EXPERIMENTAL INVESTIGATION AND NEURAL NETWORK PREDICTION OF THE PERFORMANCE OF A MIXED MODE SOLAR DRYER FOR COCONUT

The shelf life of agricultural food products may be enhanced by reducing their moisture contents, by means of a drying process. The present work aims at drying coconut yielding copra. This paper presents the design, analysis of a mixed mode solar dryer for food preservation and energy saving. In the mixed mode solar dryer, the drying cabinet absorbs solar energy directly through the transparent roof and during the same time the heated air from a solar collector is passed through a tray. Various measurements like solar radiation, mass flow rate, and moisture content and relative humidity have been observed. From previous literature four different models (Newton, Page, Henderson & Pabis and Wang & Singh) are chosen for testing the performance of mixed mode solar dryer. Selected models are evaluated by using EMD, ERMS, R2 and ðœ’2 and it is concluded that page model is more suitable for the fabricated cabinet solar dryer at air flow rate 0.009Kg/s based on the experimental analysis. The direct radiant solar energy and a convective hot air stream dry the products, resulting in longer life for the products which are also free from impurities. The experimental results are utilized to evolve a suitable mathematical model, among the different models that are chosen, for copra. This will help in designing suitable dryers for actual users. Also, a multilayer neural network approach has been used to predict the performance of a mixed mode solar dryer for drying coconut. The simulation of neural network is based on the feed forward back propagation algorithm

The Mycobacterium marinum mel2 locus displays similarity to bacterial bioluminescence systems and plays a role in defense against reactive oxygen and nitrogen species

BACKGROUND: Mycobacteria have developed a number of pathways that provide partial protection against both reactive oxygen species (ROS) and reactive nitrogen species (RNS). We recently identified a locus in Mycobacterium marinum, mel2, that plays a role during infection of macrophages. The molecular mechanism of mel2 action is not well understood. RESULTS: To better understand the role of the M. marinum mel2 locus, we examined these genes for conserved motifs in silico. Striking similarities were observed between the mel2 locus and loci that encode bioluminescence in other bacterial species. Since bioluminescence systems can play a role in resistance to oxidative stress, we postulated that the mel2 locus might be important for mycobacterial resistance to ROS and RNS. We found that an M. marinum mutant in the first gene in this putative operon, melF, confers increased susceptibility to both ROS and RNS. This mutant is more susceptible to ROS and RNS together than either reactive species alone. CONCLUSION: These observations support a role for the M. marinum mel2 locus in resistance to oxidative stress and provide additional evidence that bioluminescence systems may have evolved from oxidative defense mechanisms

A Self-Supervised Learning-based Approach to Clustering Multivariate Time-Series Data with Missing Values (SLAC-Time): An Application to TBI Phenotyping

Self-supervised learning approaches provide a promising direction for clustering multivariate time-series data. However, real-world time-series data often include missing values, and the existing approaches require imputing missing values before clustering, which may cause extensive computations and noise and result in invalid interpretations. To address these challenges, we present a Self-supervised Learning-based Approach to Clustering multivariate Time-series data with missing values (SLAC-Time). SLAC-Time is a Transformer-based clustering method that uses time-series forecasting as a proxy task for leveraging unlabeled data and learning more robust time-series representations. This method jointly learns the neural network parameters and the cluster assignments of the learned representations. It iteratively clusters the learned representations with the K-means method and then utilizes the subsequent cluster assignments as pseudo-labels to update the model parameters. To evaluate our proposed approach, we applied it to clustering and phenotyping Traumatic Brain Injury (TBI) patients in the Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI) study. Our experiments demonstrate that SLAC-Time outperforms the baseline K-means clustering algorithm in terms of silhouette coefficient, Calinski Harabasz index, Dunn index, and Davies Bouldin index. We identified three TBI phenotypes that are distinct from one another in terms of clinically significant variables as well as clinical outcomes, including the Extended Glasgow Outcome Scale (GOSE) score, Intensive Care Unit (ICU) length of stay, and mortality rate. The experiments show that the TBI phenotypes identified by SLAC-Time can be potentially used for developing targeted clinical trials and therapeutic strategies.Comment: Submitted to the Journal of Biomedical Informatic

Identifying TBI Physiological States by Clustering Multivariate Clinical Time-Series Data

Determining clinically relevant physiological states from multivariate time series data with missing values is essential for providing appropriate treatment for acute conditions such as Traumatic Brain Injury (TBI), respiratory failure, and heart failure. Utilizing non-temporal clustering or data imputation and aggregation techniques may lead to loss of valuable information and biased analyses. In our study, we apply the SLAC-Time algorithm, an innovative self-supervision-based approach that maintains data integrity by avoiding imputation or aggregation, offering a more useful representation of acute patient states. By using SLAC-Time to cluster data in a large research dataset, we identified three distinct TBI physiological states and their specific feature profiles. We employed various clustering evaluation metrics and incorporated input from a clinical domain expert to validate and interpret the identified physiological states. Further, we discovered how specific clinical events and interventions can influence patient states and state transitions.Comment: 10 pages, 7 figures, 2 table

Analysis of spatial variability of turmeric (Curcuma longa L. syn. C. domestica Val.) yield in India

This paper examines the yield variability of turmeric in India. The data on maximum, minimum and mean fresh rhizome yield of 200 experiments from secondary sources of published research papers/reports were collected. These data were utilized with an objective to calculate summary statistics and to identify maximum and minimuln yields and yield differences obtained in each state. The summary statistics indicated that in India maximum yield ranged between 4.59 and 95.0 t ha-1 with a mean of 28.48 ± 14.29 t ha-1. The Ininimum yield ranged between 1.34 and 56.25 t ha-1 with a mean of 13.73 ± 18.60 t ha-1 and the mean yield ranged from 3.09 to 72.68 t ha-1 with a mean of 22.99 ±11.60 t ha-1. The maximum yield obtained in all the cases was due to adoption of improved produCtion techniques such as variety, manure and better crop management practices compared to minhnum yield of control treatment. The yield differences between maximum and minimum ranged from 6.69 to 93.50 t ha-1. &nbsp

Human Macrophages Exhibit GM-CSF Dependent Restriction of Mycobacterium tuberculosis Infection via Regulating Their Self-Survival, Differentiation and Metabolism

GM-CSF is an important cytokine that regulates the proliferation of monocytes/macrophages and its various functions during health and disease. Although growing evidences support the notion that GM-CSF could play a major role in immunity against tuberculosis (TB) infection, the mechanism of GM-CSF mediated protective effect against TB remains largely unknown. Here in this study we examined the secreted levels of GM-CSF by human macrophages from different donors along with the GM-CSF dependent cellular processes that are critical for control of M. tuberculosis infection. While macrophage of different donors varied in their ability to produce GM-CSF, a significant correlation was observed between secreted levels of GM-CSF, survial of macrophages and intra-macrophage control of Mycobacterium tuberculosis bacilli. GM-CSF levels secreted by macrophages negatively correlated with the intra-macrophage M. tuberculosis burden, survival of infected host macrophages positively correlated with their GM-CSF levels. GM-CSF-dependent prolonged survival of human macrophages also correlated with significantly decreased bacterial burden and increased expression of self-renewal/cell-survival associated genes such as BCL-2 and HSP27. Antibody-mediated depletion of GM-CSF in macrophages resulted in induction of significantly elevated levels of apoptotic/necrotic cell death and a simultaneous decrease in autophagic flux. Additionally, protective macrophages against M. tuberculosis that produced more GM-CSF, induced a stronger granulomatous response and produced significantly increased levels of IL-1β, IL-12 and IL-10 and decreased levels of TNF-α and IL-6. In parallel, macrophages isolated from the peripheral blood of active TB patients exhibited reduced capacity to control the intracellular growth of M. tuberculosis and produced significantly lower levels of GM-CSF. Remarkably, as compared to healthy controls, macrophages of active TB patients exhibited significantly altered metabolic state correlating with their GM-CSF secretion levels. Altogether, these results suggest that relative levels of GM-CSF produced by human macrophages plays a critical role in preventing cell death and maintaining a protective differentiation and metabolic state of the host cell against M. tuberculosis infection

Visible light induced photocatalytic activity of Nb2O5/carbon cluster/Cr2O3 composite materials

Nano-sized Nb2O5/carbon cluster/Cr2O3 composite material was prepared by the calcination of NbCl5/chromium acetylacetonate/epoxy resin complex under an argon atmosphere. The Pt-loaded Nb2O5/carbon cluster/Cr2O3 composite material shows the photocatalytic activity under visible light irradiation. The composite material successfully decomposed the water into H2 and O2 in the [H2]/[O2] ratio of 2. Electron spin resonance spectral examination suggests a two-step electron transfer in the process of Nb2O5 → carbon cluster → Cr2O3 → Pt

Epidemiology of Pediatric Traumatic Brain Injury and Hypothalamic-Pituitary Disorders in Arizona

Traumatic brain injury (TBI) in children can result in long-lasting social, cognitive, and neurological impairments. In adults, TBI can lead to endocrinopathies (endocrine system disorders), but this is infrequently reported in children. Untreated endocrinopathies can elevate risks of subsequent health issues, such that early detection in pediatric TBI survivors can initiate clinical interventions. To understand the risk of endocrinopathies following pediatric TBI, we identified patients who had experienced a TBI and subsequently developed a new-onset hypothalamic regulated endocrinopathy (n = 498). We hypothesized that pediatric patients who were diagnosed with a TBI were at higher risk of being diagnosed with a central endocrinopathy than those without a prior diagnosis of TBI. In our epidemiological assessment, we identified pediatric patients enrolled in the Arizona Health Care Cost Containment System (AHCCCS) from 2008 to 2014 who were diagnosed with one of 330 TBI International Classification of Diseases (ICD)-9 codes and subsequently diagnosed with one of 14 central endocrinopathy ICD-9 codes. Additionally, the ICD-9 code data from over 600,000 Arizona pediatric patients afforded an estimate of the incidence, prevalence, relative risk, odds ratio, and number needed to harm, regarding the development of a central endocrinopathy after sustaining a TBI in Arizona Medicaid pediatric patients. Children with a TBI diagnosis had 3.22 times the risk of a subsequent central endocrine diagnosis compared with the general population (±0.28). Pediatric AHCCCS patients with a central endocrine diagnosis had 3.2-fold higher odds of a history of a TBI diagnosis than those without an endocrine diagnosis (±0.29). Furthermore, the number of patients with a TBI diagnosis for one patient to receive a diagnosis of a central endocrine diagnosis was 151.2 (±6.12). Female subjects were more likely to present with a central endocrine diagnosis after a TBI diagnosis compared to male subjects (64.1 vs. 35.9%). These results are the first state-wide epidemiological study conducted to determine the risk of developing a hypothalamic-pituitary disorder after a TBI in the pediatric population. Our results contribute to a body of knowledge demonstrating a TBI etiology for idiopathic endocrine disorders, and thus advise physicians with regard to TBI follow-up care that includes preventive screening for endocrine disorders.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]