Inhibitory co-receptor Lag3 supports Foxp3+ regulatory T cell function by restraining Myc-dependent metabolic programming.

Lymphocyte activation gene 3 (Lag3) is an inhibitory co-receptor expressed on activated T cells and has been proposed to regulate regulatory T (Treg) cell function. However, its precise modality and mechanisms remain elusive. We generated Treg cell-specific Lag3-mutant mouse models and found that Lag3 was essential for Treg cell control of autoimmunity. RNA sequencing analysis revealed that Lag3 mutation altered genes associated with metabolic processes, especially Myc target genes. Myc expression in Lag3-mutant Treg cells was increased to the level seen in conventional T helper (Th)1-type effector cells and directly correlated with their metabolic profiles and in vivo suppressive functions. The phosphatidylinositol 3-kinase (PI3K)-Akt-Rictor pathway was activated in Lag3-mutant Treg cells, and inhibiting PI3K, Rictor, or lactate dehydrogenase A (Ldha), a key Myc target enzyme converting pyruvate to lactate, was sufficient to restore normal metabolism and suppressive function in Lag3-mutant Treg cells. These findings indicate that Lag3 supports Treg cell suppression partly by tuning Myc-dependent metabolic programming.

Anti-inflammatory Roles of Glucocorticoids Are Mediated by Foxp3+ Regulatory T Cells via a miR-342-Dependent Mechanism.

Glucocorticoids (GC) are the mainstay treatment option for inflammatory conditions. Despite the broad usage of GC, the mechanisms by which GC exerts its effects remain elusive. Here, utilizing murine autoimmune and allergic inflammation models, we report that Foxp3+ regulatory T (Treg) cells are irreplaceable GC target cells in vivo. Dexamethasone (Dex) administered in the absence of Treg cells completely lost its ability to control inflammation, and the lack of glucocorticoid receptor in Treg cells alone resulted in the loss of therapeutic ability of Dex. Mechanistically, Dex induced miR-342-3p specifically in Treg cells and miR-342-3p directly targeted the mTORC2 component, Rictor. Altering miRNA-342-3p or Rictor expression in Treg cells dysregulated metabolic programming in Treg cells, controlling their regulatory functions in vivo. Our results uncover a previously unknown contribution of Treg cells during glucocorticoid-mediated treatment of inflammation and the underlying mechanisms operated via the Dex-miR-342-Rictor axis.

IFN-Induced Protein with Tetratricopeptide Repeats 2 Limits Autoimmune Inflammation by Regulating Myeloid Cell Activation and Metabolic Activity.

Besides antiviral functions, type I IFN expresses potent anti-inflammatory properties and is being widely used to treat certain autoimmune conditions, such as multiple sclerosis. In a murine model of multiple sclerosis, experimental autoimmune encephalomyelitis, administration of IFN-ß effectively attenuates the disease development. However, the precise mechanisms underlying IFN-ß-mediated treatment remain elusive. In this study, we report that IFN-induced protein with tetratricopeptide repeats 2 (Ifit2), a type I and type III IFN-stimulated gene, plays a previously unrecognized immune-regulatory role during autoimmune neuroinflammation. Mice deficient in Ifit2 displayed greater susceptibility to experimental autoimmune encephalomyelitis and escalated immune cell infiltration in the CNS. Ifit2 deficiency was also associated with microglial activation and increased myeloid cell infiltration. We also observed that myelin debris clearance and the subsequent remyelination were substantially impaired in Ifit2-/- CNS tissues. Clearing myelin debris is an important function of the reparative-type myeloid cell subset to promote remyelination. Indeed, we observed that bone marrow-derived macrophages, CNS-infiltrating myeloid cells, and microglia from Ifit2-/- mice express cytokine and metabolic genes associated with proinflammatory-type myeloid cell subsets. Taken together, our findings uncover a novel regulatory function of Ifit2 in autoimmune inflammation in part by modulating myeloid cell function and metabolic activity.

Impact of climate change on future flood susceptibility projections under shared socioeconomic pathway scenarios in South Asia using artificial intelligence algorithms.

This study investigated the impact of climate change on flood susceptibility in six South Asian countries Afghanistan, Bangladesh, Bhutan, Bharat (India), Nepal, and Pakistan-under two distinct Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6 and SSP5-5.8, for 2041-2060 and 2081-2100. To predict flood susceptibility, we employed three artificial intelligence (AI) algorithms: the K-nearest neighbor (KNN), conditional inference random forest (CIRF), and regularized random forest (RRF). Predictions were based on data from 2452 historical flood events, alongside climatic variables measured over monthly, seasonal, and annual timeframes. The innovative aspect of this research is the emphasis on using climatic variables across these progressively condensed timeframes, specifically addressing eight precipitation factors. The performance evaluation, employing the area under the receiver operating characteristic curve (AUC) metric, identified the RRF model as the most accurate, with the highest AUC of 0.94 during the testing phase, followed by the CIRF (AUC = 0.91) and the KNN (AUC = 0.86). An analysis of variable importance highlighted the substantial role of certain climatic factors, namely precipitation in the warmest quarter, annual precipitation, and precipitation during the wettest month, in the modeling of flood susceptibility in South Asia. The resultant flood susceptibility maps demonstrated the influence of climate change scenarios on susceptibility classifications, signalling a dynamic landscape of flood-prone areas over time. The findings revealed variable trends under different climate change scenarios and periods, with marked differences in the percentage of areas classified as having high and very high flood susceptibility. Overall, this study advances our understanding of how climate change affects flood susceptibility in South Asia and offers an essential tool for assessing and managing flood risks in the region.

Improving the prediction of wildfire susceptibility on Hawai'i Island, Hawai'i, using explainable hybrid machine learning models.

This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawai'i Island, Hawai'i. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms - Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) - were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics - sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision-Recall Curves (PRCs) - were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawai'i Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.

Microcystis abundance is predictable through ambient bacterial communities: A data-oriented approach.

The number of cyanobacterial harmful algal blooms (cyanoHABs) has increased, leading to the widespread development of prediction models for cyanoHABs. Although bacteria interact closely with cyanobacteria and directly affect cyanoHABs occurrence, related modeling studies have rarely utilized microbial community data compared to environmental data such as water quality. In this study, we built a machine learning model, the multilayer perceptron (MLP), for the prediction of Microcystis dynamics using both bacterial community and weekly water quality data from the Daechung Reservoir and Nakdong River, South Korea. The modeling performance, indicated by the R2 value, improved to 0.97 in the model combining bacterial community data with environmental factors, compared to 0.78 in the model using only environmental factors. This underscores the importance of microbial communities in cyanoHABs prediction. Through the post-hoc analysis of the MLP models, we revealed that nitrogen sources played a more critical role than phosphorus sources in Microcystis blooms, whereas the bacterial amplicon sequence variants did not have significant differences in their contribution to each other. Similar to the MLP model results, bacterial data also had higher predictability in multiple linear regression (MLR) than environmental data. In both the MLP and MLR models, Microscillaceae showed the strongest association with Microcystis. This modeling approach provides a better understanding of the interactions between bacteria and cyanoHABs, facilitating the development of more accurate and reliable models for cyanoHABs prediction using ambient bacterial data.

Cutting Edge: Steroid Responsiveness in Foxp3+ Regulatory T Cells Determines Steroid Sensitivity during Allergic Airway Inflammation in Mice.

Glucocorticoids are a highly effective first-line treatment option for many inflammatory diseases, including asthma. Some patients develop a steroid-resistant condition, yet, the cellular and molecular mechanisms underlying steroid resistance remain largely unknown. In this study, we used a murine model of steroid-resistant airway inflammation and report that combining systemic dexamethasone and intranasal IL-27 is able to reverse the inflammation. Foxp3+ regulatory T cells (Tregs) were required during dexamethasone/IL-27 treatment of steroid-resistant allergic inflammation, and importantly, direct stimulation of Tregs via glucocorticoid or IL-27 receptors was essential. Mechanistically, IL-27 stimulation in Tregs enhanced expression of the agonistic glucocorticoid receptor-α isoform. Overexpression of inhibitory glucocorticoid receptor-ß isoform in Tregs alone was sufficient to elicit steroid resistance in a steroid-sensitive allergic inflammation model. Taken together, our results demonstrate for the first time, to our knowledge, that Tregs are instrumental during steroid resistance and that manipulating steroid responsiveness in Tregs may represent a novel strategy to treat steroid refractory asthma.

Direct visualization of single-molecule membrane protein interactions in living cells.

Interactions between membrane proteins are poorly understood despite their importance in cell signaling and drug development. Here, we present a co-immunoimmobilization assay (Co-II) enabling the direct observation of membrane protein interactions in single living cells that overcomes the limitations of currently prevalent proximity-based indirect methods. Using Co-II, we investigated the transient homodimerizations of epidermal growth factor receptor (EGFR) and beta-2 adrenergic receptor (ß2-AR) in living cells, revealing the differential regulation of these receptors' dimerizations by molecular conformations and microenvironment in a plasma membrane. Co-II should provide a simple, rapid, and robust platform for visualizing both weak and strong protein interactions in the plasma membrane of living cells.

Cutting Edge: IL-27 Attenuates Autoimmune Neuroinflammation via Regulatory T Cell/Lag3-Dependent but IL-10-Independent Mechanisms In Vivo.

IL-27 regulates immune responses in inflammation. The underlying mechanism of IL-27 functions has long been attributed to its ability to induce IL-10 production in activated CD4 T cells. In this study, we report that Foxp3+ regulatory T cells (Tregs) are the main target cells of IL-27, mediating its immunoregulatory functions in vivo. Systemically delivered IL-27 efficiently prevents the development of experimental autoimmune encephalomyelitis, an autoimmune inflammation in the CNS. However, it failed to do so upon Treg depletion. IL-27 signaling in Tregs was necessary, as transferring Tregs deficient in IL-27Rα or Lag3, a downstream molecule induced by IL-27, was unable to protect mice from experimental autoimmune encephalomyelitis. IL-27 efficiently induced IL-10 expression in CD4 T cells in vitro; however, we found no evidence supporting IL-27-induced IL-10 induction in CD4 T cells in vivo. Taken together, our results uncover an irreplaceable contribution of Tregs during IL-27-mediated control of inflammation.

Automatic Classification of the Korean Triage Acuity Scale in Simulated Emergency Rooms Using Speech Recognition and Natural Language Processing: a Proof of Concept Study.

BACKGROUND: Rapid triage reduces the patients' stay time at an emergency department (ED). The Korean Triage Acuity Scale (KTAS) is mandatorily applied at EDs in South Korea. For rapid triage, we studied machine learning-based triage systems composed of a speech recognition model and natural language processing-based classification. METHODS: We simulated 762 triage cases that consisted of 18 classes with six types of the main symptom (chest pain, dyspnea, fever, stroke, abdominal pain, and headache) and three levels of KTAS. In addition, we recorded conversations between emergency patients and clinicians during the simulation. We used speech recognition models to transcribe the conversation. Bidirectional Encoder Representation from Transformers (BERT), support vector machine (SVM), random forest (RF), and k-nearest neighbors (KNN) were used for KTAS and symptom classification. Additionally, we evaluated the Shapley Additive exPlanations (SHAP) values of features to interpret the classifiers. RESULTS: The character error rate of the speech recognition model was reduced to 25.21% through transfer learning. With auto-transcribed scripts, support vector machine (area under the receiver operating characteristic curve [AUROC], 0.86; 95% confidence interval [CI], 0.81-0.9), KNN (AUROC, 0.89; 95% CI, 0.85-0.93), RF (AUROC, 0.86; 95% CI, 0.82-0.9) and BERT (AUROC, 0.82; 95% CI, 0.75-0.87) achieved excellent classification performance. Based on SHAP, we found "stress", "pain score point", "fever", "breath", "head" and "chest" were the important vocabularies for determining KTAS and symptoms. CONCLUSION: We demonstrated the potential of an automatic KTAS classification system using speech recognition models, machine learning and BERT-based classifiers.

SPICE-IT: Smart COVID-19 pandemic controlled eradication over NDN-IoT.

Internet of things (IoT) application in e-health can play a vital role in countering rapidly spreading diseases that can effectively manage health emergency scenarios like pandemics. Efficient disease control also requires monitoring of Standard operating procedure (SOP) follow-up of the population in the disease-prone area with a cost-effective reporting and responding mechanism to register any violation. However, the IoT devices have limited resources and the application requires delay-sensitive data transmission. Named Data Networking (NDN) can significantly reduce content retrieval delays but inherits cache overflow and network congestion challenges. Therefore, we are motivated to present a novel smart COVID-19 pandemic-controlled eradication over NDN-IoT (SPICE-IT) mechanism. SPICE-IT introduces autonomous monitoring in indoor environments with efficient pull-based reporting mechanism that records violations at local servers and cloud server. Intelligent face mask detection and temperature monitoring mechanism examines every person. Cloud server controls the response action from the centre with an adaptive decision-making mechanism. Long short-term memory (LSTM) based caching mechanism reduces the cache overflow and overall network congestion problem.

Treg-specific IL-27Rα deletion uncovers a key role for IL-27 in Treg function to control autoimmunity.

Dysregulated Foxp3+ Treg functions result in uncontrolled immune activation and autoimmunity. Therefore, identifying cellular factors modulating Treg functions is an area of great importance. Here, using Treg-specific Il27ra-/- mice, we report that IL-27 signaling in Foxp3+ Tregs is essential for Tregs to control autoimmune inflammation in the central nervous system (CNS). Following experimental autoimmune encephalomyelitis (EAE) induction, Treg-specific Il27ra-/- mice develop more severe EAE. Consistent with the severe disease, the numbers of IFNγ- and IL-17-producing CD4 T cells infiltrating the CNS tissues are greater in these mice. Treg accumulation in the inflamed CNS tissues is not affected by the lack of IL-27 signaling in Tregs, suggesting a functional defect of Il27ra-/- Tregs. IL-10 production by conventional CD4 T cells and their CNS accumulation are rather elevated in Treg-specific Il27ra-/- mice. Analysis with Treg fate-mapping reporter mice further demonstrates that IL-27 signaling in Tregs may control stability of Foxp3 expression. Finally, systemic administration of recombinant IL-27 in Treg-specific Il27ra-/- mice fails to ameliorate the disease even in the presence of IL-27-responsive conventional CD4 T cells. These findings uncover a previously unknown role of IL-27 in regulating Treg function to control autoimmune inflammation.

Towards Energy Efficient Home Automation: A Deep Learning Approach.

Home Automation Systems (HAS) attracted much attention during the last decade due to the developments in new wireless technologies, such as Bluetooth 4.0, 5G, WiFi 6, etc. In order to enable automation as a service in smart homes, a number of challenges must be addressed, such as fulfilling the electrical energy demands, scheduling the operational time of appliances, applying machine learning models in real-time, optimal human appliances interaction, etc. In order to address the aforementioned challenges and control the wastage of energy due to the lifestyle of the home users, we propose a system for automatically controlling the energy consumption by employing machine and deep learning techniques to smart home networks. The proposed system works in three phases, (1) feature extraction and classification based on 1-dimensional Deep Convolutional Neural Network (1D-DCNN) which extract important energy patterns from the historic energy data, (2) a load forecasting system based on Long-short Term Memory (LSTM) is proposed to forecast the load based on the extracted features in phase 1 and (3) a scheduling algorithm based on the forecasted data obtained from phase 2 is designed to schedule the operational time of smart home appliances. The proposed scheme efficiently automates the smart home appliances to consume less energy while adapting to the lifestyle of smart home users. The validation of the proposed scheme is tested with a number of simulation scenarios incorporating datasets from authentic data sources. The simulation results show that the proposed smart home automation system can be a game-changer in fulfilling the energy demands of the home users without installing renewable and other energy sources in the future.

Enhancements and Challenges in CoAP-A Survey.

The Internet of Engineering Task (IETF) developed a lighter application protocol (Constrained Application Protocol (CoAP)) for the constrained IoT devices operating in lossy environments. Based on UDP, CoAP is a lightweight and efficient protocol compared to other IoT protocols such as HTTP, MQTT, etc. CoAP also provides reliable communication among nodes in wireless sensor networks in addition to features such as resource observation, resource discovery, congestion control, etc. These capabilities of CoAP have enabled the implementation of CoAP in various domains ranging from home automation to health management systems. The use of CoAP has highlighted its shortcomings over the time. To overcome shortcomings of CoAP, numerous enhancements have been made in basic CoAP architecture. This survey highlights the shortcomings of basic CoAP architecture and enhancements made in it throughout the time. Furthermore, existing challenges and issue in the current CoAP architecture are also discussed. Finally, some applications with CoAP implementation are mentioned in order to realize the viability of CoAP in real world use cases.

Correction to: MicroRNA-9 regulates survival of chondroblasts and cartilage integrity by targeting protogenin.

Following publication of the original article [1], the authors reported that Figs. 3 and 6 are incorrect.

Lung-Infiltrating Foxp3+ Regulatory T Cells Are Quantitatively and Qualitatively Different during Eosinophilic and Neutrophilic Allergic Airway Inflammation but Essential To Control the Inflammation.

Understanding functions of Foxp3+ regulatory T cells (Tregs) during allergic airway inflammation remains incomplete. In this study, we report that, during cockroach Ag-induced allergic airway inflammation, Foxp3+ Tregs are rapidly mobilized into the inflamed lung tissues. However, the level of Treg accumulation in the lung was different depending on the type of inflammation. During eosinophilic airway inflammation, ∼30% of lung-infiltrating CD4 T cells express Foxp3, indicative of Tregs. On the contrary, only ∼10% of infiltrating CD4 T cells express Foxp3 during neutrophilic airway inflammation. Despite the different accumulation, the lung inflammation and inflammatory T cell responses were aggravated following Treg depletion, regardless of the type of inflammation, suggesting regulatory roles for Tregs. Interestingly, however, the extent to which inflammatory responses are aggravated by Treg depletion was significantly greater during eosinophilic airway inflammation. Indeed, lung-infiltrating Tregs exhibit phenotypic and functional features associated with potent suppression. Our results demonstrate that Tregs are essential regulators of inflammation, regardless of the type of inflammation, although the mechanisms used by Tregs to control inflammation may be shaped by environmental cues available to them.

Nanostructured ZnO films on stainless steel are highly safe and effective for antimicrobial applications.

The safety and effectiveness of antimicrobial ZnO films must be established for general applications. In this study, the antimicrobial activity, skin irritation, elution behavior, and mechanical properties of nanostructured ZnO films on stainless steel were evaluated. ZnO nanoparticle (NP) and ZnO nanowall (NW) structures were prepared with different surface roughnesses, wettability, and concentrations using an RF magnetron sputtering system. The thicknesses of ZnO NP and ZnO NW were approximately 300 and 620 nm, respectively, and ZnO NW had two diffraction directions of [0002] and [01-10] based on high-resolution transmission electron microscopy. The ZnO NW structure demonstrated 99.9% antimicrobial inhibition against Escherichia coli, Staphylococcus aureus, and Penicillium funiculosum, and no skin irritation was detected using experimental rabbits. Approximately 27.2 ± 3.0 µg L-1 Zn ions were eluted from the ZnO NW film at 100 °C for 24 h, which satisfies the WHO guidelines for drinking water quality. Furthermore, the Vickers hardness and fracture toughness of ZnO NW films on stainless steel were enhanced by 11 and 14% compared to those of the parent stainless steel. Based on these results, ZnO NW films on STS316L sheets are useful for household supplies, such as water pipes, faucets, and stainless steel containers.

Integrative analysis of the Lake Simcoe watershed (Ontario, Canada) as a socio-ecological system.

Striving for long-term sustainability in catchments dominated by human activities requires development of interdisciplinary research methods to account for the interplay between environmental concerns and socio-economic pressures. In this study, we present an integrative analysis of the Lake Simcoe watershed, Ontario, Canada, as viewed from the perspective of a socio-ecological system. Key features of our analysis are (i) the equally weighted consideration of environmental attributes with socioeconomic priorities and (ii) the identification of the minimal number of key socio-hydrological variables that should be included in a parsimonious watershed management framework, aiming to establish linkages between urbanization trends and nutrient export. Drawing parallels with the concept of Hydrological Response Units, we used Self-Organizing Mapping to delineate spatial organizations with similar socio-economic and environmental attributes, also referred to as Socio-Environmental Management Units (SEMUs). Our analysis provides evidence of two SEMUs with contrasting features, the "undisturbed" and "anthropogenically-influenced", within the Lake Simcoe watershed. The "undisturbed" cluster occupies approximately half of the Lake Simcoe catchment (45%) and is characterized by low landscape diversity and low average population density <0.4 humans ha-1. By contrast, the socio-environmental functional properties of the "anthropogenically-influenced" cluster highlight the likelihood of a stability loss in the long-run, as inferred from the distinct signature of urbanization activities on the tributary nutrient export, and the loss of subwatershed sensitivity to natural mechanisms that may ameliorate the degradation patterns. Our study also examines how the SEMU concept can augment the contemporary integrated watershed management practices and provides directions in order to promote environmental programs for lake conservation and to increase public awareness and engagement in stewardship initiatives.