NOD2/RICK-dependent β-defensin 2 regulation is protective for nontypeable Haemophilus influenzae-induced middle ear infection.

Middle ear infection, otitis media (OM), is clinically important due to the high incidence in children and its impact on the development of language and motor coordination. Previously, we have demonstrated that the human middle ear epithelial cells up-regulate β-defensin 2, a model innate immune molecule, in response to nontypeable Haemophilus influenzae (NTHi), the most common OM pathogen, via TLR2 signaling. NTHi does internalize into the epithelial cells, but its intracellular trafficking and host responses to the internalized NTHi are poorly understood. Here we aimed to determine a role of cytoplasmic pathogen recognition receptors in NTHi-induced β-defensin 2 regulation and NTHi clearance from the middle ear. Notably, we observed that the internalized NTHi is able to exist freely in the cytoplasm of the human epithelial cells after rupturing the surrounding membrane. The human middle ear epithelial cells inhibited NTHi-induced β-defensin 2 production by NOD2 silencing but augmented it by NOD2 over-expression. NTHi-induced β-defensin 2 up-regulation was attenuated by cytochalasin D, an inhibitor of actin polymerization and was enhanced by α-hemolysin, a pore-forming toxin. NOD2 silencing was found to block α-hemolysin-mediated enhancement of NTHi-induced β-defensin 2 up-regulation. NOD2 deficiency appeared to reduce inflammatory reactions in response to intratympanic inoculation of NTHi and inhibit NTHi clearance from the middle ear. Taken together, our findings suggest that a cytoplasmic release of internalized NTHi is involved in the pathogenesis of NTHi infections, and NOD2-mediated β-defensin 2 regulation contributes to the protection against NTHi-induced otitis media

Plant Location Selection for Food Production by Considering the Regional and Seasonal Supply Vulnerability of Raw Materials

A production capacity analysis considering market demand and raw materials is very important to design a new plant. However, in the food processing industry, the supply uncertainty of raw materials is very high, depending on the production site and the harvest season, and further, it is not straightforward to analyze too complex food production systems by using an analytical optimization model. For these reasons, this study presents a simulation-based decision support model to select the right location for a new food processing plant. We first define three supply vulnerability factors from the standpoint of regional as well as seasonal instability and present an assessment method for supply vulnerability based on fuzzy quantification. The evaluated vulnerability scores are then converted into raw material supply variations for food production simulation to predict the quarterly production volume of a new food processing plant. The proposed selection procedure is illustrated using a case study of semiprocessed kimchi production. The best plant location is proposed where we can reduce and mitigate risks when supplying raw material, thereby producing a target production volume steadily

Comparative analysis of multiple classification models to improve PM10 prediction performance

With the increasing requirement of high accuracy for particulate matter prediction, various attempts have been made to improve prediction accuracy by applying machine learning algorithms. However, the characteristics of particulate matter and the problem of the occurrence rate by concentration make it difficult to train prediction models, resulting in poor prediction. In order to solve this problem, in this paper, we proposed multiple classification models for predicting particulate matter concentrations required for prediction by dividing them into AQI-based classes. We designed multiple classification models using logistic regression, decision tree, SVM and ensemble among the various machine learning algorithms. The comparison results of the performance of the four classification models through error matrices confirmed the f-score of 0.82 or higher for all the models other than the logistic regression model

Query-Efficient Black-Box Red Teaming via Bayesian Optimization

The deployment of large-scale generative models is often restricted by their potential risk of causing harm to users in unpredictable ways. We focus on the problem of black-box red teaming, where a red team generates test cases and interacts with the victim model to discover a diverse set of failures with limited query access. Existing red teaming methods construct test cases based on human supervision or language model (LM) and query all test cases in a brute-force manner without incorporating any information from past evaluations, resulting in a prohibitively large number of queries. To this end, we propose Bayesian red teaming (BRT), novel query-efficient black-box red teaming methods based on Bayesian optimization, which iteratively identify diverse positive test cases leading to model failures by utilizing the pre-defined user input pool and the past evaluations. Experimental results on various user input pools demonstrate that our method consistently finds a significantly larger number of diverse positive test cases under the limited query budget than the baseline methods. The source code is available at https://github.com/snu-mllab/Bayesian-Red-Teaming.Comment: ACL 2023 Long Paper - Main Conferenc

Negative pressure wound therapy for soft tissue injuries around the foot and ankle

<p>Abstract</p> <p>Background</p> <p>This study was performed to evaluate the results of negative pressure wound therapy (NPWT) in patients with open wounds in the foot and ankle region.</p> <p>Materials and methods</p> <p>Using a NPWT device, 16 patients were prospectively treated for soft tissue injuries around the foot and ankle. Mean patient age was 32.8 years (range, 3–67 years). All patients had suffered an acute trauma, due to a traffic accident, a fall, or a crush injury, and all had wounds with underlying tendon or bone exposure. Necrotic tissues were debrided before applying NPWT. Dressings were changed every 3 or 4 days and treatment was continued for 18.4 days on average (range, 11–29 days).</p> <p>Results</p> <p>Exposed tendons and bone were successfully covered with healthy granulation tissue in all cases except one. The sizes of soft tissue defects reduced from 56.4 cm²to 42.9 cm²after NPWT (mean decrease of 24%). In 15 of the 16 cases, coverage with granulation tissue was achieved and followed by a skin graft. A free flap was needed to cover exposed bone and tendon in one case. No major complication occurred that was directly attributable to treatment. In terms of minor complications, two patients suffered scar contracture of grafted skin.</p> <p>Conclusion</p> <p>NPWT was found to facilitate the rapid formation of healthy granulation tissue on open wounds in the foot and ankle region, and thus, to shorten healing time and minimize secondary soft tissue defect coverage procedures.</p