Subdividing Stress Groups into Eustress and Distress Groups Using Laterality Index Calculated from Brain Hemodynamic Response

A stress group should be subdivided into eustress (low-stress) and distress (high-stress) groups to better evaluate personal cognitive abilities and mental/physical health. However, it is challenging because of the inconsistent pattern in brain activation. We aimed to ascertain the necessity of subdividing the stress groups. The stress group was screened by salivary alpha-amylase (sAA) and then, the brain’s hemodynamic reactions were measured by functional near-infrared spectroscopy (fNIRS) based on the near-infrared biosensor. We compared the two stress subgroups categorized by sAA using a newly designed emotional stimulus-response paradigm with an international affective picture system (IAPS) to enhance hemodynamic signals induced by the target effect. We calculated the laterality index for stress (LIS) from the measured signals to identify the dominantly activated cortex in both the subgroups. Both the stress groups exhibited brain activity in the right frontal cortex. Specifically, the eustress group exhibited the largest brain activity, whereas the distress group exhibited recessive brain activity, regardless of positive or negative stimuli. LIS values were larger in the order of the eustress, control, and distress groups; this indicates that the stress group can be divided into eustress and distress groups. We built a foundation for subdividing stress groups into eustress and distress groups using fNIRS.

Modelling and Optimisation of UHF band EW Based WTA Problem within the Scope of Threat Assessment

The classical weapon target allocation (WTA) problem has been evaluated within the scope of electronic warfare (EW) threat assessment with an electromagnetic effect-based jammer- tactical radio engagement approach. As different from the literature, optimum allocation of non-directional jammers operating at different operating UHF frequencies under constraints to RF emitters is aimed in this study. The values of the targets are modelled using an original threat assessment algorithm developed that takes into account operating frequencies, jamming distance, and weather conditions. The computed jammer-target effect matrix has been solved under different scenarios according to the efficiency and cost constraints. It is seen at the end of the simulations that the allocation results for EW applications largely depend on the effect ratio used. The better results are taken in the case of under 0.5 effect ratio. Finally, jammer-radio allocation problem specified at the suggested model is solved successfully and effectively.

AttCRISPR: a spacetime interpretable model for prediction of sgRNA on-target activity

Background More and more Cas9 variants with higher specificity are developed to avoid the off-target effect, which brings a significant volume of experimental data. Conventional machine learning performs poorly on these datasets, while the methods based on deep learning often lack interpretability, which makes researchers have to trade-off accuracy and interpretability. It is necessary to develop a method that can not only match deep learning-based methods in performance but also with good interpretability that can be comparable to conventional machine learning methods. Results To overcome these problems, we propose an intrinsically interpretable method called AttCRISPR based on deep learning to predict the on-target activity. The advantage of AttCRISPR lies in using the ensemble learning strategy to stack available encoding-based methods and embedding-based methods with strong interpretability. Comparison with the state-of-the-art methods using WT-SpCas9, eSpCas9(1.1), SpCas9-HF1 datasets, AttCRISPR can achieve an average Spearman value of 0.872, 0.867, 0.867, respectively on several public datasets, which is superior to these methods. Furthermore, benefits from two attention modules—one spatial and one temporal, AttCRISPR has good interpretability. Through these modules, we can understand the decisions made by AttCRISPR at both global and local levels without other post hoc explanations techniques. Conclusion With the trained models, we reveal the preference for each position-dependent nucleotide on the sgRNA (short guide RNA) sequence in each dataset at a global level. And at a local level, we prove that the interpretability of AttCRISPR can be used to guide the researchers to design sgRNA with higher activity.

A druggable addiction to de novo pyrimidine biosynthesis in diffuse midline glioma

Diffuse midline glioma (DMG) is a uniformly fatal pediatric cancer driven by oncohistones that do not readily lend themselves to drug development. To identify therapeutic targets for DMG, we conducted a genome-wide CRIPSR screen for DMG metabolic vulnerabilities, which revealed a DMG selective dependency on the de novo pathway for pyrimidine biosynthesis. The dependency is specific to pyrimidines as there is no selectivity for suppression of de novo purine biosynthesis. A clinical stage inhibitor of DHODH (a rate limiting enzyme in the de novo pathway) generates DNA damage and induces apoptosis through suppression of replication forks--an on target effect, as shown by uridine rescue. MALDI mass spectroscopy imaging demonstrates that BAY2402234 accumulates in brain at therapeutically relevant concentrations, suppresses de novo pyrimidine biosynthesis in vivo, and prolongs survival of mice bearing intracranial DMG xenografts. Our results highlight BAY2402234, a brain-penetrant DHODH inhibitor, as a promising therapy against DMGs.

The Atypical Antipsychotic Quetiapine Induces Multiple Antibiotic Resistance in Escherichia coli

Atypical antipsychotic (AAP) medication is a critical tool for treating symptoms of psychiatric disorders. While AAPs primarily target dopamine (D2) and serotonin (5HT2A and 5HT1A) receptors, they also exhibit intrinsic antimicrobial activity as an off-target effect. Because AAPs are often prescribed to patients for many years, a potential risk associated with long-term AAP use is the unintended emergence of bacteria with antimicrobial resistance (AMR). Here, we show that exposure to the AAP quetiapine at estimated gut concentrations promotes AMR in Escherichia coli after six weeks. Quetiapine-exposed isolates exhibited an increase in minimal inhibitory concentrations (MICs) for ampicillin, tetracycline, ceftriaxone, and levofloxacin. By whole genome sequencing analysis, we identified mutations in genes that confer AMR, including the repressor for the multiple antibiotic resistance mar operon (marR), and real-time RT-qPCR analysis showed increased levels of marA, acrA, and tolC mRNAs and a reduced level of ompF mRNA in the isolates carrying marR mutations. To determine the contribution of each marR mutation to AMR, we constructed isogenic strains carrying individual mutant marR alleles in the parent background and re-evaluated their resistant phenotypes using MIC and RT-qPCR assays. While marR mutations induced a robust activity of the mar operon, they resulted in only a modest increase in MICs. Interestingly, although these marR mutations did not fully recapitulate the AMR phenotype of the quetiapine-exposed isolates, we show that marR mutations promote growth fitness in the presence of quetiapine. Our findings revealed an important link between the use of AAPs and AMR development in E. coli.

Multisensory stimuli shift perceptual priors to facilitate rapid behavior

Multisensory stimuli speed behavioral responses, but the mechanisms subserving these effects remain disputed. Historically, the observation that multisensory reaction times (RTs) outpace models assuming independent sensory channels has been taken as evidence for multisensory integration (the “redundant target effect”; RTE). However, this interpretation has been challenged by alternative explanations based on stimulus sequence effects, RT variability, and/or negative correlations in unisensory processing. To clarify the mechanisms subserving the RTE, we collected RTs from 78 undergraduates in a multisensory simple RT task. Based on previous neurophysiological findings, we hypothesized that the RTE was unlikely to reflect these alternative mechanisms, and more likely reflected pre-potentiation of sensory responses through crossmodal phase-resetting. Contrary to accounts based on stimulus sequence effects, we found that preceding stimuli explained only 3–9% of the variance in apparent RTEs. Comparing three plausible evidence accumulator models, we found that multisensory RT distributions were best explained by increased sensory evidence at stimulus onset. Because crossmodal phase-resetting increases cortical excitability before sensory input arrives, these results are consistent with a mechanism based on pre-potentiation through phase-resetting. Mathematically, this model entails increasing the prior log-odds of stimulus presence, providing a potential link between neurophysiological, behavioral, and computational accounts of multisensory interactions.

Mango Endophyte and Epiphyte Microbiome Composition during Fruit Development and Post-Harvest Stages

The influence of the development stage and post-harvest handling on the microbial composition of mango fruit plays a central role in fruit health. Hence, the composition of fungal and bacterial microbiota on the anthoplane, fructoplane, stems and stem-end pulp of mango during fruit development and post-harvest handling were determined using next-generation sequencing of the internal transcribed spacer and 16S rRNA regions. At full bloom, the inflorescence had the richest fungal and bacterial communities. The young developing fruit exhibited lower fungal richness and diversities in comparison to the intermediate and fully developed fruit stages on the fructoplane. At the post-harvest stage, lower fungal and bacterial diversities were observed following prochloraz treatment both on the fructoplane and stem-end pulp. Ascomycota (52.8%) and Basidiomycota (43.2%) were the most dominant fungal phyla, while Penicillium, Botryosphaeria, Alternaria and Mucor were detected as the known post-harvest decay-causing fungal genera. The Cyanobacteria (35.6%), Firmicutes (26.1%) and Proteobacteria (23.1%) were the most dominant bacterial phyla. Changes in the presence of Bacillus subtilis following post-harvest interventions such as prochloraz suggested a non-target effect of the fungicide. The present study, therefore, provides the primary baseline data on mango fungal and bacterial diversity and composition, which can be foundational in the development of effective disease (stem-end rot) management strategies.

Bacteria modulate tamoxifen-induced death via host fatty acid metabolism

Tamoxifen is a selective estrogen receptor (ER) modulator that is used to treat ER positive breast cancer, but that at high doses kills both ER-positive and ER negative breast cancer cells. We recapitulate this off-target effect in Caenorhabditis elegans, which does not have an ER ortholog. We find that different bacteria dramatically modulate tamoxifen toxicity in C. elegans, with a three-order of magnitude difference between animals fed Escherichia coli, Comamonas aquatica, and Bacillus subtilis. Remarkably, host fatty acid (FA) biosynthesis mitigates tamoxifen toxicity, and different bacteria provide the animal with different FAs, resulting in distinct FA profiles. Surprisingly these bacteria modulate tamoxifen toxicity by different death mechanisms, some of which are modulated by FA supplementation and others by antioxidants. Together, this work reveals a complex interplay between microbiota, FA metabolism and tamoxifen toxicity that may provide a blueprint for similar studies in more complex mammals.

Forecast of Waterway Cargo Turnover Volume Based on Genetic Algorithm to Optimize Neural Network Parameters

The traditional BP neural network is difficult to achieve the target effect in the prediction of waterway cargo turnover. In order to improve the accuracy of waterway cargo turnover forecast, a waterway cargo turnover forecast model was created based on genetic algorithm to optimize neural network parameters. The genetic algorithm overcomes the trap that the general iterative method easily falls into, that is, the “endless loop” phenomenon that occurs when the local minimum is small, and the calculation time is small, and the robustness is high. Using genetic algorithm optimized BP neural network to predict waterway cargo turnover, and the empirical analysis of the waterway cargo turnover forecast is carried out. The results obtained show that the neural network waterway optimized by genetic algorithm has a higher accuracy than the traditional BP neural network for predicting waterway cargo turnover, and the optimization model can long-term analysis of the characteristics of waterway cargo turnover changes shows that the prediction effect is far better than traditional neural networks.