Screening of Tomatoes for Their Resistance to Salinity and Drought Stress

In the study, 55 tomato genotypes have been investigated for their responses against salinity stresses in 48 day old early plant growth stage. For these purposes, several morphological and physiological measurements and analysis have been done in stressed plants. Shoot and root dry weights, plant height, leaf number, leaf area, relative water content, stomatal conductance, leaf osmotic potential, leaf water potential, shoot K, Ca and Cl concentrations were measured and analyzed. Salt and drought tolerant and sensitive (intolerant) genotypes have been found out according to the responses of the tomato genotypes to the above mentioned morphological and physiological parameters. At the end of the study, the fifty-five tomato genotypes were classified as tolerant, mildly tolerant or susceptible. Shoot dry weight, plant total leaf area, leaf water potential, leaf osmotic potential, stomatal conductance, K, Ca, Na and Cl concentrations in shoot and root, K/Na, Ca/Na, membrane injury index and visual appearance of damages were more relevant parameter for screening studies. Keywords: Stress, saline, water, tolerance, selection, breedin

A cone-beam X-ray computed tomography data collection designed for machine learning

Unlike previous works, this open data collection consists of X-ray cone-beam (CB) computed tomography (CT) datasets specifically designed for machine learning applications and high cone-angle artefact reduction. Forty-two walnuts were scanned with a laboratory X-ray set-up to provide not only data from a single object but from a class of objects with natural variability. For each walnut, CB projections on three different source orbits were acquired to provide CB data with different cone angles as well as being able to compute artefact-free, high-quality ground truth images from the combined data that can be used for supervised learning. We provide the complete image reconstruction pipeline: raw projection data, a description of the scanning geometry, pre-processing and reconstruction scripts using open software, and the reconstructed volumes. Due to this, the dataset can not only be used for high cone-angle artefact reduction but also for algorithm development and evaluation for other tasks, such as image reconstruction from limited or sparse-angle (low-dose) scanning, super resolution, or segmentation

Ancient manuring of Amazonian Dark Earths as assessed by molecular markers.

Analytical procedures and the applicability of this methods to detect ancient human manuring in those soils, which were as far as we know, not used before in the humid tropics, will be discussed

Ai-Drugnet: a Network-Based Deep Learning Model For Drug Repurposing and Combination therapy in Neurological Disorders

Discovering effective therapies is difficult for neurological and developmental disorders in that disease progression is often associated with a complex and interactive mechanism. Over the past few decades, few drugs have been identified for treating Alzheimer\u27s disease (AD), especially for impacting the causes of cell death in AD. Although drug repurposing is gaining more success in developing therapeutic efficacy for complex diseases such as common cancer, the complications behind AD require further study. Here, we developed a novel prediction framework based on deep learning to identify potential repurposed drug therapies for AD, and more importantly, our framework is broadly applicable and may generalize to identifying potential drug combinations in other diseases. Our prediction framework is as follows: we first built a drug-target pair (DTP) network based on multiple drug features and target features, as well as the associations between DTP nodes where drug-target pairs are the DTP nodes and the associations between DTP nodes are represented as the edges in the AD disease network; furthermore, we incorporated the drug-target feature from the DTP network and the relationship information between drug-drug, target-target, drug-target within and outside of drug-target pairs, representing each drug-combination as a quartet to generate corresponding integrated features; finally, we developed an AI-based Drug discovery Network (AI-DrugNet), which exhibits robust predictive performance. The implementation of our network model help identify potential repurposed and combination drug options that may serve to treat AD and other diseases

Ad-Syn-Net: Systematic Identification of alzheimer\u27s Disease-Associated Mutation and Co-Mutation Vulnerabilities Via Deep Learning

Alzheimer\u27s disease (AD) is one of the most challenging neurodegenerative diseases because of its complicated and progressive mechanisms, and multiple risk factors. Increasing research evidence demonstrates that genetics may be a key factor responsible for the occurrence of the disease. Although previous reports identified quite a few AD-associated genes, they were mostly limited owing to patient sample size and selection bias. There is a lack of comprehensive research aimed to identify AD-associated risk mutations systematically. to address this challenge, we hereby construct a large-scale AD mutation and co-mutation framework (\u27AD-Syn-Net\u27), and propose deep learning models named Deep-SMCI and Deep-CMCI configured with fully connected layers that are capable of predicting cognitive impairment of subjects effectively based on genetic mutation and co-mutation profiles. Next, we apply the customized frameworks to data sets to evaluate the importance scores of the mutations and identified mutation effectors and co-mutation combination vulnerabilities contributing to cognitive impairment. Furthermore, we evaluate the influence of mutation pairs on the network architecture to dissect the genetic organization of AD and identify novel co-mutations that could be responsible for dementia, laying a solid foundation for proposing future targeted therapy for AD precision medicine. Our deep learning model codes are available open access here: https://github.com/Pan-Bio/AD-mutation-effectors

Experimental cerebral malaria progresses independently of the Nlrp3 inflammasome

Cerebral malaria is the most severe complication of Plasmodium falciparum infection in humans and the pathogenesis is still unclear. Using the P. berghei ANKA infection model of mice, we investigated a potential involvement of Nlrp3 and the inflammasome in the pathogenesis of cerebral malaria. Nlrp3 mRNA expression was upregulated in brain endothelial cells after exposure to P. berghei ANKA. Although Β-hematin, a synthetic compound of the parasites heme polymer hemozoin, induced the release of IL-1Β in macrophages through Nlrp3, we did not obtain evidence for a role of IL-1Β in vivo . Nlrp3 knock-out mice displayed a delayed onset of cerebral malaria; however, mice deficient in caspase-1, the adaptor protein ASC or the IL-1 receptor succumbed as WT mice. These results indicate that the role of Nlrp3 in experimental cerebral malaria is independent of the inflammasome and the IL-1 receptor pathway.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/69193/1/764_ftp.pd

How gaming tourism affects tourism development through word-of-mouth communication regarding a destination: applying the integrated satisfaction theory

This study applies the concept of integrated satisfaction to investigate the effects of satisfaction with gaming and non-gaming experience on word-of-mouth communication regarding gaming destination. A survey in Macau (n = 298) indicates that integrated satisfaction has a partial mediating effect on the relationship between non-gaming satisfaction and word-of-mouth, and integrated satisfaction has a moderating and partial mediating effect on the relationship between gaming satisfaction and word-of-mouth. Therefore, gaming tourism enlarges the effect of the non-gaming tourism experience. Besides, gaming activities cause positive word-of-mouth communication for repeat tourists. This study extends our knowledge in gaming tourism and integrated satisfaction theory