Microbial network for waste activated sludge cascade utilization in an integrated system of microbial electrolysis and anaerobic fermentation

Background: Bioelectrochemical systems have been considered a promising novel technology that shows an enhanced energy recovery, as well as generation of value-added products. A number of recent studies suggested that an enhancement of carbon conversion and biogas production can be achieved in an integrated system of microbial electrolysis cell (MEC) and anaerobic digestion (AD) for waste activated sludge (WAS). Microbial communities in integrated system would build a thorough energetic and metabolic interaction network regarding fermentation communities and electrode respiring communities. The characterization of integrated community structure and community shifts is not well understood, however, it starts to attract interest of scientists and engineers. Results: In the present work, energy recovery and WAS conversion are comprehensively affected by typical pre-treated biosolid characteristics. We investigated the interaction of fermentation communities and electrode respiring communities in an integrated system of WAS fermentation and MEC for hydrogen recovery. A high energy recovery was achieved in the MECs feeding WAS fermentation liquid through alkaline pretreatment. Some anaerobes belonging to Firmicutes (Acetoanaerobium, Acetobacterium, and Fusibacter) showed synergistic relationship with exoelectrogens in the degradation of complex organic matter or recycling of MEC products (H-2). High protein and polysaccharide but low fatty acid content led to the dominance of Proteiniclasticum and Parabacteroides, which showed a delayed contribution to the extracellular electron transport leading to a slow cascade utilization of WAS. Conclusions: Efficient pretreatment could supply more short-chain fatty acids and higher conductivities in the fermentative liquid, which facilitated mass transfer in anodic biofilm. The overall performance of WAS cascade utilization was substantially related to the microbial community structures, which in turn depended on the initial pretreatment to enhance WAS fermentation. It is worth noting that species in AD and MEC communities are able to build complex networks of interaction, which have not been sufficiently studied so far. It is therefore important to understand how choosing operational parameters can influence reactor performances. The current study highlights the interaction of fermentative bacteria and exoelectrogens in the integrated system

Construction and validation of a deterioration model for elderly COVID-19 Sub-variant BA.2 patients

RationaleCOVID-19 pandemic has imposed tremendous stress and burden on the economy and society worldwide. There is an urgent demand to find a new model to estimate the deterioration of patients inflicted by Omicron variants.ObjectiveThis study aims to develop a model to predict the deterioration of elderly patients inflicted by Omicron Sub-variant BA.2.MethodsCOVID-19 patients were randomly divided into the training and the validation cohorts. Both Lasso and Logistic regression analyses were performed to identify prediction factors, which were then selected to build a deterioration model in the training cohort. This model was validated in the validation cohort.Measurements and main resultsThe deterioration model of COVID-19 was constructed with five indices, including C-reactive protein, neutrophil count/lymphocyte count (NLR), albumin/globulin ratio (A/G), international normalized ratio (INR), and blood urea nitrogen (BUN). The area under the ROC curve (AUC) showed that this model displayed a high accuracy in predicting deterioration, which was 0.85 in the training cohort and 0.85 in the validation cohort. The nomogram provided an easy way to calculate the possibility of deterioration, and the decision curve analysis (DCA) and clinical impact curve analysis (CICA)showed good clinical net profit using this model.ConclusionThe model we constructed can identify and predict the risk of deterioration (requirement for ventilatory support or death) in elderly patients and it is clinically practical, which will facilitate medical decision making and allocating medical resources to those with critical conditions

C-reactive protein to lymphocyte ratio is a significant predictive factor for poor short-term clinical outcomes of SARS-CoV-2 BA.2.2 patients

ObjectiveThe aim of the present study is to assess the utility of C-reactive protein to Lymphocyte Ratio (CLR) in predicting short-term clinical outcomes of patients infected by SARS-CoV-2 BA.2.2.MethodsThis retrospective study was performed on 1,219 patients with laboratory-confirmed SARS-CoV-2 BA.2.2 to determine the association of CLR with short-term clinical outcomes. Independent Chi square test, Rank sum test, and binary logistic regression analysis were performed to calculate mean differences and adjusted odds ratios (aORs) with their 95% CI, respectively.ResultsOver 8% of patients admitted due to SARS-CoV-2 BA.2.2. were critically ill. The best cut-off value of CLR was 21.25 in the ROC with a sensitivity of 72.3% and a specificity of 86%. After adjusting age, gender, and comorbidities, binary logistic regression analysis showed that elevated CLR was an independent risk factor for poor short-term clinical outcomes of COVID-19 patients.ConclusionC-reactive protein to Lymphocyte Ratio is a significant predictive factor for poor short-term clinical outcomes of SARS-CoV-2 BA.2.2 inflicted patients

Deep heterogeneous superpixel neural networks for image analysis and feature extraction

Lately, deep convolutional neural networks are rapidly transforming and enhancing computer vision accuracy and performance, and pursuing higher-level and interpretable object recognition. Superpixel-based methodologies have been used in conventional computer vision research where their efficient representation has superior effects. In contemporary computer vision research driven by deep neural networks, superpixel-based approaches mainly rely on oversegmentation to provide a more efficient representation of the imagery data, especially when the computation is too expensive in time or memory to perform in pairwise similarity regularization or complex graphical probabilistic inference. In this dissertation, we proposed a novel superpixel-enabled deep neural network paradigm by relaxing some of the prior assumptions in the conventional superpixel-based methodologies and exploring its capabilities in the context of advanced deep convolutional neural networks. This produces novel neural network architectures that can achieve higher-level object relation modeling, weakly supervised segmentation, high explainability, and facilitate insightful visualizations. This approach has the advantage of being an efficient representation of the visual signal and has the capability to dissect out relevant object components from other background noise by spatially re-organizing visual features. Specifically, we have created superpixel models that join graphical neural network techniques and multiple-instance learning to achieve weakly supervised object detection and generate precise object bounding without pixel-level training labels. This dissection and the subsequent learning by the architecture promotes explainable models, whereby the human users of the models can see the parts of the objects that have led to recognition. Most importantly, this neural design's natural result goes beyond abstract rectangular bounds of an object occurrence (e.g., bounding box or image chip), but instead approaches efficient parts-based segmented recognition. It has been tested on commercial remote sensing satellite imagery and achieved success. Additionally, We have developed highly efficient monocular indoor depth estimation based on superpixel feature extraction. Furthermore, we have demonstrated state-of-theart weakly supervised object detection performance on two contemporary benchmark data sets, MS-COCO and VOC 2012. In the future, deep learning techniques based on superpixel-enabled image analysis can be further optimized in accuracy and computational performance; and it will also be interesting to evaluate in other research domains, such as those involving medical imagery, infrared imagery, or hyperspectral imagery.Includes bibliographical references