Forward Approach Research Articles

Decreased Expression of Soluble Epoxide Hydrolase Suppresses Murine Choroidal Neovascularization.

Neovascular or "wet" age-related macular degeneration (nAMD) is a leading cause of blindness among older adults. Choroidal neovascularization (CNV) is a major pathological feature of nAMD, in which abnormal new blood vessel growth from the choroid leads to irreversible vision loss. There is a critical need to develop novel therapeutic strategies to address limitations of the current anti-vascular endothelial growth factor biologics. Previously, we identified soluble epoxide hydrolase (sEH) as a possible therapeutic target for CNV through a forward chemical genetic approach. The purpose of this study was to validate sEH as a target by examining retinal expression of sEH protein and mRNA by immunohistochemistry and RNAscope in situ hybridization, respectively, and to assess the efficacy of an adeno-associated virus (AAV) vector designed to knock down the sEH gene, Ephx2, in the murine laser-induced (L-) CNV model. nAMD patient postmortem eye tissue and murine L-CNV showed overexpression of sEH in photoreceptors and retinal pigment epithelial cells. Ephx2 knockdown significantly reduced CNV and normalized mRNA expression levels of CNV-related inflammatory markers. Thus, this study further establishes sEH as a promising therapeutic target against CNV associated with nAMD.

A Study on Magnetotelluric Characteristics of Magmatic Geothermal Systems

The Magnetotelluric (MT) method is a widely used and effective method of exploring geothermal resources because it can reveal geological information at a great depth and is cost effective. In order to further improve the reliability and rationality of MT data interpretation, MT responses for a typical hydrothermal system and a hot dry rock (HDR) and partial melting system are investigated by a finite-element (FE) forward modeling approach based on unstructured tetrahedral grids that can handle with complex-shaped geothermal systems. These two geothermal models, designed by the 3ds Max software, are comprised of a clay cap, a reservoir, and a heat source, and are discretized into tetrahedral elements by TetGen software. The results show that the apparent resistivities at the broadband of frequencies are mainly affected by the shallow low-resistivity clay cap due to its strong shielding effects, and the induction arrows effectively reflect the boundary of the clay cap. The conductive heat-conducting path and heat source (1 S/m) considered in the models of the HDR and partial melting system could cause significant changes in the apparent resistivities and induction arrows at low frequencies. It suggests that in addition to the apparent resistivities, the induction arrows should be taken into consideration in MT data processing and inversion for better lateral resolution when exploring geothermal resources.

Kinase signalling in excitatory neurons regulates sleep quantity and depth.

Progress has been made in the elucidation of sleep and wakefulness regulation at the neurocircuit level1,2. However, the intracellular signalling pathways that regulate sleep and the neuron groups in which these intracellular mechanisms work remain largely unknown. Here, using a forward genetics approach in mice, we identify histone deacetylase 4 (HDAC4) as a sleep-regulating molecule. Haploinsufficiency of Hdac4, a substrate of salt-inducible kinase 3 (SIK3)3, increased sleep. By contrast, mice that lacked SIK3 or its upstream kinase LKB1 in neurons or with a Hdac4S245A mutation that confers resistance to phosphorylation by SIK3 showed decreased sleep. These findings indicate that LKB1-SIK3-HDAC4 constitute a signalling cascade that regulates sleep and wakefulness. We also performed targeted manipulation of SIK3 and HDAC4 in specific neurons and brain regions. This showed that SIK3 signalling in excitatory neurons located in the cerebral cortex and the hypothalamus positively regulates EEG delta power during non-rapid eye movement sleep (NREMS) and NREMS amount, respectively. A subset of transcripts biased towards synaptic functions was commonly regulated in cortical glutamatergic neurons through the expression of a gain-of-function allele of Sik3 and through sleep deprivation. These findings suggest that NREMS quantity and depth are regulated by distinct groups of excitatory neurons through common intracellular signals. This study provides a basis for linking intracellular events and circuit-level mechanisms that control NREMS.

Mutation of Os4BGlu18, monolignol β-glucosidase, improves salinity insensitivity in a rice

Rice is a salt-sensitive crop, and its development and yield are greatly affected by salinity. Using a forward genetics approach, the salt-insensitive line, sitl2, was selected from a core population (M 10 ) induced by gamma-ray irradiation. Under salt treatment, the sitl2 mutant showed greater fresh weight, chlorophyll content, and lower hydrogen peroxide (H 2 O 2 ) levels than the wild type (WT). In addition, the sitl2 mutant showed a lower Na+ content in the shoots and roots under salinity stress. A single-base guanine deletion of Os4BGlu18 , which hydrolyzes monolignol-glucoside by monolignol β-glucosidase, was found by whole genome re-sequencing. Subcellular localization of Os4BGlu18 and the mutant form (Os4BGlu18p.Gly115fs) GFP-tagged proteins were found in the cytosol of rice protoplasts. The expression levels of Os4BGlu18 and most UDP-glucosyltransferase genes examined were lower in the mutant than in the WT. Monolignols were extracted in higher amounts from WT shoots and roots than from the sitl2 mutant; however, monolignol glucoside was extracted in higher amounts in sitl2 . Phloroglucinol-HCl staining showed that sitl2 had more intense and higher lignin content than WT. These results suggest that sitl2 might confer salt tolerance caused by the mutation of Os4BGlu 18 and could be used as a new resource for breeding programs to enhance salt tolerance in rice. • Using a forward genetics approach, a salt-insensitive TILLING line ( sitl2 ) was selected, which could be useful as a new resource for breeding programs. • The sitl2 mutant exhibited a single-base guanine deletion in Os4BGlu18 and a frameshift with a premature stop. • Mutation of Os4BGlu18 affected the expression of lignin biosynthetic pathway genes and UDP-glucosyltransferase genes, and caused high accumulation of lignin in the vascular bundles of shoots and roots in sit2.

Assessing the long-term low-temperature thermal evolution of the central Indian Bundelkhand craton with a complex apatite and zircon (U-Th)/He dataset

Modern approaches in low-temperature thermochronometry are capable of extracting long-term thermal histories from cratonic settings that may elucidate potential drivers of deep-time phases of intracontinental burial and erosion. Here, we assess the utilization of the Radiation Damage Accumulation and Annealing Model for apatite (RDAAM) and zircon (ZRDAAM) to track the long-term low-temperature thermal evolution of the Archean Bundelkhand craton and the surrounding undeformed strata of the ∼1.7–0.9 Ga Vindhyan successions in central India. We correspondingly interpret a complex basement and detrital zircon and apatite (U-Th)/He (ZHe and AHe, respectively) dataset in light of observed model limitations and known geologic context. ZHe and AHe dates from across the craton reveal a significant (>300 Myr) date inversion between the two systems within grains with moderate to high effective uranium (eU) concentrations. Inverse thermal models utilizing current ZRDAAM and RDAAM parameters are not capable of reproducing observed coupled basement ZHe and AHe data for the same thermal history. However, meaningful thermal information can be extracted from AHe inverse models coupled with a forward modeling approach applied to detrital ZHe data from Vindhyan deposits, which have notably lower eU concentrations and yield significantly older ZHe dates (between ∼1,475 and 575 Ma) than basement zircon. Resulting thermal models indicate that the Bundelkhand craton experienced peak burial temperatures of ∼150°C between 850 and 475 Ma, followed by a major crustal cooling event at ∼350–310 Ma, possibly driven by late Paleozoic glaciations and/or epeirorogenic uplift. Inverse models including AHe data require a Deccan Traps related thermal perturbation between ∼66 and 65 Ma, and we suspect that this event overprinted basement zircon with moderate to high eU concentrations. Although the effects of zonation, grain morphology, and/or uncertainties in damage-annealing parameters contribute to disparities between predicted and observed AHe and ZHe dates, these factors alone cannot account for the major ZHe and AHe date inversion observed from the Bundelkhand craton. Instead, it is likely the case that current damage-dependent models for ⁴He diffusion are not adequately calibrated at the resolution necessary to predict short-lived thermal perturbations that occurred in a late phase relative to a prolonged period of extensive damage accumulation.

A Forward Future-Based Approach to Optimizing Agriculture and Climate Change Adaptation in Lower Eastern Kenya

Kenya’s vulnerability to climate variability and change has been compounded by dependence on rain-fed agriculture with constrained capacity to adapt, a rapidly growing population, low-mechanized and low-input smallholder agricultural systems, and compromised soil fertility. The Ukraine war, COVID-19 and the desert locust invasion have only amplified the prevailing sensitivity to shocks in the agriculture sector, creating an emphasis on the need to strengthen local agricultural production to reduce reliance on imports. This paper seeks to assess the opportunities for improving agriculture adaptation and resilience based on future expected changes in climate, length of the growing period and agro-ecologies. The study uses 2020 as the baseline year and explores changes in agro-ecological zones (AEZs) in “near future” 2040 through two representative concentration pathways, 4.5 and 8.5, representing a medium carbon emissions and a dire emissions future, respectively. Google Earth Engine and R Statistics are used in data-processing. Down-scaled climate projections from CIMP5 are used for future analyses combined with static soil suitability and drainage data. Fuzzy logic is used to normalize inputs and compute the agro-ecological zones (AEZ). Interesting results emerge from the study that validate the hypothesis that the seasons and production potential are shifting. Lowland drylands will experience an increasingly long growing period, creating the potential for diversifying production systems from rangelands to agro-pastoral systems, with the capacity to grow more drought-resistant crops and the potential to take advantage of increased runoff for water harvesting. Midland highland areas, which form part of the food basket areas, have already started experiencing a reduction in the length of the growing period and agricultural potential. In these areas, resilience mechanisms will need to consider the expected future reduction in rain-fed agricultural potential, gendered preferences, convergence of technology and indigenous coping mechanisms, and drought-resilience-focused diversification.

A forward genetics approach identifies pathogenicity genes in Fusarium oxysporum f. sp. carthami, a fungus causing vascular wilt disease of safflower

Vascular wilt disease caused by Fusarium oxysporum f. sp. carthami (Foc) is one of the biggest constraints for safflower production in India. Understanding the basis of pathogenicity and molecular dissection of its complex processes is of immense economic importance for the effective management of the wilt disease in safflower. In this study, a forward genetic approach was employed as an unbiased tool to identify the candidate pathogenicity-related genes. Agrobacterium mediated random T-DNA mutagenesis in Foc resulted in the generation of 178 Foc transformants. A hydroponics-based pathogenicity screening of generated mutants led to the identification of 12 avirulent mutants. Genome walking with two of the single insertion mutants revealed T-DNA insertion in the intergenic region of one mutant, while in the other mutant T-DNA was inserted in the coding region of a transcription factor. The genes identified in the present study can be targeted by host-delivered RNAi to generate transgenic safflower lines resistant to Foc.

Synthesis, Characterization and Evaluation of Some Novel 2-Mercaptobenzothiazole Derivatives: In Silico and Experimental Approach

Recently, QSAR technique and molecular docking have become widespread in designing new series of compounds, depending upon the different models developed, before actually synthesizing and testing them. Due to the diverse activity profile of substituted Benzothiazole, it has become a pharmacophore of interest. The main motive of the study was to design some novel 2- Mercaptobenzothiazole molecules with potential anti-inflammatory effect. Thus, in order to get a better insight into the structural features of 2- Mercaptobenzothiazole, a series of analogues were taken from the literature and 2D, 3D QSAR models were developed and molecular docking and ADME studies were also performed. For 2D QSAR study, stepwise forwarding variable selection method, coupled with Partial Least Square (SWF-PLS) regression-based method, was used to generate the best 2D model giving r2= 0.9425, q2=0.9018, F-value=90.0118, pred_r2=0.8976. In contrast, the forward stepwise variable selection k-nearest neighbour molecular field analysis approach was used to generate best 3D QSAR model (q2=0.8171 and pred_r2=0.7006). Docking studies were performed on 29 derivatives of 2-Mercaptobenzothiazoles. The ligands were prepared and docked against protein S. aureus TYrRS with a resolution of 3.2Å. The three derivatives K6, K18 and K20 were found to have the best docking scores (-48.44, -47.46, -48.52). Further, synthesis of these three derivatives was carried out. Spectral characterization was carried out by IR, 1H NMR and 13C NMR and all the structures were found to be in agreement with the data obtained. In vitro anti-inflammatory evaluation of these compounds was carried out by following the protein denaturation method and HRBC membrane stabilization method. Compound K20 displayed potential anti-inflammatory activity, which may be attributed to the electronegative group on the phenyl ring substituted on the pyrazole part of the pharmacophore.

Physics-Informed Neural Network Method and Application to Nuclear Reactor Calculations: A Pilot Study

In this paper, the physics-informed neural network (PINN) method is investigated and applied to nuclear reactor physics calculations with neutron diffusion models. The reactor problems were introduced with both fixed-source and eigenvalue modes. For the fixed-source problem, the loosely coupled reactor model was solved with the forward PINN approach, and then, the model was used to optimize the neural network hyperparameters. For the k-eigenvalue problem, which is unique for reactor calculations, the forward PINN approach was modified to expand the capability of solving for both the fundamental eigenvalue and the associated eigenfunction. This was achieved by using a free learnable parameter to approximate the eigenvalue and a novel regularization technique to exclude null solutions from the PINN framework. Both single-energy-group and multiple-energy-group diffusion models were examined in the work to demonstrate the PINN capabilities of solving systems of coupled partial differential equations in reactor problems. A series of numerical examples was tested to demonstrate the viability of the PINN approach. The PINN solution was compared against the finite element method solution for the neutron flux and the power iteration solution for the k-eigenvalue. The error in the predicted flux ranged from 0.63% for simple fixed-source problems up to about 15% for the two-group k-eigenvalue problem. The deviations in the predicted k-eigenvalues from the power iteration solver ranged from 0.13% to 0.92%. These generally acceptable results preliminarily justified the feasibility of PINN applications in reactor problems. The advantageous application potentials as well as the observable computational deficits of the PINN approaches are discussed along with the pilot study.

Methodical study implicating the effectiveness of Microbial treatment over Xylanase Enzymatic treatment for Pulp Bio-bleaching

This research work evaluated a straight forward approach for pulp bio-bleaching by growing the xylanase producing microorganism ( Bacillus pumilus ) directly onto the soda pulp and then comparing the results of this approach with effect of the extracted xylanase enzyme from the same microorganism. The kappa number for the bacterial treated pulp sample decreased by 26.4% whereas after the enzymatic treatment only 10.3% decrease was found. Physical properties of the paper also improved more significantly in case of bacterial treated pulp samples (52.3% increase in the tear factor, 5% improvement in tensile strength) as compared to the paper formed after enzymatic treatment of pulp (27.57% increase in tear factor, 2.9% increase in tensile strength). The results suggest a novel method of pulp bleaching by direct growth of B. pumilus which is much simpler and time efficient method. The paper quality improved considerably following this approach in comparison to the extracted enzyme. • Direct approach for Pulp bio-bleaching was examined. • Bacillus pumilus SSF onto soda pulp was performed. • Xylanase enzyme was extracted from the same strain B. pumilus . • Indirect enzymatic treatment was also assessed. • Both methods for pulp bio-bleaching were compared critically. • Direct SSF approach showed significant results in comparison to enzymatic treatment.

A potential EARLY FLOWERING 3 homolog in Chlamydomonas is involved in the red/violet and blue light signaling pathways for the degradation of RHYTHM OF CHLOROPLAST 15.

Light plays a major role in resetting the circadian clock, allowing the organism to synchronize with the environmental day and night cycle. In Chlamydomonas the light-induced degradation of the circadian clock protein, RHYTHM OF CHLOROPLAST 15 (ROC15), is considered one of the key events in resetting the circadian clock. Red/violet and blue light signals have been shown to reach the clock via different molecular pathways; however, many of the participating components of these pathways are yet to be elucidated. Here, we used a forward genetics approach using a reporter strain that expresses a ROC15-luciferase fusion protein. We isolated a mutant that showed impaired ROC15 degradation in response to a wide range of visible wavelengths and impaired light-induced phosphorylation of ROC15. These results suggest that the effects of different wavelengths converge before acting on ROC15 or at ROC15 phosphorylation. Furthermore, the mutant showed a weakened phase resetting in response to light, but its circadian rhythmicity remained largely unaffected under constant light and constant dark conditions. Surprisingly, the gene disrupted in this mutant was found to encode a protein that possessed a very weak similarity to the Arabidopsis thaliana EARLY FLOWERING 3 (ELF3). Our results suggest that this protein is involved in the many different light signaling pathways to the Chlamydomonas circadian clock. However, it may not influence the transcriptional oscillator of Chlamydomonas to a great extent. This study provides an opportunity to further understand the mechanisms underlying light-induced clock resetting and explore the evolution of the circadian clock architecture in Viridiplantae.

Harnessing insect olfactory neural circuits for detecting and discriminating human cancers

There is overwhelming evidence that presence of cancer alters cellular metabolic processes, and these changes are manifested in emitted volatile organic compound (VOC) compositions of cancer cells. Here, we take a novel forward engineering approach by developing an insect olfactory neural circuit-based VOC sensor for cancer detection. We obtained oral cancer cell culture VOC-evoked extracellular neural responses from in vivo insect (locust) antennal lobe neurons. We employed biological neural computations of the antennal lobe circuitry for generating spatiotemporal neuronal response templates corresponding to each cell culture VOC mixture, and employed these neuronal templates to distinguish oral cancer cell lines (SAS, Ca9-22, and HSC-3) vs. a non-cancer cell line (HaCaT). Our results demonstrate that three different human oral cancers can be robustly distinguished from each other and from a non-cancer oral cell line. By using high-dimensional population neuronal response analysis and leave-one-trial-out methodology, our approach yielded high classification success for each cell line tested. Our analyses achieved 76–100% success in identifying cell lines by using the population neural response (n = 194) collected for the entire duration of the cell culture study. We also demonstrate this cancer detection technique can distinguish between different types of oral cancers and non-cancer at different time-matched points of growth. This brain-based cancer detection approach is fast as it can differentiate between VOC mixtures within 250 ms of stimulus onset. Our brain-based cancer detection system comprises a novel VOC sensing methodology that incorporates entire biological chemosensory arrays, biological signal transduction, and neuronal computations in a form of a forward-engineered technology for cancer VOC detection.

Integrating machine learning with electronic health record data to facilitate detection of prolactin level and pharmacovigilance signals in olanzapine-treated patients.

Background and aimAvailable evidence suggests elevated serum prolactin (PRL) levels in olanzapine (OLZ)-treated patients with schizophrenia. However, machine learning (ML)-based comprehensive evaluations of the influence of pathophysiological and pharmacological factors on PRL levels in OLZ-treated patients are rare. We aimed to forecast the PRL level in OLZ-treated patients and mine pharmacovigilance information on PRL-related adverse events by integrating ML and electronic health record (EHR) data.MethodsData were extracted from an EHR system to construct an ML dataset in 672×384 matrix format after preprocessing, which was subsequently randomly divided into a derivation cohort for model development and a validation cohort for model validation (8:2). The eXtreme gradient boosting (XGBoost) algorithm was used to build the ML models, the importance of the features and predictive behaviors of which were illustrated by SHapley Additive exPlanations (SHAP)-based analyses. The sequential forward feature selection approach was used to generate the optimal feature subset. The co-administered drugs that might have influenced PRL levels during OLZ treatment as identified by SHAP analyses were then compared with evidence from disproportionality analyses by using OpenVigil FDA.ResultsThe 15 features that made the greatest contributions, as ranked by the mean (|SHAP value|), were identified as the optimal feature subset. The features were gender_male, co-administration of risperidone, age, co-administration of aripiprazole, concentration of aripiprazole, concentration of OLZ, progesterone, co-administration of sulpiride, creatine kinase, serum sodium, serum phosphorus, testosterone, platelet distribution width, α-L-fucosidase, and lipoprotein (a). The XGBoost model after feature selection delivered good performance on the validation cohort with a mean absolute error of 0.046, mean squared error of 0.0036, root-mean-squared error of 0.060, and mean relative error of 11%. Risperidone and aripiprazole exhibited the strongest associations with hyperprolactinemia and decreased blood PRL according to the disproportionality analyses, and both were identified as co-administered drugs that influenced PRL levels during OLZ treatment by SHAP analyses.ConclusionsMultiple pathophysiological and pharmacological confounders influence PRL levels associated with effective treatment and PRL-related side-effects in OLZ-treated patients. Our study highlights the feasibility of integration of ML and EHR data to facilitate the detection of PRL levels and pharmacovigilance signals in OLZ-treated patients.

A novel temperature prediction method without using energy equation based on physics-informed neural network (PINN): A case study on plate- circular/square pin-fin heat sinks

This study introduces a new physics-informed neural networks (PINN)-based prediction method to determine the temperature pattern of fluid and fins when flow passes over plate-circular/ plate-square pin fin heat sinks (PCPFHS / PSPFHS). The proposed method is based on calculating the velocity pattern on the fins' surface. For this target, a training algorithm based on the feed-forward neural network (FNN) (110 layers of learnable weights and 16 neurons in each layer), a nonlinear activation function (rectified linear unit, "ReLU") and the Adam method for optimization is used. The training algorithm is fed by transient large eddy simulation (LES) results at every 0.01 s time step for the total physical time of 100 s. According to the input parameter type, the training:validation ratio is varied between 70:30 and 90:10 in order to keep the coefficient of determination (R2) at its maximum. The automatic differentiation employed the forward accumulation approach to reduce calculation costs, while the transient training matrix fed the neural network. The adaptive gradient (AdaGrad) method is also to improve convergence process and its speed up. Based on the developed calculation tools, the temperature pattern for the flow and over the fins are calculated according to the energy balance on the fin surface and the transient pattern of velocity predicted by PINN. After careful validation with experimental data and sensitivity analysis on the number of neurons and layers, the thermal behavior of PCPFHS and PSPFHS are determined using the conduction heat transfer equation inside the fins via the finite element method and by assuming a heat balance between the fins' surface and airflow. As a result of the proposed method, it is possible to reduce the number of equations in the calculation process of these parameters. According to the results, it is found that PSPFHS has an average Nusselt number which is 9.63% greater than the one in PCPFHS. However, compared to PCPFHS, PSPFHS shows a -17.78% reduced vorticity ratio at Re = 4,865. The results indicated that, for long calculation times (for instance, at 2,000 s physical time), the PINN method reduces calculation costs up to 35% compared to the technique that directly solves the energy conservation in the whole domain.

Testing sea-level and carbonate production effects on stratal architecture of a distally steepened carbonate ramp (Upper Miocene, Menorca): A 3D forward modelling approach

Although distally steepened carbonate ramps have been studied by numerous researchers, the processes that control the development of these carbonate systems, including tectonics, differential carbonate production along the ramp profile, or antecedent physiography of the slopes, are an ongoing discussion. We use a stratigraphic forward model to test different hypotheses to unravel controls over distally steepened ramp development, referenced to the well-known Upper Miocene Menorca carbonate ramp (Spain). Sensitivity tests show that distally steepened ramps develop under complex interaction among accommodation, carbonate production and sediment transport parameters. Ramp slope initiation is favoured by still stands and falls of sea-level, in a setting with high-frequency sea-level fluctuations with amplitude between 20 m and 40 m. Low-frequency and higher amplitude sea-level fluctuations of about 115 m tend to form models with no significant slope development. The impact of antecedent slope on the geometry of ramps is determined by the paleoslope inclination, with flat to subhorizontal paleosurfaces resulting in ramps that mirror the antecedent slope. In contrast, steeper paleosurfaces tend to result in ramps with well-defined slopes. Our models, therefore, show that the ramp profile becomes more influenced by the depth constraints on the carbonate sediment producers than by the geometry of the underlying topography as the inclination of the paleosurface increases. The presented models also show that seagrass-dominated shallow carbonate production tends to result in steep slopes due to the low-transport characteristic imposed by seagrass trapping. This steepness can, however, be altered by the introduction of high transport sediment grains from deeper carbonate producers, which fill the slopes and more distal sections of the ramp profile.

Ameliorated artificial hummingbird algorithm for coordinated wind-solar-thermal generation scheduling problem in multiobjective framework

This paper proposed an optimization technique, namely ameliorated artificial hummingbird algorithm (AAHA), that blends artificial hummingbird algorithm (AHA) with simplex search strategy (SSS) to solve the coordinated wind-solar-thermal generation scheduling problem. The AAHA simulates the foraging behaviour of hummingbirds for food, including guided, territorial, and migration foraging. Guided foraging helps in the higher exploration in the initial stages, and territorial foraging performs the exploitation in its neighbourhood. Migration foraging explores the search space. The SSS enhances the weak territorial and migration foraging of AHA by improving the exploitation mechanism. The proposed method is simple and has less dependency on parameters to adjust. The solar and wind units are committed to ascertaining their share for uninterrupted supply. The price penalty method is applied to unify the emission of gaseous pollutants due to thermal generation with operating costs. To reduce the use of coal, renewable energy sources have been considered in this problem which results in reducing the pollutants’ emissions and saving in fuel costs. To solve the dynamic multivariable constrained optimization problem, the forward approach has been implemented. The performance of the proposed algorithm is tested on different electric test systems, and a statistical test justifies the results.

Multivariable analysis to determine risk factors associated with abortion in mares.

Risk factors associated with equine reproductive efficiency have been identified along with those associated specifically with early pregnancy loss (EPL). In contrast, no studies have reported risk factors associated with abortion (loss between day 70 and 300 post-cover). Given the causes of abortion differ to those of EPL, likely too will the risk factors. A retrospective cohort study was carried out to identify risk factors associated with abortion in UK and Irish based Thoroughbreds, collecting data on 20 exposure variables over a five-year period. A generalized linear mixed model was utilized to evaluate the associations between exposure variables and abortion, with clustering of observations accounted for at the mare and farm level. Variables with a likelihood ratio test (LRT) p value <0.2 were entered into the model in a forward stepwise approach. Pregnancy outcome was available on 4,439 pregnancies from 2,510 mares. Having had two or more prior abortions (odds ratio (OR) 7.91, 95% confidence interval (CI) 2.86, 21.88), conceiving on the second or subsequent covered estrous cycle (OR 1.84, 95% CI 1.22, 2.78) and conceiving multiple conceptuses (OR 1.68, 95% CI 1.02, 2.76) were associated with an increased risk of abortion compared to null parous, first estrous cycle covers and singleton conceptions respectively. Increasing paternal age (OR 0.95, 95% CI 0.90, 0.99) was associated with a decreasing risk of abortion. Mare and farm variance were not significant in the final model, LRT p=0.43. These findings provide evidence-based data to inform Thoroughbred breeding management practices to help mitigate abortion risk.

A MATHEMATICAL MODEL TO ADDRESS OUT-OF-SCHOOL CHILDREN MENACE FOR ACTUALIZATION OF SUSTAINABLE DEVELOPMENT IN NIGERIA

The rising number of out-of-school children (OOSC) constitutes a major obstacle to growth and development in Nigeria. Despite various institutional frameworks and policy initiatives, Nigeria accounts for the highest number of OOSC worldwide with one out of every five OOSC globally residing in Nigeria. In an attempt to characterize dynamics of OOSC and how it could be tackled to fount sustainable development in Nigeria, a new mathematical model was formulated. The validity of the model was examined using some mathematical theorems and the model equilibria were derived. The inclusive schooling ratio, an analytic parameter that quantified the extent to which the rising OOSC was being tackled to fount development, was computed. The stability properties of the model were studied via stability theory of differential equations based on the derived inclusive schooling ratio. Sensitivity analysis was conducted for some major parameters following the normalized forward sensitivity index approach to examine the relative importance of the model parameters to OOSC expansion and contraction. Numerical simulation was later conducted to justify the theoretical results and the results of the simulation showed that efforts to fount development through minimization of OOSC were fruitful if the inclusive schooling ratio was greater than one otherwise the menace of OOSC persisted. The policy implication of the result is that tackling the menace of OOSC to fount sustainable development in Nigeria is a long-term process and any policies designed to pursue the course must be sustained.

Identification and function analysis of yellow-leaf mutant (YX-yl) of broomcorn millet

BackgroundBroomcorn millet is highly tolerant to drought and barren soil. Changes in chlorophyll content directly affect leaf color, which subsequently leadsleading to poor photosynthetic performance and reduced crop yield. Herein, we isolated a yellow leaf mutant (YX-yl) using a forward genetics approach and evaluated its agronomic traits, photosynthetic pigment content, chloroplast ultrastructure, and chlorophyll precursors. Furthermore, the molecular mechanism of yellowing was explored using transcriptome sequencing.ResultsThe YX-yl mutant showed significantly decreased plant height and low yield. The leaves exhibited a yellow-green phenotype and poor photosynthetic capacity during the entire growth period. The content of chlorophyll a, chlorophyll b, and carotenoids in YX-yl leaves was lower than that in wild-type leaves. Chlorophyll precursor analysis results showed that chlorophyll biosynthesis in YX-yl was hindered by the conversion of porphobilinogen to protoporphyrin IX. Examination of chloroplast ultrastructure in the leaves revealed that the chloroplasts of YX-yl accumulated on one side of the cell. Moreover, the chloroplast structure of YX-yl was degraded. The inner and outer membranes of the chloroplasts could not be distinguished well. The numbers of grana and grana thylakoids in the chloroplasts were low. The transcriptome of the yellowing mutant YX-yl was sequenced and compared with that of the wild type. Nine chlorophyll-related genes with significantly different expression profiles were identified: PmUROD, PmCPO, PmGSAM, PmPBDG, PmLHCP, PmCAO, PmVDE, PmGluTR, and PmPNPT. The proteins encoded by these genes were located in the chloroplast, chloroplast membrane, chloroplast thylakoid membrane, and chloroplast matrix and were mainly involved in chlorophyll biosynthesis and redox-related enzyme regulation.ConclusionsYX-yl is an ideal material for studying pigment metabolism mechanisms. Changes in the expression patterns of some genes between YX-yl and the wild type led to differences in chloroplast structures and enzyme activities in the chlorophyll biosynthesis pathway, ultimately resulting in a yellowing phenotype in the YX-yl mutant. Our findings provide an insight to the molecular mechanisms of leaf color formation and chloroplast development in broomcorn millet.

A Kit Mutation Associated with Black-Eyed White Phenotype in the Grey Red-Backed Vole, Myodes rufocanus

Mutations in genes causing leucism are often accompanied by serious disease limiting individual fitness in laboratory animals. Mammalian species with all-white fur exist in the wild; however, the causative genes have not yet been identified, because of the difficulty of the forward genetic approach—comparisons between individuals with different phenotypes. In this study, we conducted linkage analyses using six candidate gene markers on a wild leucistic female grey red-backed vole (Myodes rufocanus) and inbreeding F1 and F2 offspring displaying variations in coat colors, agouti, and leucism. We found that the white-furred variant was associated with a specific Kit allele characterized by a nonsynonymous substitution at amino acid position 604, leading to the replacement of glycine with arginine. In addition, the leucistic phenotype was accompanied by a small portion of black hairs, which increased with growth. Our results showed a candidate gene causing leucism in viable wild animals and provided an opportunity to consider the necessity to study growth-associated traits for coat color analyses.