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Abstract The Inert Higgs Doublet model (IDM), assisted by Peccei-Quinn (PQ) symmetry, offers a simple but natural framework of a dark sector that accommodates Weakly Interacting Massive Particle (WIMP) and axion as dark matter components. Spontaneous breaking of U(1) PQ symmetry, which was originally proposed as an elegant solution to the strong charge-parity (CP) problem, also ensures the stability of WIMP through a residual ℤ2 symmetry. Interestingly, additional fields necessitated by PQ symmetry further enrich the dark sector. These include a scalar field proprietor for axion DM and a vector-like quark (VLQ) that acts as a portal for the dark sector through Yukawa interactions. Moreover, this combination of the axion and WIMP components satisfies the observed DM relic density and reopens the phenomenologically exciting region of the IDM parameter space where the WIMP mass falls between 100 - 550 GeV. We investigate the model-independent pair production of VLQs exploring this region at the Large Hadron Collider (LHC), incorporating the effects of next-to-leading order (NLO) QCD corrections. After production, each VLQ decays into a top or bottom quark accompanied by an inert scalar, a consequence of the residual ℤ2 symmetry. Utilising relevant observables with a leptonic search channel and employing multivariate analysis, we demonstrate the ability of this analysis to exclude a significant portion of the parameter space with an integrated luminosity of 300 fb −1.

This article introduces a predictive deep learning model called MLP-SDAE, which combines Multilayer Perceptron (MLP) and Stacked Denoising Auto-encoder (SDAE) techniques. Our model, MLP-SDAE is trained using Stacked Denoising Auto-Encoder for feature selection, and backpropagation is employed within the MLP structure. We have incorporated dropout to enhance the model’s performance and prevent overfitting. The primary objective of the MLP-SDAE model is to identify associations among genes that have undergone significant alterations from a normal to a diseased state based on their expression behaviors. This concept allows us to predict disease-mediating genes and their altered associations. The methodology involves calculating gene-based correlation coefficients and selecting a subset of genes based on this analysis. We have demonstrated the effectiveness of our methods using four gene expression datasets related to human leukemia, lung, colon, and breast cancer. As a result, we have identified several potentially important genes, such as CACLA, HBA, IGFBP3, EFGR, TFN, TP53, LI6, and TMTC1, which may play a crucial role in developing these cancers. Furthermore, we conducted a comprehensive comparative study with other deep learning techniques, including Recurrent Neural Network (RNN), Deep Belief Network (DBN), Deep Boltzmann Machine (DBM), Auto-encoder (AE), and Denoising Auto-encoder (DAE). Our results have been validated through biochemical pathway analysis, t-tests, F-score, Gene Ontology (GO) identification, and the NCBI database. These validations demonstrate that our proposed MLP-SDAE model outperforms existing methods.

Abstract The Covid-19 pandemic outbreak has accelerated tremendous efforts to discover a therapeutic strategy that targets severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to control viral infection. Various viral proteins have been identified as potential drug targets, however, to date, no specific therapeutic cure is available against the SARS-CoV-2. To address this issue, the present work reports a systematic cheminformatic approach to identify the potent andrographolide derivatives that can target methyltransferases of SARS-CoV-2, i.e. nsp14 and nsp16 which are crucial for the replication of the virus and host immune evasion. A consensus of cheminformatics methodologies including virtual screening, molecular docking, ADMET profiling, molecular dynamics simulations, free-energy landscape analysis, molecular mechanics generalized born surface area (MM-GBSA), and density functional theory (DFT) was utilized. Our study reveals two new andrographolide derivatives (PubChem CID: 2734589 and 138968421) as natural bioactive molecules that can form stable complexes with both proteins via hydrophobic interactions, hydrogen bonds and electrostatic interactions. The toxicity analysis predicts class four toxicity for both compounds with LD50 value in the range of 500–700 mg/kg. MD simulation reveals the stable formation of the complex for both the compounds and their average trajectory values were found to be lower than the control inhibitor and protein alone. MMGBSA analysis corroborates the MD simulation result and showed the lowest energy for the compounds 2734589 and 138968421. The DFT and MEP analysis also predicts the better reactivity and stability of both the hit compounds. Overall, both andrographolide derivatives exhibit good potential as potent inhibitors for both nsp14 and nsp16 proteins, however, in-vitro and in vivo assessment would be required to prove their efficacy and safety in clinical settings. Moreover, the drug discovery strategy aiming at the dual target approach might serve as a useful model for inventing novel drug molecules for various other diseases.

Stratigraphic sites in north-west Svalbard record at least six significant glacial advances in the last 200 Ky, and sediments deposited under locally high relative sea level during intervening interstadials or interglacials contain abundant benthic foraminifera. We present a biostratigraphic record from the Kongsfjordhallet site in the Kongsfjorden area that covers five high relative sea-level events and stretches back to the Middle Pleistocene (>195 Kya). Five foraminiferal assemblages have been identified: (1) Cassidulina reniforme – Elphidium clavatum — Islandiella helenae; (2) Cassidulina neoteretis – Cassidulina reniforme; (3) Cassidulina reniforme – Cibicides lobatulus – Cassidulina neoteretis; (4) Cibicides lobatulus – Cassidulina reniforme – Elphidium clavatum; and (5) Cassidulina reniforme – Elphidium clavatum assemblages. The assemblages suggest a distal glaciomarine environment with variable influence of seasonal sea ice and nutrient availability in the Kongsfjorden region during the five deglaciation events. The foraminiferal data are also compared with foraminiferal records from other sites in Svalbard and the adjacent Arctic Ocean.

Abstract Larvae of the elephant mosquitoes, Toxorhynchites spp. (Diptera: Culicidae) are predacious on larvae of other mosquito species and some small aquatic organisms; this predatory behavior can be applied in (mosquito) vector control. The present study examined the feeding behavior of Toxorhynchites splendens on Aedes albopictus in relation to search area [volume of water (X1)] and prey density (X2), prey instars, predatory preference and larvae's functional response on variable prey densities. Experiments were conducted to determine changes in the feeding activity of T. splendens with different search areas and showed that rate of prey consumption was inversely proportional to the search area as evidenced by a negative value of X1 in the regression equation and positively related to prey density. The non-linear polynomial logistic regression estimated a significant linear parameter (P1 0.05), expressing that all the instars of prey were equally susceptible to the predator. Toxorhynchites splendens preferred to consume Ae. albopictus larvae rather than Tubifex when supplied together as a food source.

Abstract We explore a two-component dark matter scenario in an extended Kim-Shifman-Vainshtein-Zakharov (KSVZ) axion framework. This hybrid setup incorporates an extra SU(2) L complex singlet scalar whose lightest component plays the role of one of the dark matter, while the QCD axion of the KSVZ model acts as a second dark matter candidate. In this work, we focus on accentuating the role of vector-like quark that naturally emerges in the KSVZ extension on the dark matter and collider phenomenology. Here, we demonstrate that the presence of this colored particle can significantly affect the allowed dark matter parameter space of the scalar dark matter by opening up additional co-annihilation as well as the direct detection channels. Moreover, the interaction between the color particle with the top quark and scalar dark matter provides a unique topology to generate a boosted-top pair with considerable missing transverse momentum at the LHC. Using jet substructure variables and multivariate analysis, here we show that one can already exclude a vast region of parameter space with 139 fb−1 integrated luminosity at 14 TeV LHC.

The low responsivity of conventional Silicon photodiodes in ultraviolet and near-infrared regimes restricts their utility as broadband photodetectors (BBPDs). Despite ongoing investigations into various p-n heterostructures for Silicon-based BBPDs, challenges such as high dark current (I _dark ), low collection efficiency, low detectivity, and compatibility issues with large-scale Silicon-based devices persist. In this context, we have fabricated relatively unexplored n-Er _2 O _3 /p-Si heterojunction-based BBPDs. Polycrystalline Er _2 O _3 thin films (∼110 nm) were deposited on p-Si 〈100〉 substrates by radio frequency magnetron sputtering. Although this process induces a microstrain of approximately 0.022 and a dislocation density of about 0.00303/nm ^2 , the presence of optically active defects is minimal, indicated by a low Urbach energy (∼0.35 eV). X-ray photoelectron spectroscopy (XPS) analysis confirms staggered band alignment at the heterointerface, facilitating efficient charge carrier separation and transport. Consequently, the In/p-Si/n-Er _2 O _3 /In device demonstrated significant BBPD properties– low I _dark ∼0.15 μ A (at +5 V), photo-to-dark current ratio (PDCR) ∼6.5 (at +5 V, 700 nm) with a maximum photoresponsivity ∼22.3 A W ^−1 , and impressive detectivity (∼10 ^13 Jones) even in UV-C region where traditional silicon-based photodetectors respond feebly. The device also demonstrates transient photo-response across an ultrawide spectrum (254 nm–1200 nm) with a fast rise time/fall time ∼79 ms/∼86 ms (at −5 V for 600 nm illumination). This work establishes a straightforward and reliable method for proper material engineering, surface texturing, staggered heterojunction formation, and high-performance BBPD fabrication with prominent broad-spectrum responsivity, sizeable detectivity, and fast response. The integration of these BBPDs with Silicon opens possibilities for their use in electronic devices containing optical switches for communications and broadband image sensors, enhancing their utility in various applications.

The primary objective of this study is to measure the laminar burning velocity of perfectly premixed hydrogen-air, methane-air, ethane-air, ethylene-air, and propane-air flames, at near-cryogenic temperatures and atmospheric pressure. Initial fuel-air mixture temperatures as low as 160 K were investigated. The experimental methodology was validated by comparing the results obtained with those from previous studies available in the literature and with numerical simulations using five different chemical mechanisms. First, for all fuels, the laminar burning velocity evolution as a function of the equivalence ratio followed the same trend at 295 K and 240 K. Regardless of the equivalence ratio and mixture composition, the laminar burning velocity decreased by 22 to 44% for hydrogen, methane, ethane, ethylene, and propane flames when the temperature was decreased from 295 K to 240 K. Second, for stoichiometric conditions, the laminar burning velocity decreased by about 50% for all fuels, when the temperature was decreased by 100 K, from 295 to 195 K. These experimental results were in excellent agreement with laminar burning velocities calculated by a power law of the ratio of unburned gas temperature to ambient temperature, with exponent values from the literature, obtained for temperatures above the ambient. All five chemical mechanisms provided a very good agreement, within or near experimental uncertainties, for most of the fuels and conditions investigated, even at low temperatures. Overall, this study provides valuable information on the laminar burning velocities of various hydrocarbon and hydrogen fuels at near-cryogenic temperatures, which can be useful for the design of cryogenic storage systems and the validation of chemical kinetic models for conditions below ambient temperatures.

AbstractSimplistic linear methods for predicting crop yield leave out important factors like climate, rainfall, soil, irrigation, and land characteristics. Recent literature points to use of individual Machine Learning (ML) and Artificial Intelligence (AI) models for better prediction of crop yield. However, such methods have not been used in the Indian context. Moreover, given the diversity of land, climate, soil and irrigation facilities in the country, it is necessary to develop an ensemble approach incorporating a significant number of ML algorithms to have a better prediction of crop yield across geographies of the country. The purpose of this paper is to: (a) develop scenario-specific algorithms for ML models and identify the best fit model for yield prediction and (b) develop an ensemble approach synthesizing the ML models for better overall prediction of crop yield in India.

Background: COVID-19 pandemic induced lockdown and the social restrictions had a profound impact on the circadian rhythm driven sleep-wake schedule of the home confined population. Aims and Objective: Our study explored the effect of COVID-19 pandemic induced lockdown on sleep wake pattern and personal well-being of medical students of West Bengal Materials and Methods: An observational, questionnaire-based online survey was conducted using the URL linked Google form. The online survey was conducted through social media platforms as per CHERRIES checklist guideline. Information on demographic profile, before and after lockdown sleep-wake pattern, social wellbeing and general lifestyle was obtained. Any student with any sleeping disorder or on drugs was excluded. Mean (SE), median, range, proportion was calculated as per attributes. Fisher’s exact chi square test and paired t test was done to test association and to compare means of the attributes. Results: The mean age of students was 20.26 years. The average daily time spent on electronic media got doubled during lockdown. Sleep disorder was reported by 53.0% male and 47.0% students. More than half of respondents reported increased napping during daytime, irregular timing for meal intake and low mood. Anxiety was present in every two out of three participants. Females gained more weight during lockdown. The gender difference in sleep duration became significant. Lockdown effect on the average daily media time was significant. The sleep disturbance at night and physical inactivity was significantly more among female students. Conclusion: COVID-19 lockdown leads to delayed sleep-wake cycle, irregular meal timings and excessive digital exposure among medical students with gender based differential impact.

Mosquitoes transmit major human diseases, resulting in millions of fatalities each year and the development of chemical insecticide resistance, leading to a rebound in vectorial capacity. Plants could be used as a mosquito repellent alternative. The purpose of this study was to evaluate the biocontrol potentiality of ethyl acetate extract of fruit pericarp of Alangium salviifolium against Culex quinquefasciatus larvae. 100% larval mortality was recorded after 72 h of exposure with 50, 40 ppm, and 30 ppm concentrations of ethyl acetate extract against 3rd instar mosquito larvae. The bioactive compound responsible for larval mortality was isolated by TLC (Rf value of 0.33). 3rd instar larvae were found to be the most susceptible (LC50 = 3.60 ppm) among all the instars and corresponding LC50 values were 4.45 ppm and 4.52 ppm for 2nd and 4th instars larvae respectively after 72 h of exposure. The mortality rate of all larval instars was directly proportional to the concentration of bioactive compounds. Three-way ANOVA analysis revealed that larval instars, the concentration of bioactive compound, and time of exposure had a significant effect on larval mortality. In the bioactive TLC fraction of A. salviifolium (Rf value of 0.33), FT-IR spectroscopy analysis revealed the presence of numerous functional groups. GC-MS analysis revealed the presence of Benzoyl bromide and 3-Amino -5 (2-Furyl) Pyrazole in the extract. The compounds were also studied on non-target organisms such as Anisops sardea, 4th instar larvae of Chironomus sp. and Diplonychus annulatum, and in all the cases no abnormalities were recorded.

Abstract The phenomenon of two or more genes affecting the expression of each other in various ways in the development of a single character of an organism is known as gene interaction. Gene interaction not only applies to normal human traits but to the diseased samples as well. Thus, an analysis of gene interaction could help us to differentiate between the normal and the diseased samples or between the two/more phases any diseased samples. At the first stage of this work we have used restricted Boltzmann machine model to find such significant interactions present in normal and/or cancer samples of every gene pairs of 20 genes of colorectal cancer data set (GDS4382) along with the weight/degree of those interactions. Later on, we are looking for those interactions present in adenoma and/or carcinoma samples of the same 20 genes of colorectal cancer data set (GDS1777). The weight/degree of those interactions represents how strong/weak an interaction is. At the end we will create a gene regulatory network with the help of those interactions, where the regulatory genes are identified by using Naïve Bayes Classifier. Experimental results are validated biologically by comparing the interactions with NCBI databases.

Abstract Objectives This study assessed the predatory potentiality of two unexplored fishes, Puntius tetrazona and Hyphessobrycon rosaceus on Culex vishnui subgroup larvae in order to utilize natural resources to diminish mosquito population. Larval feeding rate was evaluated in laboratory under varying prey density and volume of water. The experiment was extended to semi field condition. Results Puntius tetrazona and H. rosaceus consumed from 66 to 600 and from 87 to 718 Cx. vishnui larvae respectively in laboratory condition at 10 prey density levels (100–1000 larvae) at an increment of 100 larvae at 2 l water volume. In semi field condition, a 78% reduction in larval density was observed at day 30 post introduction of P. tetrazona, whereas 91% reduction was noted on day 21 for H. rosaceus and in the subsequent samples no mosquito larvae were found in ditches. Withdrawal of predators from the ditches resulted gradual increase in larval density. Laboratory and semi field bioassay of both the species indicated their potentiality as efficient mosquito larval predator though H. rosaceus exhibited better performance than P. tetrazona. It is recommended to utilize these natural resources to diminish mosquito population in the countries of their native range.

Background: Literatures reported psychosocial and behavioural problems among adolescents in different parts of India but research among tribes was quite less in spite of marginalised section. The study was conducted to screen for the prevalence of psychosocial symptoms and strengths and to find out factors with increased score among the tribal adolescents of a district of West Bengal. Methods: This community based cross sectional, descriptive study was done among 190 tribal adolescents with a pre designed, semi structured proforma including strength and difficulties questionnaire (SDQ). Anthropometric measurements were recorded. Census was done for selection of participants. Main findings: Out of 190 adolescents, 60.0% were male and six females were married. The illiteracy rate was two times in mothers. Proportion of overweight was significantly more in older adolescents (p

Background and objectives: In integrated vector management programmes, major emphasis is given on theapplication of biocontrol agents. In the present study, the hemipteran water bug, Laccotrephes griseus(Hemiptera: Nepidae) was evaluated to establish its functional response against the immature Culexquinquefasciatus Say in the laboratory.Methods: Adult water bugs (L. griseus) were collected from different ponds in Bankura, West Bengal, India.Predation rate was determined in semi-field conditions. Functional response, regression equation analysis andrate of predation were also determined in the presence of an alternative prey.Results: The long-term predation experiment (up to 30 days) revealed that L. griseus is a very good predatorof mosquito larvae. The functional curve analysis of L. griseus exhibited a Type II functional response. Theirhandling times and coefficient of attack rates were 29.37 min and 2.17 respectively. Regression equationsshowed that consumption rate was directly proportional to prey and predator densities and inversely proportionalto the search area. The selectivity index, niche width and food breadth were also determined.Interpretation and conclusion: The present study revealed that L. griseus can consume a good number ofmosquito larvae in the laboratory conditions. The long-term bioassay also indicates their predatory responseagainst mosquito larvae in the semi-field conditions. The result of selectivity index, niche width and foodbreadth were also highest for Cx. quinquefasciatus. In these respects, the water bugs L. griseus are indicativeof their release in the aquatic habitat where Cx. quinquefasciatus is the most dominant mosquito species.

Most of the gene expression data analysis algorithms require the entire gene expression matrix without any missing values. Hence, it is necessary to devise methods which would impute missing data values accurately. There exist a number of imputation algorithms to estimate those missing values. This work starts with a microarray dataset containing multiple missing values. We first apply the modified version of the fuzzy theory based existing method LRFDVImpute to impute multiple missing values of time series gene expression data and then validate the result of imputation by genetic algorithm (GA) based gene ranking methodology along with some regular statistical validation techniques, like RMSE method. Gene ranking, as far as our knowledge, has not been used yet to validate the result of missing value estimation. Firstly, the proposed method has been tested on the very popular Spellman dataset and results show that error margins have been drastically reduced compared to some previous works, which indirectly validates the statistical significance of the proposed method. Then it has been applied on four other 2-class benchmark datasets, like Colorectal Cancer tumours dataset (GDS4382), Breast Cancer dataset (GSE349-350), Prostate Cancer dataset, and DLBCL-FL (Leukaemia) for both missing value estimation and ranking the genes, and the results show that the proposed method can reach 100% classification accuracy with very few dominant genes, which indirectly validates the biological significance of the proposed method.