Miracle-dz/UniFAR-FTUs huggingface dataset:
How does estrogen work on autophagy?

ABSTRACT Macroautophagy/autophagy is vital for intracellular quality control and homeostasis. Therefore, careful regulation of autophagy is very important. In the past 10 years, a number of studies have reported that estrogenic effectors affect autophagy. However, some results, especially those regarding the modulatory effect of 17β-estradiol (E2) on autophagy seem inconsistent. Moreover, several clinical trials are already in place combining both autophagy inducers and autophagy inhibitors with endocrine therapies for breast cancer. Not all patients experience benefit, which further confuses and complicates our understanding of the main effects of autophagy in estrogen-related cancer. In view of the importance of the crosstalk between estrogen signaling and autophagy, this review summarizes the estrogenic effectors reported to affect autophagy, subcellular distribution and translocation of estrogen receptors, autophagy-targeted transcription factors (TFs), miRNAs, and histone modifications regulated by E2. Upon stimulation with estrogen, there will always be opposing functional actions, which might occur between different receptors, receptors on TFs, TFs on autophagy genes, or even histone modifications on transcription. The huge signaling network downstream of estrogen can promote autophagy and reduce overstimulated autophagy at the same time, which allows autophagy to be regulated by estrogen in a restricted range. To help understand how the estrogenic regulation of autophagy affects cell fate, a hypothetical model is presented here. Finally, we discuss some exciting new directions in the field. We hope this might help to better understand the multiple associations between estrogen and autophagy, the pathogenic mechanisms of many estrogen-related diseases, and to design novel and efficacious therapeutics. Abbreviations: AP-1, activator protein-1; HATs, histone acetyltransferases; HDAC, histone deacetylases; HOTAIR, HOX transcript antisense RNA
Design and synthesis of novel organometallic complexes using boronated phenylalanine derivatives as potential anticancer agents

Abstract Drug design and discovery studies are important because of the prevalence of diseases without available medical cures. New anticancer agents are particularly urgent because of the high mortality rate associated with cancer. A series of mononuclear gold (III) and platinum (II) complexes based on boronated phenylalanine (BPA) were designed and synthesized using 4,4’-dimethyl-2,2’-dipyridyl (L1) or 1,10-phenanthroline-5,6-dion (L2) ligands to obtain promising anticancer drug candidates. Proton nuclear magnetic resonance, infrared, mass spectrometry, and elemental analyses were utilized for chemical characterizations. Cell viability, cancer cell colony formation, endothelial tube formation, and cytoskeleton staining assays were performed using A549 lung adenocarcinoma and human umbilical vein endothelial cells (HUVECs) to investigate preliminary pharmacological activities. L1-based platinum (II) complex (BPA-L1-Pt) was the most promising complex, and has similar activity with the approved chemotherapy drug cis-platinum. Half maximal inhibitory concentration values for BPA-L1-Pt were 9.15 µM on A549s and 16.61 µM on HUVECs; the values for cis-platinum were 5.24 µM on A549s and 23.14 µM on HUVECs. Consequently, further synthesis studies should be performed to boost the cancer cell selectivity feature of BPA by varying metal and ligand types.
Web-based and print journal-based scholarly communication in the XML research field: A look at the intellectual structure

As part of a research project that aims to identify the similarities and differences between Web-based and print journal-based scholarly communication, this paper compares the intellectual structure of the XML research field revealed from author co-citation analysis of research papers published on the Web as indexed by ResearchIndex and that derived from print journals as indexed by SCI. Considerable differences are observed and some media specific features are identified. Results from this study demonstrate the importance and the feasibility of the use of multiple data sources in citation analysis studies of scholarly communication, and evidence for a developing “two-tier” scholarly communication system.
Enhance Accuracy of Hierarchical Text Categorization Based on Deep Learning Network Using Embedding Strategies

Hierarchical text categorization is a task that aims to assign predefined categories to text documents with hierarchical constraint. Recently, deep learning techniques has shown many success results in various fields, especially, in text categorization. In our previous work called Shared Hidden Layer Neural Network (SHL-NN), it has shown that sharing information between levels can improve a performance of the model. However, this work is based on a sequence of unsupervised word embedding vectors, so the performance should be limited. In this paper, we propose a supervised document embedding specifically designed for hierarchical text categorization based on Autoencoder, which is trained from both words and labels. To enhance the embedding vectors, the document embedding strategies are invented to utilize the class hierarchy information in the training process. To transfer the prediction result from the parent classes, the shared information technique has been improved to be more flexible and efficient. The experiment was conducted on three standard benchmarks: WIPO-C, WIPO-D and Wiki comparing to two baselines: SHL-NN and a top-down based SVM framework with TF-IDF inputs called “HR-SVM. ” The results show that the proposed model outperforms all baselines in terms of F1 macro.
Artificial Intelligent System for Automatic Depression Level Analysis Through Visual and Vocal Expressions

A human being’s cognitive system can be simulated by artificial intelligent systems. Machines and robots equipped with cognitive capability can automatically recognize a humans mental state through their gestures and facial expressions. In this paper, an artificial intelligent system is proposed to monitor depression. It can predict the scales of Beck depression inventory II (BDI-II) from vocal and visual expressions. First, different visual features are extracted from facial expression images. Deep learning method is utilized to extract key visual features from the facial expression frames. Second, spectral low-level descriptors and mel-frequency cepstral coefficients features are extracted from short audio segments to capture the vocal expressions. Third, feature dynamic history histogram (FDHH) is proposed to capture the temporal movement on the feature space. Finally, these FDHH and audio features are fused using regression techniques for the prediction of the BDI-II scales. The proposed method has been tested on the public Audio/Visual Emotion Challenges 2014 dataset as it is tuned to be more focused on the study of depression. The results outperform all the other existing methods on the same dataset.
The Development and Validation of Food Atlas for Portion Size Estimation in the Balkan Region

Assessment of portion sizes is an important factor for the accuracy of food consumption surveys. The objective of this study was to develop and validate a food atlas of commonly consumed foods in the Balkan region in order to improve the accuracy of portion size estimation for food consumption surveys. A list of 135 foods and their portion sizes was based on previously conducted food consumption surveys in this region. Food was cooked, measured and served in three or four portion sizes right before being photographed. A validation study was conducted through the visual perception method. Without receiving training on usage of the food picture book, participants were asked to evaluate two portion sizes of 20 selected foods by comparison with a photo series of each food. Portion sizes were evaluated by 18 nutrition professionals and 17 lay individuals who had no nutritional education. Mean differences and the standard deviations of the mean differences (SD) between the portions estimated by each participant and the served portion were calculated. The percentages of participants who selected the correct, adjacent or distant portion size also were calculated. The number of food items that were quantified within the predefined acceptable range (i.e., mean difference < ∣ 0·7 ∣ and SD < 1) was 16 (80%) among lay individuals and 17 (85%) among nutritional professionals. Among 16 photo series that were assessed as “acceptable,” the percentage of all participants, who selected the correct picture, was between 44.3 and 82.9%, with an average of 60.2%. Only three foods were assessed correctly by <50% participants. The percentage of participants who selected the correct or adjacent serving size was above 98% for both lay and professional evaluators. This is the first food atlas containing representative foods and recipes commonly consumed in the Balkan region. However, further adjustments of the methodology should include larger number of food items to be tested, involvement of more participants and provision of training for the users of the food atlas. This food atlas could be used in food consumption surveys in the Balkan region after further testing and validation.
Parallel Separable 3D Convolution for Video and Volumetric Data Understanding

For video and volumetric data understanding, 3D convolution layers are widely used in deep learning, however, at the cost of increasing computation and training time. Recent works seek to replace the 3D convolution layer with convolution blocks, e.g. structured combinations of 2D and 1D convolution layers. In this paper, we propose a novel convolution block, Parallel Separable 3D Convolution (PmSCn), which applies m parallel streams of n 2D and one 1D convolution layers along different dimensions. We first mathematically justify the need of parallel streams (Pm) to replace a single 3D convolution layer through tensor decomposition. Then we jointly replace consecutive 3D convolution layers, common in modern network architectures, with the multiple 2D convolution layers (Cn). Lastly, we empirically show that PmSCn is applicable to different backbone architectures, such as ResNet, DenseNet, and UNet, for different applications, such as video action recognition, MRI brain segmentation, and electron microscopy segmentation. In all three applications, we replace the 3D convolution layers in state-of-the art models with PmSCn and achieve around 14% improvement in test performance and 40% reduction in model size and on average.
Linear and Logistic Regression Based Monitoring for Resource Management in Cloud Networks

Understanding and implementing the effective techniques to manage the infrastructure resources of cloud datacenter has recently become important. The energy consumption and ineffective resource utilization can lead to an increase in the operational cost of cloud provider side, which in turn increases the cloud services cost at cloud consumer side. One of the effective techniques to address these issues in cloud datacenters is the server consolidation by allowing multiple virtual machines (VMs) include varying workload to host in a single physical machine. This leads to an increase in the resource utilization, and reduced power consumption by turning off the idle physical machines. However, consolidating the virtual machines due to varying workload in cloud applications can cause a violation of service level agreement. In this paper, we propose a model based on linear and logistic regression to detect overloaded hosts by dynamically generating rules based on historical data of hosts and datacenter in order to update association functions to address and adapt the changes of different types of workloads running on the cloud provider datacenter. The experiments and simulation results based on dynamic workloads show the proposed algorithm significantly outperforms the other competitive host detection algorithms.
Top-k Critical Vertices Query on Shortest Path

Shortest path query is one of the most fundamental and classic problems in graph analytics, which returns the complete shortest path between any two vertices. However, in many real-life scenarios, only critical vertices on the shortest path are desirable and it is unnecessary to search for the complete path. This paper investigates the shortest path sketch by defining a top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="ma-ieq1-2808495.gif"/></alternatives></inline-formula> critical vertices (<inline-formula> <tex-math notation="LaTeX">$k$</tex-math><alternatives><inline-graphic xlink:href="ma-ieq2-2808495.gif"/></alternatives> </inline-formula>CV) query on the shortest path. Given a source vertex <inline-formula><tex-math notation="LaTeX">$s$ </tex-math><alternatives><inline-graphic xlink:href="ma-ieq3-2808495.gif"/></alternatives></inline-formula> and target vertex <inline-formula><tex-math notation="LaTeX">$t$</tex-math><alternatives> <inline-graphic xlink:href="ma-ieq4-2808495.gif"/></alternatives></inline-formula> in a graph, <inline-formula> <tex-math notation="LaTeX">$k$</tex-math><alternatives><inline-graphic xlink:href="ma-ieq5-2808495.gif"/></alternatives> </inline-formula>CV query can return the top-<inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="ma-ieq6-2808495.gif"/></alternatives></inline-formula> significant vertices on the shortest path <inline-formula><tex-math notation="LaTeX">$SP(s,t)$</tex-math><alternatives> <inline-graphic xlink:href="ma-ieq7-2808495.gif"/></alternatives></inline-formula>. The significance of the vertices can be predefined. The key strategy for seeking the sketch is to apply off-line preprocessed distance oracle to accelerate on-line real-time queries. This allows us to omit unnecessary vertices and obtain the most representative sketch of the shortest path directly. We further explore a series of methods and optimizations to answer <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives> <inline-graphic xlink:href="ma-ieq8-2808495.gif"/></alternatives></inline-formula>CV query on both centralized and distributed platforms, using exact and approximate approaches, respectively. We evaluate our methods in terms of time, space complexity and approximation quality. Experiments on large-scale real-world networks validate that our algorithms are of high efficiency and accuracy.
Design and Information Architectures for an Unmanned Aerial Vehicle Cooperative Formation Tracking Controller

This paper addresses a well-documented open problem on the tracking target of the multiple unmanned aerial vehicles (UAVs) subject to the inconsistent of attitude and position in the process of flight. The main contribution is to study a UAV cooperative formation control of tracking a moving target and analyze the flight stability of the designed controller when only knowing the UAV local information. The path planning of a single UAV and multiple UAV cooperative formation tracking of a moving target is studied. First, the feedback control is used to ensure heading convergence for the path planning of a single UAV. Furthermore, we use a variable airspeed controller to achieve the desired angular spacing and build the information architecture using graph theory for the UAV formation tracking of moving targets. The communication data management module at the cloud stores and transmits to UAV by the ground station. The ground command can maintain a UAV control center which gets access to the cloud for the UAV activity management. In addition, the design of a distributed control law is achieved based on the type of information construction between UAVs. The simulation results show that the designed controller is robust for tracking moving targets and achieves good flight-stability when tracking such targets.
Explaining variations in test ordering in primary care: protocol for a realist review

Introduction Studies have demonstrated the existence of significant variation in test-ordering patterns in both primary and secondary care, for a wide variety of tests and across many health systems. Inconsistent practice could be explained by differing degrees of underuse and overuse of tests for diagnosis or monitoring. Underuse of appropriate tests may result in delayed or missed diagnoses; overuse may be an early step that can trigger a cascade of unnecessary intervention, as well as being a source of harm in itself. Methods and analysis This realist review will seek to improve our understanding of how and why variation in laboratory test ordering comes about. A realist review is a theory-driven systematic review informed by a realist philosophy of science, seeking to produce useful theory that explains observed outcomes, in terms of relationships between important contexts and generative mechanisms. An initial explanatory theory will be developed in consultation with a stakeholder group and this ‘programme theory’ will be tested and refined against available secondary evidence, gathered via an iterative and purposive search process. This data will be analysed and synthesised according to realist principles, to produce a refined ‘programme theory’, explaining the contexts in which primary care doctors fail to order ‘necessary’ tests and/or order ‘unnecessary’ tests, and the mechanisms underlying these decisions. Ethics and dissemination Ethical approval is not required for this review. A complete and transparent report will be produced in line with the RAMESES standards. The theory developed will be used to inform recommendations for the development of interventions designed to minimise ‘inappropriate’ testing. Our dissemination strategy will be informed by our stakeholders. A variety of outputs will be tailored to ensure relevance to policy-makers, primary care and pathology practitioners, and patients. Prospero registration number CRD42018091986
On the Usage of the Probability Integral Transform to Reduce the Complexity of Multi-Way Fuzzy Decision Trees in Big Data Classification Problems

We present a new distributed fuzzy partitioning method to reduce the complexity of multi-way fuzzy decision trees in Big Data classification problems. The proposed algorithm builds a fixed number of fuzzy sets for all variables and adjusts their shape and position to the real distribution of training data. A two-step process is applied : 1) transformation of the original distribution into a standard uniform distribution by means of the probability integral transform. Since the original distribution is generally unknown, the cumulative distribution function is approximated by computing the q-quantiles of the training set; 2) construction of a Ruspini strong fuzzy partition in the transformed attribute space using a fixed number of equally distributed triangular membership functions. Despite the aforementioned transformation, the definition of every fuzzy set in the original space can be recovered by applying the inverse cumulative distribution function (also known as quantile function). The experimental results reveal that the proposed methodology allows the state-of-the-art multi-way fuzzy decision tree (FMDT) induction algorithm to maintain classification accuracy with up to 6 million fewer leaves.
Using the Tsetlin Machine to Learn Human-Interpretable Rules for High-Accuracy Text Categorization With Medical Applications

Medical applications challenge today’s text categorization techniques by demanding both high accuracy and ease-of-interpretation. Although deep learning has provided a leap forward in regard to accuracy, this leap comes at the sacrifice of interpretability. In this paper, we introduce a text categorization approach that leverages the recently introduced Tsetlin Machine to address this accuracy-interpretability challenge. Briefly, we represent the terms of a text as propositional variables. From these variables, we capture categories using simple propositional formulae, such as: IF “rash” AND “reaction” AND “penicillin” THEN Allergy. The Tsetlin Machine learns these formulae from labeled text, utilizing conjunctive clauses to represent the particular facets of each category. Therefore, also the absence of terms (negated features) can be used for categorization purposes. Our empirical comparisons with Naïve Bayes classifiers, decision trees, linear support vector machines (SVMs), random forest, long short-term memory (LSTM) neural networks, and other techniques, are quite conclusive. Using relatively simple propositional formulae, the accuracy of the Tsetlin Machine either outperforms or performs approximately on par with the best evaluated methods on both the 20 Newsgroups and IMDb datasets, as well as on a clinical dataset containing authentic electronic health records (EHRs). On average, the Tsetlin Machine delivers the best recall and precision scores across the datasets. The main merit of the proposed approach is thus its capacity for producing human-interpretable rules, while at the same time achieving acceptable accuracy. We believe that our novel approach can have a significant impact on a wide range of text analysis applications, providing a promising starting point for deeper natural language understanding with the Tsetlin Machine.
Mixed Peptide-Conjugated Chitosan Matrices as Multi-Receptor Targeted Cell-Adhesive Scaffolds

Biomaterials are important for cell and tissue engineering. Chitosan is widely used as a scaffold because it is easily modified using its amino groups, can easily form a matrix, is stable under physiological conditions, and is inactive for cell adhesion. Chitosan is an excellent platform for peptide ligands, especially cell adhesive peptides derived from extracellular matrix (ECM) proteins. ECM proteins, such as collagen, fibronectin, and laminin, are multifunctional and have diverse cell attachment sites. Various cell adhesive peptides have been identified from the ECM proteins, and these are useful to design functional biomaterials. The cell attachment activity of peptides is influenced by the solubility, conformation, and coating efficiency to solid materials, whereas immobilization of peptides to a polysaccharide such as chitosan avoids these problems. Peptide–chitosan matrices promote various biological activities depending on the peptide. When the peptides are immobilized to chitosan, the activity of the peptides is significantly enhanced. Further, mixed peptide–chitosan matrices, conjugated with more than one peptide on a chitosan matrix, interact with multiple cellular receptors and promote specific biological responses via receptor cross-talk. Receptor cross-talk is important for mimicking the biological activity of ECM and the proteins. The mixed peptide–chitosan matrix approach is useful to develop biomaterials as a synthetic ECM for cell and tissue engineering.
Big Data application on signal processing systems

One of the tasks of system identification within the modern signal processing system is the task of identification of unknown error-correction code (ECC). System identification can be done using various methods. Anyway, the more knowledge one has about the possible system structure provides better solution for system identification. Here we take a look into digital communication systems uses ECC, which are the major part of modern communication systems are. We create a new practical approach for identification of a priori unknown ECC being used over the transmission channel of the communication system which is examined for identification purposes. The approach is based as Big Data application which can provide new source for the study. Shown that usage of new approach provides better speed for system identification making the process of identification faster, more reliable, with better quality for solution found and with lower requirements for quality of the transmission channel.