Miracle-dz/UniFAR-FTUs huggingface dataset:
Proximate and Fatty Acid Composition of the Keeled Mullet (Liza carinata) from the North East Mediterranean Sea

The proximate composition and the fatty acid profile of keeled mullet (Liza carinata) caught off the Yumurtalik Bay located on the North-East Mediterranean Sea were investigated. The average crude protein content of keeled mullet was found to be 17.18%, while the lipid content was determined as 1.53 %. The percentages of total saturated fatty acids (SFA), monounsaturated fatty acids (MUFA) and the polyunsaturated fatty acids (PUFA) were found as 41.64%, 16.78% and 26.96 %, respectively. Palmitic acid, stearic acid and lignoceric acid among the saturated fatty acids; palmitoleic acid and oleic acid among the monounsaturated fatty acids; and arachidonic acid, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) among the polyunsaturated fatty acids, were identified as the major fatty acids. The average EPA content was 10.99% whereas the average DHA content was 8.48%.
PAR4 activation involves extracellular loop-3 and transmembrane residue Thr153.

Protease activated receptor 4 (PAR4) mediates sustained thrombin signaling in platelets and is required for a stable thrombus. PAR4 is activated by proteolysis of the N-terminus to expose a tethered ligand. The structural basis for PAR4 activation and the location of its ligand binding site (LBS) are unknown. Using hydrogen/deuterium exchange (H/D exchange), computational modeling and signaling studies, we determined the molecular mechanism for tethered ligand-mediated PAR4 activation. H/D exchange identified that the LBS is composed by transmembrane domain 3 (TM3) and TM7. Unbiased computational modeling further predicted an interaction between Gly48 from the tethered ligand and Thr153 from the LBS. Mutating Thr153 significantly decreased PAR4 signaling. H/D exchange and modeling also showed that extracellular loop 3 (ECL3) serves as a gatekeeper for the interaction between the tethered ligand and LBS. A naturally occurring sequence variant (P310L, rs2227376) and two experimental mutations (S311A and P312L) determined that the rigidity conferred by prolines in ECL3 are essential for PAR4 activation. Finally, we examined the role of the polymorphism at position 310 in venous thromboembolism (VTE) using the INVENT consortium multi-ancestry GWAS meta-analysis. Individuals with the PAR4 Leu310 allele had a 15% relative risk reduction for VTE (odds ratio [OR]: 0.85; 95% CI: 0.77-0.94) compared to the Pro310 allele. These data are consistent with our H/D exchange, molecular modeling, and signaling studies. In conclusion, we have uncovered the structural basis for PAR4 activation and identified a previously unrecognized role for PAR4 in VTE.
A Three-Parameter Rank-Frequency Relation in Natural Languages

We present that, the rank-frequency relation in textual data follows f \propto r^{-\alpha}(r+\gamma)^{-\beta}, where f is the token frequency and r is the rank by frequency, with (\alpha, \beta, \gamma) as parameters. The formulation is derived based on the empirical observation that d^2 (x+y)/dx^2 is a typical impulse function, where (x,y)=(\log r, \log f). The formulation is the power law when \beta=0 and the Zipf–Mandelbrot law when \alpha=0. We illustrate that \alpha is related to the analytic features of syntax and \beta+\gamma to those of morphology in natural languages from an investigation of multilingual corpora.
Modeling and Experimental Study of the Localized Electrochemical Micro Additive Manufacturing Technology Based on the FluidFM

In this work, the localized electrochemical micro additive manufacturing technology based on the FluidFM (fluidic force microscope) has been introduced to fabricate micro three-dimensional overhang metal structures at sub-micron resolution. It breaks through the localized deposition previously achieved by micro-anode precision movement, and the micro-injection of the electrolyte is achieved in a stable electric field distribution. The structure of electrochemical facilities has been designed and optimized. More importantly, the local electrochemical deposition process has been analyzed with positive source diffusion, and the mathematical modeling has been revealed in the particle conversion process. A mathematical model is proposed for the species flux under the action of pulsed pressure in an innovatively localized liquid feeding process. Besides, the linear structure, bulk structure, complex structure, and large-area structure of the additive manufacturing are analyzed separately. The experimental diameter of the deposited cylinder structure is linearly fitted. The aspect ratio of the structure is greater than 20, the surface roughness value is between 0.1–0.2 μm at the surface of bulk structures, and the abilities are verified for deposition of overhang, hollow complex structures. Moreover, this work verifies the feasibility of 3D overhang array submicron structure additive manufacturing, with the application of pulsed pressure. Furthermore, this technology opens new avenues for the direct fabrication of nano circuit interconnection, tiny sensors, and micro antennas.
Learning to Tag OOV Tokens by Integrating Contextual Representation and Background Knowledge

Neural-based context-aware models for slot tagging have achieved state-of-the-art performance. However, the presence of OOV(out-of-vocab) words significantly degrades the performance of neural-based models, especially in a few-shot scenario. In this paper, we propose a novel knowledge-enhanced slot tagging model to integrate contextual representation of input text and the large-scale lexical background knowledge. Besides, we use multi-level graph attention to explicitly model lexical relations. The experiments show that our proposed knowledge integration mechanism achieves consistent improvements across settings with different sizes of training data on two public benchmark datasets.
Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network

Emotion recognition plays an important role in the field of human–computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate human emotion owing to its convenience and mobility. Deep neural network (DNN) approaches using an EEG for emotion recognition have recently shown remarkable improvement in terms of their recognition accuracy. However, most studies in this field still require a separate process for extracting handcrafted features despite the ability of a DNN to extract meaningful features by itself. In this paper, we propose a novel method for recognizing an emotion based on the use of three-dimensional convolutional neural networks (3D CNNs), with an efficient representation of the spatio-temporal representations of EEG signals. First, we spatially reconstruct raw EEG signals represented as stacks of one-dimensional (1D) time series data to two-dimensional (2D) EEG frames according to the original electrode position. We then represent a 3D EEG stream by concatenating the 2D EEG frames to the time axis. These 3D reconstructions of the raw EEG signals can be efficiently combined with 3D CNNs, which have shown a remarkable feature representation from spatio-temporal data. Herein, we demonstrate the accuracy of the emotional classification of the proposed method through extensive experiments on the DEAP (a Dataset for Emotion Analysis using EEG, Physiological, and video signals) dataset. Experimental results show that the proposed method achieves a classification accuracy of 99.11%, 99.74%, and 99.73% in the binary classification of valence and arousal, and, in four-class classification, respectively. We investigate the spatio-temporal effectiveness of the proposed method by comparing it to several types of input methods with 2D/3D CNN. We then verify the best performing shape of both the kernel and input data experimentally. We verify that an efficient representation of an EEG and a network that fully takes advantage of the data characteristics can outperform methods that apply handcrafted features.
Antibody-Drug Conjugates and Targeted Treatment Strategies for Hepatocellular Carcinoma: A Drug-Delivery Perspective

Increased understanding of cancer biology, pharmacology and drug delivery has provided a new framework for drug discovery and product development that relies on the unique expression of specific macromolecules (i.e., antigens) on the surface of tumour cells. This has enabled the development of anti-cancer treatments that combine the selectivity of antibodies with the efficacy of highly potent chemotherapeutic small molecules, called antibody-drug conjugates (ADCs). ADCs are composed of a cytotoxic drug covalently linked to an antibody which then selectively binds to a highly expressed antigen on a cancer cell; the conjugate is then internalized by the cell where it releases the potent cytotoxic drug and efficiently kills the tumour cell. There are, however, many challenges in the development of ADCs, mainly around optimizing the therapeutic/safety benefits. These challenges are discussed in this review; they include issues with the plasma stability and half-life of the ADC, its transport from blood into and distribution throughout the tumour compartment, cancer cell antigen expression and the ADC binding affinity to the target antigen, the cell internalization process, cleaving of the cytotoxic drug from the ADC, and the cytotoxic effect of the drug on the target cells. Finally, we present a summary of some of the experimental ADC strategies used in the treatment of hepatocellular carcinoma, from the recent literature.
Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning

Recognition of motor imagery intention is one of the hot current research focuses of brain-computer interface (BCI) studies. It can help patients with physical dyskinesia to convey their movement intentions. In recent years, breakthroughs have been made in the research on recognition of motor imagery task using deep learning, but if the important features related to motor imagery are ignored, it may lead to a decline in the recognition performance of the algorithm. This paper proposes a new deep multi-view feature learning method for the classification task of motor imagery electroencephalogram (EEG) signals. In order to obtain more representative motor imagery features in EEG signals, we introduced a multi-view feature representation based on the characteristics of EEG signals and the differences between different features. Different feature extraction methods were used to respectively extract the time domain, frequency domain, time-frequency domain and spatial features of EEG signals, so as to made them cooperate and complement. Then, the deep restricted Boltzmann machine (RBM) network improved by t-distributed stochastic neighbor embedding(t-SNE) was adopted to learn the multi-view features of EEG signals, so that the algorithm removed the feature redundancy while took into account the global characteristics in the multi-view feature sequence, reduced the dimension of the multi-visual features and enhanced the recognizability of the features. Finally, support vector machine (SVM) was chosen to classify deep multi-view features. Applying our proposed method to the BCI competition IV 2a dataset we obtained excellent classification results. The results show that the deep multi-view feature learning method further improved the classification accuracy of motor imagery tasks.
Pathophysiology of Peripheral Arterial Disease (PAD): A Review on Oxidative Disorders

Peripheral arterial disease (PAD) is an atherosclerotic disease that affects a wide range of the world’s population, reaching up to 200 million individuals worldwide. PAD particularly affects elderly individuals (>65 years old). PAD is often underdiagnosed or underestimated, although specificity in diagnosis is shown by an ankle/brachial approach, and the high cardiovascular event risk that affected the PAD patients. A number of pathophysiologic pathways operate in chronic arterial ischemia of lower limbs, giving the possibility to improve therapeutic strategies and the outcome of patients. This review aims to provide a well detailed description of such fundamental issues as physical exercise, biochemistry of physical exercise, skeletal muscle in PAD, heme oxygenase 1 (HO-1) in PAD, and antioxidants in PAD. These issues are closely related to the oxidative stress in PAD. We want to draw attention to the pathophysiologic pathways that are considered to be beneficial in order to achieve more effective options to treat PAD patients.
An Application of the Orthogonal Matching Pursuit Algorithm in Space-Time Adaptive Processing

The article presents a new space-time adaptive processing (STAP) method for target detection in a heterogeneous and non-stationary environment. In study it was proven that it is possible to estimate the clutter covariance matrix (CCM) in STAP by using the MIMO (Multiple Input Multiple Output) radar geometry model and the orthogonal matching pursuit (OMP) algorithm. For the estimation of spatio-temporal spectrum of clutter and target, a model of joint sparse recovery was established. As a result, clutter suppression and target detection in a heterogeneous environment will be achieved. In addition, the proposed method uses a single snapshot of the radar data cube, which eliminates the need for access to all training cells.
Reduced Sialylation and Bioactivity of the Antifibrotic Protein Serum Amyloid P in the Sera of Patients with Idiopathic Pulmonary Fibrosis

Pulmonary fibrosis is a chronic and generally fatal disorder characterized by progressive formation of scar-like tissue in the lungs. Sialic acids are often found as the terminal sugar on extracellular glycoconjugates such as protein glycosylations. Sialidases, also known as neuraminidases, desialylate glycoconjugates. Serum amyloid P (SAP), a pentameric serum glycoprotein that has two sialic acids on each polypeptide, inhibits the differentiation of monocytes into fibrocytes and promotes human PBMCs to accumulate high extracellular levels of IL-10. When SAP is desialylated with sialidase, the effects of SAP on fibrocyte differentiation and IL-10 accumulation are strongly inhibited. Intriguingly, in patients with pulmonary fibrosis, there are increased levels of sialidase activity in the bronchoalveolar lavage fluid, increased levels of sialidases in the lungs, and decreased levels of SAP in the sera. To elucidate the role of SAP desialylation in idiopathic pulmonary fibrosis (IPF) pathogenesis, we purified SAP from the serum of IPF patients and healthy controls and measured the extent of sialylation and bioactivity of the purified SAP. We find that some IPF patients have abnormally high levels of the sialidase NEU3 in their sera and that the SAP in the sera of IPF patients has an abnormally high extent of desialylation and an abnormally low ability to inhibit fibrocyte differentiation and induce extracellular IL-10 accumulation by PBMC. These results suggest that SAP desialylation may play a role in IPF pathogenesis and that inhibiting NEU3 could be a potential therapeutic target for IPF.
Repulsive guidance molecules lock growth differentiation factor 5 in an inhibitory complex

Significance Repulsive guidance molecules (RGMs) are cell surface proteins that control processes ranging from brain development to iron metabolism. Dysregulation of RGM function leads to severe diseases. RGMs can bind to the cell guidance receptor Neogenin (NEO1) and act as coreceptors for the bone morphogenetic proteins (BMPs). Here, we determined high-resolution structures of all three human RGMs in complex with the BMP ligand growth differentiation factor 5 (GDF5) and the ternary complex of GDF5 with RGM and NEO1. Our structural and functional analyses reveal that RGM competes with the GDF5 receptor. This study shows that RGMs can act as inhibitors of a BMP ligand (GDF5), and not always as activators of BMP signaling, as previously reported. Repulsive guidance molecules (RGMs) are cell surface proteins that regulate the development and homeostasis of many tissues and organs, including the nervous, skeletal, and immune systems. They control fundamental biological processes, such as migration and differentiation by direct interaction with the Neogenin (NEO1) receptor and function as coreceptors for the bone morphogenetic protein (BMP)/growth differentiation factor (GDF) family. We determined crystal structures of all three human RGM family members in complex with GDF5, as well as the ternary NEO1–RGMB–GDF5 assembly. Surprisingly, we show that all three RGMs inhibit GDF5 signaling, which is in stark contrast to RGM-mediated enhancement of signaling observed for other BMPs, like BMP2. Despite their opposite effect on GDF5 signaling, RGMs occupy the BMP type 1 receptor binding site similar to the observed interactions in RGM–BMP2 complexes. In the NEO1–RGMB–GDF5 complex, RGMB physically bridges NEO1 and GDF5, suggesting cross-talk between the GDF5 and NEO1 signaling pathways. Our crystal structures, combined with structure-guided mutagenesis of RGMs and BMP ligands, binding studies, and cellular assays suggest that RGMs inhibit GDF5 signaling by competing with GDF5 type 1 receptors. While our crystal structure analysis and in vitro binding data initially pointed towards a simple competition mechanism between RGMs and type 1 receptors as a possible basis for RGM-mediated GDF5 inhibition, further experiments utilizing BMP2-mimicking GDF5 variants clearly indicate a more complex mechanism that explains how RGMs can act as a functionality-changing switch for two structurally and biochemically similar signaling molecules.
Myoferlin Is a Yet Unknown Interactor of the Mitochondrial Dynamics’ Machinery in Pancreas Cancer Cells

Pancreas ductal adenocarcinoma is one of the deadliest cancers where surgery remains the main survival factor. Mitochondria were described to be involved in tumor aggressiveness in several cancer types including pancreas cancer. We have previously reported that myoferlin controls mitochondrial structure and function, and demonstrated that myoferlin depletion disturbs the mitochondrial dynamics culminating in a mitochondrial fission. In order to unravel the mechanism underlying this observation, we explored the myoferlin localization in pancreatic cancer cells and showed a colocalization with the mitochondrial dynamic machinery element: mitofusin. This colocalization was confirmed in several pancreas cancer cell lines and in normal cell lines as well. Moreover, in pancreas cancer cell lines, it appeared that myoferlin interacted with mitofusin. These discoveries open-up new research avenues aiming at modulating mitofusin function in pancreas cancer.
Pulmonary delivery of ORMDL3 short hairpin RNA – a potential tool to regulate allergen-induced airway inflammation

Abstract Aim/Purpose: Exposure to various allergens has been shown to increase expression of ORMDL3 in the lung in models of allergic asthma. Studies using genetically modified (transgenic or knock out) mice have revealed some of the functions of ORMDL3 in asthma pathogenesis, although amid debate. The goal of this study was to use targeted post-transcriptional downregulation of ORMDL3 in allergen-challenged wild-type (WT) mice by RNA interference to further elucidate the functional role of ORMDL3 in asthma pathogenesis and evaluate a potential therapeutic option. Methods: Allergen (ovalbumin [OVA])-challenged WT mice were administered intranasally (i.n) with a single dose of five short hairpin RNA (shRNA) constructs with different target sequence for murine ORMDL3 cloned in a lentiviral vector or with the empty vector (control). Mice were evaluated for allergen-induced airway hyperresponsiveness (AHR) and various features of airway inflammation after 72 hours. Results: I.n administration of a single dose of ORMDL3 shRNAs to OVA-challenged mice resulted in reduction of ORMDL3 gene expression in the lungs associated with a significant reduction in AHR to inhaled methacholine and in the number of inflammatory cells recruited in the airways, specifically eosinophils, as well as in airway mucus secretion compared to OVA-challenged mice that received the empty vector. Administration of ORMDL3 shRNAs also significantly inhibited levels of IL-13, eotaxin-2 and sphingosine in the lungs. Additionally, ORMDL3 shRNAs significantly inhibited the allergen-mediated increase in monohexyl ceramides C22:0 and C24:0. Conclusions: Post-transcriptional down regulation of ORMDL3 in allergic lungs using i.n-delivered ORMDL3 shRNA (akin to inhaled therapy) attenuates development of key features of airway allergic disease, confirming the involvement of ORMDL3 in allergic asthma pathogenesis and serving as a model for a potential therapeutic strategy.
Handling Rare Entities for Neural Sequence Labeling

One great challenge in neural sequence labeling is the data sparsity problem for rare entity words and phrases. Most of test set entities appear only few times and are even unseen in training corpus, yielding large number of out-of-vocabulary (OOV) and low-frequency (LF) entities during evaluation. In this work, we propose approaches to address this problem. For OOV entities, we introduce local context reconstruction to implicitly incorporate contextual information into their representations. For LF entities, we present delexicalized entity identification to explicitly extract their frequency-agnostic and entity-type-specific representations. Extensive experiments on multiple benchmark datasets show that our model has significantly outperformed all previous methods and achieved new start-of-the-art results. Notably, our methods surpass the model fine-tuned on pre-trained language models without external resource.
A Simple and Effective Unified Encoder for Document-Level Machine Translation

Most of the existing models for document-level machine translation adopt dual-encoder structures. The representation of the source sentences and the document-level contexts are modeled with two separate encoders. Although these models can make use of the document-level contexts, they do not fully model the interaction between the contexts and the source sentences, and can not directly adapt to the recent pre-training models (e.g., BERT) which encodes multiple sentences with a single encoder. In this work, we propose a simple and effective unified encoder that can outperform the baseline models of dual-encoder models in terms of BLEU and METEOR scores. Moreover, the pre-training models can further boost the performance of our proposed model.
Graph-to-Tree Learning for Solving Math Word Problems

While the recent tree-based neural models have demonstrated promising results in generating solution expression for the math word problem (MWP), most of these models do not capture the relationships and order information among the quantities well. This results in poor quantity representations and incorrect solution expressions. In this paper, we propose Graph2Tree, a novel deep learning architecture that combines the merits of the graph-based encoder and tree-based decoder to generate better solution expressions. Included in our Graph2Tree framework are two graphs, namely the Quantity Cell Graph and Quantity Comparison Graph, which are designed to address limitations of existing methods by effectively representing the relationships and order information among the quantities in MWPs. We conduct extensive experiments on two available datasets. Our experiment results show that Graph2Tree outperforms the state-of-the-art baselines on two benchmark datasets significantly. We also discuss case studies and empirically examine Graph2Tree’s effectiveness in translating the MWP text into solution expressions.