Enrichment and characterization of a bacteria consortium capable of heterotrophic nitrification and aerobic denitrification at low temperature

Nitrogen removal in wastewater treatment plants is usually severely inhibited under cold temperature. The present study proposes bioaugmentation using psychrotolerant heterotrophic nitrification-aerobic denitrification consortium to enhance nitrogen removal at low temperature. A functional consortium has been successfully enriched by stepped increase in DO concentration. Using this consortium, the specific removal rates of ammonia and nitrate at 10 degrees C reached as high as 3.1 mg N/(g SS h) and 9.6 mg N/ (g SS h), respectively. PCR-DGGE and clone library analysis both indicated a significant reduction in bacterial diversity during enrichment. Phylogenetic analysis based on nearly full-length 16S rRNA genes showed that Alphaproteobacteria. Deltaproteobacteria and particularly Bacteroidetes declined while Gammaproteobacteria (all clustered into Pseudomonas sp.) and Betaproteobacteria (mainly Rhodoferax ferrireducens) became dominant in the enriched consortium. It is likely that Pseudomonas spp. played a major role in nitrification and denitrification, while R. ferrireducens and its relatives utilized nitrate as both electron acceptor and nitrogen source. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000312926400021&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Agricultural EngineeringBiotechnology & Applied MicrobiologyEnergy & FuelsSCI(E)EIPubMed31ARTICLE151-15712

CLR: Channel-wise Lightweight Reprogramming for Continual Learning

Continual learning aims to emulate the human ability to continually accumulate knowledge over sequential tasks. The main challenge is to maintain performance on previously learned tasks after learning new tasks, i.e., to avoid catastrophic forgetting. We propose a Channel-wise Lightweight Reprogramming (CLR) approach that helps convolutional neural networks (CNNs) overcome catastrophic forgetting during continual learning. We show that a CNN model trained on an old task (or self-supervised proxy task) could be ``reprogrammed" to solve a new task by using our proposed lightweight (very cheap) reprogramming parameter. With the help of CLR, we have a better stability-plasticity trade-off to solve continual learning problems: To maintain stability and retain previous task ability, we use a common task-agnostic immutable part as the shared ``anchor" parameter set. We then add task-specific lightweight reprogramming parameters to reinterpret the outputs of the immutable parts, to enable plasticity and integrate new knowledge. To learn sequential tasks, we only train the lightweight reprogramming parameters to learn each new task. Reprogramming parameters are task-specific and exclusive to each task, which makes our method immune to catastrophic forgetting. To minimize the parameter requirement of reprogramming to learn new tasks, we make reprogramming lightweight by only adjusting essential kernels and learning channel-wise linear mappings from anchor parameters to task-specific domain knowledge. We show that, for general CNNs, the CLR parameter increase is less than 0.6\% for any new task. Our method outperforms 13 state-of-the-art continual learning baselines on a new challenging sequence of 53 image classification datasets. Code and data are available at https://github.com/gyhandy/Channel-wise-Lightweight-ReprogrammingComment: ICCV 202

Free Radical Damage in Ischemia-Reperfusion Injury: An Obstacle in Acute Ischemic Stroke after Revascularization Therapy

Acute ischemic stroke is a common cause of morbidity and mortality worldwide. Thrombolysis with recombinant tissue plasminogen activator and endovascular thrombectomy are the main revascularization therapies for acute ischemic stroke. However, ischemia-reperfusion injury after revascularization therapy can result in worsening outcomes. Among all possible pathological mechanisms of ischemia-reperfusion injury, free radical damage (mainly oxidative/nitrosative stress injury) has been found to play a key role in the process. Free radicals lead to protein dysfunction, DNA damage, and lipid peroxidation, resulting in cell death. Additionally, free radical damage has a strong connection with inducing hemorrhagic transformation and cerebral edema, which are the major complications of revascularization therapy, and mainly influencing neurological outcomes due to the disruption of the blood-brain barrier. In order to get a better clinical prognosis, more and more studies focus on the pharmaceutical and nonpharmaceutical neuroprotective therapies against free radical damage. This review discusses the pathological mechanisms of free radicals in ischemia-reperfusion injury and adjunctive neuroprotective therapies combined with revascularization therapy against free radical damage

A pilot study on acute inflammation and cancer: a new balance between IFN-γ and TGF-β in melanoma

Recent data have redefined the concept of inflammation as a critical component of tumor progression. However, there has been little development on cases where inflammation on or near a wound and a tumor exist simultaneously. Therefore, this pilot study aims to observe the impact of a wound on a tumor, to build a new mouse tumor model with a manufactured surgical wound representing acute inflammation, and to evaluate the relationship between acute inflammation or wound healing and the process of tumor growth. We focus on the two phases that are present when acute inflammation influences tumor. In the early phase, inhibitory effects are present. The process that produces these effects is the functional reaction of IFN-γ secretions from a wound inflammation. In the latter phase, the inhibited tumor is made resistant to IFN-γ through the release of TGF-β to balance the inflammatory factor effect on the tumor cells. A pair of cytokines IFN-γ/TGF-β established a new balance to protect the tumor from the interference effect of the inflammation. The tumor was made resistant to IFN-γ through the release of TGF-β to balance the inflammatory effect on the tumor cells. This balance mechanism that occurred in the tumor cells increased proliferation and invasion. In vitro and in vivo experiments have confirmed a new view of clinical surgery that will provide more detailed information on the evaluation of tumors after surgery. This study also provides a better understanding of the relationship between tumor and inflammation, as well as tumor cell attacks on inflammatory factors

xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence

Data-driven AI promises support for pathologists to discover sparse tumor patterns in high-resolution histological images. However, from a pathologist's point of view, existing AI suffers from three limitations: (i) a lack of comprehensiveness where most AI algorithms only rely on a single criterion; (ii) a lack of explainability where AI models tend to work as 'black boxes' with little transparency; and (iii) a lack of integrability where it is unclear how AI can become part of pathologists' existing workflow. Based on a formative study with pathologists, we propose two designs for a human-AI collaborative tool: (i) presenting joint analyses of multiple criteria at the top level while (ii) revealing hierarchically traceable evidence on-demand to explain each criterion. We instantiate such designs in xPath -- a brain tumor grading tool where a pathologist can follow a top-down workflow to oversee AI's findings. We conducted a technical evaluation and work sessions with twelve medical professionals in pathology across three medical centers. We report quantitative and qualitative feedback, discuss recurring themes on how our participants interacted with xPath, and provide initial insights for future physician-AI collaborative tools.Comment: 31 pages, 11 figure

Inflammation-related proteomics demonstrate landscape of fracture blister fluid in patients with acute compartment syndrome

BackgroundBlisters are tense vesicles or bullae that arise on swollen skin and are found in a wide range of injuries. As a complication of fracture, fracture blisters are considered soft tissue injuries, which often lead to adverse effects such as prolonged preoperative waiting time and increased risk of surgical site infection. However, our previous study found that in patients with acute compartment syndrome, fracture blisters may be a form of compartment pressure release, but the specific mechanism has not been revealed. Here, we mapped out the proteomic landscape of fracture blister fluid for the first time and compared its expression profile to cupping and burn blisters.MethodsFirst, fluid samples were collected from 15 patients with fracture blisters, 7 patients with cupping blisters, and 9 patients with burn blisters. Then, the expression levels of 92 inflammatory proteins were measured using the Olink Target 96 Inflammation panel. Protein profiles were compared across the three groups using Differential Protein Expression Analysis and Principal Component Analysis (PCA).ResultsFracture blisters had significantly higher levels of 50 proteins in comparison to cupping and 26 proteins in comparison to burn blisters. Notably, PCA showed fracture blisters closely resembled the protein expression profile of burn blisters but were distinct from the protein expression profile of cupping blisters.ConclusionOur study provides the first characterization of fracture blister fluid using proteomics, which provides a valuable reference for further analysis of the difference between blisters caused by fractures and those caused by other pathogenic factors. This compendium of proteomic data provides valuable insights and a rich resource to better understand fracture blisters

Complex 3D microfluidic architectures formed by mechanically guided compressive buckling.

Microfluidic technologies have wide-ranging applications in chemical analysis systems, drug delivery platforms, and artificial vascular networks. This latter area is particularly relevant to 3D cell cultures, engineered tissues, and artificial organs, where volumetric capabilities in fluid distribution are essential. Existing schemes for fabricating 3D microfluidic structures are constrained in realizing desired layout designs, producing physiologically relevant microvascular structures, and/or integrating active electronic/optoelectronic/microelectromechanical components for sensing and actuation. This paper presents a guided assembly approach that bypasses these limitations to yield complex 3D microvascular structures from 2D precursors that exploit the full sophistication of 2D fabrication methods. The capabilities extend to feature sizes <5 μm, in extended arrays and with various embedded sensors and actuators, across wide ranges of overall dimensions, in a parallel, high-throughput process. Examples include 3D microvascular networks with sophisticated layouts, deterministically designed and constructed to expand the geometries and operating features of artificial vascular networks