Miracle-dz/UniFAR-FTUs huggingface dataset:
Active-Workspaces: A Dynamic Collaborative Business Process Model for Disease Surveillance Systems

Flexibility and change at both design-and run-time are fast becoming the Rule rather than the Exception in disease surveillance processes. This is attributed to the diversity in public health threats, to continuous advances in domain knowledge, the increase in expert knowledge, and the diverse and heterogeneous nature of contextual variables. Disease surveillance is one such processes and it is characterized by collaborative work and decision making between users with heterogeneous profiles on processes designed on-the-fly. A model for disease surveillance processes should thus natively support flexible workflow design and enactment as well as human interactions. We show in this paper how the Active Workspaces model proposed by Badouel et al. for distributed collaborative systems provides this support.
Inositol Trisphosphate Kinase and Diphosphoinositol Pentakisphosphate Kinase Enzymes Constitute the Inositol Pyrophosphate Synthesis Pathway in Plants

Inositol pyrophosphates (PP-InsPs) are an emerging class of “high-energy” intracellular signaling molecules containing one or two diphosphate groups attached to an inositol ring, with suggested roles in bioenergetic homeostasis and inorganic phosphate (Pi) sensing. Information regarding the biosynthesis of these unique class of signaling molecules in plants is scarce, however the enzymes responsible for their biosynthesis in other eukaryotes have been well described. Here we report the characterization of the two Arabidopsis VIP kinase domains, a newly discovered activity of the Arabidopsis ITPK1 and ITPK2 enzymes, and the subcellular localization of the enzymes involved in the synthesis of InsP6 and PP-InsPs. Our data indicate that AtVIP1-KD and AtVIP2-KD act primarily as 1PP-specific Diphosphoinositol Pentakisphosphate Kinases (PPIP5) Kinases. The AtITPK enzymes, in contrast, can function as InsP6 kinases, and thus are the missing enzyme in the plant PP-InsP synthesis pathway. Together, these enzyme classes can function in plants to produce PP-InsPs, which have been implicated in signal transduction and Pi sensing pathways. We measured a higher InsP7 level (increased InsP7/InsP8 ratio) in vip1/vip2 double loss-of-function mutants, and an accumulation of InsP8 (decreased InsP7/InsP8 ratio) in the 35S:VIP2 overexpression line relative to wild-type plants. We also report that enzymes involved in the synthesis of InsPs and PP-InsPs accumulate within the nucleus and cytoplasm of plant cells. Our work defines a molecular basis for understanding how plants synthesize PP-InsPs which is crucial for determining the roles of these signaling molecules in processes such as Pi sensing. SIGNIFICANCE STATEMENT Inositol pyrophosphate signaling molecules are of agronomic importance as they can control complex responses to the limited nutrient, phosphate. This work fills in the missing steps in the inositol pyrophosphate synthesis pathway and points to a role for these molecules in the plant cell nucleus. This is an important advance that can help us design future strategies to increase phosphate efficiency in plants.
Structural genomic applied on the rust fungus Melampsora larici-populina reveals two candidate effector proteins adopting cystine-knot and nuclear transport factor 2-like protein folds

Rust fungi are plant pathogens that secrete an arsenal of effector proteins interfering with plant functions and promoting parasitic infection. Effectors are often species-specific, evolve rapidly, and display low sequence similarities with known proteins or domains. How rust fungal effectors function in host cells remains elusive, and biochemical and structural approaches have been scarcely used to tackle this question. In this study, we used a strategy based on recombinant protein production in Escherichia coli to study eleven candidate effectors of the leaf rust fungus Melampsora larici-populina. We successfully purified and solved the three-dimensional structure of two proteins, MLP124266 and MLP124017, using NMR spectroscopy. Although both proteins show no sequence similarity with known proteins, they exhibit structural similarities to knottin and nuclear transport factor 2-like proteins, respectively. Altogether, our findings show that sequence-unrelated effectors can adopt folds similar to known proteins, and encourage the use of biochemical and structural approaches to functionally characterize rust effector candidates.
Severe population bottleneck and cranial morphology change in the Mednyi Island subspecies of Arctic fox Vulpes lagopus (Carnivora: Canidae)

Abstract Arctic foxes, Vulpes lagopus living on Mednyi Island suffered a drastic decline in population size in the late 1970s due to an outbreak of mange epizootic. This dramatic fall in numbers rendered the subspecies endangered, and the concomitant loss of variability resulted in a population bottleneck. Here, we investigate whether differences in cranial morphology between Mednyi Island Arctic foxes and Bering Island Arctic foxes could be attributed to the severe population bottleneck suffered by the Mednyi population in the 1970s. We used morphometric traits as proxies for genetic data to provide estimates of FST. Results show higher FST estimates for the Mednyi population than for the Bering population, which we interpret as a bottleneck signature. FST results also indicate a pattern of divergence between the two populations consistent with random genetic drift. Bottleneck detection is critical for the interpretation of the demographic history of the endangered Mednyi Island Arctic fox, with consequences for conservation management.
Repertoire Sequencing of B Cells Elucidates the Role of UNG and Mismatch Repair Proteins in Somatic Hypermutation in Humans

The generation of high-affinity antibodies depends on somatic hypermutation (SHM). SHM is initiated by the activation-induced cytidine deaminase (AID), which generates uracil (U) lesions in the B-cell receptor (BCR) encoding genes. Error-prone processing of U lesions creates a typical spectrum of point mutations during SHM. The aim of this study was to determine the molecular mechanism of SHM in humans; currently available knowledge is limited by the number of mutations analyzed per patient. We collected a unique cohort of 10 well-defined patients with bi-allelic mutations in genes involved in base excision repair (BER) (UNG) or mismatch repair (MMR) (MSH2, MSH6, or PMS2) and are the first to present next-generation sequencing (NGS) data of the BCR, allowing us to study SHM extensively in humans. Analysis using ARGalaxy revealed selective skewing of SHM mutation patterns specific for each genetic defect, which are in line with the five-pathway model of SHM that was recently proposed based on mice data. However, trans-species comparison revealed differences in the role of PMS2 and MSH2 in strand targeting between mice and man. In conclusion, our results indicate a role for UNG, MSH2, MSH6, and PMS2 in the generation of SHM in humans comparable to their function in mice. However, we observed differences in strand targeting between humans and mice, emphasizing the importance of studying molecular mechanisms in a human setting. The here developed method combining NGS and ARGalaxy analysis of BCR mutation data forms the basis for efficient SHM analyses of other immune deficiencies.
Advancing Leaf-Out and Flowering Phenology is Not Matched by Migratory Bird Arrivals Recorded in Hunting Guide's Journal in Aroostook County, Maine

Abstract Historical records have the potential to temporally and spatially expand ecological studies to places and periods that garnered the attention of earlier naturalists. Few historical or contemporary scientific studies have examined the local-to-regional ecological effects of climate change in northern Maine. Recently uncovered journals of L.S. Quackenbush, a hunting guide in mid-20th century Aroostook County, ME, provide an opportunity to incorporate new historical ecological data into climate change research.The leaf-out and flowering phenology observations in the Quackenbush journals are closely tied to spring temperatures and match the direction, though not the magnitude, of changes found in southern New England. Comparisons of Quackenbush's bird records to contemporary observations are less straightforward, but help fill an important gap in regional migratory bird phenology studies. Quackenbush's routine observations, recorded daily in a rural outpost in northern Maine, provide an important contribution to climate change research in a data-poor region and highlight a type of record that may be available in other rural areas.
A new features extraction technique for occluded object recognition based on boundaries' pixels description

Abstract: The ability to model the object and to distinguish it from a large number of database has been investigated by many researches working in the computer vision and pattern recognition field. In this paper, an object was modeled using boundaries pixel distance. The invariant has been resulted from the distance of each boundaries pixel to their central point. By performing polynomial regression on each set of sorted distances, a unique set of numerical features from the coefficients of this polynomial function has been produced. The experiments show that the coefficient of linear function from boundaries’ distance plot of each object has produced better recognition than polynomial function of degree more than one.
Antimicrobial effect of chinese creeper (Mikania Micrantia) leaf extract to E. Coli (Escherichia Coli) causing diarrhea

The study utilizes an experimental quantitative method. It was conducted in the science laboratory 5th floor Administration Building of the University of the Visayas. Antimicrobial activity of the leaf extract of Chinese creeper (Mikania micantha) used in traditional folk medicines were screened against E. coli (Escherichia coli) compared to Tetracycline, pharmaceutical drug. The highest antimicrobial potentially was exhibited by the Tetracyclic drug, followed by the different concentrations (100%, 75%, 50%, and 25%). The leaf extract of M. micantha can be considered to be equally potent as the most common effect antibiotic, such as Tetracycline. A sensitivity test performed with commonly used sensitivity test resulted in the appearance of drug and plant resistance of the bacteria tested. A comparison of data in the inhibition zones of E. coli (Escherichia coli) showed that M. micantha leaf extractand Tetracycline were effective against bacteria strain tested. Furthermore, the results suggest that traditional folk medicine could be used as a guide on the continuing search for new natural products with potential medicinal properties.
Chlamydia and Its Many Ways of Escaping the Host Immune System

The increasing number of new cases of Chlamydia infection worldwide may be attributed to the pathogen's ability to evade various host immune responses. Summarized here are means of evasion utilized by Chlamydia enabling survival in a hostile host environment. The pathogen's persistence involves a myriad of molecular interactions manifested in a variety of ways, e.g., formation of membranous intracytoplasmic inclusions and cytokine-induced amino acid synthesis, paralysis of phagocytic neutrophils, evasion of phagocytosis, inhibition of host cell apoptosis, suppression of antigen presentation, and induced expression of a check point inhibitor of programmed host cell death. Future studies could focus on the targeting of these molecules associated with immune evasion, thus limiting the spread and tissue damage caused by this pathogen.
Machine Learning Models of Breast Cancer Risk Prediction

Breast cancer is the most common cancer in women both in the developed and less developed world. Early detection based on clinical features can greatly increase the chances for successful treatment. Our goal was to construct a breast cancer prediction model based on machine learning algorithms. A total of 10 potential clinical features like age, BMI, glucose, insulin, HOMA, leptin, adiponectin, resistin, and MCP-1 were collected from 116 patients. In this report, most commonly used machine learning model such as decision tree (DT), random forest (RF), K-nearest neighbors (KNN), support vector machine (SVM), logistic regression (LR), and artificial neural network (ANN) models were tested for breast cancer prediction. A repeated 10-fold cross-validation model was used to rank variables on the randomly split dataset. The accuracy of DT, RF, SVM, LR, ANN, and KNN was 0.71, 0.71, 0.77, 0.80, 0.81, and 0.86 respectively. However, The KNN model showed most higher accuracy with area under receiver operating curve, sensitivity, and specificity of 0.95, 0.80, 0.91. Therefore, identification of breast cancer patients correctly would create care opportunities such as monitoring and adopting intervention plans may benefit the quality of care in long-term.
To study the effect of BT (Bacillus thruingiensis) cotton cultivation on soil health

To Study the Effect of Bt (Bacillus thuringiensis) Cotton Cultivation on Soil Health Name of student: Manohari Kumari Registration Number: CUP/MPhil-PhD/SEES/EVS/2009-2010/04 Degree for which submitted: M.Phil Name of supervisor: Dr. Sunil Mittal Centre: Centre of Environmental Science and Technology School of Studies: School of Environment and Earth Sciences
Design and development of an autonomous navigation system for an omni-directional four-wheeled mobile robot

A navigation system developed for an omni-directional wheeled mobile robot, called the Omnibot, is presented. This system is developed to enable the Omnibot to autonomously navigate, in a collision-free manner, along predefined paths in indoor structured office or factory-like environments. The navigation system is composed of four integrated subsystems: localization, pathfollowing, velocity control, and obstacle detection. The path-following subsystem is responsible for driving the Omnibot along a given path based on feedback about its location relative to its environment. A localization system that uses a combination of odometry and a novel indoor GPS-like system provides the necessary estimates of the Omnibot’s position and orientation (i.e., pose). Using the pose updates from the localization subsystem, the path-following subsystem is able to compute motion commands to drive the Omnibot along the path. Execution of these motion commands is performed by the velocity control subsystem, which uses feedback control to regulate the angular velocities of the motors driving the Omnibot’s wheels to produce the required motion of the robot. To ensure collision-free navigation, the Omnibot is equipped with an array of infrared distance sensors for detecting obstacles around its perimeter. Interaction between a human operator and the Omnibot is facilitated with a user-control interface running on a remote workstation. The interface allows the operator to visualize the Omnibot’s location within a 3D model of its indoor workspace and provides a means to input commands. Testing of the developed system is performed, and the results confirm its effectiveness at enabling the Omnibot to perform collision-free autonomous navigation in an indoor structured environment.
A Brief Study of Approaches to Text Feature Selection

With reducing cost of storage devices, increasing amounts of data is being stored and processed for extracting intelligence. Classification and clustering have been two major approaches in generating data abstraction. Over the last few years, text data is dominating the types of data shared and stored. Some of the sources of such datasets are mobile data, e-commerce, and wide-range of continuously expanding social-networking services. Within each of these sources, the nature of data differs drastically from formal language text to Twitter or SMS slangs thereby leading to the need for different ways of processing the data for making meaningful summarization. Such summaries could effectively be used for business advantage. Processing of such data requires identifying appropriate set of features both for efficiency and effectiveness. In the current Chapter, we propose to discuss approaches to text feature selection and make a comparative study.
Field Studies on the Pea Leaf Miner Liriomyza huidobrensis (Blanchard) and its Associated Ectoparasitoid Diglyphus isaea (Walker)

Afield study was carried out at the experimental farm of Sakha Agricultural Research station , Kafr El-Sheikh, Egypt during two seasons 2015/2016 and 2016/2017. The highest peak of abundance of L. huidobrensis total larvae on Pisum sativum plants recorded in the third week of December 2015 and represented by 62 larvae /100 leaflets. The highest peak for healthy larvae found in the third week of March 2016 and represented by 51 larvae. While the highest peak of abundance for the parasitized L. huidobrensis larvae by D. isaea found in the second week of January 2016 and represented by16 larvae. and unknown larval mortality recorded in the fourth week of December 2015 and represented by 11 larvae. The average number of the total larvae, healthy larvae, parasitized larvae by D. iseae and unknown larval mortality were recorded with an average of 27.3 ±1.7, 30.4 ±1.7, 4.1 ±0.11 and 3.5 ±0.6 larvae, respectively. The average percentage of the healthy larvae recorded 73.3% and the average percentage of parasitism caused by D. isaea was 14.5% while, the percentage of unknown larval mortality recorded 9.6% during the first season 2015/16. The highest peak of abundance of L. huidobrensis total larvae on Pisum sativum plants recorded in the second week of January 2017 and represented by 70 larvae /100 leaflets. The highest peak for healthy larvae found in the third week of December 2016 and represented by 41 larvae. While the highest peak of abundance for the parasitized L. huidobrensis larvae by D. isaea found in the second week of January and represented by 16 larvae. and unknown larval mortality recorded in the second week of January 2017 and represented by 21 larvae. The monthly average number of the total larvae, healthy larvae, parasitized larvae by D. iseae un known larval mortality were recorded with an average 28, 18.4, 2.9 and 6.6, respectively. The average percentage of the healthy larvae recorded 76.2% and the average percentage of parasitism caused by D. isaea was 6.6% while, the percentage of unknown larval mortality recorded 17.3% during the second season 2016/17.
A Morphology based Approach for Segmentation and Tumor Detection from MRI Images in Noisy Environment and Performance Evaluation

: In the field of medical image handling, location of brain tumor from Magnetic resonance image (MRI) examine has become to be an active amongst the most dynamic exploration. Detection of the tumor is the fundamental goal of the framework. In this paper, MRI brain image is utilized to tumor recognition process. This framework incorporates test the brain image process, image separating, segmentation, and morphological operation, tumor detection. In recent years many image processing procedures are widely used on medical images to detect tumors at an early and treatment stages. When the images are added with noise the difficulties are primarily associated with the detection of tumors. In such cases the intensity thresholding might fail to identify the tumors. Edges characterize boundaries are the fundamental importance in image processing. The image edge detection reduces the data by filtering and preserving the important structural attributes. So understanding the edge detection algorithms is essential. Here the morphology based region of interest segmentation combined with watershed transform of MRI tumor image is performed and comparative analysis in noisy environment such as Gaussian, salt and pepper, Poisson and speckle is performed. Here different edge detection filters such as average, Gaussian and laplacian, sobel and prewit, unsharp and log in presence of noise. The parameters such as Cross correlation, Peak signal to noise ratio, Mean absolute error for different operators are evaluated. The motivation behind the morphological operators is to separate the tumor part of the image. In this paper, the brain image is considered for examination and recognition. At first the region of interest is found, that recognizes the specific substance of the image and set the boundary of it. Morphological operations are utilized for edge identification. The four parameters, such as Probabilistic Rand Index (PRI), Variation of Information (VOI), Global Consistency Error (GCE), Structural similarity (SSIM) have been utilized to evaluate the performance of segmentation.
Oxidative stress in retinal pigment epithelial cells increased endogenous complement-dependent inflammatory and angiogenic responses - independent from exogenous complement sources

Oxidative stress-induced damage of the retinal pigment epithelium (RPE) together with chronic inflammation has been suggested as major contributors to retinal diseases. Here, we examine the effects of oxidative stress and endogenous complement components on the RPE and its pro-inflammatory and –angiogenic responses. The RPE cell line, ARPE-19, treated with H2O2 reduced cell-cell contacts, increased marker for epithelial–mesenchymal transition but showed less cell death. Stressed ARPE-19 cells increased the expression of complement receptors CR3 and C5aR1. CR3 was co-localized with cell-derived complement protein C3, which was observed in its activated form in ARPE-19 cells. C3 as well as its regulators CFH and properdin accumulated in ARPE-19 cells after oxidative stress independent from external complement sources. This cell-associated complement accumulation promoted nlrp3 and foxp3 expression and subsequent increased secretion of pro-inflammatory and pro-angiogenic factors. The complement-associated ARPE-19 reaction to oxidative stress, independent from external complement source, was increased by the PARP-inhibitor olaparib. Our results indicated that RPE cell-derived complement proteins and receptors are involved in RPE cell homeostasis following oxidative stress and should be considered as targets for treatment developments for retinal degeneration. GRAPHICAL ABSTRACT We show a functional link between oxidative stress, complement receptors, endogenous complement proteins, pro-angiogenic and -inflammatory responses in ARPE-19 cells. These effects are independent from extracellularly added complement proteins or receptor ligands. We suggest an oxidative stress-associated autocrine mechanism of complement receptor regulation in ARPE-19 cells in connection with upregulated intracellular proteases. HIGHLIGHTS Oxidative stress accumulates complement proteins and receptors in RPE cells Oxidative stress activates the RPE inflammasome without external complement proteins Oxidative stress increases foxp3 expression and IL-8/VEGF secretion in RPE cells Olaparib enhances pro-inflammatory response of RPE