Can sympathetic induction be a convenient technique for breeding Amblypharyngodon mola (Hamilton, 1822)?

Mola carplet (Amblypharyngodon mola) is one of the most popular small fish species of the Indian subcontinent. There are limited studies on captive breeding of this species, which is important for aquaculture and the conservation prospects of this species. The conventional induced breeding method using an inducing agent (GnRHa and dopamine antagonist) is one of the most effective and prevalent methods of breeding fish. It is a laborious and time-consuming process, particularly in mass fish breeding and in lieu of that, a less time-consuming method - sympathetic induction of the broodstock, is used in some regions of India, particularly in big carp fish. However, this method has not been reported in commercial-scale breeding of small indigenous fish species. Therefore, an experiment was conducted to compare the spawning efficiency of Amblypharyngodon mola bred by sympathetic induction with the conventional complete induced breeding method. The spawning performance in terms of latency period, relative fecundity, fertilization rate, incubation period, and hatching rates of sympathetically induced Amblypharyngodon mola were compared to completely induced Amblypharyngodon mola brooders. Although the latency period (7.8 hrs), relative fecundity (39 nos./g), fertilization rates (81.61%) and spawning efficiency coefficient (0.681) were better in conventionally induced fish, but lower post-spawning mortality (1.29%) and better hatching rates (86.21%) were observed in sympathetically induced fish. The results indicate that quality offspring of Amblypharyngodon mola could be obtained in terms of survivability through sympathetic breeding. Sympathetic induction breeding could be a cost-effective, convenient, time-saving method of mass-scale breeding and aquaculture of Amblypharyngodon mola.

Extracting film thickness and optical constants from spectrophotometric data by evolutionary optimization.

In this paper, we propose a simple and elegant method to extract the thickness and the optical constants of various films from the reflectance and transmittance spectra in the wavelength range of 350 - 1000 nm. The underlying inverse problem is posed here as an optimization problem. To find unique solutions to this problem, we adopt an evolutionary optimization approach that drives a population of candidate solutions towards the global optimum. An ensemble of Tauc-Lorentz Oscillators (TLOs) and an ensemble of Gaussian Oscillators (GOs), are leveraged to compute the reflectance and transmittance spectra for different candidate thickness values and refractive index profiles. This model-based optimization is solved using two efficient evolutionary algorithms (EAs), namely genetic algorithm (GA) and covariance matrix adaptation evolution strategy (CMAES), such that the resulting spectra simultaneously fit all the given data points in the admissible wavelength range. Numerical results validate the effectiveness of the proposed approach in estimating the optical parameters of interest.

Methodical Approach to Inactivate Any Microbial Element like SARS-CoV2.

The proposed paper discusses Far UVC could assassinate microbes without harming healthy tissues. The plasma ion generation will increase the ion (O2 -) generation in abundance along with hydrogen ion (H+), and at the same time, the negative hydroxyl radical (OH-) formed will merge with the positive (H+) ion of the virus to break the structure of it. The silver nanoparticle which is present in the diffuser with thermostat support is very effective for destroying the microbial elements by heating the gel present within the diffuser. The gel will mix in the environment, and it will also increase the activity of T cell generation and act as an immunoglobulin booster in the human body while inhaling it. In the proposed device, we are using warm humidified CO2 strategic therapy in low dose which is able to suppress any microbial element like SARS-CoV2.

Lipid-induced monokine cyclophilin-A promotes adipose tissue dysfunction implementing insulin resistance and type 2 diabetes in zebrafish and mice models of obesity.

Several studies have implicated obesity-induced macrophage-adipocyte cross-talk in adipose tissue dysfunction and insulin resistance. However, the molecular cues involved in the cross-talk of macrophage and adipocyte causing insulin resistance are currently unknown. Here, we found that a lipid-induced monokine cyclophilin-A (CyPA) significantly attenuates adipocyte functions and insulin sensitivity. Targeted inhibition of CyPA in diet-induced obese zebrafish notably reduced adipose tissue inflammation and restored adipocyte function resulting in improvement of insulin sensitivity. Silencing of macrophage CyPA or pharmacological inhibition of CyPA by TMN355 effectively restored adipocytes' functions and insulin sensitivity. Interestingly, CyPA incubation markedly increased adipocyte inflammation along with an impairment of adipogenesis, however, mutation of its cognate receptor CD147 at P309A and G310A significantly waived CyPA's effect on adipocyte inflammation and its differentiation. Mechanistically, CyPA-CD147 interaction activates NF-κB signaling which promotes adipocyte inflammation by upregulating various pro-inflammatory cytokines gene expression and attenuates adipocyte differentiation by inhibiting PPARγ and C/EBPß expression via LZTS2-mediated downregulation of ß-catenin. Moreover, inhibition of CyPA or its receptor CD147 notably restored palmitate or CyPA-induced adipose tissue dysfunctions and insulin sensitivity. All these results indicate that obesity-induced macrophage-adipocyte cross-talk involving CyPA-CD147 could be a novel target for the management of insulin resistance and type 2 diabetes.

Capturing functional relations in fluid-structure interaction via machine learning.

While fluid-structure interaction (FSI) problems are ubiquitous in various applications from cell biology to aerodynamics, they involve huge computational overhead. In this paper, we adopt a machine learning (ML)-based strategy to bypass the detailed FSI analysis that requires cumbersome simulations in solving the Navier-Stokes equations. To mimic the effect of fluid on an immersed beam, we have introduced dissipation into the beam model with time-varying forces acting on it. The forces in a discretized set-up have been decoupled via an appropriate linear algebraic operation, which generates the ground truth force/moment data for the ML analysis. The adopted ML technique, symbolic regression, generates computationally tractable functional forms to represent the force/moment with respect to space and time. These estimates are fed into the dissipative beam model to generate the immersed beam's deflections over time, which are in conformity with the detailed FSI solutions. Numerical results demonstrate that the ML-estimated continuous force and moment functions are able to accurately predict the beam deflections under different discretizations.

Comparing data driven and physics inspired models for hopping transport in organic field effect transistors.

The past few decades have seen an uptick in the scope and range of device applications of organic semiconductors, such as organic field-effect transistors, organic photovoltaics and light-emitting diodes. Several researchers have studied electrical transport in these materials and proposed physical models to describe charge transport with different material parameters, with most disordered semiconductors exhibiting hopping transport. However, there exists a lack of a consensus among the different models to describe hopping transport accurately and uniformly. In this work, we first evaluate the efficacy of using a purely data-driven approach, i.e., symbolic regression, in unravelling the relationship between the measured field-effect mobility and the controllable inputs of temperature and gate voltage. While the regressor is able to capture the scaled mobility well with mean absolute error (MAE) ~ O(10-2), better than the traditionally used hopping transport model, it is unable to derive physically interpretable input-output relationships. We then examine a physics-inspired renormalization approach to describe the scaled mobility with respect to a scale-invariant reference temperature. We observe that the renormalization approach offers more generality and interpretability with a MAE of the ~ O(10-1), still better than the traditionally used hopping model, but less accurate as compared to the symbolic regression approach. Our work shows that physics-based approaches are powerful compared to purely data-driven modelling, providing an intuitive understanding of data with extrapolative ability.