Dual-Branch Temperature Scaling Calibration for Long-Tailed Recognition

The calibration for deep neural networks is currently receiving widespread attention and research. Miscalibration usually leads to overconfidence of the model. While, under the condition of long-tailed distribution of data, the problem of miscalibration is more prominent due to the different confidence levels of samples in minority and majority categories, and it will result in more serious overconfidence. To address this problem, some current research have designed diverse temperature coefficients for different categories based on temperature scaling (TS) method. However, in the case of rare samples in minority classes, the temperature coefficient is not generalizable, and there is a large difference between the temperature coefficients of the training set and the validation set. To solve this challenge, this paper proposes a dual-branch temperature scaling calibration model (Dual-TS), which considers the diversities in temperature parameters of different categories and the non-generalizability of temperature parameters for rare samples in minority classes simultaneously. Moreover, we noticed that the traditional calibration evaluation metric, Excepted Calibration Error (ECE), gives a higher weight to low-confidence samples in the minority classes, which leads to inaccurate evaluation of model calibration. Therefore, we also propose Equal Sample Bin Excepted Calibration Error (Esbin-ECE) as a new calibration evaluation metric. Through experiments, we demonstrate that our model yields state-of-the-art in both traditional ECE and Esbin-ECE metrics

Single-step electrochemical functionalization of double-walled carbon nanotube (DWCNT) membranes and the demonstration of ionic rectification

Carbon nanotube (CNT) membranes allow the mimicking of natural ion channels for applications in drug delivery and chemical separation. Double-walled carbon nanotube membranes were simply functionalized with dye in a single step instead of the previous two-step functionalization. Non-faradic electrochemical impedance spectra indicated that the functionalized gatekeeper by single-step modification can be actuated to mimic the protein channel under bias. This functional chemistry was proven by a highly efficient ion rectification, wherein the highest experimental rectification factor of ferricyanide was up to 14.4. One-step functionalization by electrooxidation of amine provides a simple and promising functionalization chemistry for the application of CNT membranes

centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation

Unsupervised Domain Adaptation (UDA) approaches address the covariate shift problem by minimizing the distribution discrepancy between the source and target domains, assuming that the label distribution is invariant across domains. However, in the imbalanced domain adaptation (IDA) scenario, covariate and long-tailed label shifts both exist across domains. To tackle the IDA problem, some current research focus on minimizing the distribution discrepancies of each corresponding class between source and target domains. Such methods rely much on the reliable pseudo labels' selection and the feature distributions estimation for target domain, and the minority classes with limited numbers makes the estimations more uncertainty, which influences the model's performance. In this paper, we propose a cross-domain class discrepancy minimization method based on accumulative class-centroids for IDA (centroIDA). Firstly, class-based re-sampling strategy is used to obtain an unbiased classifier on source domain. Secondly, the accumulative class-centroids alignment loss is proposed for iterative class-centroids alignment across domains. Finally, class-wise feature alignment loss is used to optimize the feature representation for a robust classification boundary. A series of experiments have proved that our method outperforms other SOTA methods on IDA problem, especially with the increasing degree of label shift

RETRACTED: A study on expression level and nutritional status of IGFBP-2 after left neck anastomosis combined with placement of feeding nutritional applicators carrying ^(125)I particles in the treatment of esophageal cancer

BackgroundTo explore the changes and significance of the expression level and nutritional status of human insulin-like growth factor binding protein-2 (IGFBP2) after the treatment of esophageal cancer with left neck anastomosis combined with placement of feeding nutritional applicators carrying ^(125)I particles.MethodsA total of 110 patients with esophageal cancer (observation group: left neck anastomosis combined with placement of feeding nutritional applicators carrying ^(125)I particles) and 100 healthy people (control group) were enrolled at the same period. Then enzyme-linked immunosorbent assay (ELISA) was carried out to detect level of IGFBP-2. Lymphocyte count and serum albumin were measured by immune analyzer and automatic protein analyzer to evaluate nutritional status. Logistic regression analysis was used to analyze the relationship between serum IGFBP-2, nutritional status and prognosis of esophageal cancer after combined treatment.ResultsThe albumin, lymphocyte absolute value and PNI detection value of the control group were lower than those of the observation group 1 month after treatment, and the difference was statistically significant compared with the control group. The detection value of IGFBP-2 in early patients before and after treatment was lower than that in middle and late patients, and the detection values of albumin, lymphocyte absolute value and PNI were higher than those in middle and late patients, the differences were statistically significant. Serum IGFBP-2 level was negatively correlated with PNI, and albumin and lymphocyte absolute value were positively correlated with PNI. The detection value of IGFBP-2 in patients with good prognosis was significantly lower than that in patients with poor prognosis, and the detection values of albumin, lymphocyte absolute value and PNI were significantly higher than those in patients with poor prognosis. The AUC (0.887,95% CI: 0.799-0.975) of IGFBP-2, albumin, lymphocyte absolute value and PNI in predicting poor prognosis of esophageal cancer was the largest, and the sensitivity and specificity were 94.12% and 92.47%, respectively.ConclusionsLeft neck anastomosis combined with ^(125)I particle application nutritional tube is helpful for the decrease of serum IGFBP-2 and the increase of various nutritional status indicators, which is beneficial for the improvement of the patient’s condition

Role of astrocytes in sleep deprivation: accomplices, resisters, or bystanders?

Sleep plays an essential role in all studied animals with a nervous system. However, sleep deprivation leads to various pathological changes and neurobehavioral problems. Astrocytes are the most abundant cells in the brain and are involved in various important functions, including neurotransmitter and ion homeostasis, synaptic and neuronal modulation, and blood–brain barrier maintenance; furthermore, they are associated with numerous neurodegenerative diseases, pain, and mood disorders. Moreover, astrocytes are increasingly being recognized as vital contributors to the regulation of sleep-wake cycles, both locally and in specific neural circuits. In this review, we begin by describing the role of astrocytes in regulating sleep and circadian rhythms, focusing on: (i) neuronal activity; (ii) metabolism; (iii) the glymphatic system; (iv) neuroinflammation; and (v) astrocyte–microglia cross-talk. Moreover, we review the role of astrocytes in sleep deprivation comorbidities and sleep deprivation-related brain disorders. Finally, we discuss potential interventions targeting astrocytes to prevent or treat sleep deprivation-related brain disorders. Pursuing these questions would pave the way for a deeper understanding of the cellular and neural mechanisms underlying sleep deprivation-comorbid brain disorders