Miracle-dz/UniFAR-FTUs huggingface dataset:
Onto based cluster labelling and incremental system for information retrieval

Document clustering is utilised for data retrieval, past task of labels to cluster individuals enhances quick retrieval, and Existing framework doles out labels because of standard terms show in archives. However, semantic marks are first taking into Document semantic relationship, the incremental calculation for the versatile framework. The proposed work allocates onto mark because of scientific cate-gorisation, i.e. ontology-based; word net Synsets and gleams coordinating and incremental dynamicity is accomplished through naming. The assessment is done utilising f-measure and figuring speed, contrasted and benchmark K-Means, K-Means without labels. Thus semantic labelling is designed more efficient than traditional document clustering methodologies and can be implemented for real-time internet document clustering applications.
PLACENTAL FACTORS IMPLICATED IN INTRAUTERINE GROWTH RESTRICTION

Intrauterine growth restriction (IUGR) is defined as the failure of the fetus to reach its genetic growth potential.It is one of the most frequent obstetrics problem in developing countries affecting approximately 30 million infants every year. The placenta is least understood human organ, which plays a crucial role in normal fetal growth. Any disturbance in placenta development leads to pregnancy complications like IUGR and until now no therapy interventions are available for this pathology. This review emphasises on the possible mechanisms in placenta like differential expression of proteins, deregulated pathways, DNA methylation changes that contribute to the pathogenesis of IUGR and also explains the various possible treatment modalities for placental repair and development.
Mutations of a redundant α -tubulin gene affect C. elegans early embryonic cleavage via MEI-1/katanin dependent and independent pathways

The C. elegans zygote supports both meiosis and mitosis within a common cytoplasm. The meiotic spindle is small and is located anteriorly, whereas the first mitotic spindle fills the zygote. The C. elegans microtubule-severing complex, katanin, is encoded by the mei-1 and mei-2 genes and is solely required for oocyte meiotic spindle formation; ectopic mitotic katanin activity disrupts mitotic spindles. Here we characterize two mutations that rescue the lethality caused by ectopic MEI-1/MEI-2. Both mutations are gain-of-function alleles of tba-2 α -tubulin. These tba-2 alleles do not prevent MEI-1/MEI-2 microtubule localization but interfere with its activity. TBA-1 and TBA-2 are redundant for viability, but when katanin activity is limiting, TBA-2 is preferred over TBA-1 by katanin. This is similar to what we previously reported for the β -tubulins. Removing both preferred α and β isoforms results in normal development, suggesting the katanin isoform preferences are not absolute. We conclude that while the C. elegans embryo expresses redundant α and β tubulins isoforms, they nevertheless have subtle functional specializations. Finally, we identified a dominant tba-2 allele that disrupts both meiotic and mitotic spindle formation independent of MEI-1/MEI-2 activity. Genetic studies suggest that this tba-2 mutation has a “poisonous” effect on microtubule function.
A summer and winter’s tale: factors influencing avian community composition and species abundances in lowland subtropical floodplain forests in central eastern Australia

The ecology of avian community assembly in subtropical climate areas with seasonal and year-to-year variability is complex and poorly understood. To test for variation in year–year and seasonal (summer–winter) avian community composition and species abundances, we established 10 transects (200 m long) and sampled twice yearly for 7 years. To differentiate year–year and seasonal (summer–winter) patterns from the potential disturbance effects caused by human activities associatedwith music festivals (events),we monitored community composition and species abundances on sites close to disturbance areas (impact) and sites distant from disturbances (control). Impacts from large scale music events included loud noise, and thousands of vehicle and people movements on a daily basis. Raw count, abundance-weighted, and feeding guild data were analysed using multivariate and univariate methods. Seasonal (summer–winter) patterns of food resource availability in dominant forest trees (fruit and nectar resources) were identified. We found strong signals for the influence of seasonality and seasonal resource availability on community composition and feeding guild representation (nectivores and frugivores). We detected somewhat weaker effects for location relative to disturbance (control vs impact) and weak effects for sample timing associated with disturbance from the events. Avian community composition showed high similarity between control and impact sites, was dynamic in space and time (year to year) and showed strong local and regional trends in response to seasonality. Avian species abundances were greater in less disturbed (control) forest habitats, highlighting the fundamental value of conservation reserves in protecting higher quality avian habitats.
A review on current and future prospective of Cancer Classification through Deep Learning

Cancer is the second foremost origin of death in the world, next to heart disease. The name cancer refers to more than a thousand sicknesses illustrate by out of direct development & replication of multiple cells. Due to this reason of cancer analysis, utilization of microarray datasets along with machine learning methods escalating in the current research scenario. Classification is one of the very broadly used datamining techniques to build a model that describes & distinguishes data classes in a manner to be used to predict the class of unseen instances. In machine learning, features are chosen manually for a classifier. With Deep learning features, extraction and modelling steps are automatic. Deep learning is one of the most significant among machine learning that requires computing system to iteratively perform calculations to identified patterns by itself. Deep learning use training data to discover underlying patterns, build models & make predictions based on the best fit model. In the last decades, there has been a growing interest of addressing cancer classification using deep learning due to their positive revival of neural networks and connectionism from the genuine integration of the latest advances in parallel processing enabled by coprocessors. Here the review of deep learning for classification in bioinformatics presenting examples of current research. Additionally, we discuss Deep learning and convolutional neural network working principles to provide a useful and comprehensive perspective, this paper presents three works DeepGen, SDAE, Enhance Feature learning in a brief description of each study. We believe that this review will provide valuable insights and serve as a starting point for the researcher to apply deep learning approaches for classification in Gene expression dataset.
Biochemical and Molecular Inhibition of Plastidial Carbonic Anhydrase Reduces the Incorporation of Acetate into Lipids in Cotton Embryos and Tobacco Cell Suspensions and Leaves 1

Two cDNAs encoding functional carbonic anhydrase (CA) enzymes were recently isolated from a non-photosynthetic, cotyledon library of cotton ( Gossypium hirsutum ) seedlings with putative plastid-targeting sequences (GenBank accession nos. AF132854 and AF132855). Relative CA transcript abundance and enzyme activity increased 9 and 15 times, respectively, in cotton embryos during the maximum period of reserve oil accumulation. Specific sulfonamide inhibitors of CA activity significantly reduced the rate of [ 14 C]acetate incorporation into total lipids in cotton embryos in vivo, and in embryo plastids in vitro, suggesting a role for CA in plastid lipid biosynthesis. CA inhibitors did not affect acetyl-coenzyme A carboxylase activity or total storage protein synthesis. Similar results were obtained for two other plant systems: cell suspensions (and isolated plastids therefrom) of tobacco ( Nicotiana tabacum ), and chloroplasts isolated from leaves of transgenic CA antisense-suppressed tobacco plants (5% of wild-type CA activity). In addition, tobacco cell suspensions treated with the CA inhibitor ethoxyzolamide showed a substantial loss of CO 2 compared with controls. The rate of [ 14 C]acetate incorporation into lipid in cell suspensions was reduced by limiting external [CO 2 ] (scrubbed air), and this rate was further reduced in the presence of ethoxyzolamide. Together, these results indicate that a reduction of CA activity (biochemical or molecular inhibition) impacts the rate of plant lipid biosynthesis from acetate, perhaps by impairing the ability of CA to efficiently “trap” inorganic carbon inside plastids for utilization by acetyl-coenzyme A carboxylase and the fatty acid synthesis machinery. of thin-layer chromatography (TLC)- fractionated lipid classes in comparison with triolein standards. CA activity was determined electrometrically in cell free homoge- nates. Poly(A (cid:2) ) RNA was isolated from cotton embryos and the ratios of CA to actin transcripts were evaluated by northern-blot analyses. results experiments.
EXPERIMENTAL OF ADVERTISEMENT WATCHING

The given article describes the experiment, which has tested the main hypothesis that primary school-aged children will consume larger amounts of food, and snacks while watching some cartoon including food advertising. It is concluded that our research results have demonstrated the food advertising influence on snacks while watching television. It has been noted that such eating behavior caused by food advertising may additionally facilitate the epidemic of obesity. It has been proved that in view of the findings the necessity to make efforts to reduce advertising of unhealthy food for children is flagrant.
in \-jun Transgenic Murine Cells1

The oncogene jun encodes a transcription factor of the VP-1 family. In mice carrying viral juif (v-jun) as a transgene, wounding is a prerequisite for tumorigenesis, suggesting collaboration between the transgene and a wound-related event. To define possible candidates for this collaborative process, we examined the effect of several wound-related polypeptide growth factors on cells from transgenic mice. Tumor necrosis factor a and interleukin In induce anchorage independence in embryo fibroblasts and tumor cell revertants from these mice. This effect was specific for the two cytokines and was restricted to cells from \-jun transgenic mice. Anchor age independence required the continued presence of the cytokines. Transfection of transgenic cells with a v-juit expression plasmid also induced anchorage independence and a tumorigenic phenotype in transgenic tu mor cell revertants. However, there was no correlation between anchorage independence, expression of Jun, and AP-1 activity. These results suggest that while increased transgene expression can enhance the growth prop erties of \-jun transgenic cells, there exist other cytokine-dependent mech anisms that have a similar effect. Although these agents affect both AP-1 transactivation po tential and DNA binding in the transgenic cells, the changes are not correlated with the inhibition of growth. epidermal fibroblast y-interferon, platelet-derived growth-promoting effects of TNF-a and IL-1a nontransgenic NIH controls. both forms the efficiency of agar colony formation of that this effect was not dependent on the presence of the jun transgene. Indeed, TGF-ÃŸis known to confer anchorage independence on normal mouse cells (52-55). We picked three agar colonies of I-7.1, two induced with TNF-a and one with IL-1a, and propagated them in the presence of the respective cytokines for 3 weeks. All three subcolonies induced tumors in nude mice, indicating enhanced growth in culture was correlated with acquisition of oncogenicity in the an imal. These results demonstrate that TNF-a and specifically increase anchorage independence and induce tumorigenicity in three independently derived populations of transgenic
The Collaborative Gaming for Business in Pervasive Networks

— Ubiquitous collaboration should be provided for pervasively children and business people in not relation to their socio-economical conditions. This ubiquity is freely and openly to access various chances and resources in networked communities for global citizens. New business and knowledge based economy can be engendered by these active people. In this regard, I will discuss needs, backgrounds, design, and practices of gaming collaboration for educing business senses. It was designed as one of the network based ubiquitous collaboration (e.g. Ubiquitous Jigsaw ). I will articulate the Ubiquitous Jigsaw toward organizational development in business. And finally all things considered, I introduce practices of business gaming using this collaboration.
s B Activity Depends on RsbU in Staphylococcus aureus

Derivatives of the widely used laboratory strain Staphylococcus aureus NCTC8325, which are natural rsbU mutants, were shown to be unable to produce RsbU, a positive regulator of the alternative sigma factor s B . The lack of RsbU prevented the heat-dependent production of s B -controlled transcripts and resulted in reduced H 2 O 2 and UV tolerance, enhanced alpha-hemolysin activity, and the inability to produce the alkaline shock protein Asp23. After 48 h of growth, rsbU mutant strains failed to accumulate staphyloxanthin, the major stationary-phase carotenoid. Transcription of Asp23 was found to be exclusively controlled by s B , making it an excellent target for the study of s B activity in S. aureus . Reporter gene experiments, using the ﬁreﬂy luciferase gene ( luc 1 ) fused to the s B -dependent promoter(s) of asp23 , revealed that s B is almost inactive in 8325 derivatives. cis complementation of the 8325 derivative BB255 with the wild-type rsbU gene from strain COL produced the rsbU 1 derivative GP268, a strain possessing a s B activity proﬁle comparable to that of the rsbU 1 wild-type strain Newman. In GP268, the heat inducibility of s B -dependent genes, Asp23 production, alpha-hemolysin activity, pigmentation, and susceptibility to H 2 O 2 were restored to the levels observed in strain Newman, clearly demonstrating that
Discrete-time differentiators in closed-loop control systems: experiments on electro-pneumatic system and rotary inverted pendulum

This paper is dedicated to the experimental analysis of discrete-time differentiators implemented in closed-loop control systems. To this end, two laboratory setups, namely an electro-pneumatic system and a rotary inverted pendulum have been used to implement 25 different differentiators including the exact differentiators and a linear filter. Since the selected laboratory setups behave differently in the case of dynamic response and noise characteristics, it is expected that the results remain valid for a wide range of control applications. The validity of several theoretical results, which have been already reported in the literature based on the analytical analysis and numerical simulations
Stiffness prediction on elastography images and neuro-fuzzy based segmentation for thyroid cancer detection.

The elastography method detects metastatic changes by measuring the stiffness of tissues. Estimation of elasticities from elastography images facilitates more precise identification of the metastatic region and detection of the same. In this study, an automated segmentation algorithm is proposed that calculates pixel-wise elasticity values to detect thyroid cancer from elastography images. This intensity to elasticity conversion is achieved by constructing a fuzzy inference system using an adaptive neuro-fuzzy inference system supported by two meta-heuristic algorithms: genetic algorithm and particle swarm optimization. Pixels of the input color images (red, green, and blue) are replaced by equivalent elasticity values (in kilo Pascal) and are stored in a two-dimensional array to form an "elasticity matrix. " The elasticity matrix is then segmented into three regions, namely, suspicious, near-suspicious, and non-suspicious, based on the elasticity measures, where the threshold limits are calculated using the fuzzy entropy maximization method optimized by the differential evolution algorithm. Segmentation performances are evaluated by Kappa and the dice similarity co-efficient, and average values achieved are 0.94±0.11 and 0.93±0.12, respectively. Sensitivity and specificity values achieved by the proposed method are 86.35±0.34% and 97.67±0.40%, respectively, showing an overall accuracy of 93.50±0.42%. Results justify the importance of pixel stiffness for segmentation of thyroid nodules in elastography images.
Gene Knockout and Metabolome Analysis of SLC22A4

Division of Pharmaceutical Sciences, Graduate School of Natural Science and Technology, Kanazawa University, Kanazawa 920-1192, Japan, Department of Gastroenterology, Kanazawa University Hospital, Kanazawa 920-0934, Japan, Department of Histology and Embryology, Graduate School of Medical Science, Kanazawa University, Kanazawa 920-0934, Japan, Institute for Advanced Biosciences, Keio University, Yamagata 997-0035, Japan, Division of Transgenic Animal Science, Advanced Science Research Center, Kanazawa University, Kanazawa 920-0934, Japan, Department of Gastroenterology, Fukuoka University Chikushi Hospital, Fukuoka 818-8502, Japan, Division of
Antigen Exposure and Persistence Cell Responses in Different Conditions of Functional Heterogeneity of Memory CD4 T

Memory CD4 T cell responses are functionally and phenotypically heterogeneous. In the present study, memory CD4 T cell responses were analyzed in different models of Ag-speciﬁc immune responses differing on Ag exposure and/or persistence. Ag-speciﬁc CD4 T cell responses for tetanus toxoid, HSV, EBV, CMV, and HIV-1 were compared. Three distinct patterns of T cell response were observed. A dominant single IL-2 CD4 T cell response was associated with the model in which the Ag can be cleared. Polyfunctional (single IL-2 plus IL-2/IFN- (cid:1) plus single IFN- (cid:1) ) CD4 T cell responses were associated with Ag persistence and low Ag levels. A dominant single IFN- (cid:1) CD4 T cell response was associated with the model of Ag persistence and high Ag levels. The results obtained supported the hypothesis that the different patterns observed were substantially inﬂuenced by different conditions of Ag exposure and persistence. The Journal of Immunology, 2005, 174: 1037–1045. and 3) chronic and progressive HIV-1 infection as a model of Ag persistence under conditions of noncontrolled virus replication and high levels of viral Ag; and 4) primary CMV and HIV-1 infections as models of acute Ag exposure and high levels of viral Ag. The changes of the memory CD4 T cell responses associated with the variation in Ag exposure and persistence within the four models were also investigated. Our results provide a complete characterization of memory CD4 T cell responses against some of the most diffuse human viruses. Furthermore, the investigation of the Ag-speciﬁc CD4 T cell responses in different models of Ag exposure and persistence indicated that the functional heterogeneity of memory CD4 T cells is strongly modulated by the Ag exposure and persistence.
The Kernel Based Multiple Instances Learning Algorithm for Object Tracking

To realize real time object tracking in complex environments, a kernel based MIL (KMIL) algorithm is proposed. The KMIL employs the Gaussian kernel function to deal with the inner product used in the weighted MIL (WMIL) algorithm. The method avoids computing the pos-likely-hood and neg-likely-hood many times, which results in a much faster tracker. To track an object with different motion, the searching areas for cropping the instances are varied according to the object’s size. Furthermore, an adaptive classifier updating strategy is presented to handle with the occlusion, pose variations and illumination changes. A similar score range is defined with respect to two given thresholds and a similar score from the second frame. Then, the learning rate will be set to be a small value when a similar score is out of the range. In contrast, a big learning rate is used. Finally, we compare its performance with that of the state-of-art algorithms on several classical videos. The experimental results show that the presented KMIL algorithm is faster and robust to the partial occlusion, pose variations and illumination changes.
Global sizing optimisation using dual-level response surface method based on mixed-resolution central composite design for permanent magnet synchronous generators

This study presents a global sizing design optimisation of a permanent magnet synchronous generator (PMSG) using the three-dimensional finite-element analysis (3D FEA). To build an optimal parametric model structure, the efficiency improvement of the PMSG is taken as the main objective, where iron and copper losses were minimised. A dual-level response surface methodology (D-RSM) with a window-zoom-in approach for a variable-speed-range analysis as a global optimisation technique is employed to find out the optimal design variables of the objective function. The D-RSM using mixed-resolution central composite design (MR-CCD), full factorial design, central composite design (CCD), and box-Behnken design are applied to optimise the geometry with very small error. Analysis of variance and multi-level RSM plots are used to check the adequacy of fit. However, the MR-CCD exceeds the range of the boundary in the design region. Hence, a modified MR-CCD is used that improves the efficiency and proposes the parameter settings to manufacture the high-class quality wind generator. The validation of the analytical and numerical fashions is successfully achieved through rigorous FEA, and the experimental verifications perfectly marked the theoretical and significance optimisation design.
Initialization methods for 2D/3D registration of medical images during Orthopedic surgeries

—2D/3D registration is the process of aligning 3D volume data to a 2D image. Typically, during orthopedic trauma surgeries, 3D volume data is acquired prior to the surgery to visualize the extent of the injury and also plan the operation. During the surgery, 2D ﬂuoroscopic images are acquired to visualize the position of surgical instruments and understand the progress of the operation. However, the depth perception from these 2D images becomes difﬁcult. By employing 2D/3D registration to overlay pre-operative imaging modalities, doctors may be presented a better way to visualize the progress of the surgery. Although several approaches have been proposed in the literature to deal with 2D/3D registration, they are limited by the need for a good initial alignment. This requirement for accurate initial alignment is the main reason hindering the adoption of 2D/3D registration in clinical practice. In this thesis, we propose automated initialization strategies, the output of which can be used by any common 2D/3D registration algorithm for further ﬁne tuning. A library based approach is to used to study the initialization problem. Four parameters of the six dimensional parameter space are sampled and their projections are stored prior to the surgery. We proposed frequency and feature based methods to retrieve the correct library match which is used for the initialization during the surgery. The feasibility of the approach is demonstrated by initializing an intensity based 2D/3D registration method with the automatically obtained estimation of the initialization transformation parameters. For ﬁxed C-arm cases with available ground truth the initialization was successful for 87 . 41% for various anatomies such as the head, pelvis, spine, knees and feet. We further tested the approach in a completely un-calibrated scenario using a mobile C-arm. Visual evaluation of the results revealed a success rate of 88 . 4% for the initialization. Successful results were also obtained even in the presence of additional surgical instruments indicating the robustness of the proposed approach.