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To explore the mechanism of action of Bacillus velezensis against sweet cherry soft rot disease, the inhibitory effect of Bacillus velezensis KT against Rhizopus stolonifer LC-7 in vitro and in inoculated sweet cherry fruits, and the effect of B. velezensis KT on defense-related enzyme activities were investigated. Besides, the expression of genes related to secondary metabolite synthesis and biological control in B. velezensis KT during the interaction with R. stolonifer was also analyzed by transcriptomics. The results showed that the percentage inhibition of 1 × 109 CFU/mL B. velezensis KT suspension against mycelial growth of R. stolonifer LC-7 was 87.12%, and the percentage of spore germination and germ length of R. stolonifer LC-7 in the presence of B. velezensis KT were 21.54% and 11.45 μm, respectively. At 48 h after being treated with 1 × 109 CFU/mL B. velezensis KT suspension, the incidence of soft rot in cherry fruits was about 20% and was about 80% lower than that in the control group. B. velezensis KT suspension inhibited the activity of polyphenoloxidase (PPO) and lipoxidase (LOX), increased the activity of peroxidase (POD), phenylalanine ammonia lyase (PAL) chitinase (CHI) and β-1,3-glucanase (GLU), and improved disease resistance and antioxidant capacity in cherry fruits. It was observed by scanning electron microscopy that after co-cultured with B. velezensis KT, the morphology of the mycelia and spores of R. stolonifer LC-7 was distorted and malformed. Transcriptomic analysis showed that the expression of pksIJ in the biosynthetic gene cluster for antibiotic bacillaene and tuaAGE in the biosynthetic gene cluster for teichuronic acid were up-regulated, and the differentially expressed genes were mainly concentrated in ribonucleoprotein complex assembly, subunit assembly and ribosomal assembly during biological processes. The B. velezensis KT could directly inhibit R. stolonifer LC-7 probably through nutritional and spatial competition and secreting anti-bacterial substances and could also induce disease resistance in sweet cherry fruits. The results showed that B. velezensis KT had an obvious control effect on sweet cherry soft rot caused by R. stolonifer LC-7, making it a potential biocontrol agent.

Radar jamming recognition is a key step in electronic countermeasures, and accurate and sufficient labeled samples are essential for supervised learning-based recognition methods. However, in real practice, collected radar jamming samples often have weak labels (i.e., noisy-labeled or unlabeled ones), which degrade recognition performance. Additionally, recognition performance is hindered by limitations in capturing the global features of radar jamming. The Transformer (TR) has advantages in modeling long-range relationships. Therefore, a weakly supervised Transformer is proposed to address the issues of performance degradation under weak supervision. Specifically, complementary label (CL) TR, called RadarCL-TR, is proposed to improve radar jamming recognition accuracy with noisy samples. CL learning and a cleansing module are successively utilized to detect and remove potentially noisy samples. Thus, the adverse influence of noisy samples is mitigated. Additionally, semi-supervised learning (SSL) TR, called RadarSSL-PL-TR, is proposed to boost recognition performance under unlabeled samples via pseudo labels (PLs). Network generalization is improved by training with pseudo-labeling unlabeled samples. Moreover, the RadarSSL-PL-S-TR is proposed to further promote recognition performance, where a selection module identifies reliable pseudo-labeling samples. The experimental results show that the proposed RadarCL-TR and RadarSSL-PL-S-TR outperform comparison methods in recognition accuracy by at least 7.07 % and 6.17 % with noisy and unlabeled samples, respectively. [ABSTRACT FROM AUTHOR]

Recently, transformer has gradually attracted interest for its excellence in modeling the long-range dependencies of spatial-spectral features in HSI. However, transformer has the problem of the quadratic computational complexity due to the self-attention mechanism, which is heavier than other models and thus has limited adoption in HSI processing. Fortunately, the recently emerging state space model-based Mamba shows great computational efficiency while achieving the modeling power of transformers. Therefore, in this paper, we first proposed spectral-spatial Mamba (SS-Mamba) for HSI classification. Specifically, SS-Mamba mainly includes a spectral-spatial token generation module and several stacked spectral-spatial Mamba blocks. Firstly, the token generation module converts any given HSI cube to spatial and spectral tokens as sequences. And then these tokens are sent to stacked spectral-spatial mamba blocks (SS-MB). Each SS-MB includes two basic mamba blocks and a spectral-spatial feature enhancement module. The spatial and spectral tokens are processed separately by the two basic mamba blocks, correspondingly. Moreover, the feature enhancement module modulates spatial and spectral tokens using HSI sample's center region information. Therefore, the spectral and spatial tokens cooperate with each other and achieve information fusion within each block. The experimental results conducted on widely used HSI datasets reveal that the proposed SS-Mamba requires less processing time compared with transformer. The Mamba-based method thus opens a new window for HSI classification. [ABSTRACT FROM AUTHOR]

Background and Aims: Nonalcoholic fatty liver disease (NAFLD) is a global health problem, and dietary intervention is still considered one of the primary interventions. This study aimed to examine cross-sectional associations between dietary and serum levels of folate and NAFLD. Methods: We conducted a study of 7543 adults who participated in the National Health and Nutrition Examination Survey, 2009–2018. NAFLD status was determined by a fatty liver index (FLI) value ≥60. Multivariable logistic regression models were used to estimate associations between folate and NAFLD. Results: Almost half (45%) of the patients were classified as having NAFLD based on the FLI. In the fully adjusted model, participants in the highest quartile of dietary total folate and food folate were found to have a lower prevalence of NAFLD than those in the lowest quartile [odds ratio (OR)quartile 4 versus 1 = 0.582; 95% confidence interval (CI) = 0.350–0.968; and ORquartile 4 versus 1 = 0.737; 95% CI = 0.611–0.888, respectively], and the fourth quartile values of serum total folate and 5-methyl-tetrahydrofolate were significantly negatively associated with NAFLD prevalence (ORquartile 4 versus 1 = 0.664; 95% CI = 0.495–0.891; and ORquartile 4 versus 1 = 0.712; 95% CI = 0.532–0.954, respectively). Subgroup analyses revealed that this beneficial association was more significant in women (ORquartile 4 versus 1 = 0.526; 95% CI = 0.329–0.843; pinteraction < 0.001) than in men (ORquartile 4 versus 1 = 0.805; 95% CI = 0.546–1.186). Conclusions: Higher dietary folate intake and serum folate levels are associated with a lower NAFLD prevalence among U.S. adults and the trend is more pronounced among women, indicating opportunities for dietary NAFLD interventions. [ABSTRACT FROM AUTHOR]

A radiation-hardened delay-locked loop (DLL) applied for multiple frequency ranges is proposed in this paper. Novel and effective radiation-hardened techniques are applied in charge pump (CP) and voltage-controlled delay line (VCDL) to avoid inverted lock error and missing pulses error caused by single-event transient (SET). In order to ensure a wide operation frequency range, a frequency detection module is designed to detect the frequency of input signals and adjust the current source array of VCDL so as to change its delay time. Besides, an accelerate module is designed to reduce the lock time of DLL by providing an extra discharge current. According to the simulation results, the proposed DLL shows good performance when ion strikes occur and can work properly in a wide operation frequency range from 3.7 GHz to 7.4 GHz. Also, the proposed accelerate module helps to reduce lock time by 22.2%. [ABSTRACT FROM AUTHOR]

Background: Current artificial intelligence (AI) in histopathology typically specializes on a single task, resulting in a heavy workload of collecting and labeling a sufficient number of images for each type of cancer. Heterogeneous transfer learning (HTL) is expected to alleviate the data bottlenecks and establish models with performance comparable to supervised learning (SL). Methods: An accurate source domain model was trained using 28,634 colorectal patches. Additionally, 1000 sentinel lymph node patches and 1008 breast patches were used to train two target domain models. The feature distribution difference between sentinel lymph node metastasis or breast cancer and CRC was reduced by heterogeneous domain adaptation, and the maximum mean difference between subdomains was used for knowledge transfer to achieve accurate classification across multiple cancers. Result: HTL on 1000 sentinel lymph node patches (L-HTL-1000) outperforms SL on 1000 sentinel lymph node patches (L-SL-1-1000) (average area under the curve (AUC) and standard deviation of L-HTL-1000 vs. L-SL-1-1000: 0.949 ± 0.004 vs. 0.931 ± 0.008, p value = 0.008). There is no significant difference between L-HTL-1000 and SL on 7104 patches (L-SL-2-7104) (0.949 ± 0.004 vs. 0.948 ± 0.008, p value = 0.742). Similar results are observed for breast cancer. B-HTL-1008 vs. B-SL-1-1008: 0.962 ± 0.017 vs. 0.943 ± 0.018, p value = 0.008; B-HTL-1008 vs. B-SL-2-5232: 0.962 ± 0.017 vs. 0.951 ± 0.023, p value = 0.148. Conclusions: HTL is capable of building accurate AI models for similar cancers using a small amount of data based on a large dataset for a certain type of cancer. HTL holds great promise for accelerating the development of AI in histopathology. [ABSTRACT FROM AUTHOR]

First-line treatment for osteosarcoma includes chemotherapy and surgery. However, the five-year survival rate of refractory osteosarcoma remains unsatisfactory. Osteosarcoma cancer stem cells, possessing stemness and chemoresistance, are one of the critical causes of poor response to chemotherapy. Elucidating regulatory signaling pathways of osteosarcoma cancer stem cells may provide a rationale for improving regimens against chemoresistant osteosarcoma. Methotrexate (MTX)-resistant osteosarcoma cells were established. microRNA expression profiles were used for detecting differentially expressed microRNA in resistant clones and the parental cells. microRNA target databases were employed to predict potential microRNA and mRNA interactions. Flow cytometry was performed to measure stem cell marker Prominin-1 (CD133)-positive cells. Immunofluorescence staining was applied to detect CD133 expression. miR-197-3p mimic or anti-miR-197-3p stably transfected cells were used to generate xenograft models. In the study, we found that miR-197-3p was increased in MTX-resistant cell lines. Overexpression of miR-197-3p enhanced the expression of cancer stem cell markers CD133, Octamer-binding protein 4 (OCT4), Transcription factor SOX-2 (SOX2), and Homeobox protein NANOG (NANOG), as well as chemoresistance-associated genes ATP-dependent translocase ABCB1 (ABCB1) and Broad substrate specificity ATP-binding cassette transporter ABCG2 (ABCG2), whereas miR-197-3p knockdown inhibited stemness and recovered sensitivity to MTX. We also classified the tumor suppressor Speckle-type POZ protein-like (SPOPL) as a target of miR-197-3p. The miR-197-3p mutation that could not combine SPOPL promoter regions was unable to sustain stemness or chemoresistance. Collectively, we discovered miR-197-3p conferred osteosarcoma stemness and chemotherapy resistance by targeting SPOPL, prompting promising therapeutic candidates for refractory osteosarcoma treatment. [ABSTRACT FROM AUTHOR]

Convolutional Neural Network (CNN) has been proved excellent for hyperspectral image(HSI) classification on the basis of large training set. However, the lack of available samples degrades the performance of CNNs in practical HSI classification. Although transfer learning is a typical strategy to address the issue of limited training samples, such methods tend to reload source model's feature extractors directly ignoring their generalization. Furthermore, most existing adaptive transfer learning algorithms pay attention to spectral features of HSI while the obvious difference between RGB image and HSI's spatial features is overlooked. In this paper, the proposed method can solve the mentioned problems to some extent. Firstly, the proposed adaptive heterogeneous transfer (AHT) can measure their transferability and find appropriate solution to transfer knowledge. It computes feature extractors' transferability based on feature maps and influences the learning process of target model through backpropagation, therefore, target model is prone to receive knowledge from feature extractors with high generalization. Secondly, the proposed method lays emphasis on HSI's spatial features. It adopts the methodology of spatial attention and calculates the regularization loss based on spatial features. Experiments were conducted on two widely used HSI datasets. The results demonstrate the method's effectiveness when few samples are available. [ABSTRACT FROM AUTHOR]

Recently, deep learning methods, especially convolutional neural networks (CNNs), have achieved good performance for hyperspectral image (HSI) classification. However, due to limited training samples of HSIs and the high volume of trainable parameters in deep models, training deep CNN-based models is still a challenge. To address this issue, this study investigates contrastive learning (CL) as a pre-training strategy for HSI classification. Specifically, a supervised contrastive learning (SCL) framework, which pre-trains a feature encoder using an arbitrary number of positive and negative samples in a pair-wise optimization perspective, is proposed. Additionally, three techniques for better generalization in the case of limited training samples are explored in the proposed SCL framework. First, a spatial–spectral HSI data augmentation method, which is composed of multiscale and 3D random occlusion, is designed to generate diverse views for each HSI sample. Second, the features of the augmented views are stored in a queue during training, which enriches the positives and negatives in a mini-batch and thus leads to better convergence. Third, a multi-level similarity regularization method (MSR) combined with SCL (SCL–MSR) is proposed to regularize the similarities of the data pairs. After pre-training, a fully connected layer is combined with the pre-trained encoder to form a new network, which is then fine-tuned for final classification. The proposed methods (SCL and SCL–MSR) are evaluated on four widely used hyperspectral datasets: Indian Pines, Pavia University, Houston, and Chikusei. The experiment results show that the proposed SCL-based methods provide competitive classification accuracy compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Obesity has become a health threat and hard enough to deal with. Evidences show that metformin could inhibit adipogenesis and combat obesity, while its mechanisms remain to be elucidated more comprehensively. In this study, we found that administration of metformin could combat obesity of mice induced by high-fat diet (HFD), indicated by strikingly decreased body weight and weight of inguinal white adipose tissue (iWAT) and epidydimal white adipose tissue (eWAT) compared with the control group. Mechanically, we revealed that metformin could inhibit protein expression of FTO, leading to increased m6A methylation levels of cyclin D1 (Ccnd1) and cyclin dependent kinase 2 (Cdk2), two crucial regulators in cell cycle. Ccnd1 and Cdk2 with increased m6A levels were recognised by YTH m6A RNA binding protein 2 (YTHDF2), causing an YTHDF2-dependent decay and decreased protein expressions. In consequence, mitotic clonal expansion (MCE) process was blocked and adipogenesis was inhibited. [ABSTRACT FROM AUTHOR]