Miracle-dz/UniFAR-FTUs huggingface dataset:
DOES CRANIOPLASTY REDUCE DISABILITY IN CASES WITH POST TRAUMATIC SKULL BONE DEFECTS? OBSERVATIONAL STUDY IN ASSIUT UNIVERSITY HOSPITALS, EGYPT.

Abstract: Introduction: Permanent infirmity (disability) is defined as a functional limitation that restricts the ability of a person to perform the daily activities. The incidence of cranial bone defects related to head trauma have increased over the past decades. Cranioplasty could impact the work capability and employability. This study designed to assess the effect of cranioplasty as a method of skull repair on work capability of the patients and their employ‌ability. Subjects and Methods: The authors compared the work capability and employment of 35 patients with traumatic cranial bone defects after head trauma treated in the Neurosurgery Department of Assiut University Hospitals, Egypt from January 2013 to January 2018 before and after cranioplasty. Results: Before cranioplasty, 9 patients recorded that they were able to work without limitations according to the WHO classification, 6 patients recorded their ability to work with limitations and 20 recorded being unfit of working. After surgical reconstruction, 26 patients recorded they are able of working without limitations, 5 patients recorded they are able to work with limitations and 4 recorded being unfit to work for a variety of reasons. Conclusions: Cranioplasty is an efficient protective and cosmetic reconstructive operation, contributing to reduce disability and helping early return to work after traumatic cranial bone defects. This outcome may contribute to improved social and economic state of patients with cranial bone defects.
What Happens Where During Disasters? A Workflow for the Multifaceted Characterization of Crisis Events Based on Twitter Data

Twitter data are a valuable source of information for rescue and helping activities in case of natural disasters and technical accidents. Several methods for disaster&#8208; and event&#8208;related tweet filtering and classification are available to analyse social media streams. Rather than processing single tweets, taking into account space and time is likely to reveal even more insights regarding local event dynamics and impacts on population and environment. This study focuses on the design and evaluation of a generic workflow for Twitter data analysis that leverages that additional information to characterize crisis events more comprehensively. The workflow covers data acquisition, analysis and visualization, and aims at the provision of a multifaceted and detailed picture of events that happen in affected areas. This is approached by utilizing agile and flexible analysis methods providing different and complementary views on the data. Utilizing state&#8208;of&#8208;the&#8208;art deep learning and clustering methods, we are interested in the question, whether our workflow is suitable to reconstruct and picture the course of events during major natural disasters from Twitter data. Experimental results obtained with a data set acquired during hurricane Florence in September 2018 demonstrate the effectiveness of the applied methods but also indicate further interesting research questions and directions.
Causes and consequences of immune variation in a wild mammal

The immune system provides protection against parasites and is crucial for survival, but mounting, maintaining and controlling an immune response is expensive. Under limited resources these costs can lead to investment trade-offs between life history traits in order to maximize an individual's fitness. Understanding how these trade-offs relate to immunity can be important in understanding individual variation in fitness and the broader ecological implications that this may have in a population. In the wild there is evidence of trade-offs between life history traits and immunity, but there are relatively few studies which have measured specific aspects of the immune system under natural parasitic exposure. Using reagents developed in domestic sheep, I measured an unusually broad range of immune markers in a wild population of Soay sheep on the island of Hirta, St Kilda, Scotland. These include: T cell subsets (CD4+, CD8+, CD4+ & CD8+ naïve, gamma delta and Foxp3), anti-T.circumcincta (T. circ) antibody isotopes, (IgA, IgE, IgG), leukocyte subtypes (neutrophil:lymphocyte ratio & eosinophils), and leukocyte telomere length (LTL). I found that, in a year under high selection pressure for survival, anti-T. circ IgG positively predicted survival across all ages and for both sexes. Additionally, females had higher proportions of naïve T cells than males; a previously unreported sex difference in a wild mammal. In chapter 2, analysis of lambs in early life found higher growth rates associated with low antibody measures, while lower growth rates related to low antibody measures and high levels of inflammatory marker. I also found that male lambs with high anti-T. circ IgE and IgG were less likely to survive over-winter, contrary to the findings across all ages in chapter 1. In chapter 3, I detected an increase in LTL attrition with age in males >3 years, but this was not significant in females or in younger animals. In male lambs, high investment in horn growth was related to reduced LTL. Changes in LTL were independent of variation in leukocyte cell populations. The data in this thesis demonstrate the complexity of immune variation in the wild, and highlight the value of multiple ecologically relevant markers to understanding the evolutionary implications of resource trade-offs.
A Machine‐Learning‐Based Model for Water Quality in Coastal Waters, Taking Dissolved Oxygen and Hypoxia in Chesapeake Bay as an Example

Hypoxia is a big concern in coastal waters as it affects ecosystem health, fishery yield, and marine water resources. Accurately modeling coastal hypoxia is still very challenging even with the most advanced numerical models. A data‐driven model for coastal water quality is proposed in this study and is applied to predict the temporal‐spatial variations of dissolved oxygen (DO) and hypoxic condition in Chesapeake Bay, the largest estuary in the United States with mean summer hypoxic zone extending about 150 km along its main axis. The proposed model has three major components including empirical orthogonal functions analysis, automatic selection of forcing transformation, and neural network training. It first uses empirical orthogonal functions to extract the principal components, then applies neural network to train models for the temporal variations of principal components, and finally reconstructs the three‐dimensional temporal‐spatial variations of the DO. Using the first 75% of the 32‐year (1985–2016) data set for training, the model shows good performance for the testing period (the remaining 25% data set). Selection of forcings for the first mode points to the dominant role of streamflow in controlling interannual variability of bay‐wide DO condition. Different from previous empirical models, the approach is able to simulate three‐dimensional variations of water quality variables and it does not use in situ measured water quality variables but only external forcings as model inputs. Even though the approach is used for the hypoxia problem in Chesapeake Bay, the methodology is readily applicable to other coastal systems that are systematically monitored.
Staging Melanocytic Skin Neoplasms Using High-Level Pixel-Based Features

The formation of malignant neoplasm can be seen as deterioration of a pre-malignant skin neoplasm in its functionality and structure. Distinguishing melanocytic skin neoplasms is a challenging task due to their high visual similarity with different types of lesions and the intra-structural variants of melanocytic neoplasms. Besides, there is a high visual likeliness level between different lesion types with inhomogeneous features and fuzzy boundaries. The abnormal growth of melanocytic neoplasms takes various forms from uniform typical pigment network to irregular atypical shape, which can be described by border irregularity of melanocyte lesion image. This work proposes analytical reasoning for the human-observable phenomenon as a high-level feature to determine the neoplasm growth phase using a novel pixel-based feature space. The pixel-based feature space, which is comprised of high-level features and other color and texture features, are fed into the classifier to classify different melanocyte neoplasm phases. The proposed system was evaluated on the PH2 dermoscopic images benchmark dataset. It achieved an average accuracy of 95.1% using a support vector machine (SVM) classifier with the radial basis function (RBF) kernel. Furthermore, it reached an average Disc similarity coefficient (DSC) of 95.1%, an area under the curve (AUC) of 96.9%, and a sensitivity of 99%. The results of the proposed system outperform the results of other state-of-the-art multiclass techniques.
A novel decentralized method based on the system engineering concept for reliability‐security constraint unit commitment in restructured power environment

In the deregulated power environment, including Central operator (CO) and Micro Grids (MGs), different parts of the network are dedicated to the private sector, and each of them seeks to increase their profits independently. The CO and MGs should cooperate and collaborate in terms of operating, security and reliability in the whole power system. This article proposes a new method based on a System of System (SoS) concept for the secure and economic hourly generation scheduling to find optimal operational point. The main methodology includes three steps. In the first step, the power system is divided into several subsystems by using a spectral clustering partitioning technique to reduce converge time by decentralizes methods. And also load forecasting based on a Gaussian probability distribution function to avoid conventional calculation and considering uncertainty of the loads has been presented. To find a similar scenario, and reduction scenario with low probability, the probabilistic method has been addressed. The main contribution of this method is removing scenarios with low value of probabilities and scenarios which are similar to each other. In fact, the reduced set must include scenarios which are scattered appropriately in the uncertain space while holding high probabilities. In order to estimate the similarity (distance) between two scenarios the Kantorovich distance is implemented. In the second step, the hierarchical Bi‐level optimization approach is used to execute the decentralized decision making to solve the Security Constraints Unit Commitment (SCUC) problem between CO and MGs. Regarding all physical relations and shared data among CO and MGs, the SoS concept and Bi‐level optimization are presented to find the optimal operating point of autonomous systems. In the third step, a random number of generators will be select. Hence, the initial iteration number is set. In this step, sampling from state space to classifying reliability object achieved (The expected energy not supplied and loss of load probability are the reliability criterion). The presented method is evaluated using a 6‐bus, the RTS 24‐bus, 118‐bus, and 4672‐bus as an IEEE test systems. The suggested structure has been implemented by GAMS, and the results illustrate the usefulness of the presented methodology. To comparing proposed approach with the centralized method, the results illustrate improving total operational costs and security (in RTS‐24($603,209), 118 bus ($2 562 154) and 4672‐bus ($9 185 168)) in scenario 3 near to 9%, 10% and 8% respectively. Similarly, by comparison (in three test systems) with genetic algorithm these improvements are near to 23%, 22% and 13% respectively.
PhysioPong: Serious Game Aplicado ao Processo de Reabilitação Física de Amputados de Membro Superior

The present study describes the proposal a solution computing based on the context of the Serious Game, to support in the process of physical rehabilitation of patients amputees of upper limbs. Therefore, when we developed a serious game PhysioPong, we expect that the proposal serves as a training tool, integrated into the technical monitoring of electromyography signal to assist in the early stages of the rehabilitation process of these patients. This training as physically strengthens the muscles so that they can later be protected, as well as provide the patient with a motivator environment for the treatment. Furthermore, another tool functionality is to create an environment for physical therapists to follow the progress of these individuals over the rehabilitation process. Nowadays, serious games have had been employed as a tool that can be used for several purposes, bringing a ludic experience of the players. The PhysioPong was developed by using the Unity game engine, combined with a sensor node module that aims to collect data from the myoelectric signals generates by a muscular contraction of an individual, being possible to perform actions in a game. To further measure the present purpose have been performing a usability test, furthermore scientific experiments with amputees patients. These experiments were carried together by physiotherapists of the physic rehabilitation service. The evaluation performed about the tool indicated promising results with a usability and mechanic appropriated, basing the use of PhysioPong.
MOLECULAR EVOLUTION '99 Concerted Evolution: Molecular Mechanism and Biological Implications

A surprisingly large fraction of the eukaryotic genome is repetitive. More than one third of the human genome consists of interspersed repetitive DNA, and tandemly repeated DNA sequences may occupy as much as 10% of the human genome. The majority of the repetitive sequences are nongenic; the rest encode multigene families. The genomic organization of repetitive DNA sequences takes different forms: these repetitive sequences either disperse throughout the genome, as with short interspersed sequences (SINEs), long interspersed sequences (LINEs), and transposable elements, or, like tRNA genes and human histone genes, they may cluster in one or a few chromosomal regions. Multigene families, including those for ribosomal RNAs (rRNAs) and small nuclear RNAs (snRNAs) (Pavelitz et al. 1995), as well as noncoding sequences such as satellite DNA, minisatellite sequences, and microsatellite sequences (Charlesworth et al. 1994), are often arranged in tandem arrays. Despite the abundance of repetitive DNA in the genomes of eukaryotic organisms, the biological functions, if any, of noncoding repetitive sequences remain elusive. However, most repetitive sequences, whether coding or noncoding, exhibit an unexpected property: they evolve in a concerted fashion.
Bot Detection on Social Networks Using Persistent Homology

The growth of social media in recent years has contributed to an ever-increasing network of user data in every aspect of life. This volume of generated data is becoming a vital asset for the growth of companies and organizations as a powerful tool to gain insights and make crucial decisions. However, data is not always reliable, since primarily, it can be manipulated and disseminated from unreliable sources. In the field of social network analysis, this problem can be tackled by implementing machine learning models that can learn to classify between humans and bots, which are mostly harmful computer programs exploited to shape public opinions and circulate false information on social media. In this paper, we propose a novel topological feature extraction method for bot detection on social networks. We first create weighted ego networks of each user. We then encode the higher-order topological features of ego networks using persistent homology. Finally, we use these extracted features to train a machine learning model and use that model to classify users as bot vs. human. Our experimental results suggest that using the higher-order topological features coming from persistent homology is promising in bot detection and more effective than using classical graph-theoretic structural features.
Viscerotropic Leishmaniasis; Diagnosis and species detection using the polymerase chain reaction with kinetoplast DNA-specific primers in Balb/c mice

The study involves three groups of Balb/c mice, the first and second groups inoculated in the hind footpad (1 x 106), (3 x 106) stationary phase promastigotes of L. tropica respectively, the third group inoculated phosphate buffer saline. The aim of this study was to establish a murine model for human viscerotropic leishmaniasis, and the dissemination of L. tropica to visceral organs tissues of BALB/c mice in order to find out the ability to diagnosis the disease by polymerase chain reaction (PCR) as recent application. The samples of liver tissues were collected and DNA was extracted by Promega DNA kit purification. The polymerase chain reaction (PCR) with kDNA specific primers was used firstly to distinguish the species of parasite which isolate from patient had cutaneous lesion acquired in Iraq where only L. major and L. tropica were expected to be the causative agents. The size difference between the PCR products of L. major and L. tropica allowed differential diagnosis. The smaller product (650 bp) could be identified as derived from L. major, whereas the larger product (800 bp) was due either to L. tropica or to a member of the L. donovani complex which yielded the same size of band. The same primers were used to detect the dissemination of parasites to visceral organs via extracted DNA from liver tissues of infected animals. PCR application was very high sensitive and early diagnosis in liver tissues as a substrate for PCR reaction, all animals of group (1) which infected with (106) promastigotes were negative result at (15) days post-infection, whereas three animals from group (2) which infected with (3x106) promastigotes showed positive result at the same period. All animals except one from group (1) showed positive results at (30) days post-infection. But at the later sequences days all animals were positive.
HIGH PRICE FLUCTUATIONS: A MAJOR MARKETING PROBLEM AND A DETERRENT TO SMALLHOLDERS MARKET PARTICIPATION IN GHANA

Dr. Alfred Mensah Business & Management and Healthcare: Tower Hill and Holborn Study Centres, Arden University, UK. Manuscript Info Abstract . Manuscript History Received: 05 June 2020 Final Accepted: 10 July 2020 Published: August 2020
Forest fragmentation and loss reduce richness, availability, and specialization in tropical hummingbird communities

Hummingbirds are important pollinators of many native Neotropical plants but their abundance and diversity in landscapes dominated by intensive human uses such as agriculture have rarely been examined, despite such land‐uses prevailing in the tropics. We examined how tropical deforestation affects hummingbird community structure in premontane forest patches embedded in a tropical countryside of Coto Brus Canton, Costa Rica. We captured hummingbirds in fourteen landscapes representing a gradient in patch size and forest amount, and tested for the effects of these variables on (1) hummingbird captures at flowers (pollinator availability); (2) species richness; and (3) filtering of functional traits. After accounting for sampling effects, both hummingbird availability and species richness declined by 40% and 50%, respectively, across the gradient in deforestation that we observed (9–66% forest within 1000 m). Focal patch size was the strongest predictor, even after statistically accounting for the amount of forest and matrix composition of landscapes. These reductions in availability and richness were well predicted by functional traits; morphologically specialized species with the capacity to transport long‐distance outcrossed pollen and low functional redundancy within the pollinator network showed the greatest sensitivity to landscape change. We hypothesize that declines in hummingbird availability, diversity, and functional traits are important mechanisms driving the observed pollen limitation of ornithophilous flowers in fragmented tropical landscapes. Efforts to conserve large forest patches and enhance matrix permeability are critical for maintaining forest hummingbird communities and pollination services under current and predicted deforestation regimes.
Motion sensor data anonymization by time-frequency filtering

Recent advances in wireless actimetry sensors allow recognizing human real-time activities with mobile devices. Although the analysis of data generated by these devices can have many benefits for healthcare, these data also contains private information about users without their awareness and may even cause their re-identification. In this paper, we propose a privacy-preserving framework for activity recognition. The method consists of a two-step process. First, acceleration signals are encoded in the time-frequency domain by three different linear transforms. Second, we propose a method to anonymize the acceleration signals by filtering in the time-frequency domain. Finally, we evaluate our approach for the three different linear transforms with a neural network classifier by comparing the performances for activity versus identity recognition. We extensively study the validity of our framework with a reference dataset: results show an accurate activity recognition (85%) while limiting the re-identifation rate (32%). This represents a large utility improvement (19%) against a slight privacy decrease (10%) compared to state-of-the-art baseline.
Influence of drinking diet with low content of deuterium on the indicators of the estrous cycle and the adaptive capabilities of the organism in female rats in the presenile age

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Milk Evolution with Emphasis on the Atlantogenata

The milk composition of each species is unique, because it has evolved according to specific needs and adaptation to the environment. As milk data from more species became available, an evolutionary trend shows a phylogenetic distinction between Eutherian taxa regarding milk composition. For example, differences in the composition and properties of nutrients have been reported between carnivores and herbivores, as well as between ruminants and non-ruminants. Although limited, data of the three subclades of the Eutheria (Xenarthra, Afrotheria and Boreoeutheria) suggest that a phylogenetic distinction in milk composition may also exist between them. In this review, the validity of such a distinction is investigated with emphasis on the Atlantogenata.