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Psychiatric diseases have a strong heritable component known to not be restricted to DNA sequence-based genetic inheritance alone but to also involve epigenetic factors in germ cells. Initial evidence suggested that sperm RNA is causally linked to the transmission of symptoms induced by traumatic experiences. Here, we show that alterations in long RNA in sperm contribute to the inheritance of specific trauma symptoms. Injection of long RNA fraction from sperm of males exposed to postnatal trauma recapitulates the effects on food intake, glucose response to insulin and risk-taking in adulthood whereas the small RNA fraction alters body weight and behavioural despair. Alterations in long RNA are maintained after fertilization, suggesting a direct link between sperm and embryo RNA.

Forecasting tropical storm intensities is a very challenging issue. In recent years, dynamical models have improved considerably. However, for intensity forecasts more improvement is necessary. Dynamical models have different kinds of biases. Considering a multimodel consensus could eliminate some of the biases resulting in improved intensity forecasts as compared to the individual models. Apart from the ensemble mean, the construction of multimodel consensuses has always contributed to somewhat improved forecasts. The Florida State University (FSU) multimodel superensemble is one that, over the years, has systematically provided improved forecasts for hurricanes, numerical weather prediction, and seasonal climate forecasts. The present study considers an artificial neural network (ANN), based on biological principles, for the construction of a multimodel ensemble. ANN has been used for constructing multimodel consensus forecasts for tropical cyclone intensities. This study uses the generalized regression neural network (GRNN) method for the construction of consensus intensity forecasts for the Atlantic basin. Hurricane seasons 2012–16 are considered. Results show that with only five input models improved guidance for tropical storm intensities may be obtained. The consensus using GRNN mostly outperforms all the models included in the study and the ensemble mean. Forecast errors at the longer forecast leads are considerably less for this multimodel superensemble based on the generalized regression neural network. The skill and correlations of different models along with the developed consensus are provided in our analysis. Results suggest that this consensus forecast may be used for operational guidance and for planning and emergency evacuation management. Possibilities for future improvements of the consensus based on new advances in statistical algorithms are also indicated.

The polymers poly(vinyl alcohol) (PVA) is used as matrices to synthesize a nanocomposite with reduced graphene oxide (rGO). The structural and optical properties of the rGO and the nanocomposites (rGO–PVA) are studied by XRD, FTIR analysis, FESEM studies, Raman spectroscopy and UV–VIS absorption spectroscopy analysis. Interaction of PVA polymer chains with rGO is confirmed from FTIR study. The bandgap of the PVA and rGO–PVA nanocomposites has been studied from UV–VIS absorption spectrum. The refractive index and optical dielectric constants of PVA, GO, rGO and rGO–PVA nanocomposites have been discussed from optical spectrum analysis. The visual structures of the GO, rGO and rGO–PVA nanocomposites are observed from FESEM study. The electric modulus M *( ɷ ) formalism used in the analysis enabled us to distinguish and separate the relaxation processes, dominated by marked conductivity in the ε *(ɷ) representation. In the ceramics studies, the relaxation times are thermally activated and the dipole process has a clearly non-Debye behaviour. The relaxation process is described with the use of the activation energy of approximately E A  = 0.12 eV and the characteristic relaxation time, τ 0  = 2.07 × 10 –7  s. The dielectric property of the nanocomposite (rGO–PVA) is studied in zero magnetic field and in magnetic field (H) up to 1.2 T. From these data, magnetodielectric effects are obtained as the variation of real ( ε ′) and imaginary ( ε ″) parts of complex dielectric constant with H at some frequencies. In our study at 100 kHz for the increase of H from zero to 1 T  ε′ decreases by 2.5% in rGO–PVA. This fact is indicative of the interaction between rGO filler particles and PVA polymer chains.

The slope of symmetry energy at the saturation density ( L 0 ) as estimated recently from the measured value of neutron skin thickness in 208 Pb and 48 Ca nuclei overlaps only marginally which leads to the issue of density dependence of symmetry energy unresolved. We have studied the role of density dependence of symmetry energy in the sub-barrier fusion cross-section and the astrophysical S -factor for asymmetric nuclei. The required nucleon densities are generated from different families of non-relativistic and relativistic mean-field models which correspond to a wide range of neutron skin thickness or L 0 . The results are presented for the barrier parameters, cross-section, and astrophysical S -factor for several asymmetric nuclei involving O, Ca, Ni, and Sn isotopes. The cross-sections for the neutron-rich nuclei show a strong dependence on the behavior of symmetry energy or the neutron skin thickness. The increase in skin thickness lowers the height of the barrier as well as its width which enhances the values of the S -factor by more than an order of magnitude.

This paper addresses the development of predictive models for distinguishing pre-symptomatic infections from uninfected individuals. Our machine learning experiments are conducted on publicly available challenge studies that collected whole-blood transcriptomics data from individuals infected with HRV, RSV, H1N1, and H3N2. We address the problem of identifying discriminatory biomarkers between controls and eventual shedders in the first 32 h post-infection. Our exploratory analysis shows that the most discriminatory biomarkers exhibit a strong dependence on time over the course of the human response to infection. We visualize the feature sets to provide evidence of the rapid evolution of the gene expression profiles. To quantify this observation, we partition the data in the first 32 h into four equal time windows of 8 h each and identify all discriminatory biomarkers using sparsity-promoting classifiers and Iterated Feature Removal. We then perform a comparative machine learning classification analysis using linear support vector machines, artificial neural networks and Centroid-Encoder. We present a range of experiments on different groupings of the diseases to demonstrate the robustness of the resulting models.

Compliance risk issues of banks in India as reflected in the regulatory actions encompass non-compliance with regulatory directives relating to fraud risk management, cyber security framework, SWIFT related operational control, asset classification and provisioning, and anti-money laundering standards. Based on data of monetary penalties imposed by the Reserve Bank of India, and banks' disclosures of divergence in reporting of non-performing assets and resultant overstatement of profit, this paper analyses causes of compliance failure and preventive measures adopted by the banking regulator. It has been observed that occurrence of many severe loss events are directly linked to non-compliance with various regulations issued by the Reserve Bank of India. This paper also explores significance of recent decision to engage independent chief compliance officer at regulated banks to resolve the complex non-compliance syndrome.

The article demonstrates the effect of the respiration rate of fig fruit at different storage temperatures (5–35 °C) and the designing of its modified atmospheric packaging. The average O 2 consumption rates R O 2 and CO 2 evolution rate R CO 2 of fig fruits within 5–35 °C were varied from 4.02 to 26.18 mL kg −1  h −1 and 4.89 to 29.19 mL kg −1 h −1 , respectively. The respiration rate at 35 °C was increased up to 6.51 times in terms of O 2 and 5.97 times in terms of CO 2 than the respiration rate at 5 °C. The results showed that under steady state condition at 35 °C there were almost 43.3% and 42.8% decrease in R O 2 and R CO 2 values with respect to increase in time. The enzyme kinetics model in combination with the Arrhenius equation precisely predicted the respiration rate of fig fruit at different storage temperatures. The maximum respiration rate (V m ) for the enzyme kinetics model was increased from 10.910 to 45.620 mL kg −1  h −1 in terms of O 2 and from 12.670 to 50.310 mL kg −1  h −1 in terms of CO 2 . The designing of modified atmospheric packaging was done to evaluate the influence of time and temperature on the respiration rate of fig fruit with various packaging materials. The modified atmosphere in polypropylene package with equilibrium concentration for O 2 (0.0878) and CO 2 (0.0971) was established within a period of 14 h was found to be the best suitable packaging material for extending the shelf life of fig fruit.

Silymarin is a hepatoprotective agent, having poor water solubility and oral absorption of about 23 - 47%, leading to low bioavailability of the drug. The aim of the present study is to improve the solubility and dissolution rate and in turn the hepatoprotective activity of the drug, by formulating its inclusion complex with beta (β)-cyclodextrin, using different methods. The phase solubility analysis indicates the formation of 1:1 molar inclusion complex of the drug with beta cyclodextrin. Apparent stability constant for Silymarin (K(c)) was 722 K(-1) with β-cyclodextrin complex. The inclusion complexes were prepared by four different methods, namely, physical mixing, kneading, co-precipitation, and solvent evaporation. The prepared complexes were characterized using differential scanning colorimetry, scanning electron microscopy, and x-ray diffractometry. The inclusion complex prepared by the co-precipitation methods exhibits an overall best result, with respect to the formulation of sustained release formulations.

One of the frontiers of today’s nuclear physics research is the synthesis of Super Heavy Elements (SHE). Fusion-fission dynamics, namely the competition between quasi fission and fusion is one of the key challenges to optimize the SHE. To have an insight into the dynamics, one requires the study of fission fragment mass and angular distribution near barrier energies for heavy-ion induced fission reactions. Recent successful installation of linear accelerators in India offers a unique opportunity to study the dynamics of nuclear reactions and formation process of SHE. For the effective utilization of these current, as well as upcoming facilities, development of novel detectors to study reaction dynamics, formation process of SHE with heavier projectiles and higher beam energies is needed. Gaseous detectors have undergone a rapid improvement in terms of spatial, temporal and energy resolution, rate capability, radiation hardness, ion feedback etc., ushering in a new genre of micro-structured devices based on semi-conductor technology, commonly known as Micro-Pattern Gaseous Detectors (MPGDs). Although many of the MPGD structures were primarily developed for high-rate tracking of charged particles in high energy physics experiments, stability of operation, simplicity of construction and relatively low cost make these detectors suitable for other applications, such as low-energy nuclear physics experiments. The present activities encompass a detailed evaluation of the operational conditions of Micromesh-Multi Wire and THGEM-Multi Wire hybrid detector operated in low-pressure isobutane gas with a view to optimizing their use in the detection of charged particles and fission fragments.

Three polymers, poly(vinyl alcohol) (PVA), poly(acrylic acid) (PAA) and poly(methyl methacrylate) (PMMA), are used as matrices to synthesize three nanocomposites each with reduced graphene oxide (RGO) fillers (5 wt%). The dielectric properties of these nanocomposites, RGO–PVA, RGO–PAA and RGO–PMMA, are studied in zero magnetic field and in magnetic field ( H ) up to 1.2 T. From these data magnetodielectric effects are obtained as the variation of ε ′ and ε ″ —the real and imaginary parts of complex dielectric constant with H at some frequencies. Thus at 100 kHz for the increase of H from zero to 1 T ε ′ decreases by 5% in RGO–PVA and by 4% in RGO–PAA, whereas ε ′ increases by 4% in RGO–PMMA. The observed magnetodielectric effects, though small, are significant. They show both decrease and increase of ε ′ depending on the polymer. This fact is the indicative of the interaction between RGO filler particles and polymer chains.