Mechanistic insights into the changes of biological activity and physicochemical characteristics in Lacticaseibacillus paracasei fortified milk powder during storage.

To clarify the change mechanism of biological activity and physicochemical characteristics in Lacticaseibacillus paracasei JY025 fortified milk powder (LFMP) during storage, morphological observation, JY025 survival, storage stability, and metabolomics of LFMP were determined during the storage period in this study. The results showed that the LFMP had a higher survival rate of JY025 compared with the bacterial powder of JY025 (LBP) during storage, which suggested that milk powder matrix could reduce strain JY025 mortality under prolonged storage in the LFMP samples. The fortification of strain JY025 also affected the stability of milk powder during the storage period. There was lower water activity and higher glass transition temperature in LFMP samples compared with blank control milk powder (BCMP) during storage. Moreover, the metabolomics results of LFMP indicated that vitamin degradation, Maillard reaction, lipid oxidation, tricarboxylic acid cycle, and lactobacilli metabolism are interrelated and influence each other to create complicated metabolism networks.

Discovery of Fusarium pseudograminearum Causing Crown Rot of Winter Wheat in Xinjiang Uygur Autonomous Region, China.

Fusarium pseudograminearum is an important plant pathogen that invades many crops (Zhang et al. 2018). Since it was first discovered in Australia in 1951, F. pseudograminearum has been reported in many countries and regions and caused huge economic losses (Burgess et al. 2001). In 2012, crown rot of wheat caused by F. pseudograminearum was discovered for the first time in Henan Province, China (Li et al. 2012). Wheat (Triticum aestivum L.) is one of the most important food crops in Xinjiang Uygur Autonomous Region (XUAR), with 1.07 million hectares cultivated in 2020. In June 2023, a survey of crown rot disease was carried out in winter wheat cv. Xindong 20 in Hotan area, XUAR, China (80.148907°E, 37.051474°N). About 5% of wheat plants showed symptoms of crown rot such as browning of the stem base and white head. The disease was observed in 85% of wheat fields. In order to identify the pathogens, 36 pieces of diseased stem basal tissue, 0.5 cm in length, were collected and sterilized with 75% alcohol for 30s and 5% NaOCl solution for 2 min, then rinsed three times with sterile water and placed on potato dextrose agar (PDA) medium at 25°C. A total of 27 isolates with consistent morphological characteristics were obtained using single-spore technique (Leslie and Summerell. 2006), and the isolation rate was 75%. The isolates grew rapidly on PDA, produced large numbers of fluffy white hyphae, and pink pigment accumulated in the medium. The isolates were grown on 2% mung bean flour medium and identified by morphological and molecular methods. Macroconidia were abundant, relatively slender, curved to almost straight, commonly two to seven septate, and averaged 22 to 72 × 1.8 to 4.9 µm. Microconidia were not observed. The morphological characters are consistent with Fusarium (Aoki and O'Donnell. 1999). Two isolates (LP-1 and LP-3) were selected for molecular identification. Primers EF1/EF2 (5'-ATGGGTAAGGARGACAAGAC-3'/5'-GGARGTACCAGTSATCATG-3') were used to amplify a portion of the EF-1α gene (O'Donnell et al. 1998). The two 696 bp PCR products were sequenced and submitted to GenBank. The EF-1α gene sequences (GenBank Accession No: PP062794 and PP062795) shared 99.9% identity (695/696) with published F.pseudograminearum sequences (e.g., OP105187, OP105184, OP105179, OP105173). The identification was further confirmed by F. pseudograminearum species-specific PCR primers Fp1-1/Fp1-2 (Aoki and O'Donnell. 1999). The expected PCR products of 518 bp were produced only in F. pseudograminearum. Pathogenicity tests of LP-1 and LP-3 isolates were performed on 7-day-old seedlings of winter wheat cv. Xindong 20 using the drip inoculation method with a 10-µl of a 106 macroconidia ml-1 suspension near the stem base (Xu et al. 2017). The experiment was repeated five times in a 20 to 25°C greenhouse. Control seedlings were treated with sterile water. After 4 weeks, wheat seedling death and crown browning occurred in the inoculated plants with over 90% incidence. No symptoms were observed in the control plants. The pathogen was reisolated from the inoculated plants by the method described above and identified by morphological and PCR amplification using F. pseudograminearum species-specific primers Fp1-1/Fp1-2. No F. pseudograminearum was isolated from the control plants, fulfilling Koch's postulates. To our knowledge, this is the first report of F. pseudograminearum causing crown rot of winter wheat in XUAR of China. Since F. pseudograminearum can cause great damage to wheat, one of the most important food crops in China, necessary measures should be taken to prevent the spread of F. pseudograminearum to other regions.

Clinical and genetic characteristics of myotonia congenita in Chinese population.

Myotonia congenita (MC) is a rare hereditary muscle disease caused by variants in the CLCN1 gene. Currently, the correlation of phenotype-genotype is still uncertain between dominant-type Thomsen (TMC) and recessive-type Becker (BMC). The clinical data and auxiliary examinations of MC patients in our clinic were retrospectively collected. Electromyography was performed in 11 patients and available family members. Whole exome sequencing was conducted in all patients. The clinical and laboratory data of Chinese MC patients reported from June 2004 to December 2022 were reviewed. A total of 11 MC patients were included in the study, with a mean onset age of 12.64 ± 2.73 years. The main symptom was muscle stiffness of limbs. Warm-up phenomenon and percussion myotonia were found in all patients. Electromyogram revealed significant myotonic charges in all patients and two asymptomatic carriers, while muscle MRI and biopsy showed normal or nonspecific changes. Fourteen genetic variants including 6 novel variants were found in CLCN1. Ninety-eight Chinese patients were re-analyzed and re-summarized in this study. There were no significant differences in the demographic data, clinical characteristics, and laboratory findings between 52 TMC and 46 BMC patients. Among the 145 variants in CLCN1, some variants, including the most common variant c.892 G>A, could cause TMC in some families and BMC in others. This study expanded the clinical and genetic spectrum of Chinese patients with MC. It was difficult to distinguish between TMC and BMC only based on the clinical, laboratory, and genetic characteristics.

Advances in systemic immune inflammatory indices in non-small cell lung cancer: A review.

Lung cancer is one of the most prevalent cancers globally, with non-small cell lung cancers constituting the majority. These cancers have a high incidence and mortality rate. In recent years, a growing body of research has demonstrated the intricate link between inflammation and cancer, highlighting that inflammation and cancer are inextricably linked and that inflammation plays a pivotal role in cancer development, progression, and prognosis of cancer. The Systemic Immunoinflammatory Index (SII), comprising neutrophil, lymphocyte, and platelet counts, is a more comprehensive indicator of the host's systemic inflammation and immune status than a single inflammatory index. It is widely used in clinical practice due to its cost-effectiveness, simplicity, noninvasiveness, and ease of acquisition. This paper reviews the impact of SII on the development, progression, and prognosis of non-small cell lung cancer.

A risk warning method for steady-state power quality based on VMD-LSTM and fuzzy model.

The risk warning for steady-state power quality in the power grid is essential for its prevention and management. However, current risk warning methods fall short in predicting the power quality trend while accounting for potential risks. Consequently, this study introduces a novel steady-state power quality risk warning method utilizing VMD-LSTM and a fuzzy model. Firstly, a power quality index prediction method based on variational mode decomposition (VMD) and long short-term memory (LSTM) is proposed. This approach significantly enhances prediction accuracy. Secondly, a power quality risk warning method incorporating kernel density estimation (KDE) and a fuzzy model is proposed, which systematically addresses the uncertainty associated with power quality risks. To validate the effectiveness and practicality of the proposed method, experiments are conducted using field monitoring data from a residential load in southern China. The results affirm the reliability and applicability of the proposed method. The simulation results show that the median error of prediction of power quality indexes by the proposed method is 5.03 % during the evaluated time period, and the prediction accuracy is mostly maintained above 90 %.

Biosynthetic potential of uncultured anammox community bacteria revealed through multi-omics analysis.

Microbial secondary metabolites (SMs) and their derivatives have been widely used in medicine, agriculture, and energy. Growing needs for renewable energy and the challenges posed by antibiotic resistance, cancer, and pesticides emphasize the crucial hunt for new SMs. Anaerobic ammonium-oxidation (anammox) systems harbor many uncultured or underexplored bacteria, representing potential resources for discovering novel SMs. Leveraging HiFi long-read metagenomic sequencing, 1,040 biosynthetic gene clusters (BGCs) were unearthed from the anammox microbiome with 58% being complete and showcasing rich diversity. Most of them showed distant relations to known BGCs, implying novelty. Members of the underexplored lineages (Chloroflexota and Planctomycetota) and Proteobacteria contained lots of BGCs, showcasing substantial biosynthetic potential. Metaproteomic results indicated that Planctomycetota members harbored the most active BGCs, particularly those involved in producing potential biofuel-ladderane. Overall, these findings underscore that anammox microbiomes could serve as valuable resources for mining novel BGCs and discovering new SMs for practical application.

Surface N Sites Mediate the Formation of Li Metal Cluster@N-Enriched Hierarchically Porous Carbon for Ultrahigh-Capacity Lithium Storage.

Graphite is the popular anode material of current lithium-ion batteries (LIBs). However, its low specific capacity and poor lithium intercalation potential hinder its use for high-power and large-scale energy storage. To meet the demand for energy storage, novel anode materials with high capacity, fast chargeable capability, and long cycle life are of great interest. Herein, we demonstrate an advanced nitrogen-enriched hierarchical porous carbon serving as a lithiophilic anode material for ultrahigh capacity and long-life LIBs. NHPC-700 (under optimal synthetic conditions), featuring a high surface area, rich N-doping, high porosity, and partially graphitized nanosheet structures, is successfully fabricated from a Schiff-base copolymer via a template-incipient wetness impregnation method. NHPC-700 exhibits an ultrahigh reversible lithium storage capacity of 2796 mA h g-1 at 0.1 A g-1 while still maintaining a high capacity of 526 mA h g-1 at 10 A g-1 after 1000 cycles. Theoretical and experimental studies reveal that this remarkable Li storage performance can be attributed to the large number of N lithiophilic sites on the inner surface of the small mesoporous pores. These sites guide Li metal nucleation in the initial period and control well the volume variation during charge/discharge cycles, thus exhibiting excellent cycle stability and great potential for practical application.

Exercise training influence on cognitive capacity and mental health within chronic obstructive pulmonary disease - A pilot study.

Objective: Although pulmonary rehabilitation and regular exercise have improved negative emotions and cognitive capacity within cases of chronic obstructive pulmonary disease (COPD), influence by exercise training upon different cognitive and memory functions in COPD is still controversial. This investigation aimed to assess whether cognitive performance and mental health are affected by the benefits of exercise training within cases of COPD. Materials and Methods: This pilot investigation included thirty-three patients with Global Initiative for Chronic Obstructive Lung Disease stage ≥B. Based on the subjects' rights, all included patients could choose to join either the exercise group or the control group, according to their free will. Twelve patients were assigned to receive exercise treatment over a 2-month period, while the remaining 16 patients were assigned to the control group. Cognitive capacity outcomes were measured using the Wechsler Memory Scale-III Word List Test, Stroop task, and psychomotor vigilance task (PVT). Mood states were assessed through the Beck Depression Inventory (BDI) and Beck Anxiety Inventory (BAI). Results: Most cases demonstrated major improvement for BDI and BAI scorings post-60-day therapy. During PVT, the omission rate decreased, while the hit rate increased, indicating an improvement in attention performance. Furthermore, this investigation found a significant increase in immediate verbal and recognition memory for word-list test. However, no major performance shifts were found on Stroop analysis. Conclusion: This investigation demonstrated that a 2-month exercise training program resulted in significant improvement in negative emotions, immediate memory, recognition memory, and attention.

Maintenance pembrolizumab therapy in patients with metastatic HER2-negative breast cancer with prior response to chemotherapy.

PURPOSE: Accumulating toxicities hinder indefinite chemotherapy for many patients with metastatic/recurrent HER2-negative breast cancer. We conducted a phase II trial of pembrolizumab monotherapy following induction chemotherapy to determine the efficacy of maintenance immunotherapy in patients with metastatic HER2-negative inflammatory breast cancer (IBC) and non-IBC triple-negative breast cancer (TNBC) and a biomarker study. PATIENTS AND METHODS: Patients with a complete response (CR), partial response (PR), or stable disease (SD) after at least 3 cycles of chemotherapy for HER2-negative breast cancer received pembrolizumab, regardless of programmed death-ligand 1 expression. Pembrolizumab (200 mg) was administered every 3 weeks until disease progression, intolerable toxicity, or 2 years of pembrolizumab exposure. The endpoints included the 4-month disease control rate (DCR), progression-free survival (PFS), overall survival (OS), and response biomarkers in the blood. RESULTS: Of 43 treated patients, 11 had metastatic IBC and 32 non-IBC TNBC. The 4-month DCR was 58.1% (95% CI, 43.4%-72.9%). For all patients, the median PFS was 4.8 months (95% CI, 3.0-7.1 months). The toxicity profile was similar to the previous pembrolizumab monotherapy study. Patients with high T-cell clonality at baseline had a longer PFS with pembrolizumab treatment than did those with low T-cell clonality (10.4 vs. 3.6 months, p = 0.04). Patients who achieved SD also demonstrated a significant increase in T-cell clonality during therapy compared to those who didn't achieve SD (20% vs. 5.9% mean increase, respectively; p = 0.04). CONCLUSIONS: Pembrolizumab monotherapy achieved durable treatment responses. Patients with a high baseline T-cell clonality had prolonged disease control with pembrolizumab.

Age and sex estimation in cephalometric radiographs based on multitask convolutional neural networks.

OBJECTIVES: Age and sex characteristics are evident in cephalometric radiographs (CRs), yet their accurate estimation remains challenging due to the complexity of these images. This study aimed to harness deep learning to automate age and sex estimation from CRs, potentially simplifying their interpretation. STUDY DESIGN: We compared the performance of 4 deep learning models (SVM, R-net, VGG16-SingleTask, and our proposed VGG16-MultiTask) in estimating age and sex from the testing dataset, utilizing a VGG16-based multitask deep learning model on 4,557 CRs. Gradient-weighted class activation mapping (Grad-CAM) was incorporated to identify sex. Performance was assessed using mean absolute error (MAE), specificity, sensitivity, F1 score, and the area under the curve (AUC) in receiver operating characteristic analysis. RESULTS: The VGG16-MultiTask model outperformed the others, with the lowest MAE (0.864±1.602) and highest sensitivity (0.85), specificity (0.88), F1 score (0.863), and AUC (0.93), demonstrating superior efficacy and robust performance. CONCLUSIONS: The VGG multitask model demonstrates significant potential in enhancing age and sex estimation from cephalometric analysis, underscoring the role of AI in improving biomedical interpretations.

Machine learning approaches for practical predicting outpatient near-future AECOPD based on nationwide electronic medical records.

In this research, we aimed to harness machine learning to predict the imminent risk of acute exacerbation in chronic obstructive pulmonary disease (AECOPD) patients. Utilizing retrospective data from electronic medical records of two Taiwanese hospitals, we identified 26 critical features. To predict 3- and 6-month AECOPD occurrences, we deployed five distinct machine learning algorithms alongside ensemble learning. The 3-month risk prediction was best realized by the XGBoost model, achieving an AUC of 0.795, whereas the XGBoost was superior for the 6-month prediction with an AUC of 0.813. We conducted an explainability analysis and found that the episode of AECOPD, mMRC score, CAT score, respiratory rate, and the use of inhaled corticosteroids were the most impactful features. Notably, our approach surpassed predictions that relied solely on CAT or mMRC scores. Accordingly, we designed an interactive prediction system that provides physicians with a practical tool to predict near-term AECOPD risk in outpatients.

Cellular heterogeneity and dynamics of the human uterus in healthy premenopausal women.

The human uterus is a complex and dynamic organ whose lining grows, remodels, and regenerates in every menstrual cycle or upon tissue damage. Here we applied single-cell RNA sequencing to profile more the 50,000 uterine cells from both the endometrium and myometrium of 5 healthy premenopausal individuals, and jointly analyzed the data with a previously published dataset from 15 subjects. The resulting normal uterus cell atlas contains more than 167K cells representing the lymphatic endothelium, blood endothelium, stromal, ciliated epithelium, unciliated epithelium, and immune cell populations. Focused analyses within each major cell type and comparisons with subtype labels from prior studies allowed us to document supporting evidence, resolve naming conflicts, and to propose a consensus annotation system of 39 subtypes. We release their gene expression centroids, differentially expressed genes, and mRNA patterns of literature-based markers as a shared community resource. We find many subtypes show dynamic changes over different phases of the cycle and identify multiple potential progenitor cells: compartment-wide progenitors for each major cell type, transitional cells that are upstream of other subtypes, and potential cross-lineage multipotent stromal progenitors that may be capable of replenishing the epithelial, stromal, and endothelial compartments. When compared to the healthy premenopausal samples, a postpartum and a postmenopausal uterus sample revealed substantially altered tissue composition, involving the rise or fall of stromal, endothelial, and immune cells. The cell taxonomy and molecular markers we report here are expected to inform studies of both basic biology of uterine function and its disorders. SIGNIFICANCE: We present single-cell RNA sequencing data from seven individuals (five healthy pre-menopausal women, one post-menopausal woman, and one postpartum) and perform an integrated analysis of this data alongside 15 previously published scRNA-seq datasets. We identified 39 distinct cell subtypes across four major cell types in the uterus. By using RNA velocity analysis and centroid-centroid comparisons we identify multiple computationally predicted progenitor populations for each of the major cell compartments, as well as potential cross-compartment, multi-potent progenitors. While the function and interactions of these cell populations remain to be validated through future experiments, the markers and their "dual characteristics" that we describe will serve as a rich resource to the scientific community. Importantly, we address a significant challenge in the field: reconciling multiple uterine cell taxonomies being proposed. To achieve this, we focused on integrating historical and contemporary knowledge across multiple studies. By providing detailed evidence used for cell classification we lay the groundwork for establishing a stable, consensus cell atlas of the human uterus.

Surface profile measurement and parameter analysis of silicon wafers in the upright state.

A novel approach, to the best of our knowledge, is presented for assessing silicon wafer surface profiles using an interferometer and vertically rotatable wafer holder. This approach significantly enhances precision and reduces costs, and outperforms traditional techniques in measurement consistency and accuracy. It effectively reduces sample distortion and positional shifts owing to the removal and reinstallation of the wafers. Using this method, a global backsurface-referenced ideal range of 0.385 µm, warp of 0.193 µm, and other parameters were obtained, demonstrating its practicality in efficiently capturing key surface profile metrics for silicon wafers. This innovation promises substantial improvements in high-volume wafer surface profile testing, overcoming prevalent technological challenges in this industry.

A type I and type II chemical biology toolbox to overcome the hypoxic tumour microenvironment for photodynamic therapy.

Photodynamic therapy (PDT) is a minimally invasive therapeutic modality employed for the treatment of various types of cancers, localized infections, and other diseases. Upon illumination, the photo-excited photosensitizer generates singlet oxygen and other reactive species, thereby inducing cytotoxicity in the target cells. The hypoxic tumour microenvironment (TME), however, poses a limitation on the supply of oxygen in tumour tissues. Moreover, under such conditions, tumour metastasis and drug resistance frequently occur, further compromising the efficacy of PDT in combating tumours. Traditionally, type I photosensitizers with lower oxygen consumption demonstrate significant potential in overcoming hypoxic environments and play a crucial role in determining the therapeutic efficacy of PDT because type I photosensitizers can generate highly cytotoxic free radicals. In comparison, type II photosensitizers exhibit high oxygen dependence. The rate of reactive oxygen species (ROS) generation in the type II process is significantly higher than that in the type I process. Thus, the efficiency and selectivity of PDT depend on the properties of the photosensitizer. Here, the recent development and application of type I and type II photosensitizers, mainly in the past year, are summarized. The design methods, electronic structures, photophysical properties, lipophilic properties, electric charge, and other molecular characteristics of these photosensitizers are discussed in detail. These modifications alter the microstructure of photosensitizers and directly impact the results of PDT. The main content of this paper will have a positive promoting and inspiring effect on the future development of PDT.

Assessment of Lung Nodule Detection and Lung CT Screening Reporting and Data System Classification Using Zero Echo Time Pulmonary MRI.

BACKGROUND: The detection rate of lung nodules has increased considerably with CT as the primary method of examination, and the repeated CT examinations at 3 months, 6 months or annually, based on nodule characteristics, have increased the radiation exposure of patients. So, it is urgent to explore a radiation-free MRI examination method that can effectively address the challenges posed by low proton density and magnetic field inhomogeneities. PURPOSE: To evaluate the potential of zero echo time (ZTE) MRI in lung nodule detection and lung CT screening reporting and data system (lung-RADS) classification, and to explore the value of ZTE-MRI in the assessment of lung nodules. STUDY TYPE: Prospective. POPULATION: 54 patients, including 21 men and 33 women. FIELD STRENGTH/SEQUENCE: Chest CT using a 16-slice scanner and ZTE-MRI at 3.0T based on fast gradient echo. ASSESSMENT: Nodule type (ground-glass nodules, part-solid nodules, and solid nodules), lung-RADS classification, and nodule diameter (manual measurement) on CT and ZTE-MRI images were recorded. STATISTICAL TESTS: The percent of concordant cases, Kappa value, intraclass correlation coefficient (ICC), Wilcoxon signed-rank test, Spearman's correlation, and Bland-Altman. The p-value <0.05 is considered significant. RESULTS: A total of 54 patients (age, 54.8 ± 11.9 years; 21 men) with 63 nodules were enrolled. Compared with CT, the total nodule detection rate of ZTE-MRI was 85.7%. The intermodality agreement of ZTE-MRI and CT lung nodules type evaluation was substantial (Kappa = 0.761), and the intermodality agreement of ZTE-MRI and CT lung-RADS classification was moderate (Kappa = 0.592). The diameter measurements between ZTE-MRI and CT showed no significant difference and demonstrated a high degree of interobserver (ICC = 0.997-0.999) and intermodality (ICC = 0.956-0.985) agreements. DATA CONCLUSION: The measurement of nodule diameter by pulmonary ZTE-MRI is similar to that by CT, but the ability of lung-RADS to classify nodes from MRI images still requires further research. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 2.

Cellular atlas of the human ovary using morphologically guided spatial transcriptomics and single-cell sequencing.

The reproductive and endocrine functions of the ovary involve spatially defined interactions among specialized cell populations. Despite the ovary's importance in fertility and endocrine health, functional attributes of ovarian cells are largely uncharacterized. Here, we profiled >18,000 genes in 257 regions from the ovaries of two premenopausal donors to examine the functional units in the ovary. We also generated single-cell RNA sequencing data for 21,198 cells from three additional donors and identified four major cell types and four immune cell subtypes. Custom selection of sampling areas revealed distinct gene activities for oocytes, theca, and granulosa cells. These data contributed panels of oocyte-, theca-, and granulosa-specific genes, thus expanding the knowledge of molecular programs driving follicle development. Serial samples around oocytes and across the cortex and medulla uncovered previously unappreciated variation of hormone and extracellular matrix remodeling activities. This combined spatial and single-cell atlas serves as a resource for future studies of rare cells and pathological states in the ovary.

[Brain magnetic resonance image registration based on parallel lightweight convolution and multi-scale fusion].

Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.

Generative interpolation and restoration of images using deep learning for improved 3D tissue mapping.

The development of novel imaging platforms has improved our ability to collect and analyze large three-dimensional (3D) biological imaging datasets. Advances in computing have led to an ability to extract complex spatial information from these data, such as the composition, morphology, and interactions of multi-cellular structures, rare events, and integration of multi-modal features combining anatomical, molecular, and transcriptomic (among other) information. Yet, the accuracy of these quantitative results is intrinsically limited by the quality of the input images, which can contain missing or damaged regions, or can be of poor resolution due to mechanical, temporal, or financial constraints. In applications ranging from intact imaging (e.g. light-sheet microscopy and magnetic resonance imaging) to sectioning based platforms (e.g. serial histology and serial section transmission electron microscopy), the quality and resolution of imaging data has become paramount. Here, we address these challenges by leveraging frame interpolation for large image motion (FILM), a generative AI model originally developed for temporal interpolation, for spatial interpolation of a range of 3D image types. Comparative analysis demonstrates the superiority of FILM over traditional linear interpolation to produce functional synthetic images, due to its ability to better preserve biological information including microanatomical features and cell counts, as well as image quality, such as contrast, variance, and luminance. FILM repairs tissue damages in images and reduces stitching artifacts. We show that FILM can decrease imaging time by synthesizing skipped images. We demonstrate the versatility of our method with a wide range of imaging modalities (histology, tissue-clearing/light-sheet microscopy, magnetic resonance imaging, serial section transmission electron microscopy), species (human, mouse), healthy and diseased tissues (pancreas, lung, brain), staining techniques (IHC, H&E), and pixel resolutions (8 nm, 2 µm, 1mm). Overall, we demonstrate the potential of generative AI in improving the resolution, throughput, and quality of biological image datasets, enabling improved 3D imaging.

Rice straw returning enhances cadmium activation by accelerating iron cycling thus hydroxyl radical production in paddy soils during drainage.

Straw returning is widely found elevating the bioavailability of cadmium (Cd) in paddy soils with unclear biogeochemical mechanisms. Here, a series of microcosm incubation experiments were conducted and spectroscopic and microscopic analyses were employed. The results showed that returning rice straw (RS) efficiently increased amorphous Fe and low crystalline Fe (II) to promote the production of hydroxyl radicals (OH) thus Cd availability in paddy soils during drainage. On the whole, RS increased OH and extractable Cd by 0.2-1.4 and 0.1-3.3 times, respectively. While the addition of RS effectively improved the oxidation rate of structural Fe (II) mineral (i.e., FeS) to enhance soil Cd activation (up to 38.5 %) induced by the increased OH (up to 69.2 %). Additionally, the existence of CO32- significantly increased the efficiency level on OH production and Cd activation, which was attributed to the improved reactivity of Fe (II) by CO32- in paddy soils. Conclusively, this study emphasizes risks of activating soil Cd induced by RS returning-derived OH, providing a new insight into evaluating the safety of straw recycling.

Violent Video Recognition by Using Sequential Image Collage.

Identifying violent activities is important for ensuring the safety of society. Although the Transformer model contributes significantly to the field of behavior recognition, it often requires a substantial volume of data to perform well. Since existing datasets on violent behavior are currently lacking, it will be a challenge for Transformers to identify violent behavior with insufficient datasets. Additionally, Transformers are known to be computationally heavy and can sometimes overlook temporal features. To overcome these issues, an architecture named MLP-Mixer can be used to achieve comparable results with a smaller dataset. In this research, a special type of dataset to be fed into the MLP-Mixer called a sequential image collage (SIC) is proposed. This dataset is created by aggregating frames of video clips into image collages sequentially for the model to better understand the temporal features of violent behavior in videos. Three different public datasets, namely, the dataset of National Hockey League hockey fights, the dataset of smart-city CCTV violence detection, and the dataset of real-life violence situations were used to train the model. The results of the experiments proved that the model trained using the proposed SIC is capable of achieving high performance in violent behavior recognition with fewer parameters and FLOPs needed compared to other state-of-the-art models.