Miracle-dz/UniFAR-FTUs huggingface dataset:
Growth Anomalies on the Coral Genera Acropora and Porites Are Strongly Associated with Host Density and Human Population Size across the Indo-Pacific

Growth anomalies (GAs) are common, tumor-like diseases that can cause significant morbidity and decreased fecundity in the major Indo-Pacific reef-building coral genera, Acropora and Porites. GAs are unusually tractable for testing hypotheses about drivers of coral disease because of their pan-Pacific distributions, relatively high occurrence, and unambiguous ease of identification. We modeled multiple disease-environment associations that may underlie the prevalence of Acropora growth anomalies (AGA) (n = 304 surveys) and Porites growth anomalies (PGA) (n = 602 surveys) from across the Indo-Pacific. Nine predictor variables were modeled, including coral host abundance, human population size, and sea surface temperature and ultra-violet radiation anomalies. Prevalence of both AGAs and PGAs were strongly host density-dependent. PGAs additionally showed strong positive associations with human population size. Although this association has been widely posited, this is one of the first broad-scale studies unambiguously linking a coral disease with human population size. These results emphasize that individual coral diseases can show relatively distinct patterns of association with environmental predictors, even in similar diseases (growth anomalies) found on different host genera (Acropora vs. Porites). As human densities and environmental degradation increase globally, the prevalence of coral diseases like PGAs could increase accordingly, halted only perhaps by declines in host density below thresholds required for disease establishment.
A Novel Rho-Like Protein TbRHP Is Involved in Spindle Formation and Mitosis in Trypanosomes

Background In animals and fungi Rho subfamily small GTPases are involved in signal transduction, cytoskeletal function and cellular proliferation. These organisms typically possess multiple Rho paralogues and numerous downstream effectors, consistent with the highly complex contributions of Rho proteins to cellular physiology. By contrast, trypanosomatids have a much simpler Rho-signaling system, and the Trypanosoma brucei genome contains only a single divergent Rho-related gene, TbRHP (Tb927.10.6240). Further, only a single RhoGAP-like protein (Tb09.160.4180) is annotated, contrasting with the >70 Rho GAP proteins from Homo sapiens. We wished to establish the function(s) of TbRHP and if Tb09.160. 4180 is a potential GAP for this protein. Methods/Findings TbRHP represents an evolutionarily restricted member of the Rho GTPase clade and is likely trypanosomatid restricted. TbRHP is expressed in both mammalian and insect dwelling stages of T. brucei and presents with a diffuse cytoplasmic location and is excluded from the nucleus. RNAi ablation of TbRHP results in major cell cycle defects and accumulation of multi-nucleated cells, coinciding with a loss of detectable mitotic spindles. Using yeast two hybrid analysis we find that TbRHP interacts with both Tb11.01. 3180 (TbRACK), a homolog of Rho-kinase, and the sole trypanosome RhoGAP protein Tb09.160. 4180, which is related to human OCRL. Conclusions Despite minimization of the Rho pathway, TbRHP retains an important role in spindle formation, and hence mitosis, in trypanosomes. TbRHP is a partner for TbRACK and an OCRL-related trypanosome Rho-GAP.
Platform-level design for a biomimetic amphibious robot

This paper addresses a platform-based design for a bio-inspired amphibious robot well-suited for multi-mode motion both on ground and under water. It concerns mechanical design, hardware implementation, software design, as well as ADAMS-based modeling. By introducing well-established software and hardware architectures, an ARM+uC/OS-II centered embedded control system is formed. With the sensor subsystem processing the gathered robotic and environmental information effectively, different motion modes will be adopted under dynamically changing environments. Preliminary testing partly verifies the feasibility of the platform-oriented, amphibious robotic prototype.
Energy Efficient Big Data Networks: Impact of Volume and Variety

In this paper, we study the impact of big data’s volume and variety dimensions on energy efficient big data networks (EEBDN) by developing a mixed integer linear programming (MILP) model to encapsulate the distinctive features of these two dimensions. First, a progressive energy efficient edge, intermediate, and central processing technique is proposed to process big data’s raw traffic by building processing nodes (PNs) in the network along the way from the sources to datacenters. Second, we validate the MILP operation by developing a heuristic that mimics, in real time, the behavior of the MILP for the volume dimension. Third, we test the energy efficiency limits of our green approach under several conditions where PNs are less energy efficient in terms of processing and communication compared to data centers. Fourth, we test the performance limits in our energy efficient approach by studying a “software matching” problem where different software packages are required to process big data. The results are then compared to the classical big data networks (CBDN) approach where big data is only processed inside centralized data centers. Our results revealed that up to 52% and 47% power saving can be achieved by the EEBDN approach compared to the CBDN approach, under the impact of volume and variety scenarios, respectively. Moreover, our results identify the limits of the progressive processing approach and in particular the conditions under which the CBDN centralized approach is more appropriate given certain PNs energy efficiency and software availability levels.
The Impact of the Massachusetts Health Care Reform on Health Care Use Among Children.

In 2006 Massachusetts enacted a major health care reform aimed at achieving near-universal coverage in the state. While other studies have found that this reform substantially affected the use of health services in general, the impact of the reform on children is largely unexplored. Children are of special interest to policymakers because it is widely believed that better health in early childhood results in large payoffs to adult health and achievement. I analyze how the reform affected the insurance coverage, health care utilization patterns, and health outcomes of children under 18 years old.
Syntax-Aware Language Modeling with Recurrent Neural Networks

Neural language models (LMs) are typically trained using only lexical features, such as surface forms of words. In this paper, we argue this deprives the LM of crucial syntactic signals that can be detected at high confidence using existing parsers. We present a simple but highly effective approach for training neural LMs using both lexical and syntactic information, and a novel approach for applying such LMs to unparsed text using sequential Monte Carlo sampling. In experiments on a range of corpora and corpus sizes, we show our approach consistently outperforms standard lexical LMs in character-level language modeling; on the other hand, for word-level models the models are on a par with standard language models. These results indicate potential for expanding LMs beyond lexical surface features to higher-level NLP features for character-level models.
Rapid Qualitative Urinary Tract Infection Pathogen Identification by SeptiFast® Real-Time PCR

Background Urinary tract infections (UTI) are frequent in outpatients. Fast pathogen identification is mandatory for shortening the time of discomfort and preventing serious complications. Urine culture needs up to 48 hours until pathogen identification. Consequently, the initial antibiotic regimen is empirical. Aim To evaluate the feasibility of qualitative urine pathogen identification by a commercially available real-time PCR blood pathogen test (SeptiFast®) and to compare the results with dipslide and microbiological culture. Design of study Pilot study with prospectively collected urine samples. Setting University hospital. Methods 82 prospectively collected urine samples from 81 patients with suspected UTI were included. Dipslide urine culture was followed by microbiological pathogen identification in dipslide positive samples. In parallel, qualitative DNA based pathogen identification (SeptiFast®) was performed in all samples. Results 61 samples were SeptiFast® positive, whereas 67 samples were dipslide culture positive. The inter-methodological concordance of positive and negative findings in the gram+, gram- and fungi sector was 371/410 (90%), 477/492 (97%) and 238/246 (97%), respectively. Sensitivity and specificity of the SeptiFast® test for the detection of an infection was 0.82 and 0.60, respectively. SeptiFast® pathogen identifications were available at least 43 hours prior to culture results. Conclusion The SeptiFast® platform identified bacterial DNA in urine specimens considerably faster compared to conventional culture. For UTI diagnosis sensitivity and specificity is limited by its present qualitative setup which does not allow pathogen quantification. Future quantitative assays may hold promise for PCR based UTI pathogen identification as a supplementation of conventional culture methods.
A reactive decision support system for intermodal freight transportation. (Un système réactif d'aide à la décision pour le transport intermodal de marchandises)

Le transport fluvial de conteneurs constitue une activite economique importante qui suscite un interet grandissant de la part de scientifiques. Considere comme durable et economique, le transport par barge a ete identifie comme etant une alternative competitive pour le transport de marchandises, en complement des modes traditionnels de transport, routier et ferroviaire. Neanmoins, les travaux de recherche en rapport avec la planification et le management du transport par barge, en particulier dans le contexte du transport intermodal, sont encore peu abondants. Le but de cette these est d’apporter une contribution dans ce domaine, par la proposition de modeles et de methodes de planification et gestion avancees, dans le cadre d’un systeme d’aide a la decision pour le transport de conteneurs par barge developpe pour accompagner les operateurs de transport. La methodologie proposee fait appel a des concepts et principes de gestion du revenu, des ressources et des services de transport pour la conception de plans de services reguliers avec horaires, au niveau tactique. Les operateurs de transport peuvent ainsi offrir des plans de transport avec des services plus flexibles pour leurs clients, tout en assurant un meilleur niveau de fiabilite. Plus de demandes de transport pourront ainsi etre satisfaites, avec globalement une plus grande satisfaction des chargeurs. Une originalite importante proposee par notre approche est l’utilisation de principes et techniques de gestion du revenu (segmentation du marche, classes tarifaires.) aussi bien au niveau operationnel de la modelisation qu’au niveau tactique. Les problemes d’optimisation sont formalises sous forme de modeles de programmation lineaire mixte en nombres entiers (PLNE), implementes et testes sous differentes configurations de reseaux de transport et differents scenarios de demandes, et ce pour chaque niveau de decision. Au niveau tactique, une nouvelle approche de resolution, combinant la recherche adaptative a voisinage large (ALNS) et la recherche taboue, est proposee pour resoudre des problemes PLNE de grande taille. Une plateforme de simulation, qui integre les niveaux tactique et operationnel de prise de decision, est proposee pour la validation du systeme d’aide a la decision sous differentes configurations : differentes topologies du reseau physique, differents parametres pour la gestion du revenu, differents degres de precision caracterisant les previsions de demande. Pour l’analyse des resultats numeriques ainsi obtenus, plusieurs types d’indicateurs de performance sont proposes et utilises.
Isolation of Primary Human Hepatocytes from Normal and Diseased Liver Tissue: A One Hundred Liver Experience

Successful and consistent isolation of primary human hepatocytes remains a challenge for both cell-based therapeutics/transplantation and laboratory research. Several centres around the world have extensive experience in the isolation of human hepatocytes from non-diseased livers obtained from donor liver surplus to surgical requirement or at hepatic resection for tumours. These livers are an important but limited source of cells for therapy or research. The capacity to isolate cells from diseased liver tissue removed at transplantation would substantially increase availability of cells for research. However no studies comparing the outcome of human hepatocytes isolation from diseased and non-diseased livers presently exist. Here we report our experience isolating human hepatocytes from organ donors, non-diseased resected liver and cirrhotic tissue. We report the cell yields and functional qualities of cells isolated from the different types of liver and demonstrate that a single rigorous protocol allows the routine harvest of good quality primary hepatocytes from the most commonly accessible human liver tissue samples.
The 3 steps of best data warehouse model design with leaning implementation for sales transaction in franchise restaurant

Doing Data Warehouse (DW) to your business or system is not only think about the trend only, but how to understand the DW knowledge itself and how to implement it. DW as a real technology of Artificial Intelligent (AI) which show up as how to think like a human, inevitably help the human particularly in making quick decision reports. Doing DW to your systems should apply to the mother knowledge of AI which we recognized as Software Engineering (SE) where we should apply best software application in more importantly we should satisfy the user. In order to make a good DW model design as user expectation then we should apply 3 steps such as collect all the needed reporting, order all the reports start from the most needed reports and mapping all the ordered reports into fact constellation schema. In this paper the 3 steps to model DW schema is applied in sale transaction in franchise restaurant as an example data. Building franchise restaurant must supported by information technology such as Data Warehouse (DW) in order to manage and control the business process, the branch, the sale, the staff and so on.
Hyperspectral Image Denoising Using Local Low-Rank Matrix Recovery and Global Spatial–Spectral Total Variation

Hyperspectral images (HSIs) are usually contaminated by various kinds of noise, such as stripes, deadlines, impulse noise, Gaussian noise, and so on, which significantly limits their subsequent application. In this paper, we model the stripes, deadlines, and impulse noise as sparse noise, and propose a unified mixed Gaussian noise and sparse noise removal framework named spatial–spectral total variation regularized local low-rank matrix recovery (LLRSSTV). The HSI is first divided into local overlapping patches, and rank-constrained low-rank matrix recovery is adopted to effectively separate the low-rank clean HSI patches from the sparse noise. Differing from the previous low-rank-based HSI denoising approaches, which process all the patches individually, a global spatial–spectral total variation regularized image reconstruction strategy is utilized to ensure the global spatial–spectral smoothness of the reconstructed image from the low-rank patches. In return, the globally reconstructed HSI further promotes the separation of the local low-rank components from the sparse noise. An augmented Lagrange multiplier method is adopted to solve the proposed LLRSSTV model, which simultaneously explores both the local low-rank property and the global spatial–spectral smoothness of the HSI. Both simulated and real HSI experiments were conducted to illustrate the advantage of the proposed method in HSI denoising, from visual/quantitative evaluations and time cost.
csrT Represents a New Class of csrA-Like Regulatory Genes Associated with Integrative Conjugative Elements of Legionella pneumophila

ABSTRACT Bacterial evolution is accelerated by mobile genetic elements. To spread horizontally and to benefit the recipient bacteria, genes encoded on these elements must be properly regulated. Among the legionellae are multiple integrative conjugative elements (ICEs) that each encode a paralog of the broadly conserved regulator csrA. Using bioinformatic analyses, we deduced that specific csrA paralogs are coinherited with particular lineages of the type IV secretion system that mediates horizontal spread of its ICE, suggesting a conserved regulatory interaction. As a first step to investigate the contribution of csrA regulators to this class of mobile genetic elements, we analyzed here the activity of the csrA paralog encoded on Legionella pneumophila ICE-βox. Deletion of this gene, which we name csrT, had no observed effect under laboratory conditions. However, ectopic expression of csrT abrogated the protection to hydrogen peroxide and macrophage degradation that ICE-βox confers to L. pneumophila. When ectopically expressed, csrT also repressed L. pneumophila flagellin production and motility, a function similar to the core genome's canonical csrA. Moreover, csrT restored the repression of motility to csrA mutants of Bacillus subtilis, a finding consistent with the predicted function of CsrT as an mRNA binding protein. Since all known ICEs of legionellae encode coinherited csrA-type IV secretion system pairs, we postulate that CsrA superfamily proteins regulate ICE activity to increase their horizontal spread, thereby expanding L. pneumophila versatility. IMPORTANCE ICEs are mobile DNA elements whose type IV secretion machineries mediate spread among bacterial populations. All surveyed ICEs within the Legionella genus also carry paralogs of the essential life cycle regulator csrA. It is striking that the csrA loci could be classified into distinct families based on either their sequence or the subtype of the adjacent type IV secretion system locus. To investigate whether ICE-encoded csrA paralogs are bona fide regulators, we analyzed ICE-βox as a model system. When expressed ectopically, its csrA paralog inhibited multiple ICE-βox phenotypes, as well as the motility of not only Legionella but also Bacillus subtilis. Accordingly, we predict that CsrA regulators equip legionellae ICEs to promote their spread via dedicated type IV secretion systems.
Understanding Quality Attributes in Microservice Architecture

As a prevalent architectural style, microservice architecture overcomes the challenges of monolithic architecture and achieves better quality by implementing small-scale microservice, rather than binding all functions into one monolith. Welldesigned microservice architecture with better quality relies on clear understanding of related quality attributes. However, current understanding of quality attributes in microservice architecture is deficient and not comprehensive. In this study, we aim to construct knowledge of quality attributes in architecture through a Systematic Literature Review (SLR), the exploratory case study and the explanatory survey. By analyzing the influential factors and the corresponding tactics of related quality attributes, our research is aimed at providing a comprehensive guide on quality improvement in microservice architecture.
Off line Arabic handwritten character using neural network

Character Recognition (CR) considered as one of the most important in the field of pattern recognition. The ultimate objectives of the Optical Character Recognition (OCR) system is to simulate the capability of reading, hence the OCR considered as artificial intelligence. In this paper, a character-handwritten recognition for the Arabic language is developed. The main aim of the system is to save time and effort Arabic OCR. In addition, to be the alternative of the typing manual due to provide it fast and reliable. The system has four main stages; preprocessing, segmentation, feature extraction, classification, and recognition. The system is off-line and depends on the image acquisition. So, after acquitted the image has to go through the main stages. The Neural Network used as a classifier. The proposed system is able to recognize as many characters as can with high accuracy rate. In addition, it is focusing on the character that has similarities and the system will also be considered about the number of dots and its position, and the connected components.
Man and the Last Great Wilderness: Human Impact on the Deep Sea

The deep sea, the largest ecosystem on Earth and one of the least studied, harbours high biodiversity and provides a wealth of resources. Although humans have used the oceans for millennia, technological developments now allow exploitation of fisheries resources, hydrocarbons and minerals below 2000 m depth. The remoteness of the deep seafloor has promoted the disposal of residues and litter. Ocean acidification and climate change now bring a new dimension of global effects. Thus the challenges facing the deep sea are large and accelerating, providing a new imperative for the science community, industry and national and international organizations to work together to develop successful exploitation management and conservation of the deep-sea ecosystem. This paper provides scientific expert judgement and a semi-quantitative analysis of past, present and future impacts of human-related activities on global deep-sea habitats within three categories: disposal, exploitation and climate change. The analysis is the result of a Census of Marine Life – SYNDEEP workshop (September 2008). A detailed review of known impacts and their effects is provided. The analysis shows how, in recent decades, the most significant anthropogenic activities that affect the deep sea have evolved from mainly disposal (past) to exploitation (present). We predict that from now and into the future, increases in atmospheric CO2 and facets and consequences of climate change will have the most impact on deep-sea habitats and their fauna. Synergies between different anthropogenic pressures and associated effects are discussed, indicating that most synergies are related to increased atmospheric CO2 and climate change effects. We identify deep-sea ecosystems we believe are at higher risk from human impacts in the near future: benthic communities on sedimentary upper slopes, cold-water corals, canyon benthic communities and seamount pelagic and benthic communities. We finalise this review with a short discussion on protection and management methods.