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after the construction of an ileostomy, the nutritional status of the patients is significantly affected. Decreased patient nutrition in both quantity and ingredients and reduced fluid intake appear to adversely affect the patient's nutritional status.Viticulture is one of the horticultural systems in which antifungal treatments can be extremely frequent, with substantial economic and environmental costs. New products, such as biofungicides, resistance inducers and biostimulants, may represent alternative crop protection strategies respectful of the environmental sustainability and food safety. Here, the main purpose was to evaluate the systemic molecular modifications induced by biocontrol products as laminarin, resistance inducers (i.e., fosetyl-Al and potassium phosphonate), electrolyzed water and a standard chemical fungicide (i.e., metiram), on the transcriptomic profile of 'Nebbiolo' grape berries at harvest. In addition to a validation of the sequencing data through real-time polymerase chain reaction (PCR), for the first-time the expression of some candidate genes in different cell-types of berry skin (i.e., epidermal and hypodermal layers) was evaluated using the laser microdissection approach. Results showed that several considered antifungal treatments do not strongly affect the berry transcriptome profile at the end of season. Although some treatments do not activate long lasting molecular defense priming features in berry, some compounds appear to be more active in long-term responses. In addition, genes differentially expressed in the two-cell type populations forming the berry skin were found, suggesting a different function for the two-cell type populations.RNA interference (RNAi) provides the means for alternative antiviral therapy. Delivery of RNAi in the form of short interfering RNA (siRNA), short hairpin RNA (shRNA) and micro-RNA (miRNA) have demonstrated efficacy in gene silencing for therapeutic applications against viral diseases. Bioinformatics has played an important role in the design of efficient RNAi sequences targeting various pathogenic viruses. However, stability and delivery of RNAi molecules have presented serious obstacles for reaching therapeutic efficacy. For this reason, RNA modifications and formulation of nanoparticles have proven useful for non-viral delivery of RNAi molecules. On the other hand, utilization of viral vectors and particularly self-replicating RNA virus vectors can be considered as an attractive alternative. In this review, examples of antiviral therapy applying RNAi-based approaches in various animal models will be described. Due to the current coronavirus pandemic, a special emphasis will be dedicated to targeting Coronavirus Disease-19 (COVID-19).In this paper the scientific literature on the association between forests, stress relief and relaxation is reviewed with the purpose to understand common patterns of research, the main techniques used for analysis, findings relevant to forest-therapy-oriented management, and knowledge gaps. The database of studies was collected with a keyword search on the Web, which returned a set of 32 studies that were included in the analysis. The main findings and patterns were identified with a text mining analysis of the abstract to search for keyword patterns across studies. The analysis indicates that most studies compared rest and relaxation performances across urban and forest environments and used a combination of self-reported measure of stress or rest collected with validate scales, e.g., the Profile of Mood of States (POMS) and the Restoration Outcome Scale (ROS), and a minority-only set of these two groups of indicators. Results of this review indicate that primary studies identified a positive association between forest exposure and mental well-being, in particular when compared to urban environments, thus suggesting that forest are effective in lowering stress levels. This study found that, to date, the characteristics of forests and characteristics of the visit are little investigated in the literature. For this reason, more research with a focus on forest variables such as tree species composition, tree density and other variables affecting forest landscape should be further investigated to inform forest management. Similarly, the characteristics of the visits (e.g., length of visit and frequency) should be further explored to provide robust forest therapy guidelines.Ultrasonic guided wave (UGW) testing is widely applied in numerous industry areas for the examination of pipelines where structural integrity is of concern. Guided wave testing is capable of inspecting long lengths of pipes from a single tool location using some arrays of transducers positioned around the pipe. Due to dispersive propagation and the multimodal behavior of UGW, the received signal is usually degraded and noisy, that reduce the inspection range and sensitivity to small defects. Therefore, signal interpretation and identifying small defects is a challenging task in such systems, particularly for buried/coated pipes, in that the attenuation rates are considerably higher compared with a bare pipe. In this work, a novel solution is proposed to address this issue by employing an advanced signal processing approach called "split-spectrum processing" (SSP) to minimize the level of background noise and enhance the signal quality. The SSP technique has already shown promising results in a limited trial for a bar pipe and, in this work, the proposed technique has been experimentally compared with the traditional approach for coated pipes. The results illustrate that the proposed technique significantly increases the signal-to-noise ratio and enhances the sensitivity to small defects that are hidden below the background noise.Since the mid-20th century, ischemic heart disease has been the world's leading cause of death. Developing effective clinical cardioprotection strategies would make a significant impact in improving both quality of life and longevity in the worldwide population. Both ex vivo and in vivo animal models of cardiac ischemia/reperfusion (I/R) injury are robustly used in research. Connexin43 (Cx43), the predominant gap junction channel-forming protein in cardiomyocytes, has emerged as a cardioprotective target. Cx43 posttranslational modifications as well as cellular distribution are altered during cardiac reperfusion injury, inducing phosphorylation states and localization detrimental to maintaining intercellular communication and cardiac conduction. Pre- (before ischemia) and post- (after ischemia but before reperfusion) conditioning can abrogate this injury process, preserving Cx43 and reducing cell death. Pre-/post-conditioning has been shown to largely rely on the presence of Cx43, including mitochondrial Cx43, which is implicated to play a major role in pre-conditioning. Posttranslational modifications of Cx43 after injury alter the protein interactome, inducing negative protein cascades and altering protein trafficking, which then causes further damage post-I/R injury. Recently, several peptides based on the Cx43 sequence have been found to successfully diminish cardiac injury in pre-clinical studies.The present study investigated the performance of three ceramic inserts in terms of the micro-geometry (nose radius and cutting edge type) with the aid of a 3D finite element (FE) model. A set of nine simulation runs was performed according to three levels of cutting speed and feed rate with respect to a predefined depth of cut and tool nose radius. The yielded results were compared to the experimental values that were acquired at identical cutting conditions as the simulated ones for verification purposes. Consequently, two more sets of nine simulations each were carried out so that a total of 27 turning simulation runs would adduce. The two extra sets corresponded to the same cutting conditions, but to different cutting tools (with varied nose radius). Moreover, a prediction model was established based on statistical methodologies such as the response surface methodology (RSM) and the analysis of variance (ANOVA), further investigating the relationship between the critical parameters (cutting speed, feed rate, and nose radius) and their influence on the generated turning force components. The comparison between the experimental values of the cutting force components and the simulated ones demonstrated an increased correlation that exceeded 89%. Similarly, the values derived from the statistical model were in compliance with the equivalent FE model values due to the verified adequacy.Accumulating evidence indicates that long non-coding RNAs (lncRNAs) have certain similarities with messenger RNAs (mRNAs) and are associated with numerous important biological processes, thereby demanding methods to distinguish them. Based on machine learning algorithms, a variety of methods are developed to identify lncRNAs, providing significant basic data support for subsequent studies. However, many tools lack certain scalability, versatility and balance, and some tools rely on genome sequence and annotation. In this paper, we propose a convenient and accurate tool "PreLnc", which uses high-confidence lncRNA and mRNA transcripts to build prediction models through feature selection and classifiers. The false discovery rate (FDR) adjusted P-value and Z-value were used for analyzing the tri-nucleotide composition of transcripts of different species. Conclusions can be drawn from the experiment that there were significant differences in RNA transcripts among plants, which may be related to evolutionary conservation and the fact that plants are under evolutionary pressure for a longer time than animals. Combining with the Pearson correlation coefficient, we use the incremental feature selection (IFS) method and the comparison of multiple classifiers to build the model. Finally, the balanced random forest was used to construct the classifier, and PreLnc obtained 91.09% accuracy for 349,186 transcripts of animals and plants. In addition, by comparing standard performance measurements, PreLnc performed better than other prediction tools.Action recognition has gained great attention in automatic video analysis, greatly reducing the cost of human resources for smart surveillance. Most methods, however, focus on the detection of only one action event for a single person in a well-segmented video, rather than the recognition of multiple actions performed by more than one person at the same time for an untrimmed video. ACSS2 inhibitor In this paper, we propose a deep learning-based multiple-person action recognition system for use in various real-time smart surveillance applications. By capturing a video stream of the scene, the proposed system can detect and track multiple people appearing in the scene and subsequently recognize their actions. Thanks to high resolution of the video frames, we establish a zoom-in function to obtain more satisfactory action recognition results when people in the scene become too far from the camera. To further improve the accuracy, recognition results from inflated 3D ConvNet (I3D) with multiple sliding windows are processed by a nonmaximum suppression (NMS) approach to obtain a more robust decision. Experimental results show that the proposed method can perform multiple-person action recognition in real time suitable for applications such as long-term care environments.