Miracle-dz/UniFAR-FTUs huggingface dataset:
Long Noncoding RNA DICER1-AS1 Functions in Methylation Regulation on the Multi-Drugresistance of Osteosarcoma Cells via miR-34a-5p and GADD45A

Osteosarcoma (OS) is a common malignant bone tumor that commonly occurs in children and adolescents. Long noncoding RNAs (lncRNAs) are recognized as a novel class of regulators of gene expression associated with tumorigenesis. However, the effect and mechanism of lncRNAs in OS tumorigenesis and drug resistance have not been characterized. The purpose of the study is to screen potential biomarker and therapeutic target against OS. We compared the lncRNA expression profiles between OS cell lines with different drug resistance levels using RNA-seq analysis and found that lncRNA DICER1-AS1 was significantly differentially expressed in multi-drugresistant OS cells SJSA-1 versus multi-drugsensitive OS cells G-292. Bisulfite Sequencing PCR (BSP) assay was performed to analyze the differential methylation status of the promoter region of DICER1-AS1 in four OS cells. Subsequently, in vitro gain- and loss-of-function experiments demonstrated the roles of DICER1-AS1 and miR-34a-5p in the multi-drugresistance of OS cells. The main findings is that DICER1-AS1 directly binds to miR-34a-5p, and their expression has a negative correlation with each other. The hypermethylation of the promoter region of DICER1-AS1 silenced its expression in the drugresistant cells SJSA-1 and MNNG/HOS. Moreover, we found that growth arrest and DNA damage-inducible alpha (GADD45A) participates in the DICER1-AS1/miR-34a-5p-regulated drug resistance of OS cells, probably via the cell cycle/pRb-E2F pathway. Our results revealed DICER1-AS1/miR-34a-5p-regulated drug resistance of OS cells, a new lncRNA-regulated network in OS tumorigenesis, suggested that DICER1-AS1 can be considered as a potential biomarker and therapeutic target against OS cells.
Toward Unifying Evolutionary Ecology and Genomics to Understand Positive Plant–Plant Interactions Within Wild Species

In a local environment, plant networks include interactions among individuals of different species and among genotypes of the same species. While interspecific interactions are recognized as main drivers of plant community patterns, intraspecific interactions have recently gained attention in explaining plant community dynamics. However, an overview of intraspecific genotype-by-genotype interaction patterns within wild plant species is still missing. From the literature, we identified 91 experiments that were mainly designed to investigate the presence of positive interactions based on two contrasting hypotheses. Kin selection theory predicts partisan help given to a genealogical relative. The rationale behind this hypothesis relies on kin/non-kin recognition, with the positive outcome of kin cooperation substantiating it. On the other hand, the elbow-room hypothesis supports intraspecific niche partitioning leading to positive outcome when genetically distant genotypes interact. Positive diversity-productivity relationship rationalizes this hypothesis, notably with the outcome of overyielding. We found that both these hypotheses have been highly supported in experimental studies despite their opposite predictions between the extent of genetic relatedness among neighbors and the level of positive interactions. Interestingly, we identified a highly significant effect of breeding system, with a high proportion of selfing species associated with the presence of kin cooperation. Nonetheless, we identified several shortcomings regardless of the species considered, such as the lack of a reliable estimate of genetic relatedness among genotypes and ecological characterization of the natural habitats from which genotypes were collected, thereby impeding the identification of selective drivers of positive interactions. We therefore propose a framework combining evolutionary ecology and genomics to establish the eco-genomic landscape of positive GxG interactions in wild plant species.
Prognostic Value of Coronary Artery Calcium Score in Hospitalized COVID-19 Patients

Background: The association of known cardiovascular risk factors with poor prognosis of coronavirus disease 2019 (COVID-19) has been recently emphasized. Coronary artery calcium (CAC) score is considered a risk modifier in the primary prevention of cardiovascular disease. We hypothesized that the absence of CAC might have an additional predictive value for an improved cardiovascular outcome of hospitalized COVID-19 patients. Materials and methods: We prospectively included 310 consecutive hospitalized patients with COVID-19. Thirty patients with history of coronary artery disease were excluded. Chest computed tomography (CT) was performed in all patients. Demographics, medical history, clinical characteristics, laboratory findings, imaging data, in-hospital treatment, and outcomes were retrospectively analyzed. A composite endpoint of major adverse cardiovascular events (MACE) was defined. Results: Two hundred eighty patients (63.2 ± 16.7 years old, 57.5% male) were included in the analysis. 46.7% patients had a CAC score of 0. MACE rate was 21.8% (61 patients). The absence of CAC was inversely associated with MACE (OR 0.209, 95% CI 0.052–0.833, p = 0.027), with a negative predictive value of 84.5%. Conclusion: The absence of CAC had a high negative predictive value for MACE in patients hospitalized with COVID-19, even in the presence of cardiac risk factors. A semi-qualitative assessment of CAC is a simple, reproducible, and non-invasive measure that may be useful to identify COVID-19 patients at a low risk for developing cardiovascular complications.
Integrated Metagenomic Assessment of Multiple Pre-harvest Control Points on Lettuce Resistomes at Field-Scale

An integrated understanding of factors influencing the occurrence, distribution, and fate of antibiotic resistance genes (ARGs) in vegetable production systems is needed to inform the design and development of strategies for mitigating the potential for antibiotic resistance propagation in the food chain. The goal of the present study was to holistically track antibiotic resistance and associated microbiomes at three distinct pre-harvest control points in an agroecosystem in order to identify the potential impacts of key agricultural management strategies. Samples were collected over the course of a single growing season (67 days) from field-scale plots amended with various organic and inorganic amendments at agronomic rates. Dairy-derived manure and compost amendment samples (n = 14), soil samples (n = 27), and lettuce samples (n = 12) were analyzed via shotgun metagenomics to assess multiple pre-harvest factors as hypothetical control points that shape lettuce resistomes. Pre-harvest factors of interest included manure collection during/post antibiotic use, manure composting, and soil amended with organic (stockpiled manure/compost) versus chemical fertilizer. Microbial community resistome and taxonomic compositions were unique from amendment to soil to lettuce surface according to dissimilarity analysis. The highest resistome alpha diversity (i.e., unique ARGs, n = 642) was detected in amendment samples prior to soil application, while the composted manure had the lowest total ARG relative abundance (i.e., 16S rRNA gene-normalized). Regardless of amendment type, soils acted as an apparent ecological buffer, i.e., soil resistome and taxonomic profiles returned to background conditions 67 d-post amendment application. Effects of amendment conditions surprisingly re-emerged in lettuce phyllosphere resistomes, with the highest total ARG relative abundances recovered on the surface of lettuce plants grown in organically-fertilized soils (i.e., compost- and manure-amended soils). Co-occurrence analysis identified 55 unique ARGs found both in the soil amendments and on lettuce surfaces. Among these, arnA and pmrF were the most abundant ARGs co-occurring with mobile genetic elements (MGE). Other prominent ARG-MGE co-occurrences throughout this pre-harvest lettuce production chain included: TetM to transposon (Clostridiodies difficile) in the manure amendment and TriC to plasmid (Ralstonia solanacearum) on the lettuce surfaces. This suggests that, even with imposing manure management and post-amendment wait periods in agricultural systems, ARGs originating from manure can still be found on crop surfaces. This study demonstrates a comprehensive approach to identifying key control points for the propagation of ARGs in vegetable production systems, identifying potential ARG-MGE combinations that could inform future surveillance. The findings suggest that additional pre-harvest and potentially post-harvest interventions may be warranted to minimize risk of propagating antibiotic resistance in the food chain.
Real Life Clinical Management and Survival in Advanced Cutaneous Melanoma: The Italian Clinical National Melanoma Registry Experience

Background Cutaneous melanoma (CM) is one of the most aggressive types of skin cancer. Currently, innovative approaches such as target therapies and immunotherapies have been introduced in clinical practice. Data of clinical trials and real life studies that evaluate the outcomes of these therapeutic associations are necessary to establish their clinical utility. The aim of this study is to investigate the types of oncological treatments employed in the real-life clinical management of patients with advanced CM in several Italian centers, which are part of the Clinical National Melanoma Registry (CNMR). Methods Melanoma-specific survival and overall survival were calculated. Multivariate Cox regression models were used to estimate the hazard ratios adjusting for confounders and other prognostic factors. Results The median follow-up time was 36 months (range 1.2-185.1). 787 CM were included in the analysis with completed information about therapies. All types of immunotherapy showed a significant improved survival compared with all other therapies (p=0.001). 75% was the highest reduction of death reached by anti-PD-1 (HR=0.25), globally immunotherapy was significantly associated with improved survival, either for anti-CTLA4 monotherapy or combined with anti-PD-1 (HR=0.47 and 0.26, respectively) and BRAFI+MEKI (HR=0.62). Conclusions The nivolumab/pembrolizumab in combination of ipilimumab and the addition of ant-MEK to the BRAFi can be considered the best therapies to improve survival in a real-world-population. The CNMR can complement clinical registries with the intent of improving cancer management and standardizing cancer treatment.
Chemokines and Pain in the Trigeminal System

Chemotactic cytokines or chemokines are a large family of secreted proteins able to induce chemotaxis. Chemokines are categorized according to their primary amino acid sequence, and in particular their cysteine residues that form disulphide bonds to maintain the structure: CC, CXC, CX3C, and XC, in which X represents variable amino acids. Among their many roles, chemokines are known to be key players in pain modulation in the peripheral and central nervous systems. Thus, they are promising candidates for novel therapeutics that could replace current, often ineffective treatments. The spinal and trigeminal systems are intrinsically different beyond their anatomical location, and it has been suggested that there are also differences in their sensory mechanisms. Hence, understanding the different mechanisms involved in pain modulation for each system could aid in developing appropriate pharmacological alternatives. Here, we aim to describe the current landscape of chemokines that have been studied specifically with regard to trigeminal pain. Searching PubMed and Google Scholar, we identified 30 reports describing chemokines in animal models of trigeminal pain, and 15 reports describing chemokines involved in human pain associated with the trigeminal system. This review highlights the chemokines studied to date at different levels of the trigeminal system, their cellular localization and, where available, their role in a variety of animal pain models.
Conserved and Unique Roles of Chaperone-Dependent E3 Ubiquitin Ligase CHIP in Plants

Protein quality control (PQC) is essential for maintaining cellular homeostasis by reducing protein misfolding and aggregation. Major PQC mechanisms include protein refolding assisted by molecular chaperones and the degradation of misfolded and aggregated proteins using the proteasome and autophagy. A C-terminus of heat shock protein (Hsp) 70-interacting protein [carboxy-terminal Hsp70-interacting protein (CHIP)] is a chaperone-dependent and U-box-containing E3 ligase. CHIP is a key molecule in PQC by recognizing misfolded proteins through its interacting chaperones and targeting their degradation. CHIP also ubiquitinates native proteins and plays a regulatory role in other cellular processes, including signaling, development, DNA repair, immunity, and aging in metazoans. As a highly conserved ubiquitin ligase, plant CHIP plays an important role in response to a broad spectrum of biotic and abiotic stresses. CHIP protects chloroplasts by coordinating chloroplast PQC both outside and inside the important photosynthetic organelle of plant cells. CHIP also modulates the activity of protein phosphatase 2A (PP2A), a crucial component in a network of plant signaling, including abscisic acid (ABA) signaling. In this review, we discuss the structure, cofactors, activities, and biological function of CHIP with an emphasis on both its conserved and unique roles in PQC, stress responses, and signaling in plants.
Non-Convex Sparse Deviation Modeling Via Generative Models

In this paper, the generative model is used to introduce the structural properties of the signal to replace the common sparse hypothesis, and a non-convex compressed sensing sparse deviation model based on the generative model (ℓq-Gen) is proposed. By establishing ℓq variant of the restricted isometry property (q-RIP) and Set-Restricted Eigenvalue Condition (q-S-REC), the error upper bound of the optimal decoder is derived when the recovered signal is within the sparse deviation range of the generator. Furthermore, it is proved that the Gaussian matrix satisfying a certain number of measurements is sufficient to ensure a good recovery for the generating function with high probability. Finally, a series of experiments are carried out to verify the effectiveness and superiority of the ℓq-Gen model.
Multichannel Overlapping Speaker Segmentation Using Multiple Hypothesis Tracking Of Acoustic And Spatial Features

An essential part of any diarization system is the task of speaker segmentation which is important for many applications including speaker indexing and automatic speech recognition (ASR) in multi-speaker environments. Segmentation of overlapping speech has recently been a key focus of this work. In this paper we explore the use of a new multimodal approach for overlapping speaker segmentation that tracks both the fundamental frequency (F0) of the speaker and the speaker’s direction of arrival (DOA) simultaneously. Our proposed multiple hypothesis tracking system, which simultaneously tracks both features, shows an improvement in segmentation performance when compared to tracking these features separately. An illustrative example of overlapping speech demonstrates the effectiveness of our proposed system. We also undertake a statistical analysis on 12 meetings from the AMI corpus and show an improvement in the HIT rate of 14.1% on average against a commonly used deep learning bidirectional long short term memory network (BLSTM) approach.
Image Coding For Machines: an End-To-End Learned Approach

Over recent years, deep learning-based computer vision systems have been applied to images at an ever-increasing pace, oftentimes representing the only type of consumption for those images. Given the dramatic explosion in the number of images generated per day, a question arises: how much better would an image codec targeting machine-consumption perform against state-of-the-art codecs targeting human-consumption? In this paper, we propose an image codec for machines which is neural network (NN) based and end-to-end learned. In particular, we propose a set of training strategies that address the delicate problem of balancing competing loss functions, such as computer vision task losses, image distortion losses, and rate loss. Our experimental results show that our NN-based codec outperforms the state-of-the-art Versa-tile Video Coding (VVC) standard on the object detection and instance segmentation tasks, achieving -37.87% and -32.90% of BD-rate gain, respectively, while being fast thanks to its compact size. To the best of our knowledge, this is the first end-to-end learned machine-targeted image codec.
Environment-Independent Wi-Fi Human Activity Recognition with Adversarial Network

Human activity recognition is an essential part of human-computer interaction systems. Environment-robust Wi-Fi-based systems for this task is still a challenging problem, due to the fact that most existing systems may drop in performance when the environment is changed. To address this issue, we in this paper present WiHARAN, a Wi-Fi-based activity recognition system that can learn environment-independent features from Channel State Information (CSI) traces. With a well-designed base network capable of extracting temporal information from spectrograms, we align the joint distribution of features and labels from multiple environments utilizing adversarial learning. Experimental results show that our system achieves better performance than state-of-the-art solutions and can improve performance in difficult environments.
Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction

Probabilistic context-free grammars (PCFGs) have been pro-posed to capture password distributions, and further been used in password guessing attacks and password strength meters. However, current PCFGs suffer from the limitation of inaccurate segmentation of password, which leads to misestimation of password probability and thus seriously affects their performance. In this paper, we propose a word extraction approach for passwords, and further present an improved PCFG model, called WordPCFG. The WordPCFG using word extraction method can precisely extract semantic segments (called word) from passwords based on cohesion and freedom of words. We evaluate our WordPCFG on six large-scale datasets, showing that WordPCFG cracks 83.04%–95.47% passwords and obtains 12.96%–71.84% improvement over the state-of-the-art PCFGs.
Riemannian Geometry on Connectivity for Clinical BCI

Riemannian BCI based on EEG covariance have won many data competitions and achieved very high classification results on BCI datasets. To increase the accuracy of BCI systems, we propose an approach grounded on Riemannian geometry that extends this framework to functional connectivity measures. This paper describes the approach submitted to the Clinical BCI Challenge-WCCI2020 and that ranked 1st on the task 1 of the competition.
An Improved Mean Teacher Based Method for Large Scale Weakly Labeled Semi-Supervised Sound Event Detection

This paper presents an improved mean teacher (MT) based method for large-scale weakly labeled semi-supervised sound event detection (SED), by focusing on learning a better student model. Two main improvements are proposed based on the authors’ previous perturbation based MT method. Firstly, an event-aware module is de-signed to allow multiple branches with different kernel sizes to be fused via an attention mechanism. By inserting this module after the convolutional layer, each neuron can adaptively adjust its receptive field to suit different sound events. Secondly, instead of using the teacher model to provide a consistency cost term, we propose using a stochastic inference of unlabeled examples to generate high quality pseudo-targets by averaging multiple predictions from the perturbed student model. MixUp of both labeled and unlabeled data is further exploited to improve the effectiveness of student model. Finally, the teacher model can be obtained via exponential moving average (EMA) of the student model, which generates final predictions for SED during inference. Experiments on the DCASE2018 task4 dataset demonstrate the ability of the proposed method. Specifically, an F1-score of 42.1% is achieved, significantly outperforming the 32.4% achieved by the winning system, or the 39.3% by the previous perturbation based method.
Vehicle 3d Localization in Road Scenes VIA a Monocular Moving Camera

Knowing the 3D locations of the surrounding vehicles is of vital importance in autonomous driving scenarios. It can be pretty challenging to make an accurate estimation from a monocular moving camera. In this paper, we present an effective vehicle 3D localization method, that utilizes 2D key-points predicted from a trained CNN to model the vehicles’ structure, from which the ground points are further inferred. An adaptive ground plane estimation method is exploited under the monocular camera for 3D geometric back-projection. Benefiting from tracking, we also take into account temporal information of the same object to ensure the trajectory consistency. Viewpoint and size knowledge are also considered for refinement. The evaluation on the KITTI benchmark for on-road vehicles shows the effectiveness of our proposed approach with promising 3D localization results.
Auditory Filterbanks Benefit Universal Sound Source Separation

For separating two arbitrary sources from monaural recordings, the encoder-separator-decoder framework is popular in recent years. We investigated three kinds of filterbanks in the encoder: free, parameterized, and fixed. We proposed parameterized Gammatone and Gammachirp filterbanks, which improved performance with fewer parameters and better interpretability. Next, the properties of different filterbanks were investigated. Through training the network, an entirely freely learned filterbank emerges with properties similar to a series of bandpass filters spaced on a nonlinear scale - similar to the auditory system. We also explored the underlying separation mechanisms learned by the network through a classic auditory segregation experiment, revealing that the model separates mixtures based on the general principle (proximity of frequency and time). In summary, results demonstrate that the separation network automatically picks up the filterbank properties and separation mechanisms that are similar to those which have developed over millions of years in humans.
Hierarchical Attention-Based Temporal Convolutional Networks for Eeg-Based Emotion Recognition

EEG-based emotion recognition is an effective way to infer the inner emotional state of human beings. Recently, deep learning methods, particularly long short-term memory recurrent neural networks (LSTM-RNNs), have made encouraging progress for in the field of emotion recognition. However, the LSTM-RNNs are time-consuming and have difficulty avoiding the problem of exploding/vanishing gradients when during training. In addition, EEG-based emotion recognition often suffers due to the existence of silent and emotional irrelevant frames from intra-channel. Not all channels carry the same emotional discriminative information. In order to tackle these problems, a hierarchical attention-based temporal convolutional networks (HATCN) for efficient EEG-based emotion recognition is proposed. Firstly, a spectrogram representation is generated from raw EEG signals in each channel to capture their time and frequency information. Secondly, temporal convolutional networks (TCNs) are utilised to automatically learn more robust/intrinsic long-term dynamic characters in emotion response. Next, a hierarchical attention mechanism is investigated that aggregates the emotional information at both the frame and channel level. The experimental results on the DEAP dataset show that our method achieves an average recognition accuracy of 0.716 and an F1-score of 0.642 over four emotional dimensions and outperforms other state-of-the-art methods in a user-independent scenario.