Adsorption Properties of Typical Lung Cancer Breath Gases on Ni-SWCNTs through Density Functional Theory

A lot of useful information is contained in the human breath gases, which makes it an effective way to diagnose diseases by detecting the typical breath gases. This work investigated the adsorption of typical lung cancer breath gases: benzene, styrene, isoprene, and 1-hexene onto the surface of intrinsic and Ni-doped single wall carbon nanotubes through density functional theory. Calculation results show that the typical lung cancer breath gases adsorb on intrinsic single wall carbon nanotubes surface by weak physisorption. Besides, the density of states changes little before and after typical lung cancer breath gases adsorption. Compared with single wall carbon nanotubes adsorption, single Ni atom doping significantly improves its adsorption properties to typical lung cancer breath gases by decreasing adsorption distance and increasing adsorption energy and charge transfer. The density of states presents different degrees of variation during the typical lung cancer breath gases adsorption, resulting in the specific change of conductivity of gas sensing material. Based on the different adsorption properties of Ni-SWCNTs to typical lung cancer breath gases, it provides an effective way to build a portable noninvasive portable device used to evaluate and diagnose lung cancer at early stage in time

Flatness-Aware Minimization for Domain Generalization

Domain generalization (DG) seeks to learn robust models that generalize well under unknown distribution shifts. As a critical aspect of DG, optimizer selection has not been explored in depth. Currently, most DG methods follow the widely used benchmark, DomainBed, and utilize Adam as the default optimizer for all datasets. However, we reveal that Adam is not necessarily the optimal choice for the majority of current DG methods and datasets. Based on the perspective of loss landscape flatness, we propose a novel approach, Flatness-Aware Minimization for Domain Generalization (FAD), which can efficiently optimize both zeroth-order and first-order flatness simultaneously for DG. We provide theoretical analyses of the FAD's out-of-distribution (OOD) generalization error and convergence. Our experimental results demonstrate the superiority of FAD on various DG datasets. Additionally, we confirm that FAD is capable of discovering flatter optima in comparison to other zeroth-order and first-order flatness-aware optimization methods.Comment: Accepted by ICCV202

The clinical value of glycosylated hemoglobin level in newly diagnosed ketosis-prone type 2 diabetes

ObjectiveTo evaluate the clinical value of glycosylated hemoglobin (HbA1c) in newly diagnosed ketosis-prone type 2 diabetes (KPD).MethodsA total of 330 patients with newly diagnosed type 2 diabetes (T2DM) hospitalized in our department with an average age of 48.72 ± 13.07 years old were selected and divided into T2DM group (193 cases) and KPD group (137 cases) according to whether they were combined with ketosis. According to the quartile level of HbA1c, they were divided into group A (HbA1c < 8.90%, 84 cases), group B (8.90%≤HbA1c < 10.70%, 86 cases), group C (10.70%≤HbA1c ≤ 12.40%, 85 cases) and group D (HbA1c > 12.40%, 75 cases). The general clinical features, laboratory indicators and islet function of each group were compared. Spearman correlation analysis was used to explore the correlation between HbA1c and β- Hydroxybutyric acid (β- HB) and islet function. ROC curve was used to analyze the sensitivity and specificity of HbA1c in diagnosing KPD, and the optimal tangent point was obtained.ResultsHbA1c, β-HB, FFA, RBG, insulin dosage, GSP, OGTT (0, 0.5, 1, 2, 3h) in KPD group were significantly higher than those in T2DM group (P< 0.001). HDL-C, IRT (0, 0.5, 1, 2, 3h), HOMA-β, HOMA-IR, HOMA-IS, ΔC30/ΔG30, AUC insulin were significantly lower than those in T2DM group (P< 0.001). With the increase of HbA1c level, the incidence of ketosis, β-HB, FFA and insulin dosage increased, while IRT (0, 0.5, 1, 2, 3h), ΔC30/ΔG30, AUC insulin, HOMA-β and HOMA-IS decreased accordingly (P< 0.001). In all newly diagnosed T2DM patients, Spearman correlation analysis showed that HbA1c was positively correlated with β-HB (r=0.539, P < 0.001), and was negatively correlated with HOMA-β (r=-0.564, P < 0.001), HOMA-IS (r=-0.517, P < 0.01, P < 0.001), HOMA-IR (r=-0.177, P < 0.001), ΔC30/ΔG30 (r=-0.427, P < 0.01) and AUC insulin (r=-0.581, P < 0.001). In ROC curve analysis, the optimal threshold for the diagnosis of KPD was 10.15%, Youden index was 0.616, area under the curve (AUC) was 0.882, sensitivity = 92.70%, specificity = 70.50%.ConclusionIn newly diagnosed T2DM patients, if HbA1c > 10.15%, it is more likely to develop KPD. Monitoring HbA1c level is conducive to timely detection of high-risk individuals with KPD and taking appropriate measures to prevent the occurrence and development of the disease

Rethinking Cross-Domain Pedestrian Detection: A Background-Focused Distribution Alignment Framework for Instance-Free One-Stage Detectors

Cross-domain pedestrian detection aims to generalize pedestrian detectors from one label-rich domain to another label-scarce domain, which is crucial for various real-world applications. Most recent works focus on domain alignment to train domain-adaptive detectors either at the instance level or image level. From a practical point of view, one-stage detectors are faster. Therefore, we concentrate on designing a cross-domain algorithm for rapid one-stage detectors that lacks instance-level proposals and can only perform image-level feature alignment. However, pure image-level feature alignment causes the foreground-background misalignment issue to arise, i.e., the foreground features in the source domain image are falsely aligned with background features in the target domain image. To address this issue, we systematically analyze the importance of foreground and background in image-level cross-domain alignment, and learn that background plays a more critical role in image-level cross-domain alignment. Therefore, we focus on cross-domain background feature alignment while minimizing the influence of foreground features on the cross-domain alignment stage. This paper proposes a novel framework, namely, background-focused distribution alignment (BFDA), to train domain adaptive onestage pedestrian detectors. Specifically, BFDA first decouples the background features from the whole image feature maps and then aligns them via a novel long-short-range discriminator.Comment: This paper published on IEEE Transactions on Image Processing on August 2023.See https://ieeexplore.ieee.org/document/1023112

Verapamil Ameliorates Hepatic Metaflammation by Inhibiting Thioredoxin-Interacting Protein/NLRP3 Pathways

Activation of thioredoxin-interacting protein (TXNIP)/nod-like receptor protein 3 (NLRP3) inflammasome plays a critical role in pathogenesis of non-alcoholic fatty liver disease. This study investigated the protective effects of verapamil on hepatic metaflammation in a rodent model of high-fat (HF) diet-induced obesity (DIO). DIO was induced in a subset of mice provided with HF diet (45% kcal fat). After 10 weeks of HF diet, verapamil was administered by intraperitoneal injection. The experimental groups included the following: (1) normal diet group, (2) normal diet + treatment with verapamil (VER) group, (3) HF control group, (4) HF+VER (25 mg/kg/day) group. After 1 week of each treatment, blood and liver tissues were collected, and glucose control, serum triglyceride (TG) level, inflammation, and TXNIP/NLRP3 inflammasome were analyzed. Verapamil administration caused no alteration in food intake. HF diet impaired glucose control and increased body weight and serum TG levels. Hepatic inflammation was aggravated in HF-fed mice, as demonstrated by increased levels of pro-inflammatory markers interleukin-1β (IL-1β) and IL-18 in the liver. On the other hand, verapamil administration significantly improved glucose control, body weight, and serum TG levels. Verapamil treatment also reduced pro-inflammatory marker levels. These improvements were accompanied by alterations in activation of TXNIP/NLRP3 inflammasome. The observed results demonstrate that verapamil ameliorates hepatic metaflammation by inhibiting TXNIP/NLRP3 pathways