been considered to be used in the treatment for diabetes mellitus (DM). This is due to a functional inter-relationship between melatonin and insulin. Elevated oxidative stress is a feature found in DM associated with diabetic neuropathy (DN), retinopathy (DR), nephropathy and cardiovascular disease. Reactive oxygen species (ROS) and nitrogen oxidative species (NOS) are usually produced in massive amounts via glucose and lipid peroxidation, and this leads to diabetic complications. At the molecular level, ROS causes damage to the biomolecules and triggers apoptosis. Melatonin, as an antioxidant and a free radical scavenger, ameliorates oxidative stress caused by ROS and NOS. Besides that, melatonin administration is proven to bring other anti-DM effects such as reducing cellular apoptosis and promoting the production of antioxidants.
Linear Bandits with Feature Feedback

This paper explores a new form of the linear bandit problem in which the algorithm receives the usual stochastic rewards as well as stochastic feedback about which features are relevant to the rewards, the latter feedback being the novel aspect. The focus of this paper is the development of new theory and algorithms for linear bandits with feature feedback. We show that linear bandits with feature feedback can achieve regret over time horizon $T$ that scales like $k\sqrt{T}$, without prior knowledge of which features are relevant nor the number $k$ of relevant features. In comparison, the regret of traditional linear bandits is $d\sqrt{T}$, where $d$ is the total number of (relevant and irrelevant) features, so the improvement can be dramatic if $k\ll d$. The computational complexity of the new algorithm is proportional to $k$ rather than $d$, making it much more suitable for real-world applications compared to traditional linear bandits. We demonstrate the performance of the new algorithm with synthetic and real human-labeled data.
Logic Rules Powered Knowledge Graph Embedding

Large scale knowledge graph embedding has attracted much attention from both academia and industry in the field of Artificial Intelligence. However, most existing methods concentrate solely on fact triples contained in the given knowledge graph. Inspired by the fact that logic rules can provide a flexible and declarative language for expressing rich background knowledge, it is natural to integrate logic rules into knowledge graph embedding, to transfer human knowledge to entity and relation embedding, and strengthen the learning process. In this paper, we propose a novel logic rule-enhanced method which can be easily integrated with any translation based knowledge graph embedding model, such as TransE . We first introduce a method to automatically mine the logic rules and corresponding confidences from the triples. And then, to put both triples and mined logic rules within the same semantic space, all triples in the knowledge graph are represented as first-order logic. Finally, we define several operations on the first-order logic and minimize a global loss over both of the mined logic rules and the transformed first-order logics. We conduct extensive experiments for link prediction and triple classification on three datasets: WN18, FB166, and FB15K. Experiments show that the rule-enhanced method can significantly improve the performance of several baselines. The highlight of our model is that the filtered Hits@1, which is a pivotal evaluation in the knowledge inference task, has a significant improvement (up to 700% improvement).
A speech measure for early stratification of fast and slow progressors of bulbar amyotrophic lateral sclerosis: lip movement jitter

Abstract Objective: To assess the utility of novel measures derived from a rapid syllable repetition task (i.e. oral dysdiadochokinesis [DDK]) in early stratification of fast and slow progressive bulbar amyotrophic lateral sclerosis (ALS) and prediction of bulbar disease progression rate. Methods: Fifty-four individuals with ALS were tracked longitudinally on their oral DDK and global bulbar/speech performance (i.e. bulbar subscore on the ALS Functional Rating Scale-Revised [ALSFRS-R]; articulation rate during passage reading) for a four-month average duration. Based on the bulbar deterioration rate over the tracked period, the participants were stratified as 14 fast bulbar progressors and 40 slow bulbar progressors using a posteriori classification approach. To determine if oral DDK performance predicts the differential bulbar disease progression trajectories in these individuals during the early stages of the tracked period (prior to significant bulbar/speech signs), twenty-two measures of lip motor performance in an oral DDK task were derived to (1) differentiate fast and slow bulbar progressors using the Receiver Operating Characteristic analysis and (2) predict bulbar disease progression rates across all individuals using linear regressions. Results: Movement jitter, a measure of temporal variability of alternating lip movement during DDK, showed 80% sensitivity and 95% specificity in differentiating fast and slow bulbar progressors early in the disease, and outperformed the ALSFRS-R bulbar subscore and articulation rate. Movement jitter also predicted bulbar disease progression rates across participants. Conclusion: Findings provided preliminary validation of the clinical value of movement jitter during oral DDK in patient stratification and bulbar disease prognosis.
Prediction of Forelimb Reach Results From Motor Cortex Activities Based on Calcium Imaging and Deep Learning

Brain-wide activities revealed by neuroimaging and recording techniques have been used to predict motor and cognitive functions in both human and animal models. However, although studies have shown the existence of micrometer-scale spatial organization of neurons in the motor cortex relevant to motor control, two-photon microscopy (TPM) calcium imaging at cellular resolution has not been fully exploited for the same purpose. Here, we ask if calcium imaging data recorded by TPM in rodent brain can provide enough information to predict features of upcoming movement. We collected calcium imaging signal from rostral forelimb area in layer 2/3 of the motor cortex while mice performed a two-dimensional lever reaching task. Images of average calcium activity collected during motion preparation period and inter-trial interval (ITI) were used to predict the forelimb reach results. The evaluation was based on a deep learning model that had been applied for object recognition. We found that the prediction accuracy for both maximum reaching location and trial outcome based on motion preparation period but not ITI were higher than the probabilities governed by chance. Our study demonstrated that imaging data encompassing information on the spatial organization of functional neuronal clusters in the motor cortex is useful in predicting motor acts even in the absence of detailed dynamics of neural activities.
Spatial-Aware Non-Local Attention for Fashion Landmark Detection

Fashion landmark detection is a challenging task even using the current deep learning techniques, due to the large variation and non-rigid deformation of clothes. In order to tackle these problems, we propose Spatial-Aware Non-Local (SANL) block, an attentive module in the deep neural network which can utilize spatial and semantic information while capturing global dependency. The attention maps are generated by Grad-CAM or a human parsing segmentation model and then fed into the SANL blocks via attention mechanism. We then establish our fashion landmark detection framework on feature pyramid network, equipped with four SANL blocks in the backbone. It is demonstrated by the experimental results on two large-scale fashion datasets that our proposed fashion landmark detection approach with the SANL blocks outperforms the current state-of-the-art methods considerably. Some supplementary experiments on fine-grained image classification also show the effectiveness of the proposed SANL block.
Photoperiodic Effects on Diurnal Rhythms in Cell Numbers of Peripheral Leukocytes in Domestic Pigs

The photoperiod is known to modulate immune cell number and function and is regarded essential for seasonal disease susceptibility. In addition, diurnal variations in the immune system are regarded important for immune competence. Whereas few studies investigated the influence of season, none investigated the specific effect of the photoperiod on these diurnal immune rhythms until now. Therefore, the present study compared diurnal rhythms in cell numbers of peripheral leukocyte types in domestic pigs held either under long day conditions (LD) or short day conditions (SD). Cosinor analyses of cell numbers of various peripheral leukocyte subtypes investigated over periods of 50 h revealed distinct photoperiodic differences in diurnal immune rhythms. Relative amplitudes of cell numbers of total leukocytes, NK cells, T cells, and monocytes in blood were higher under SD than LD. In addition, cell counts of total leukocytes, NK cells, T cells including various T cell subtypes, and eosinophils peaked earlier relative to the time of lights-on under SD than LD. In contrast, diurnal rhythms of neutrophil counts did not show photoperiodic differences. Mesor values did not differ in any leukocyte type. Generalized linear mixed model analyses revealed associations of leukocyte counts with plasma cortisol concentration and activity behavior in most investigated cell types. Moreover, the present study demonstrated photoperiodic effects on diurnal rhythms in plasma cortisol concentrations and activity behavior, which is in agreement with human and primate studies. The results of the present study imply stronger rhythmicity in leukocyte counts in general under SD. Common intrinsic mechanisms seem to regulate photoperiodic effects on diurnal rhythms in leukocyte counts, except for neutrophils, in domestic pigs. Our results reveal considerable insights into the regulation of immune rhythms in diurnally active species.