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Ensuring the quality of medicinal herbs in the herbal market is crucial. However, the genetic and physical similarities among medicinal materials have led to issues of mixing and counterfeit distribution, posing significant challenges to quality assurance. Recent advancements in deep learning technology, widely applied in the field of computer vision, have demonstrated the potential to classify images quickly and accurately, even those that can only be distinguished by experts. This study aimed to develop a classification model based on deep learning technology to distinguish RGB images of seeds from Ziziphus jujuba Mill. var. spinosa, Ziziphus mauritiana Lam., and Hovenia dulcis Thunb. Using three advanced convolutional neural network (CNN) architectures—ResNet-50, Inception-v3, and DenseNet-121—all models demonstrated a classification performance above 98% on the test set, with classification times as low as 23 ms. These results validate that the models and methods developed in this study can effectively distinguish Z. jujuba seeds from morphologically similar species. Furthermore, the strong performance and speed of these models make them suitable for practical use in quality inspection settings.

Summary: Lipid rafts in plasma membranes are thought to provide a platform for regulating signaling pathways by increasing the expression or proximity of proteins in the same pathway. Despite this understanding, the absence of direct, simultaneous observations of lipid rafts and their affiliated proteins has hindered a comprehensive assessment of their roles across various biological contexts. Amyloid-β (Aβ), a hallmark of Alzheimer’s disease (AD), is generated from the sequential cleavage of amyloid precursor proteins (APPs) by β- and γ-secretases, primarily within endosomes after APP endocytosis by canonical clathrin-mediated endocytosis in neurons. In this study, we developed a protocol for imaging APP on lipid rafts using time-of-flight secondary ion mass spectrometry (ToF-SIMS) and found that astrocyte ApoE4 contributes to an increase in APP localization on lipid rafts, subsequently elevating Aβ42 synthesis in a clathrin-independent manner in neurons.

Abstract Herein, a heterogeneous structure of Ni–Mo catalyst comprising Ni4Mo nanoalloys decorated on a MoOx matrix via electrodeposition is introduced. This catalyst exhibits remarkable hydrogen evolution reaction (HER) activity across a range of pH conditions. The heterogeneous Ni–Mo catalyst showed low overpotentials only of 24 and 86, 21 and 60, and 37 and 168 mV to produce a current density of 10 and 100 mA cm−2 (η10 and η100) in alkaline, acidic, and neutral media, respectively, which represents one of the most active catalysts for the HER. The enhanced activity is attributed to the hydrogen spillover effect, where hydrogen atoms migrate between the Ni4Mo alloys and the MoOx matrix, forming hydrogen molybdenum bronze as additional active sites. Additionally, the Ni4Mo facilitated the water dissociation process, which helps the Volmer step in the alkaline/neutral HER. Through electrochemical analysis, in situ Raman spectroscopy, and density functional theory calculations, the fast HER mechanism is elucidated.

Abstract Brain endothelial LDL receptor-related protein 1 (LRP1) is involved in the clearance of Aβ peptides across the blood-brain barrier (BBB). Here we show that endothelial deficiency of ankyrin repeat and SAM domain containing 1 A (ANKS1A) reduces both the cell surface levels of LRP1 and the Aβ clearance across the BBB. Association of ANKS1A with the NPXY motifs of LRP1 facilitates the transport of LRP1 from the endoplasmic reticulum toward the cell surface. ANKS1A deficiency in an Alzheimer’s disease mouse model results in exacerbated Aβ pathology followed by cognitive impairments. These deficits are reversible by gene therapy with brain endothelial-specific ANKS1A. In addition, human induced pluripotent stem cell-derived BBBs (iBBBs) were generated from endothelial cells lacking ANKS1A or carrying the rs6930932 variant. Those iBBBs exhibit both reduced cell surface LRP1 and impaired Aβ clearance. Thus, our findings demonstrate that ANKS1A regulates LRP1-mediated Aβ clearance across the BBB.

This study presents an accurate model for non-monotonic layout-dependent effects (LDEs) measured using 10nm-class dynamic random access memory technology. To collect the LDE measurement data, a test module with an individually addressable array of 240 transistors has been developed. The proposed test module occupies a small area of 0.1 square millimeters with a density 15 times higher than that of typical scribe-line circuits. The proposed model employs a novel empirical function to precisely describe the non-monotonic dependence on each pair of geometrical parameters, such as the diffusion lengths, lateral/vertical spacings to the adjacent shallow trench isolations, and gate-to-contact distances. Additionally, this model can be easily realized as a sub-circuit model in standard circuit simulators, requiring only two additional tuning parameters for the core transistor. The fitted model demonstrates excellent agreement with the measured values obtained from test modules (802 transistors in total), achieving mean absolute errors of 0.7% for the drain current in the saturation region and 4.7 mV for the threshold voltage.

Summary: Recent developments in genome sequencing have expanded the knowledge of genetic factors associated with late-onset Alzheimer’s disease (AD). Among them, genetic variant ε4 of the APOE gene (APOE4) confers the greatest disease risk. Dysregulated glucose metabolism is an early pathological feature of AD. Using isogenic ApoE3 and ApoE4 astrocytes derived from human induced pluripotent stem cells, we find that ApoE4 increases glycolytic activity but impairs mitochondrial respiration in astrocytes. Ultrastructural and autophagy flux analyses show that ApoE4-induced cholesterol accumulation impairs lysosome-dependent removal of damaged mitochondria. Acute treatment with cholesterol-depleting agents restores autophagic activity, mitochondrial dynamics, and associated proteomes, and extended treatment rescues mitochondrial respiration in ApoE4 astrocytes. Taken together, our study provides a direct link between ApoE4-induced lysosomal cholesterol accumulation and abnormal oxidative phosphorylation.

Background: Several studies have shown that there are no significant differences in anastomotic leakage associated with Transanal total mesorectal excision (taTME) versus laparoscopic TME (lapTME) for rectal cancer; however, little is known about late anastomotic leakage, such as that primarily found in the chronic presacral sinus. We aimed to compare the occurrence of anastomotic leakage and chronic presacral sinus in rectal cancer for taTME and lapTME. Methods: In this retrospective cohort study, data were collected for patients with rectal cancer who underwent surgery between January 2009 and September 2019. Of the 220 patients included in this study, 182 were in the lapTME group and 38 in the taTME group. We compared factors associated with anastomotic leakage and chronic presacral sinus formation between the two groups. A binary-logistic model was used to determine the risk factors for chronic presacral sinus. Results: Anastomotic leakage occurred in six patients (15.8%) in the taTME group and 36 patients (19.7%) in the lapTME group. Chronic presacral sinus occurred in three patients (7.9%) in the taTME group and 15 patients (8.2%) in the lapTME group. There was no significant difference in anastomotic leakage or chronic presacral sinus between groups (P = 0.569 and P = 1.000, respectively). Pathologic stage III or higher was significantly associated with chronic presacral sinus formation (P = 0.006). Conclusion: There were no significant differences between taTME and lapTME regarding the incidence of anastomotic leakage or chronic presacral sinus. Almost one-third of anastomotic leakages developed into chronic presacral sinus.

The integration of hyperspectral imaging with machine learning algorithms has presented a promising strategy for the non-invasive and rapid detection of plant metabolites. For this study, we developed prediction models using partial least squares regression (PLSR) and boosting algo-rithms (such as AdaBoost, XGBoost, and LightGBM) for five metabolites in Brassica juncea leaves: total chlorophyll, phenolics, flavonoids, glucosinolates, and anthocyanins. To enhance the model performance, we employed several spectral data preprocessing methods and feature-selection al-gorithms. Our results showed that the boosting algorithms generally outperformed the PLSR models in terms of prediction accuracy. In particular, the LightGBM model for chlorophyll and the AdaBoost model for flavonoids improved the prediction performance, with R2p = 0.71–0.74, com-pared to the PLSR models (R2p = 0.53–0.58). The final models for the glucosinolates and anthocya-nins performed sufficiently for practical uses such as screening, with R2p = 0.82–0.85 and RPD = 2.4–2.6. Our findings indicate that the application of a single preprocessing method is more effective than utilizing multiple techniques. Additionally, the boosting algorithms with feature selection ex-hibited superior performance compared to the PLSR models in the majority of cases. These results highlight the potential of hyperspectral imaging and machine learning algorithms for the non-destructive and rapid detection of plant metabolites, which could have significant implications for the field of smart agriculture.

Partial least squares regression (PLSR) prediction models were developed using hyperspectral imaging for noninvasive detection of the five most representative functional components in Brassica juncea leaves: chlorophyll, carotenoid, phenolic, glucosinolate, and anthocyanin contents. The region of interest for functional component analysis was chosen by polygon selection and the extracted average spectra were used for model development. For pre-processing, 10 combinations of Savitzky–Golay filter (S. G. filter), standard normal variate (SNV), multiplicative scatter correction (MSC), 1st-order derivative (1st-Der), 2nd-order derivative (2nd-Der), and normalization were applied. Root mean square errors of calibration (RMSEP) was used to assess the performance accuracy of the constructed prediction models. The prediction model for total anthocyanins exhibited the highest prediction level (RV2 = 0.8273; RMSEP = 2.4277). Pre-processing combination of SNV and 1st-Der with spectral data resulted in high-performance prediction models for total chlorophyll, carotenoid, and glucosinolate contents. Pre-processing combination of S. G. filter and SNV gave the highest prediction rate for total phenolics. SNV inclusion in the pre-processing conditions was essential for developing high-performance accurate prediction models for functional components. By enabling visualization of the distribution of functional components on the hyperspectral images, PLSR prediction models will prove valuable in determining the harvest time.

Recently, the Internet of things (IoT) became useful in various applications based on the web communication technology. The IoT has great potential in several service domains including cultural, educational, or medical areas. We consider a recommendation technique suitable for the IoT-based service. A personalized recommender system often relies on user preferences for better suggestions. We notice that we need a different recommendation approach in the IoT platform. While the conventional recommendation approaches rely on user preferences provided by users, these approaches may not be suitable for the IoT environment. The conventional systems utilize user ratings for items to compose recommendation list. This implies that the systems require additional user activities such as adding their preferences. We notice that the IoT environment can naturally provide user information such as users’ item selection history without users’ additional actions. We propose a recommendation model that does not require users’ additional actions and is more suitable for the IoT environment. We examine the usability of the bandwagon effect to build a new recommender system based on users’ selection history. We first consider the bandwagon effects in movie recommendation domain and show its usefulness for the IoT. We then suggest how to use the bandwagon effect in recommender systems with IoT.