Diverse Representation Embedding for Lifelong Person Re-Identification

Lifelong Person Re-Identification (LReID) aims to continuously learn from successive data streams, matching individuals across multiple cameras. The key challenge for LReID is how to effectively preserve old knowledge while incrementally learning new information, which is caused by task-level domain gaps and limited old task datasets. Existing methods based on CNN backbone are insufficient to explore the representation of each instance from different perspectives, limiting model performance on limited old task datasets and new task datasets. Unlike these methods, we propose a Diverse Representations Embedding (DRE) framework that first explores a pure transformer for LReID. The proposed DRE preserves old knowledge while adapting to new information based on instance-level and task-level layout. Concretely, an Adaptive Constraint Module (ACM) is proposed to implement integration and push away operations between multiple overlapping representations generated by transformer-based backbone, obtaining rich and discriminative representations for each instance to improve adaptive ability of LReID. Based on the processed diverse representations, we propose Knowledge Update (KU) and Knowledge Preservation (KP) strategies at the task-level layout by introducing the adjustment model and the learner model. KU strategy enhances the adaptive learning ability of learner models for new information under the adjustment model prior, and KP strategy preserves old knowledge operated by representation-level alignment and logit-level supervision in limited old task datasets while guaranteeing the adaptive learning information capacity of the LReID model. Compared to state-of-the-art methods, our method achieves significantly improved performance in holistic, large-scale, and occluded datasets.Comment: 11 pages,7 Tables,3 Figure

Evolution of Chatbots in Nursing Education: Narrative Review

Abstract BackgroundThe integration of chatbots in nursing education is a rapidly evolving area with potential transformative impacts. This narrative review aims to synthesize and analyze the existing literature on chatbots in nursing education. ObjectiveThis study aims to comprehensively examine the temporal trends, international distribution, study designs, and implications of chatbots in nursing education. MethodsA comprehensive search was conducted across 3 databases (PubMed, Web of Science, and Embase) following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram. ResultsA total of 40 articles met the eligibility criteria, with a notable increase of publications in 2023 (n=28, 70%). Temporal analysis revealed a notable surge in publications from 2021 to 2023, emphasizing the growing scholarly interest. Geographically, Taiwan province made substantial contributions (n=8, 20%), followed by the United States (n=6, 15%) and South Korea (n=4, 10%). Study designs varied, with reviews (n=8, 20%) and editorials (n=7, 18%) being predominant, showcasing the richness of research in this domain. ConclusionsIntegrating chatbots into nursing education presents a promising yet relatively unexplored avenue. This review highlights the urgent need for original research, emphasizing the importance of ethical considerations

Design, synthesis, and biological evaluation of piperazine derivatives involved in the 5-HT1AR/BDNF/PKA pathway

AbstractIn this study, four series of piperazine derivatives were designed, synthesised and subjected to biological test, and compound 6a with potential antidepressant activity was obtained. An affinity assay of compound 6a with 5-hydroxytryptamine (serotonin, 5-HT)1A receptor (5-HT1AR) was undertaken, and the effects on the 5-HT level in the brains of mice were also tested. The results showed that compound 6a had the best affinity with 5-HT1AR (Ki = 1.28 nM) and significantly increased the 5-HT level. The expression levels of 5-HT1AR, BDNF, and PKA in the hippocampus were analysed by western blot and immunohistochemistry analyses. The results showed that the expression of 5-HT1AR, BDNF, and PKA in the model group was reduced compared to that of the control group, and compound 6a could reverse this phenomenon. Molecular docking was performed to investigate the interactions of the studied compound 6a with 5-HT1AR on the molecular level

Data-driven rapid detection of Helicobacter pylori infection through machine learning with limited laboratory parameters in Chinese primary clinics

Background: Helicobacter pylori (H. pylori) is a significant global health concern, posing a high risk for gastric cancer. Conventional diagnostic and screening approaches are inaccessible, invasive, inaccurate, time-consuming, and expensive in primary clinics. Objective: This study aims to apply machine learning (ML) models to detect H. pylori infection using limited laboratory parameters from routine blood tests and to investigate the association of these biomarkers with clinical outcomes in primary clinics. Methods: A retrospective analysis with three ML and five ensemble models was conducted on 1409 adults from Hubei Provincial Hospital of Traditional Chinese Medicine. evaluating twenty-three blood test parameters and using the C14 urea breath test as the gold standard for diagnosing H. pylori infection. Results: In our comparative study employing three different feature selection strategies, Random Forest (RF) model exhibited superior performance over other ML and ensemble models. Multiple evaluation metrics underscored the optimal performance of the RF model (ROC = 0.951, sensitivity = 0.882, specificity = 0.906, F1 = 0.906, accuracy = 0.894, PPV = 0.908, NPV = 0.880) without feature selection. Key biomarkers identified through importance ranking and shapley additive Explanations (SHAP) analysis using the RF model without feature selection include White Blood Cell Count (WBC), Mean Platelet Volume (MPV), Hemoglobin (Hb), Red Blood Cell Count (RBC), Platelet Crit (PCT), and Platelet Count (PLC). These biomarkers were found to be significantly associated with the presence of H. pylori infection, reflecting the immune response and inflammation levels. Conclusion: Abnormalities in key biomarkers could prompt clinical workers to consider H. pylori infection. The RF model effectively identifies H. pylori infection using routine blood tests, offering potential for clinical application in primary clinics. This ML approach can enhance diagnosis and screening, reducing medical burdens and reliance on invasive diagnostics

Synthesis and Evaluation of Antidepressant Activities of 5-Aryl-4,5-dihydrotetrazolo [1,5-a]thieno[2,3-e]pyridine Derivatives

In this study, we synthetized a series of 5-aryl-4,5-dihydrotetrazolo[1,5-a]thieno[2,3-e]pyridine derivatives containing tetrazole and other heterocycle substituents, i.e., triazole, methyltriazole, and triazolone. The forced swim test (FST) and tail suspension test (TST) were used to evaluate the antidepressant activity of the target compounds. The compound 5-[4-(trifluoromethyl)phenyl]-4,5-dihydrotetrazolo[1,5-a]thieno[2,3-e]pyridine (4i) showed the highest antidepressant activity, with a reduced immobility time of 55.33% when compared with the control group. Using an open-field test, compound 4i was shown to not affect spontaneous activity of mice. The determination of in vivo 5-hydroxytryptamine (5-HT) concentration showed that compound 4i may have an effect in the mouse brain. The biological activities of all synthetized compounds were verified by molecular docking studies. Compound 4i showed significant interactions with residues of the 5-HT1A receptor homology model

Design, synthesis, and biological evaluation of piperazine derivatives involved in the 5-HT_1AR/BDNF/PKA pathway

In this study, four series of piperazine derivatives were designed, synthesised and subjected to biological test, and compound 6a with potential antidepressant activity was obtained. An affinity assay of compound 6a with 5-hydroxytryptamine (serotonin, 5-HT)1A receptor (5-HT1AR) was undertaken, and the effects on the 5-HT level in the brains of mice were also tested. The results showed that compound 6a had the best affinity with 5-HT1AR (Ki = 1.28 nM) and significantly increased the 5-HT level. The expression levels of 5-HT1AR, BDNF, and PKA in the hippocampus were analysed by western blot and immunohistochemistry analyses. The results showed that the expression of 5-HT1AR, BDNF, and PKA in the model group was reduced compared to that of the control group, and compound 6a could reverse this phenomenon. Molecular docking was performed to investigate the interactions of the studied compound 6a with 5-HT1AR on the molecular level. </p

Novel GPR120 Agonists with Improved Pharmacokinetic Profiles for the Treatment of Type 2 Diabetes

GPR120 is a promising target for the treatment of type 2 diabetes (T2DM), which is activated by free fatty acids (FFAs) and stimulates the release of glucagon-like peptide-1(GLP-1). GLP-1, as an incretin, can enhance glucose-dependent secretion of insulin from pancreatic beta cells and reduce blood glucose. In this study, a series of novel GPR120 agonists were designed and synthesized to improve the stability and hydrophilicity of the phenylpropanoic acid GPR120 agonist TUG-891. Compound 11b showed excellent GPR120 agonistic activity and pharmacokinetic properties, and could reduce the blood glucose of normal mice in a dose-dependent manner. In addition, no hypoglycemic side effects were observed even at a dose of 100 mg/kg. Moreover, 11b showed good anti-hyperglycemic effects in diet-induced obese (DIO) mice. Molecular simulation illustrated that compound 11b could enter the active site of GPR120 and interact with ARG99. Taken together, the results indicate that compound 11b might be a promising drug candidate for the treatment of T2DM

Novel GPR120 Agonists with Improved Pharmacokinetic Profiles for the Treatment of Type 2 Diabetes

GPR120 is a promising target for the treatment of type 2 diabetes (T2DM), which is activated by free fatty acids (FFAs) and stimulates the release of glucagon-like peptide-1(GLP-1). GLP-1, as an incretin, can enhance glucose-dependent secretion of insulin from pancreatic beta cells and reduce blood glucose. In this study, a series of novel GPR120 agonists were designed and synthesized to improve the stability and hydrophilicity of the phenylpropanoic acid GPR120 agonist TUG-891. Compound 11b showed excellent GPR120 agonistic activity and pharmacokinetic properties, and could reduce the blood glucose of normal mice in a dose-dependent manner. In addition, no hypoglycemic side effects were observed even at a dose of 100 mg/kg. Moreover, 11b showed good anti-hyperglycemic effects in diet-induced obese (DIO) mice. Molecular simulation illustrated that compound 11b could enter the active site of GPR120 and interact with ARG99. Taken together, the results indicate that compound 11b might be a promising drug candidate for the treatment of T2DM

Effect of Post Treatment For Cu-Cr Source/Drain Electrodes on a-IGZO TFTs

We report a high-performance amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) thin-film transistor (TFT) with new copper-chromium (Cu-Cr) alloy source/drain electrodes. The TFT shows a high mobility of 39.4 cm 2 ·V − 1 ·s − 1 a turn-on voltage of −0.8 V and a low subthreshold swing of 0.47 V/decade. Cu diffusion is suppressed because pre-annealing can protect a-IGZO from damage during the electrode sputtering and reduce the copper diffusion paths by making film denser. Due to the interaction of Cr with a-IGZO, the carrier concentration of a-IGZO, which is responsible for high mobility, rises

All-Aluminum Thin Film Transistor Fabrication at Room Temperature

Bottom-gate all-aluminum thin film transistors with multi conductor/insulator nanometer heterojunction were investigated in this article. Alumina (Al2O3) insulating layer was deposited on the surface of aluminum doping zinc oxide (AZO) conductive layer, as one AZO/Al2O3 heterojunction unit. The measurements of transmittance electronic microscopy (TEM) and X-ray reflectivity (XRR) revealed the smooth interfaces between ~2.2-nm-thick Al2O3 layers and ~2.7-nm-thick AZO layers. The devices were entirely composited by aluminiferous materials, that is, their gate and source/drain electrodes were respectively fabricated by aluminum neodymium alloy (Al:Nd) and pure Al, with Al2O3/AZO multilayered channel and AlOx:Nd gate dielectric layer. As a result, the all-aluminum TFT with two Al2O3/AZO heterojunction units exhibited a mobility of 2.47 cm2/V·s and an Ion/Ioff ratio of 106. All processes were carried out at room temperature, which created new possibilities for green displays industry by allowing for the devices fabricated on plastic-like substrates or papers, mainly using no toxic/rare materials