Chemical Synthesis, Herbicidal Activity, Crop Safety, and Molecular Basis of ortho-Fluoroalkoxy Substituted Sulfonylureas as Novel Acetohydroxyacid Synthase Inhibitors.

In the face of increasing resistance to the currently used commercial herbicides and the lack of success in identifying new herbicide targets, alternative herbicides need to be developed to control unwanted monocotyledon grasses in food crops. Here, a panel of 29 novel sulfonylurea-based compounds with ortho-fluoroalkoxy substitutions at the phenyl ring were designed and synthesized. Pot assays demonstrated that two of these compounds, 6d and 6u, have strong herbicidal activities against Echinochloa crus-galli, Eleusine indica, Alopecurus aequalis, and Alopecurus japonicus Steudel at a dosage of 15 g ha-1. Furthermore, these two compounds exhibited <5% inhibition against wheat at a dosage of 30 g ha-1 under post-emergence conditions. 6u also exhibited <5% inhibition against rice at a dosage of 30 g ha-1 under both post-emergence and pre-emergence conditions. A kinetics study demonstrated that 6d and 6u are potent inhibitors of Arabidopsis thaliana acetohydroxyacid synthase (AHAS; EC 2.2.1.6) with potent Ki values of 18 ± 1.1 and 11.9 ± 4.0 nM, respectively. The crystal structure of 6u in complex with A. thaliana (At)AHAS has also been determined at 2.7 Å resolution. These new compounds represent new alternative herbicide choices to protect wheat or rice from invading grasses.

A strategy of Q-markers identification based on effect, property flavour material basis and rapid quantitative evaluation via near-infrared spectroscopy and chemometric methods for the quality control of Flos Trollii (FT).

ETHNOPHARMACOLOGICAL RELEVANCE: Flos Trollii (FT) is the dried flower of Trollius Chinensis Bunge of Ranunculaceae with the pharmacological properties of anti-inflammatory, antibacterial, antiviral, anti-oxidative. The herb FT is not only a traditional Chinese medicine (TCM) but also an extensively utilized ethnic medicine, employed by diverse ethnic groups including Mongolian, Tibetan, and Kazakh. AIM OF STUDY: FT was taken as an example to construct a strategy of quality markers (Q-markers) identification based on effect, property flavor material basis, and rapid quantitative evaluation using near-infrared (NIR) spectroscopy and chemometric methods of TCM. MATERIALS AND METHODS: Initially, the anti-inflammatory efficacy of FT from three places of origin was evaluated using the RAW264.7-cell inflammatory model, and the bitter property flavor was characterized using an electronic tongue. The high-performance liquid chromatography(HPLC) fingerprint of FT was generated, and the quality of FT from different origins was evaluated employing chemometrics. Next, potential anti-inflammatory and bitter property flavor compounds were screened utilizing a fingerprinting-effect relationship and fingerprinting-property flavor relationship model using partial least squares regression (PLSR). The Q-markers of the FT were confirmed based on the testability principle. Then, a swift, uncomplicated, and precise Q-marker content of the FT prediction model was developed by adopting NIR. RESULTS: The main common fingerprinting peaks affecting FT's efficacy and property flavor were screened. Five of these compounds, 2"-O-beta-L-galactopyranosylorientin, orientin, vitexin, veratric acid, and isoquercitrin, characterized using HPLC and ultra-high performance liquid chromatography(UPLC-QTOF-MS), could be regarded as Q-markers of FT. Q-marker content of the FT prediction model developed adopting NIR spectroscopy was rapid and effective. CONCLUSION: According to the strategy proposed in this study, a quantitative NIR spectroscopic method to identify Q-markers could be a tool to improve the QC efficiency of TCM.

Multi-platform analysis revealed the substance basis and mechanism of Wei-Tong-Xin in ameliorating ENS dysfunction for dyspepsia treatment.

ETHNOPHARMACOLOGICAL RELEVANCE: Duodenal motility disorder is a contributing factor to dyspepsia. The traditional Chinese medicine (TCM) formula Wei-Tong-Xin (WTX), originated from the famous ancient Chinese formula "Wan Ying Yuan", has been demonstrated efficacy in alleviating dyspepsia. AIM OF THE STUDY: The current study aims to elucidate the chemical composition of WTX to establish the pharmacodynamic material basis. On the basis of component, in depth to illuminate the mechanism by which WTX treats dyspepsia via constructing the comprehensive analysis of multi-platform. MATERIALS AND METHODS: The chemical constituents of WTX were systematically analyzed by UHPLC-Q-TOF-MS/MS data processing methods. Based on this, network pharmacology was employed to predict the mechanism by which WTX improved dyspepsia. The dyspepsia mouse model was constructed, and histopathology as well as intestinal permeability were assessed using H&E staining, PAS staining and FITC-dextran assay. Protein expression was detected using western blot, immunofluorescence, immunohistochemistry and ELISA kits. RESULTS: A total of 100 chemical components of WTX were preliminarily identified. Network pharmacological analysis indicated that the therapeutic mechanism of WTX in treating dyspepsia may be related to the regulation of inflammation and oxidative stress-related signaling pathways. In vivo studies showed that WTX mitigated duodenal inflammation and oxidative stress responses, repairing the intestinal mucosal barrier damaged by cisplatin (CIS). Additionally, WTX restored the number of glial cells diminished by inflammatory damage, and ameliorated the serotoninergic neuronal dysfunction caused by insufficient secretion of glia-derived neurotrophic factor (GDNF), and enhanced intestinal transit. CONCLUSIONS: In this study, a total of 100 components of the WTX extract were identified through literature review and mass spectrometry database search. Utilizing computer technology, in conjunction with pharmacodynamic and mechanistic studies, WTX has been found to restore serotoninergic neuronal function by reducing intestinal mucosal inflammatory and oxidative damage, ultimately promoting intestinal transport and treating dyspepsia.

Disruptions to protein kinase A localization in adrenal pathology.

Cell signaling fidelity requires specificity in protein-protein interactions and precise subcellular localization of signaling molecules. In the case of protein phosphorylation, many kinases and phosphatases exhibit promiscuous substrate pairing and therefore require targeting interactions to modify the appropriate substrates and avoid cross-talk among different pathways. In the past 10 years, researchers have discovered and investigated how loss of specific interactions and subcellular targeting for the protein kinase A catalytic subunit (PKAc) lead to cortisol-producing adenomas and the debilitating stress disorder adrenal Cushing's syndrome. This article reviews classical studies regarding PKA localization in glucocorticoid-producing adrenal cells and synthesizes recent evidence of disrupted PKA localization and selective regulatory interactions in adrenal pathology.

Soil moisture inversion based on multiple drought indices and RBFNN: A case study of northern Hebei Province.

Drought has a significant impact on crop growth and productivity, highlighting the critical need for precise and timely soil moisture estimation to mitigate agricultural losses. This study focuses on soil moisture retrieval in northern Hebei Province during July 2012, utilizing eight widely employed remote sensing drought indices derived from MODIS satellite data. These indices were cross-referenced with measured soil moisture levels for analysis. Based on their correlation coefficients, a composite remote sensing drought index set comprising six indices was identified. Furthermore, a radial basis function neural network (RBFNN) was employed to estimate soil relative humidity. The accuracy evaluation of the soil moisture estimation model, which integrates multiple remote sensing drought indices and the RBFNN, demonstrated clear superiority over models relying on single drought indices. The model achieved an average estimation accuracy of 87.54 % for soil relative humidity at a depth of 10 cm (SM10) and 87.36 % for a 20 cm depth (SM20). The root mean square errors (RMSE) for the test sets were 0.093 and 0.092, respectively. Validation results for July 2013 indicated that the inversion accurately reflected the actual soil moisture conditions, effectively capturing dynamic moisture changes. These results fully verify the reliability and practicability of the model. These findings introduce a novel approach to local agricultural soil moisture estimation, with significant implications for enhancing agricultural water resource management and decision-making processes.

Radical Addition Reactions: Hierarchical Ab initio Benchmark and DFT Performance Study.

We performed a hierarchical ab initio benchmark study of the gas-phase radical addition reactions of X• + C2H2 and X• + C2H4 (X•= CH3•, NH2•, OH•, SH•). The hierarchical series of ab initio methods (HF, MP2, CCSD, CCSD(T)) were paired with a hierarchal series of Dunning basis sets with and without diffuse functions ((aug)-cc-pVDZ, (aug)-cc-pVTZ, (aug)-cc-pVQZ). The HF ground-state wavefunctions were transformed into quasi-restricted orbital (QRO) reference wavefunctions to address spin contamination. Following extrapolation to the CBS limit, the energies from our highest- QRO-CCSD(T)/CBS+ level converged within 0.0-3.4 kcal mol-1 and 0.0-1.0 kcal mol-1 concerning the ab initio method and basis set, respectively. Our QRO-CCSD(T)/CBS+ reference data was used to evaluate the performance of 98 density functional theory (DFT) approximations. The MAE of the best functionals for reaction barriers and energies were: OLYP (1.9 kcal mol-1), BMK (1.0 kcal mol-1), M06-2X (0.9 kcal mol-1), MN12-SX (0.8 kcal mol-1) and CAM-B3LYP (0.8 kcal mol-1). These functionals also accurately reproduce key geometrical parameters of the stationary points within an average 2% deviation from the reference QRO-CCSD(T)/CBS+ level.

Association Analysis Provides Insights into Molecular Evolution in Salt Tolerance during Tomato Domestication.

Salt stress impairs plant growth and development, generally resulting in crop failure. Tomato domestication gave rise to a dramatic decrease in salt tolerance caused by the genetic variability of the wild ancestors. However, the nature of artificial selection in reducing tomato salt tolerance remains unclear. Here, we generated and analyzed datasets on the survival rates and sodium (Na+) and potassium (K+) concentrations of hundreds of tomato varieties from wild ancestors to contemporary breeding accessions under high salinity. Genome-wide association studies (GWAS) revealed that natural variation in the promoter region of the putative K+ channel regulatory subunit-encoding gene KSB1 (potassium channel beta subunit in Solanum lycopersicum) is associated with survival rates and root Na+/K+ ratios in tomato under salt stress. This variation is deposited in tomato domestication sweeps and contributes to modified expression of KSB1 by salt-induced transcription factor SlHY5 in response to high salinity. We further found that KSB1 interacts with the K+ channel protein KSL1 to maintain cellular Na+ and K+ homeostasis, thus enhancing salt tolerance in tomato. Our findings reveal the crucial role of the SlHY5-KSB1-KSL1 module in regulating ion homeostasis and salt tolerance during tomato domestication, elucidating that selective pressure imposed by humans on the evolutionary process provides insights into further crop improvement.

Spectral integrated neural networks (SINNs) for solving forward and inverse dynamic problems.

This study introduces an innovative neural network framework named spectral integrated neural networks (SINNs) to address both forward and inverse dynamic problems in three-dimensional space. In the SINNs, the spectral integration technique is utilized for temporal discretization, followed by the application of a fully connected neural network to solve the resulting partial differential equations in the spatial domain. Furthermore, the polynomial basis functions are employed to expand the unknown function, with the goal of improving the performance of SINNs in tackling inverse problems. The performance of the developed framework is evaluated through several dynamic benchmark examples encompassing linear and nonlinear heat conduction problems, linear and nonlinear wave propagation problems, inverse problem of heat conduction, and long-time heat conduction problem. The numerical results demonstrate that the SINNs can effectively and accurately solve forward and inverse problems involving heat conduction and wave propagation. Additionally, the SINNs provide precise and stable solutions for dynamic problems with extended time durations. Compared to commonly used physics-informed neural networks, the SINNs exhibit superior performance with enhanced convergence speed, computational accuracy, and efficiency.

Dissection of genetic architecture for desirable traits in sugarcane by integrated transcriptomics and metabolomics.

Sugarcane is an important sugar and energy crop. Breeding varieties with high yield and sugar, strong stress tolerance, as well as beneficial for mechanized harvesting are the goal of sugarcane breeder. In the present study, transcriptomics and metabolomics were conducted to explore the molecular basis for outstanding performance of five elite varieties GT42, GT44, LC05-136, YZ08-1609, and YZ05-51, along with the cross-parent CP72-1210 compared to ROC22. Transcriptomics revealed a total of 18,353 differentially expressed genes (DEGs) and several regulatory pathways, including carbon fixation, starch and sucrose metabolism, phenylpropanoids biosynthesis, flavonoid biosynthesis, cysteine and methionine metabolism, as well as zeatin biosynthesis. Expression patterns of genes involved in these pathways confirmed their role in determining the agronomic traits. Besides, metabolomics disclosed 175 differentially accumulated metabolites (DAMs), including specific metabolites of amino acids and secondary metabolites. Furthermore, conjoint analysis of transcriptomics and metabolomics highlighted the manipulation of 113 genes led to changed levels of 20 metabolites associated with carbon fixation, sucrose accumulation, phytohormone response and secondary metabolism. Finally, we depicted here a blueprint outlining the genetic basis underlying the desirable traits in sugarcane. This study will accelerate the dissection of the molecular basis for sugarcane traits and provide targets for molecular breeding.

Ge-Zhi-Jie-Jiu decoction alleviates alcoholic liver disease through multiple signaling pathways.

ETHNOPHARMACOLOGICAL RELEVANCE: Alcoholic liver disease (ALD) is a growing public health concern caused by excessive alcohol consumption, but effective treatments are limited. Ge-Zhi-Jie-Jiu decoction (JJY) is a modified traditional Chinese herbal remedy that aims to alleviate ALD. This formula contains various components such as Ge Hua, Ge Gen, Zhi Ju Zi, and other medicinal-food herbs. However, the specific pharmacotherapeutic compounds of JJY and its pharmacological mechanisms remain unclear. AIM OF THE STUDY: This study aimed to elucidate the molecular mechanism and pharmacodynamic basis of JJY in treating ALD. MATERIALS AND METHODS: UPLC-Q-Orbitrap HRMS, HPLC fingerprinting, and LC-MS techniques were used for the composition identification and quality control of JJY. The pharmacological components and molecular mechanisms of JJY in anti-ALD were then predicted using network pharmacology and molecular docking approaches. Ultimately, an acute alcoholic liver injury mouse model was developed, and the potential mechanisms were verified by hematoxylin-eosin (H&E), Oil Red O, and TUNEL staining, real-time fluorescence quantitative PCR (RT-qPCR), Western blot (WB) and molecular docking analysis. RESULTS: The results showed that the main components of JJY are organic acids, flavonoids, and isoflavonoids, in which puerarin, daidzein, glycitein, ononin, quercetin, and tectorigenin can be used as the indicator components of JJY. In addition, JJY might ameliorate ALD through several pathways, including potentially promoting alcohol metabolism via alcohol-metabolizing enzymes, and possibly inhibiting oxidative stress, inflammation and apoptosis via the Nrf2/Keap1/HO-1 and MAPK signaling pathways. Furthermore, JJY may also alleviated hepatic lipid accumulation through the PPARα signaling pathway. CONCLUSIONS: JJY has significant anti-ALD efficacy with multiple mechanisms. This study offers a solid experimental foundation for JJY's development as a medicine with anti-ALD characteristics and elucidates its probable active components.

Analysis of IgE level influencing factors of common allergens for allergic rhinitis in northeastern China.

OBJECTIVES: This thesis aims to provide patients with a preventive and therapeutic basis by analyzing IgE level influencing factors of common allergens for Allergic Rhinitis (AR). METHOD: Multiple linear regression analysis is made upon questionnaires among 749 cases of AR patients that are divided into 5 age-based groups. Perform serum-specific IgE content testing on patients. RESULTS: Cockroach being an allergen, AR patients' IgE Level is influenced by allergic history, home-raised plants and animals. For AR patients with mugwort as an allergen, allergy and asthma history could increase IgE level, respectively, ß = 4.291 and ß = 4.364. If the allergen turns out to be peanut, allergic history would increase the IgE level (ß = 0.171), however, the level would be lower in female patients compared with male patients (ß = -0.078). For patients with egg as an allergen, allergic history, home-raised plants and animals (pets) would all affect the IgE level, respectively, ß = 0.182, ß = 0.118 and ß = -0.101. CONCLUSIONS: IgE level varies according to allergic history, home-raised plants & animals, gender, furniture renewal, asthma, and ages for patients with different allergens including cockroach, mold, mugwort, peanut, egg and crab. For each kind of allergen, the IgE levels react differently to different influencing factors, thus requiring a thorough analysis of each AR patient's allergen and allergenic factors.

Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology.

INTRODUCTION: Developing somatic embryogenesis is one of the main steps in successful in vitro propagation and gene transformation in the carrot. However, somatic embryogenesis is influenced by different intrinsic (genetics, genotype, and explant) and extrinsic (e.g., plant growth regulators (PGRs), medium composition, and gelling agent) factors which cause challenges in developing the somatic embryogenesis protocol. Therefore, optimizing somatic embryogenesis is a tedious, time-consuming, and costly process. Novel data mining approaches through a hybrid of artificial neural networks (ANNs) and optimization algorithms can facilitate modeling and optimizing in vitro culture processes and thereby reduce large experimental treatments and combinations. Carrot is a model plant in genetic engineering works and recombinant drugs, and therefore it is an important plant in research works. Also, in this research, for the first time, embryogenesis in carrot (Daucus carota L.) using Genetic algorithm (GA) and data mining technology has been reviewed and analyzed. MATERIALS AND METHODS: In the current study, data mining approach through multilayer perceptron (MLP) and radial basis function (RBF) as two well-known ANNs were employed to model and predict embryogenic callus production in carrot based on eight input variables including carrot cultivars, agar, magnesium sulfate (MgSO4), calcium dichloride (CaCl2), manganese (II) sulfate (MnSO4), 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), and kinetin (KIN). To confirm the reliability and accuracy of the developed model, the result obtained from RBF-GA model were tested in the laboratory. RESULTS: The results showed that RBF had better prediction efficiency than MLP. Then, the developed model was linked to a genetic algorithm (GA) to optimize the system. To confirm the reliability and accuracy of the developed model, the result of RBF-GA was experimentally tested in the lab as a validation experiment. The result showed that there was no significant difference between the predicted optimized result and the experimental result. CONCLUTIONS: Generally, the results of this study suggest that data mining through RBF-GA can be considered as a robust approach, besides experimental methods, to model and optimize in vitro culture systems. According to the RBF-GA result, the highest somatic embryogenesis rate (62.5%) can be obtained from Nantes improved cultivar cultured on medium containing 195.23 mg/l MgSO4, 330.07 mg/l CaCl2, 18.3 mg/l MnSO4, 0.46 mg/l 2,4- D, 0.03 mg/l BAP, and 0.88 mg/l KIN. These results were also confirmed in the laboratory.

Data-driven determination of 1H-MRS basis set composition.

Purpose: Metabolite amplitude estimates derived from linear combination modeling of MR spectra depend upon the precise list of constituent metabolite basis functions used (the "basis set"). The absence of clear consensus on the "ideal" composition or objective criteria to determine the suitability of a particular basis set contributes to the poor reproducibility of MRS. In this proof-of-concept study, we demonstrate a novel, data-driven approach for deciding the basis-set composition using Bayesian information criteria (BIC). Methods: We have developed an algorithm that iteratively adds metabolites to the basis set using iterative modeling, informed by BIC scores. We investigated two quantitative "stopping conditions", referred to as max-BIC and zero-amplitude, and whether to optimize the selection of basis set on a per-spectrum basis or at the group level. The algorithm was tested using two groups of synthetic in-vivo-like spectra representing healthy brain and tumor spectra, respectively, and the derived basis sets (and metabolite amplitude estimates) were compared to the ground truth. Results: All derived basis sets correctly identified high-concentration metabolites and provided reasonable fits of the spectra. At the single-spectrum level, the two stopping conditions derived the underlying basis set with 77-87% accuracy. When optimizing across a group, basis set determination accuracy improved to 84-92%. Conclusion: Data-driven determination of the basis set composition is feasible. With refinement, this approach could provide a valuable data-driven way to derive or refine basis sets, reducing the operator bias of MRS analyses, enhancing the objectivity of quantitative analyses, and increasing the clinical viability of MRS.

Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes.

This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: linear, radial basis function (RBF), sigmoid, and polynomial degrees 1-3. Various optimization methods, including grid search, random search, genetic algorithms, differential evolution, and particle swarm optimization, were used. The radial basis function kernel achieves the highest accuracy at 93.2%, and the weighted accuracy ensemble further improves it to 94.9%. This study shows the effectiveness of these methods in agricultural research and crop improvement. Notably, optimization-based SVM classification, particularly with particle swarm optimization, saw a significant 1.7% accuracy gain in the test set, reaching 94.9% accuracy. These findings underscore the efficacy of RBF kernels and optimization techniques in improving wheat genotype classification accuracy and highlight the potential of SVMs in agricultural research and crop improvement endeavors.

Optimal interpolation approach for groundwater depth estimation.

In arid and semi-arid regions where surface water resources are scarce, groundwater is crucial. Accurate mapping of groundwater depth is vital for sustainable management practices. This study evaluated the performance of three spatial interpolation techniques - inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF) - in predicting groundwater depth distribution across Dire Dawa City, Ethiopia. The results demonstrated the superiority of the RBF method, exhibiting the lowest RMSE (3.21 m), MAE (0.16 m), and the highest R2 (0.99) compared to IDW and OK. The IDW method emerged as the next best performer (RMSE = 4.68 m, MAE = 0.16 m, R2= 0.97), followed by OK (RMSE = 5.32 m, MAE = 0.42 m, R2= 0.95). The RBF's superior accuracy aligns with findings from other semi-arid regions, underscoring its suitability for data-scarce areas like Dire Dawa. This comparative evaluation provides valuable insights for selecting the optimal interpolation method for groundwater depth mapping, supporting informed decision-making in local water resource management. The methodological approach comprised:•Implementation of three interpolation techniques, namely, inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF), utilizing 56 groundwater depth measurements from locations dispersed throughout the study area.•Cross-validation through randomly withholding 20 % of the data for validation purposes.•Comparison of the techniques based on statistical measures of accuracy, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2).

G2BFNN: Generalized geodesic basis function neural network.

Real-world data is typically distributed on low-dimensional manifolds embedded in high-dimensional Euclidean spaces. Accurately extracting spatial distribution features on general manifolds that reflect the intrinsic characteristics of data is crucial for effective feature representation. Therefore, we propose a generalized geodesic basis function neural network (G2BFNN) architecture. The generalized geodesic basis functions (G2BF) are defined based on generalized geodesic distances. The generalized geodesic distance metric (G2DM) is obtained by learning the manifold structure. To implement this architecture, a specific G2BFNN, named discriminative local preserving projection-based G2BFNN (DLPP-G2BFNN) is proposed. DLPP-G2BFNN mainly contains two modules, namely the manifold structure learning module (MSLM) and the network mapping module (NMM). In the MSLM module, a supervised adjacency graph matrix is constructed to constrain the learning of the manifold structure. This enables the learned features in the embedding subspace to maintain the manifold structure while enhancing the discriminability. The features and G2DM learned in the MSLM are fed into the NMM. Through the G2BF in the NMM, the spatial distribution features on manifold are obtained. Finally, the output of the network is obtained through the fully connected layer. Compared with the local response neural network based on Euclidean distance, the proposed network can reveal more essential spatial structure characteristics of the data. Meanwhile, the proposed G2BFNN is a generalized network architecture that can be combined with any manifold learning method, showcasing high scalability. The experimental results demonstrate that the proposed DLPP-G2BFNN outperforms existing methods by utilizing fewer kernels while achieving higher recognition performance.

The scientific nature of the linear no-threshold (LNT) model used in the system of radiological protection.

During the first half of the 20th century, it was commonly assumed that radiation-induced health effects occur only when the dose exceeds a certain threshold. This idea was discarded for stochastic effects when more knowledge was gained about the mechanisms of radiation-induced cancer. Currently, a key tenet of the international system of radiological protection is the linear no-threshold (LNT) model where the risk of radiation-induced cancer is believed to be directly proportional to the dose received, even at dose levels where the effects cannot be proven directly. The validity of the LNT approach has been questioned on the basis of a claim that only conclusions that can be verified experimentally or epidemiologically are scientific and LNT should, thus, be discarded because the system of radiological protection must be based on solid science. The aim of this publication is to demonstrate that the LNT concept can be tested in principle and fulfils the criteria of a scientific hypothesis. The fact that the system of radiological protection is also based on ethics does not render it unscientific either. One of the fundamental ethical concepts underlying the LNT model is the precautionary principle. We explain why it is the best approach, based on science and ethics (as well as practical experience), in situations of prevailing uncertainty.

A novel radial basis neural network for the Zika virus spreading model.

The motive of current investigations is to design a novel radial basis neural network stochastic structure to present the numerical representations of the Zika virus spreading model (ZVSM). The mathematical ZVSM is categorized into humans and vectors based on the susceptible S(q), exposed E(q), infected I(q) and recovered R(q), i.e., SEIR. The stochastic performances are designed using the radial basis activation function, feed forward neural network, twenty-two numbers of neurons along with the optimization of Bayesian regularization in order to solve the ZVSM. A dataset is achieved using the explicit Runge-Kutta scheme, which is used to reduce the mean square error (MSE) based on the process of training for solving the nonlinear ZVSM. The division of the data is categorized into training, which is taken as 78 %, while 11 % for both authentication and testing. Three different cases of the nonlinear ZVSM have been taken, while the scheme's correctness is performed through the matching of the results. Furthermore, the reliability of the scheme is observed by applying different performances of regression, MSE, error histograms and state transition.

Hepatic Metabolomic Responses to Low-Temperature Stress in the Invasive Turtle, Trachemys scripta elegans.

Investigating the physiological and biochemical changes of ectothermic species before entering hibernation would contribute to the understanding of how they adapt to low-temperature environments. Here, red-eared slider turtle (Trachemys scripta elegans) hatchlings were maintained under different thermal treatments (24 °C, slowly decreasing temperatures from 24 °C to 14 °C, and to 4 °C). Hepatic metabolite alterations were measured to assess the metabolic impacts of low-temperature stress in this species. Of these differentially changed metabolites, some (e.g., raffinose, spermidine, allocholic acid, taurohyocholate, 2-ketobutyric acid, acetylcysteine) were shown to decrease, while others (e.g., stearolic acid, D-mannose) increased in low-temperature treatments. Our results indicated that short-term low-temperature stress might have limited impacts on lipid and energy metabolism in this species. The changes in other metabolites (e.g., allocholic acid, taurohyocholate, spermine, acetylcysteine) might be associated with a low food intake (and thus reduced digestive performance) and weakened immune ability of low-temperature-exposed animals.

Deciphering the Genetic Basis of Kernel Composition in a Maize Association Panel.

The primary objective in contemporary maize breeding is to pursue high quality alongside high yield. Deciphering the genetic basis of natural variation in starch, protein, oil, and fiber contents is essential for manipulating kernel composition, thereby enhancing the kernel quality and meeting growing demands. Here, we identified 12 to 88 statistically significant loci associated with kernel composition traits through a genome-wide association study (GWAS) using a panel of 212 diverse inbred lines. A regional association study pinpointed numerous causal candidate genes at these loci. Coexpression and protein-protein interaction network analyses of candidate genes revealed several causal genes directly or indirectly involved in the metabolic processes related to kernel composition traits. Subsequent mutant experiment revealed that nonsense mutations in ZmTIFY12 affect starch, protein, and fiber content, whereas nonsense mutations in ZmTT12 affect starch, protein, and oil content. These findings provide valuable guidance for improving kernel quality in maize breeding efforts.