Implication of Higgs at 125 GeV within Stochastic Superspace Framework

We revisit the issue of considering stochasticity of Grassmannian coordinates in N=1 superspace, which was analyzed previously by Kobakhidze {\it et al}. In this stochastic supersymmetry(SUSY) framework, the soft SUSY breaking terms of the minimal supersymmetric Standard Model(MSSM) such as the bilinear Higgs mixing, trilinear coupling as well as the gaugino mass parameters are all proportional to a single mass parameter \xi, a measure of supersymmetry breaking arising out of stochasticity. While a nonvanishing trilinear coupling at the high scale is a natural outcome of the framework, a favorable signature for obtaining the lighter Higgs boson mass $m_h$ at 125 GeV, the model produces tachyonic sleptons or staus turning to be too light. The previous analyses took $\Lambda$, the scale at which input parameters are given, to be larger than the gauge coupling unification scale $M_G$ in order to generate acceptable scalar masses radiatively at the electroweak scale. Still this was inadequate for obtaining $m_h$ at 125 GeV. We find that Higgs at 125 GeV is highly achievable provided we are ready to accommodate a nonvanishing scalar mass soft SUSY breaking term similar to what is done in minimal anomaly mediated SUSY breaking (AMSB) in contrast to a pure AMSB setup. Thus, the model can easily accommodate Higgs data, LHC limits of squark masses, WMAP data for dark matter relic density, flavor physics constraints and XENON100 data. In contrast to the previous analyses we consider $\Lambda=M_G$, thus avoiding any ambiguities of a post-grand unified theory physics. The idea of stochastic superspace can easily be generalized to various scenarios beyond the MSSM . PACS Nos: 12.60.Jv, 04.65.+e, 95.30.Cq, 95.35.+dComment: LaTex, 35 pages, 7 figures. Minor changes in text. B-physics constraints updated with no change in conclusion. Version to be published in PR

In vitro antioxidant and antimicrobial activity of carotenoid pigment extracted from Sporobolomyces sp. isolated from natural source

The aim of the present study was to isolate and study about the antioxidant and antibacterial activity of carotenoid pigment. Sporobolomyces sp. isolated from the phyllosphere surface of rice plant has found to produce carotenoid pigment. The present investigation was carried out for antioxidant assays viz., DPPH, iron reducing and metal chelating activity. A steady increase in the antioxidant activities was observed in the carotenoid pigment with raising the pigment concentration. In the present study, the maximum antioxidation characteristics of carotenoid by DPPH, iron reducing and metal chelating assays (75.04 %, 1.88 % and 59.32 %) were achieved by pigmentation of Sporobolomyces sp. at the concentration of 100 ?g ml-1. The antibacterial activity was studied on several organisms like Enterococcus sp., Staphylococcus aureus, Streptococcus faecalis, Bacillus subtilis, Escherichia coli and Pseudomonas aeruginosa. Among the six pathogens, the pigment was found to be more effective against E. coli (2.9 cm) and S. aureus (2.6 cm). This study revealed that yeast carotenoid pigment was a potential source for its use in food and pharmaceutical applications

Chemoenzymatic Probes for Detecting and Imaging Fucose-α(1-2)-galactose Glycan Biomarkers

The disaccharide motif fucose-α(1-2)-galactose (Fucα(1-2)Gal) is involved in many important physiological processes, such as learning and memory, inflammation, asthma, and tumorigenesis. However, the size and structural complexity of Fucα(1-2)Gal-containing glycans have posed a significant challenge to their detection. We report a new chemoenzymatic strategy for the rapid, sensitive detection of Fucα(1-2)Gal glycans. We demonstrate that the approach is highly selective for the Fucα(1-2)Gal motif, detects a variety of complex glycans and glycoproteins, and can be used to profile the relative abundance of the motif on live cells, discriminating malignant from normal cells. This approach represents a new potential strategy for biomarker detection and expands the technologies available for understanding the roles of this important class of carbohydrates in physiology and disease

Effect of Implant Height Differences on the Retention and Wear Behaviour of Ball Attachment System in Mandibular Two-Implant Over Dentures: An In Vitro Study

PURPOSE: The purpose of this study was to evaluate in vitro, the effect of implant height differences on the retention and wear behaviour of ball attachment system in mandibular two-implant over dentures. MATERIALS AND METHODS: 22 wax blocks were fabricated and out of these, 2 were used as master blocks and 20 were used as prosthetic blocks. 2 implant analogs were placed in each master wax block with same height in one block and with different height in the other block. Both the master and prosthetic wax blocks were then heat cured. The attachments were placed in the master block and transferred to the prosthetic block by Direct Pick up technique. Group I had test samples placed at same height and Group II at different height. The retention force was tested from baseline to 1440 cycles with 3 time intervals simulating 1 year of clinical use, using Universal testing machine. The data obtained were then subjected to statistical analysis using ‘t’-test. The surface wear was qualitatively evaluated using Stereo microscope. RESULTS: The mean retention force for Group I and II at baseline, 360, 720, 1080, and after 1440 cycles were 24.73N, 23.49N, 22.46N, 21.07N, 19.28N and 27.13N, 25.80N, 24.16N, 22.73N and 21.85N respectively. Group II showed statistically higher retention force than Group I. Both the groups showed significant retention loss over a period of 1 year and wear on the surface of the attachments evaluated. CONCLUSION: Within the limitations of the study, the retention values obtained from the implant analogs placed at different height was significantly higher than that of at same height. But both the values were higher from the value considered minimal for overdenture stability

Queuing System on Service Interruption and Standby Server

This article deals with a Queuing model in which the presence of customers follows a Poisson distribution. Customers show up exclusively. Service rendered in a single stage follows a general scattering. Interruption happens capriciously in the server. At the point when the server gets barged in on, it gets into a Repair methodology where it is carried out in two stages vital and expanded fix arrange. To keep up a key good ways from the long queue, during the hour of extended fix process a standby server is given. This Queuing issue is enlightened by valuable variable system and the line execution measures are deduced. The model is especially pushed by numerical examination and graphical delineation. &nbsp

Spontaneous R-Parity Violation, A4A_4 Flavor Symmetry and Tribimaximal Mixing

We explore the possibility of spontaneous R parity violation in the context of $A_4$ flavor symmetry. Our model contains $SU(3)_c \times SU(2)_L \times U(1)_Y$ singlet matter chiral superfields which are arranged as triplet of $A_4$ and as well as few additional Higgs chiral superfields which are singlet under MSSM gauge group and belong to triplet and singlet representation under the $A_4$ flavor symmetry. R parity is broken spontaneously by the vacuum expectation values of the different sneutrino fields and hence we have neutrino-neutralino as well as neutrino-MSSM gauge singlet higgsino mixings in our model, in addition to the standard model neutrino- gauge singlet neutrino, gaugino-higgsino and higgsino-higgsino mixings. Because all of these mixings we have an extended neutral fermion mass matrix. We explore the low energy neutrino mass matrix for our model and point out that with some specific constraints between the sneutrino vacuum expectation values as well as the MSSM gauge singlet Higgs vacuum expectation values, the low energy neutrino mass matrix will lead to a tribimaximal mixing matrix. We also analyze the potential minimization for our model and show that one can realize a higher vacuum expectation value of the $SU(3)_c \times SU(2)_L \times U(1)_Y$ singlet sneutrino fields even when the other sneutrino vacuum expectation values are extremely small or even zero.Comment: 18 page

Texture classification of proteins using support vector machines and bio-inspired metaheuristics

6th International Joint Conference, BIOSTEC 2013, Barcelona, Spain, February 11-14, 2013[Abstract] In this paper, a novel classification method of two-dimensional polyacrylamide gel electrophoresis images is presented. Such a method uses textural features obtained by means of a feature selection process for whose implementation we compare Genetic Algorithms and Particle Swarm Optimization. Then, the selected features, among which the most decisive and representative ones appear to be those related to the second order co-occurrence matrix, are used as inputs for a Support Vector Machine. The accuracy of the proposed method is around 94 %, a statistically better performance than the classification based on the entire feature set. This classification step can be very useful for discarding over-segmented areas after a protein segmentation or identification process

The Marker State Space (MSS) Method for Classifying Clinical Samples

The development of accurate clinical biomarkers has been challenging in part due to the diversity between patients and diseases. One approach to account for the diversity is to use multiple markers to classify patients, based on the concept that each individual marker contributes information from its respective subclass of patients. Here we present a new strategy for developing biomarker panels that accounts for completely distinct patient subclasses. Marker State Space (MSS) defines "marker states" based on all possible patterns of high and low values among a panel of markers. Each marker state is defined as either a case state or a control state, and a sample is classified as case or control based on the state it occupies. MSS was used to define multi-marker panels that were robust in cross validation and training-set/test-set analyses and that yielded similar classification accuracy to several other classification algorithms. A three-marker panel for discriminating pancreatic cancer patients from control subjects revealed subclasses of patients based on distinct marker states. MSS provides a straightforward approach for modeling highly divergent subclasses of patients, which may be adaptable for diverse applications. © 2013 Fallon et al

Distribution and Abundance of Seven Spotted Ladybird Beetle (Coccinella septempunctata) Linnaeus in Different Cropping System at Pantnagar, Uttarakhand, India

The primary objective of this research is to provide insight into how abiotic factors affect the coccinellid beetle population in Pantnagar region, India. The current study was conducted at six distinct Govind Ballabh Pant University of Agriculture and Technology, Pantnagar, regions between November 2020 and April 2021. Predatory coccinellid beetle C. septumpunctata, diversity was investigated in a variety of crops, including ornamental, vegetable, fruit, and field crops. Seasonal abundance of C. septumpunctata, in different crops revealed that during the 50th standard meteorological week (SMW), there were more coccinellids in mustard fields than in other field crops. This suggests that conditions were favourable for both predators and prey. Similar to this, during the 12th SMW, rose crops had a high population of coccinellids, and during the 15th SMW, coriander had the largest population of ladybird beetles (98) among vegetable crops. Guava had the largest population of ladybird beetles in fruit orchards (75). Correlation studies between the population of ladybird beetles and weather factors revealed that in crops like mustard, rose, guava, and coriander, where high numbers of coccinellids were observed, the climatic conditions favored the coccinellids, and the availability of prey species

Heart Disease Prediction Using Fuzzy Logic-Based Image Processing and Classification Techniques

The medical field deploys heart disease prediction as a vital operation for early detection to minimize serious health risks. Researchers in this study introduce a new method for heart disease prediction which combines fuzzy logic with image processing together with classification methods. This methodology utilizes fuzzy methods to manage imprecision together with uncertainty found in medical images and numerical information for obtaining more accurate and interpretable outcomes. The initial stage requires fuzzy imputation for handling missing values and fuzzy scaling which transforms features into fuzzy sets for better representation of medical data uncertainties. Define relevant medical imaging regions through fuzzy C-Means clustering before evaluating tissue patterns for heart disease indicators by analyzing these fuzzy texture elements. The combination of fuzzy-genetic algorithms selects significant features through optimized feature space improvements while fuzzy decision trees provide clear means to rank and select features. The system utilizes Mamdani fuzzy inference systems as the final stage to classify heart disease severity based on expert model predictions. Through fuzzy support vector machine implementations the system minimizes data imprecision and overlaps to boost its classification precision. The proposed heart disease prediction method adopts fuzzy machine learning integration to optimize accuracy levels. Image segmentation occurs through Fuzzy C-Means clustering and Local Binary Patterns (LBP) extract texture features before Fuzzy Genetic Algorithms (FGA) select the features. The model received performance evaluation through assessment of its accuracy as well as sensitivity and specificity tests and AUC-ROC metric. The analysis reveals predictive strength through an AUC-ROC value of 0.96 as well as 96.4% accuracy and 93% sensitivity alongside 38% specificity. Cross-validation techniques produced average accuracy of 94% through five-fold validation tests. The integration of fuzzy logic with traditional machine learning proves effective for precise heart disease prediction as it deals effectively with medical data uncertainty and imprecision