Channel Assignment for Multiple Interface Nodes in Wireless Ad

In wireless networks, due to the broadcast property of the medium, nodes close to each other cannot simultaneously transmit over the same channel. One way to overcome this limitation is to use multiple independent channels available in the system. Although we can use a single wireless interface card to access multiple channels, such schemes can raise issues of compatibility (e.g., modication of the MAC protocol) and performance degradation (e.g., due to frequent channel switching). In this paper, we assume that nodes are equipped with multiple interface cards, and focus on the channel assignment problem for minimizing the total number of interferences among wireless links. We show that the problem is NP-hard and present distributed heuristics. We also present two centralized algorithms and show that the algorithms give constant factor approximation guarantees. We perform simulation experiments for the proposed distributed heuristic. The results show that compared to one-channel scenarios, our proposed algorithm can reduce the number of interferences by up to 85% when nodes are equipped with four interface cards. Through detailed packetlevel simulation experiments, we also show that depending on the scenario, the resulting channel assignment actually achieves up to seven times throughput improvement over the single-channel case

Wheel Impact Test by Deep Learning: Prediction of Location and Magnitude of Maximum Stress

The impact performance of the wheel during wheel development must be ensured through a wheel impact test for vehicle safety. However, manufacturing and testing a real wheel take a significant amount of time and money because developing an optimal wheel design requires numerous iterative processes of modifying the wheel design and verifying the safety performance. Accordingly, the actual wheel impact test has been replaced by computer simulations, such as Finite Element Analysis (FEA), but it still requires high computational costs for modeling and analysis. Moreover, FEA experts are needed. This study presents an aluminum road wheel impact performance prediction model based on deep learning that replaces the computationally expensive and time-consuming 3D FEA. For this purpose, 2D disk-view wheel image data, 3D wheel voxel data, and barrier mass value used for wheel impact test are utilized as the inputs to predict the magnitude of maximum von Mises stress, corresponding location, and the stress distribution of 2D disk-view. The wheel impact performance prediction model can replace the impact test in the early wheel development stage by predicting the impact performance in real time and can be used without domain knowledge. The time required for the wheel development process can be shortened through this mechanism

Euglycemic diabetic ketoacidosis development in a patient with type 2 diabetes receiving a sodium-glucose cotransporter-2 inhibitor and a carbohydrate-restricted diet

Sodium-glucose cotransporter-2 (SGLT2) inhibitors have become increasingly prescribed because of their proven protective effects on the heart and kidneys, and carbohydrate-restricted diets are popular therapeutic approaches for patients with obesity and diabetes. A 28-year-old obese woman with recently diagnosed diabetes developed euglycemic diabetic ketoacidosis (DKA) while on dapagliflozin, an SGLT2 inhibitor, and following a carbohydrate-restricted diet. She presented with nausea, vomiting, and epigastric pain. Hospital tests showed a blood glucose of 172 mg/dL, metabolic acidosis, and increased ketone levels, confirming euglycemic DKA. Treatment involved discontinuing dapagliflozin and administering fluids, glucose, and insulin. She recovered and was discharged on the fourth day. This is considered a case of euglycemic DKA induced by SGLT2 inhibitors and triggered by a carbohydrate-restricted diet. This case highlights the importance of physicians in confirming the symptoms and laboratory results of DKA, even in patients with normal blood glucose levels taking SGLT2 inhibitors and following carbohydrate-restricted diets. It is also crucial to advise patients to maintain an adequate carbohydrate intake

Variability of Response Time as a Predictor of Methylphenidate Treatment Response in Korean Children with Attention Deficit Hyperactivity Disorder

PURPOSE: Methylphenidate (MPH) is an effective medication for the treatment of attention deficit hyperactivity disorder (ADHD). However, about 30% of patients do not respond to or are unable to tolerate MPH. Based on previous findings, we hypothesized that great variability in response time (RT) among Korean children with ADHD on a computerized continuous performance attention test would be related to poor MPH treatment response. MATERIALS AND METHODS: Children (ages 6-18 years) with ADHD were recruited for a prospective 12-week, open-labeled, multicenter study to examine optimal dosage of OROS methylphenidate. Of the 144 subjects selected, 28 dropped out due to adverse events, medication noncompliance, or follow-up loss, and an additional 26 subjects with comorbid disorders were excluded from statistical analyses. We defined 'responders' as subjects who received a score of less than 18 on the attention deficit hyperactivity disorder rating scale (ARS; Korean version, K-ARS) and a score of 1 or 2 on the Clinical Global Impression-Improvement scale (CGI-I). RT variability was assessed with the ADHD diagnostic system (ADS). RESULTS: Fifty-nine (67%) subjects responded to MPH treatment. The non-responders showed greater RT variability at baseline (Mann Whitney U = 577.0, p < 0.01). Baseline RT variability was a significant predictor of MPH response (Nagelkerke R(2) = 0.136, p < 0.01). It predicted 94.9% of responder, 17.2% of non-responder and 69.3% of overall group. CONCLUSION: High RT variability may predict poor response to MPH treatment in children with ADHDope

Perspectives on single-nucleus RNA sequencing in different cell types and tissues

Single-cell RNA sequencing has become a powerful and essential tool for delineating cellular diversity in normal tissues and alterations in disease states. For certain cell types and conditions, there are difficulties in isolating intact cells for transcriptome profiling due to their fragility, large size, tight interconnections, and other factors. Single-nucleus RNA sequencing (snRNA-seq) is an alternative or complementary approach for cells that are difficult to isolate. In this review, we will provide an overview of the experimental and analysis steps of snRNA-seq to understand the methods and characteristics of general and tissue-specific snRNA-seq data. Knowing the advantages and limitations of snRNA-seq will increase its use and improve the biological interpretation of the data generated using this technique

Diffuse Interstitial Infiltrative Lung Metastasis of Malignant Melanoma: a Case Report

A diffuse interstitial infiltrative pattern of lung metastasis in a patient with malignant melanoma is rare and can be confused with benign conditions such as pulmonary edema or drug-induced pneumonitis. We experienced a case of diffuse interstitial infiltrative lung metastasis in malignant melanoma in a 37-year-old man. This case was confirmed by a transbronchial lung biopsy. We herein describe the findings on CT and positron emission tomography scan

Two-Year Changes in Diabetic Kidney Disease Phenotype and the Risk of Heart Failure: A Nationwide Population-Based Study in Korea

Background Diabetic kidney disease (DKD) is a risk factor for hospitalization for heart failure (HHF). DKD could be classified into four phenotypes by estimated glomerular filtration rate (eGFR, normal vs. low) and proteinuria (PU, negative vs. positive). Also, the phenotype often changes dynamically. This study examined HHF risk according to the DKD phenotype changes across 2-year assessments. Methods The study included 1,343,116 patients with type 2 diabetes mellitus (T2DM) from the Korean National Health Insurance Service database after excluding a very high-risk phenotype (eGFR <30 mL/min/1.73 m2) at baseline, who underwent two cycles of medical checkups between 2009 and 2014. From the baseline and 2-year eGFR and PU results, participants were divided into 10 DKD phenotypic change categories. Results During an average of 6.5 years of follow-up, 7,874 subjects developed HHF. The cumulative incidence of HHF from index date was highest in the eGFRlowPU– phenotype, followed by eGFRnorPU+ and eGFRnorPU–. Changes in DKD phenotype differently affect HHF risk. When the persistent eGFRnorPU– category was the reference, hazard ratios for HHF were 3.10 (95% confidence interval [CI], 2.73 to 3.52) in persistent eGFRnorPU+ and 1.86 (95% CI, 1.73 to 1.99) in persistent eGFRlowPU–. Among altered phenotypes, the category converted to eGFRlowPU+ showed the highest risk. In the normal eGFR category at the second examination, those who converted from PU– to PU+ showed a higher risk of HHF than those who converted from PU+ to PU–. Conclusion Changes in DKD phenotype, particularly with the presence of PU, are more likely to reflect the risk of HHF, compared with DKD phenotype based on a single time point in patients with T2DM