Miracle-dz/UniFAR-FTUs huggingface dataset:
Modifications to the EMC Algorithm for Orientation Recovery in Single Particle Imaging Experiments on X-ray Free Electron Lasers

The emergence of super-bright light sources X-ray free electron lasers(XFELs) combined with Single Particle Imaging(SPI) method, makes it possible to obtain nanometer resolution 3D structure of biological particles such as proteins or viruses without needing to freeze them. SPI relies on the “diffraction before destruction” principle, meaning that each sample only produces a single diffraction image before being destroyed by an X-ray pulse. The orientation of the particle in the beam is random for each shot. This gives rise to the problem of orientation recovery, in which an array of 2D diffraction images has to be combined into a single 3D image, necessary for the reconstruction of 3D structure of the studied particle. The orientation recovery problem is most commonly solved by the EMC algorithm, which is the most computationally expensive part of data analysis for SPI experiments. In this work we introduce several modifications to the EMC algorithm aimed at improving the quality of reconstruction and/or increasing the algorithm’s speed of convergence. We analyse the effectiveness of these modifications using simulated diffraction data.
DIAGNOSIS OF CORONAVIRUS USING CHEST X-RAY Covid-19 Detection Using Chest X-Ray

: Life is a very important topic. It tries to get effective and stable detection as soon as possible so that the patient receives proper care. It is very important in the medical field to diagnose a particular disease. Most of the techniques have been used, going beyond traditional methods. Many emerging strategies make it necessary to provide a complete overview that avoids certain aspects of finding and acquiring discovery We are developing a Machine Learning project for use in Covid diagnostics. This review focuses on modern techniques related to the development of learning machines used to diagnose human diseases in the medical field, to find interesting patterns, to make meaningless predictions, and to be useful in decision-making. In this case, we used more than 20000 X-Ray images to train the model to get better predictable results. We have pictures of Corona positive, Normal, Lungs Infection, and viral Pneumonia. Using the Best Deep Learning model we trained our model and created a predictive model. We've also made a UI for the user to use easily. We have also added a Covid-19 self-assessment form for the patient.
Optimizing Carbon Sequestration in Croplands: A Synthesis

Climate change and ensuring food security for an exponentially growing global human population are the greatest challenges for future agriculture. Improved soil management practices are crucial to tackle these problems by enhancing agro-ecosystem productivity, soil fertility, and carbon sequestration. To meet Paris climate treaty pledges, soil management must address validated approaches for carbon sequestration and stabilization. The present synthesis assesses a range of current and potential future agricultural management practices (AMP) that have an effect on soil organic carbon (SOC) storage and sequestration. Through two strategies—increasing carbon inputs (e.g., enhanced primary production, organic fertilizers) and reducing SOC losses (e.g., reducing soil erosion, managing soil respiration)—AMP can either sequester, up to 714 ± 404 (compost) kg C ha−1 y−1, having no distinct impact (mineral fertilization, no-tillage), or even reduce SOC stocks in the topsoil (bare fallow, business-as-usual). AMP can sequester between −20 ± 210 (mineral fertilization) and 714 ± 404 (compost) kg C ha−1 y−1 in the topsoil. No-tillage practices have no distinct impact, and bare fallow or “business-as-usual” scenarios even reduce SOC stocks in the topsoil. Overall, the carbon sequestration potential of the subsoil (>40 cm) requires further investigation. Moreover, climate change, permanent soil sealing, consumer behavior in dietary habits and waste production, as well as the socio-economic constraints of farmers (e.g., information exchange, long-term economic profitability) are important factors for implementing new AMPs. This calls for life-cycle assessments of those practices.
Mutations within the Open Reading Frame (ORF) including Ochre stop codon of the Surface Glycoprotein gene of SARS-CoV-2 virus erase potential seed location motifs of human non-coding microRNAs

MicroRNA are short and non-coding RNA, 18-25 nucleotides in length. They are produced at the early stage of viral infection. The roles played by cellular miRNAs and miRNA-mediated gene-silencing in the COVID-19 epidemic period is critical in order to develop novel therapeutics. We analysed SARS-CoV-2 Surface Glycoprotein (S) nucleotide sequence originating from India as well as Iran, Australia, Germany, Italy, Russia, China, Japan and Turkey and identified mutation in potential seed location of several human miRNA. Seventy single nucleotide polymorphisms (SNP) were detected in the S gene out of which, 36, 32 and 2 were cases of transitions, transversions and deletions respectively. Eleven human miRNA targets were identified on the reference S gene sequence with a score >80 in the miRDB database. Mutation A845S erased a common binding site of 7 human miRNA (miR-195-5p, miR-16-5p, miR-15b-5p, miR-15a-5p, miR-497-5p, miR-424-5p and miR-6838-5p). A synonymous mutation altered the wild type Ochre stop codon within the S gene sequence (Italy) to Opal thereby changing the seed sequence of miR-511-3p. Similar (synonymous) mutations were detected at amino acid position 659 and 1116 of the S gene where amino acids serine and threonine were retained, abolishing potential seed location for miR-219a-1-3p and miR-20b-3p respectively. The significance of this finding in reference to the strategy to use synthetic miRNA combinations as a novel therapeutic tool is discussed.
The Design and Development of Mobile Game to Promote Secure Smartphone Behaviour

Smartphones have evolved significantly in different ways over the years. Currently, Android and iOS dominate the market, followed by other operating systems. Along with the evolution of smartphones, malware, privacy, and other security issues have evolved along with it. Therefore, there is a need to develop interventions to educate and motivate users towards safe and privacy-sensitive smartphone usage behaviour while engaging with the rapidly changing features of these devices. In this paper, we introduce the design and development of a mobile persuasive game called “ PermaRun ” to improve users’ knowledge, build self-efficacy and create awareness about privacy-sensitive and security-conscious behaviours, especially as it regards to user control and handling of Android permissions and other secure smartphone behaviour. We implement nine persuasive strategies to keep the users motivated and six game design patterns to make the game engaging and more interesting for the player.
Developing Sentimental Analysis System Based on Various Optimizer

Over the past few decades, natural language processing research has not made much. However, the widespread use of deep learning and neural networks attracted attention for the application of neural networks in natural language processing. Sentiment analysis is one of the challenges of natural language processing. Emotions are things that a person thinks and feels. Therefore, sentiment analysis should be able to analyze the person’s attitude, opinions, and inclinations in text or actual text. In the case of emotion analysis, it is a priority to simply classify two emotions: positive and negative. In this paper we propose the deep learning based sentimental analysis system according to various optimizer that is SGD, ADAM and RMSProp. Through experimental result RMSprop optimizer shows the best performance compared to others on IMDB data set. Future work is to find more best hyper parameter for sentimental analysis system.
Decision-making on Human Individual Capital Investment

This article presents an approach to the feasibility of investing in human capital in the framework of the development of socio-economic systems of intellectual capital. In conditions of partial or complete uncertainty in the presence of incomplete input data, the use of tools based on artificial intelligence methods (neural networks, genetic algorithms) allows us to evaluate a wide range of indicators. The article presents non-linear mathematical models for assessing the feasibility of investing in the intellectual capital of a university, in particular, in its structural component individual human capital, namely, in the formation of an individual. Models are based on logistic regression and neural networks. The authors proposed several key factors affecting the binary resulting variable "investment effect": the level of knowledge and skills in the specialty; self-education (in terms of regularity of raising the level of information culture); employee age. The developed methodology is the basis of a decision support system that allows for the strategic development of the university based on effective investment in the human capital of its employee.
Automated Feature Category Extraction for Sentiment Analysis Employing Natural Language Processing

: Utilizing on the web buyer audits as electronic informal exchange to help buy basic leadership has turned out to be increasingly famous. The Web gives a broad wellspring of shopper audits, although one can barely read all surveys to acquire a reasonable assessment of an item or administration. A content preparing system that can outline audits, would along these lines be alluring. A subtask to be rendred by such a system is locate the general angle classifications inclined to in audit sentences, for which this paper presents two strategies. As conflicting to most existing methodologies, the principal technique introduced is an unsupervised strategy that applies affiliation govern mining on co-event recurrence information acquired from a gist to discover these perspective classifications. While not competing with cutting edge directed strategies, the proposed unsupervised technique performs superior to a few straightforward baselines, a correlative yet regulated strategy, and a managed pattern, with a F1-score of 67%. The second technique is an administered variation that outflanks existing strategies with a F1-score of 84%.
Crime Against Women: Harassment at Workplace

Compared to her male counterpart, women who are perceived to be the fairer and weaker sex are often forced to remain confined within the four walls of her home, nurturing her children and taking care of her husband and family in a patriarchal society where men are supposed to be the sole breeders. However, the traditional roles of both genders are just a myth in the competitive world of today, and not a reality where fairer sex still competes equally with its better half. Long gone are the days when a girl used to get married, without financial freedom to support herself and her family, with limited or no primary education. Also the fairer sex is well able to hold respectable professional roles in various areas of the workplace in today's world with the spread of education. A drastic change in the role of women worldwide has been brought on by globalization. Nevertheless, with the greater influx of women in India's mainstream workforce, sexual abuse in the workplace has taken on greater dimensions. Sexual harassment can be characterized as sexually colored comments, physical contact and advances, a sexual favor request or request, any other sexual nature of inappropriate physical verbal or non-verbal actions.
Launch of High Level Panel of Experts (HLPE) Report Agroecological and other innovative approaches for sustainable agriculture and food systems that enhance food security and nutrition

■ 2 Throughout this document, agriculture refers to crop and livestock production, aquaculture, fisheries and forestry. ■ 3 FAO, IFAD, UNICEF, WFP and WHO. 2020. The State of Food Security and Nutrition in the World 2020. Rome, FAO. ■ 4 FAO. 2011. Global food losses and food waste: Extent, causes and prevention. Rome. ■ 5 As described in para 20 of the CFS Voluntary Guidelines on Food Systems and Nutrition, unhealthy diets are a major risk factor of multiple forms of malnutrition. ■ 6 HLPE. 2017. Nutrition and food systems. A report by the High Level Panel of Experts. ■ 7 UN Policy Brief of June 2020. The Impact of COVID-19 on Food Security and Nutrition. New York. ■ 8 Throughout the text people in vulnerable situations will refer to women, youth, indigenous peoples and local communities. Policy recommendations Committee on World Food Security
COPD_A_259601 2449..2460

1Department of Respiratory and Critical Care Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan 410011, People’s Republic of China; 2Research Unit of Respiratory Disease, Central South University, Changsha, Hunan 410011, People’s Republic of China; 3Diagnosis and Treatment Center of Respiratory Disease, Central South University, Changsha, Hunan 410011, People’s Republic of China; 4Institute of Systems Biomedicine, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, People’s Republic of China; 5Department of Pathology, School of Basic Medical Sciences, Peking University Health Science Center, Beijing 100191, People’s Republic of China
DMSO_A_261486 2729..2741

1Department of Endocrinology, Shengjing Hospital of China Medical University, Shenyang, People’ s Republic of China; 2Department of Emergency Medicine, Shengjing Hospital of China Medical University, Shenyang, People’s Republic of China Abstract: Gestational diabetes mellitus (GDM) is associated with many adverse obstetric outcomes and neonatal outcomes, including preeclampsia, Cesarean section, and macrosomia. Active screening and early diabetes control can reduce the occurrence of adverse outcomes. Glycosylated hemoglobin (HbA1c) only reflects average blood glucose levels, but not glycemic variability (GV). Studies have shown that GV can cause a series of adverse reactions, and good control of GV can reduce the incidence of adverse pregnancy outcomes in patients with GDM. In order to provide clinicians with a better basis for diagnosis and treatment, this study reviewed the measurement, evaluation, and control of GV, the importance of GV for patients with GDM, and correlations between GV and maternal and neonatal outcomes.
Gesture Based Alphanumeric Character Detection

— Numerous techniques are projected by several researchers for written also as written character and numeral gratitude. Recognition is the method of exchange of written text into computer legible type. To achieve the most effective accuracy of any recognition system the choice of feature extraction and categorization technique is critical. The information concerning the character is collected by the options and as a result, classifiers categorize the character decidedly. For written characters there is a unit drawback love it differs from one author to another, even once the same person writes the same character variety of times there's a division in form, size, and position of the character. Newest analysis during this space has used frequent styles of the method, classifiers, and options to reduce the complexness of recognizing written text. During this paper, benefits and downsides of 2 diverse techniques of feature extraction and sorting are mentioned.
A GCN-Based Method for Extracting Power Lines and Pylons From Airborne LiDAR Data

Extracting the power lines and pylons automatically and accurately from airborne LiDAR data is a critical step in inspecting the routine power line, especially in the remote mountainous areas. However, challenges arise in using existing methods to extract the targets from large scenarios of remote mountainous areas since the terrain is undulating, and the features are difficult to distinguish. In this article, to overcome these challenges, we propose a graph convolutional network (GCN)-based method to extract power lines and pylons from Airborne LiDAR point clouds. First, data augmentation and near-ground filtering methods are developed to overcome the problems of insufficient and imbalanced samples in the LiDAR data. Then, a GCN-based framework is proposed to extract the power lines and pylons, which consist of two main modules, i.e., the neighborhood dimension information (NDI) module and the neighborhood geometry information aggregation (NGIA) module. These two modules are designed to strengthen the model’s ability to portray local geometric details. Besides, an attention fusion module is investigated to further improve the NDI and NGIA features. Finally, a line structure constraint algorithm is proposed to identify individual power lines, where the power corridor is reconstructed using a polynomial-based algorithm. Numerical experiments are conducted based on two different power line scenarios acquired in mountainous areas. The results demonstrate the superior performances of the proposed method over several existing algorithms, where the $F_{1}$ score and quality of the power line are 99.3% and 98.6%, and the results of the pylon are 96% and 92.4%, respectively. The identification rate of power line identification is above 98%.
Advances in Mechanisms and Omics Pertaining to Fruit Cracking in Horticultural Plants

Fruit cracking is a physiological disease that occurs during fruit development, which limits the quality and marketability of the fruit and causes great economic losses. Fruit cracking is affected by physiological, genetic and environmental factors. In this paper, the mechanism of fruit cracking was elaborated from cutin and cell wall, especially the gene families related to cell wall metabolism, including the polygalacturonase (PG) gene family, xylologlucan endotransglucosylase/hydrolase (XTH) gene family and expansin gene family. In addition, due to the advancement of high-throughput sequencing technology, an increasing number of horticultural plants have completed genome sequencing. This paper expounds the application of omics, including transcriptome, proteome, metabolomics and integrative omics in fruit cracking. The measures to reduce fruit cracking include using plastic rain covers and bagging, and spraying mineral and plant growth regulators. In this paper, the mechanisms of fruit cracking are reviewed at the molecular level, and the problems needing to be solved in fruit cracking research are put forward.
Associations between hemispheric asymmetry and schizophrenia-related risk genes in people with schizophrenia and people at a genetic high risk of schizophrenia

Background Schizophrenia is considered a polygenic disorder. People with schizophrenia and those with genetic high risk of schizophrenia (GHR) have presented with similar neurodevelopmental deficits in hemispheric asymmetry. The potential associations between neurodevelopmental abnormalities and schizophrenia-related risk genes in both schizophrenia and those with GHR remains unclear. Aims To investigate the shared and specific alternations to the structural network in people with schizophrenia and those with GHR. And to identify an association between vulnerable structural network alternation and schizophrenia-related risk genes. Method A total of 97 participants with schizophrenia, 79 participants with GHR and 192 healthy controls, underwent diffusion tensor imaging (DTI) scans at a single site. We used graph theory to characterise hemispheric and whole-brain structural network topological metrics. For 26 people in the schizophrenia group and 48 in the GHR group with DTI scans we also calculated their schizophrenia-related polygenic risk scores (SZ-PRSs). The correlations between alterations to the structural network and SZ-PRSs were calculated. Based on the identified genetic–neural association, bioinformatics enrichment was explored. Results There were significant hemispheric asymmetric deficits of nodal efficiency, global and local efficiency in the schizophrenia and GHR groups. Hemispheric asymmetric deficit of local efficiency was significantly positively correlated with SZ-PRSs in the schizophrenia and GHR groups. Bioinformatics enrichment analysis showed that these risk genes may be linked to signal transduction, neural development and neuron structure. The schizophrenia group showed a significant decrease in the whole-brain structural network. Conclusions The shared asymmetric deficits in people with schizophrenia and those with GHR, and the association between anomalous asymmetry and SZ-PRSs suggested a vulnerability imaging marker regulated by schizophrenia-related risk genes. Our findings provide new insights into asymmetry regulated by risk genes and provides a better understanding of the genetic–neural pathological underpinnings of schizophrenia.
Title: Genome-wide screen for new components of the Drosophila melanogaster Torso receptor tyrosine kinase pathway Authors:

Patterning of the Drosophila embryonic termini by the Torso (Tor) receptor pathway has long served as a valuable paradigm for understanding how Receptor Tyrosine Kinase (RTK) signalling is controlled. However, the mechanisms that underpin the control of Tor signalling remain to be fully understood. In particular, it is unclear how the Perforin-like protein Torsolike (Tsl) localises Tor activity to the embryonic termini. To shed light on this, together with other aspects of Tor pathway function, we conducted a genome-wide screen to identify new pathway components that operate downstream of Tsl. Using a set of molecularly-defined chromosomal deficiencies, we screened for suppressors of ligand-dependent Tor signalling induced by unrestricted Tsl expression. This approach yielded 59 genomic suppressor regions, 11 of which we mapped to the causative gene, and a further 29 that were mapped to less than 15 genes. Of the identified genes, six represent previously unknown regulators of embryonic Tor signalling. These include twins, which encodes an integral subunit of the protein phosphatase 2A complex, and α-tubulin at 84B, a major constituent of the microtubule network, suggesting that these may play an important role in terminal patterning. Together these data comprise a valuable resource for the discovery of new Tor pathway components. Many of these may also be required for other roles of Tor in development, such as in the larval prothoracic gland where Tor signalling controls the initiation of metamorphosis.
Understanding the Effect of Different Scale Information Fusion in Deep Convolutional Neural Networks

Different scale of information is an important component in computer vision systems. Recently, there are considerable researches on utilizing multi-scale information to solve the scale-invariant problems, such as GoogLeNet and FPN. In this paper, we introduce the notion of different scale information fusion (DSIF) and show that it has a significant effect on the performance of object recognition systems. We analyze the DSIF in several architecture designs, and the effect of nonlinear activations, dropout, sub-sampling and skip connections on it. This leads to clear suggestions for ways of the DSIF to choose
Voice–Nasality Interaction and Headedness in Voiceless Nasals∗

Most recent work in Element Theory assumes that nasality and true voicing are represented by the same element, where the headed element encodes voicing and the dependent element nasality (Backley, 2011; Nasukawa, 1999, 2000, 2005, et alii). This assumption is questioned here and it is proposed that the voicing–nasality contrast may be encoded the other way around. It is argued that this hypothesis is in better agreement with the means by which headedness may encode additional information at phonetic interpretation. Under the assumption of an L/H-Parameter, it is shown how, in line with Lombardi (1991) and Botma (2005), only H-systems can possibly encode voiceless (or more properly aspirated) nasals and how the hypothesis that headed |L| encodes nasality provides a better fit for these systems.