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Recent deep learning methods have achieved promising performance in infrared small target detection (IRSTD) but with high computational cost, limiting deployment or operation on resource-limited scenarios. There is an urgent need to develop both lightweight and high-precision model compression methods. In this paper, we propose a Multi-Directional Contrast Spatial Attention-driven Feature Distillation (DSAD) method for achieving quick and high-performance IRSTD. Specifically, we first extract feature maps from teacher and student networks. Then, a standard Gaussian transformation is adopted to eliminate magnitude effects. After that, a Multi-Directional Contrast Spatial Attention (DSA) is designed to capture multi-directional spatial information from teacher features, which can make student networks pay more attention to small target areas while suppressing background. Finally, we propose a Perceptual Weighted Mean Square Error (PWMSE) distillation loss by combining the DSA with feature discrepancies, guiding student networks to learn more effective information from small target features. Experimental results on the two benchmark datasets (e.g., NUDT-SIRST and NUAA-SIRST) demonstrate that our distillation method can achieve remarkable detection performance compared with the teacher counterparts on several benchmark IRSTD networks (e.g., DNANet, AMFU-Net, and DMFNet) and introduce consistent gains in inference speed (i.e., 2× more) on edge devices (NVIDIA AGX and HUAWEI Ascend-310B).

This study is the first to investigate the association between the Atherogenic Index of Plasma (AIP) and depression in women, stratified by menopausal status. A total of 9,060 subjects were enrolled from the National Health and Nutrition Examination Survey (NHANES) conducted from 2005 to 2020. AIP was computed by Log10 (triglycerides/high-density lipoprotein cholesterol). Depression was assessed using the Patient Health Questionnaire (PHQ-9), with a score of ≥10 indicating a diagnosis of depression. Multivariate logistic regression, restricted cubic splines (RCS), and subgroup analysis were employed to explore the associations between AIP and depression. In comparison to quartile 1, Multivariate logistic regression revealed that AIP in quartiles 2-4 yielded odd ratios (ORs) (95% confidence interval, 95% CI) of premenopausal women of 1.14 (0.87, 1.50), 1.06 (0.79, 1.42), and 1.49 (1.11, 2.00), and postmenopausal women of 0.88 (0.64, 1.22), 1.03 (0.76, 1.41) and 1.40 (1.04, 1.89), respectively. RCS showed a linear correlation between AIP and depression in premenopausal women and a nonlinear correlation between AIP and depression in postmenopausal women. When AIP > 0.60, premenopausal women had an increased risk of depression, while postmenopausal women had a decreased risk of depression. This study demonstrates that elevated AIP levels are significantly associated with an increased risk of depression in both premenopausal and postmenopausal women. However, the strength and direction of these associations varied between the two groups, suggesting that menopausal status may play a critical role in modulating the impact of lipid metabolism on mental health outcomes.

Tomato wilt is a widespread soilborne disease of tomato that has caused significant yield losses in many tomato growing regions of the world. Previously, it was reported that tomato wilt can be caused by many pathogens, such as Fusarium oxysporum , Ralstonia solanacearum , Ralstonia pseudosolanacearum , Fusarium acuminatum , and Plectosphaerella cucumerina . In addition, we have already reported that Fusarium brachygibbosum caused symptomatic disease of tomato wilt for the first time in China. The symptoms of tomato wilt caused by these pathogens are similar, making it difficult to distinguish them in the field. However, F. brachygibbosum specific identification method has not been reported. Therefore, it is of great importance to develop a rapid and reliable diagnostic method for Fusarium brachygibbosum to establish a more effective plan to control the disease. In this study, we designed F. brachygibbosum -specific forward primers and reverse primers with a fragment size of 283bp located in the gene encoding carbamoyl phosphate synthase arginine-specific large chain by whole genome sequence comparison analysis of the genomes of eight Fusarium spp.. We then tested different dNTP, Mg 2+ concentrations, and annealing temperatures to determine the optimal parameters for the PCR system. We evaluated the specificity, sensitivity and stability of the PCR system based on the optimized reaction system and conditions. The PCR system can specifically identify the target pathogens from different fungal pathogens, and the lower detection limit of the target pathogens is at concentrations of 10 pg/uL. In addition, we can accurately identify F. brachygibbosum in tomato samples using the optimized PCR method. These results prove that the PCR method developed in this study can accurately identify and diagnose F. brachygibbosum .

Fusarium crown rot (FCR), caused by Fusarium spp., is a devastating disease in wheat growing areas. Previous studies have shown that FCR is caused by co-infection of F. graminearum, F. pseudograminearum, F. proliferatum and F. verticillioides in Hubei Province, China. In this study, a method was developed to simultaneously detected DNAs of F. graminearum, F. pseudograminearum, F. proliferatum and F. verticillioides that can efficiently differentiate them. Whole genome sequence comparison of these four Fusarium spp. was performed and a 20 bp sequence was designed as an universal upstream primer. Specific downstream primers of each pathogen was also designed, which resulted in a 206, 482, 680, and 963 bp amplicon for each pathogen, respectively. Multiplex PCR specifically identified F. graminearum, F. pseudograminearum, F. proliferatum and F. verticillioides but not from other 46 pathogens, and the detection limit of target pathogens is about 100 pg/μl. Moreover, we accurately determined the FCR pathogen species in wheat samples using the optimized multiplex PCR method. These results demonstrate that the multiplex PCR method established in this study can efficiently and rapidly identify F. graminearum , F. pseudograminearum , F. proliferatum , and F. verticillioides , which should provide technical support for timely and targeted prevention and control of FCR.

Pseudomonas syringae pv. actinidiae ( Psa ) is the causal agent of kiwifruit canker, a serious threat to commercial kiwifruit production worldwide. Studies of the movement path and the survival time of Psa in the host are crucial for integrated management programs. Hence, we used Psa with GFPuv gene ( Psa- GFPuv) strain to investigate the movement path of Psa in leaves and branches, and the survival time of Psa in leaves under different environmental conditions. We found that the pathogen Psa spread longitudinally in the branches and leaves rather than transverse path. Additionally, the survival time of bacteria in fallen leaves under different environmental conditions were simulated by the way of Psa infecting the detached kiwifruit leaves. Psa survives the longest, up to 43 days in detached kiwifruit leaves with high humidity (above 80%) at 5 °C, and up to 32 days with low humidity (20%). At 15 °C, the Psa can survive in detached kiwifruit leaves for 20–30 days with increasing humidity. At 25 °C, it can only survive for 3 days with low humidity (20%) and 15 days with high humidity (above 80%). Furthermore, the population growth experiments showed that bacterial growth of Psa was more favorable in detached kiwifruit leaves with above 80% humidity at 5 °C. These results suggest that the survival condition of Psa in detached kiwifruit leaves is significantly affected by environmental conditions, and provide the basis for the control timing and technology of kiwifruit canker.

To uncover the evaluation information on the academic contribution of research papers cited by peers based on the content cited by citing papers, and to provide an evidence-based tool for evaluating the academic value of cited papers. CiteOpinion uses a deep learning model to automatically extract citing sentences from representative citing papers; it starts with an analysis on the citing sentences, then it identifies major academic contribution points of the cited paper, positive/negative evaluations from citing authors and the changes in the subjects of subsequent citing authors by means of Recognizing Categories of Moves (problems, methods, conclusions, etc.), and sentiment analysis and topic clustering. Citing sentences in a citing paper contain substantial evidences useful for academic evaluation. They can also be used to objectively and authentically reveal the nature and degree of contribution of the cited paper reflected by citation, beyond simple citation statistics. The evidence-based evaluation tool CiteOpinion can provide an objective and in-depth academic value evaluation basis for the representative papers of scientific researchers, research teams, and institutions. No other similar practical tool is found in papers retrieved. There are difficulties in acquiring full text of citing papers. There is a need to refine the calculation based on the sentiment scores of citing sentences. Currently, the tool is only used for academic contribution evaluation, while its value in policy studies, technical application, and promotion of science is not yet tested.

There are many scenarios such as the electronic health records where the outcome is much more difficult to collect than the covariates. We refer data consisting of both covariates and the corresponding outcomes as labeled data, and data with only covariates as unlabeled data. Semi-supervised learning combines both labeled and unlabeled data to improve a model using only labeled data and can be useful in these scenarios. In this work, we consider the linear regression problem with a semi-supervised learning data structure under high dimensionality. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation and inference of the regression parameters in linear models, especially in light of the fact that linear models may be misspecified for real-world data. This work addresses the following two questions: (1) can we use the labeled data as well as the unlabeled data to construct a semi-supervised estimator such that its convergence rate is faster than the supervised estimators? (2) can we construct confidence intervals or hypothesis tests that are guaranteed to be more efficient or powerful than those with the supervised estimators? For the first question, we establish the minimax lower bound for the linear regression coefficients estimation in the semi-supervised setting and show that this lower bound cannot be achieved by supervised estimators using the labeled data only. We close this gap by proposing an optimal semi-supervised estimator with estimates for the conditional mean function as pseudo-labels for unlabeled data, which attains the lower bound provided that the imputation for the conditional mean function is consistent with a proper rate. To tackle the problem that without any model assumptions for the conditional mean, we cannot tell whether the imputation is consistent or not, we further propose a safe semi-supervised estimator. We view it safe, because this estimator is always at least as good as the supervised estimators regardless of the quality of the imputation. We also extend our idea to the aggregation of multiple semi-supervised estimators caused by different misspecifications of the conditional mean function. This allows us to borrow the predictive strength from a best imputation model to improve the supervised estimator. To answer the second question, based on the optimal semi-supervised estimator, we propose the efficient estimator for semi-supervised inference, which is fully efficient if the unknown conditional mean function is estimated consistently, but may not be more efficient than the supervised approach otherwise. We also further develop a safe inference procedure, which usually does not aim to provide fully efficient inference, but is guaranteed to be no worse than the supervised approach, no matter whether the linear model is correctly specified or the conditional mean function is consistently estimated. Extensive numerical simulations and real data analysis are conducted to illustrate our theoretical results.

Height significantly influences interpersonal communication, yet the underlying reason for this positive correlation remains unclear. Two studies were conducted with preschoolers to explore this phenomenon. Study 1 aimed to assess whether preschoolers perceive taller peers as having greater warmth and competence traits and if they use these traits for peer selection ( N  = 104, M age = 53.90 months). Study 2 investigated whether preschoolers prioritize traits or height in their peer selection process ( N  = 117, M age = 60.80 months). The results of Study 1 show that preschoolers perceive taller peers as warmer and more competent, leading them to prefer taller peers as friends. Conversely, Study 2 indicates that preschoolers prioritize warmth and competence traits over height when selecting peers. These findings suggest that taller individuals gain an advantage in peer selection due to the warmth and competence traits associated with height.

Recent studies have suggested that correlated neural signals generated by speech processing networks can be identified from brain electromagnetic recordings. We studied the dynamic property of 40-Hz gamma band steady-state auditory responses evoked by speech and non-speech stimuli, and found that these two categories of signals are processed differentially over the left and right auditory cortex. We applied an envelope-based Hilbert-Huang decomposition of the data, and extracted signals from a functional network that is only correlated with speech stimuli. We believe these are evidences that speech signals are preferentially processed at a longer (syllabic) time scales than that of non-speech (phonetic). In a separate study, the envelope correlated neural signals have been used to successfully classify the perceived and imagined syllabic rhythms from EEG. We then developed a new method of geometrically accurate spline surface Laplacian (SSL) to estimate the radial current source density of the signal. Numerical simulations and real EEG data have shown that the new method is more accurate than traditional spherical SS. In a third experiment we recorded EEG when subjects were asked to listen or imagine a small set of selected sentences. We apply the new SSL to estimate the anatomical origin of the signal that tracks the internal copy of imagined speech, and apply a fast wavelet transform technique to counter the lag and compression uncertainty of imagined speech.

Semi-supervised learning has attracted significant attention due to the proliferation of applications featuring limited labeled data but abundant unlabeled data. In this paper, we examine the statistical inference problem in an assumption-lean framework which involves a high-dimensional regression parameter, defined by minimizing the least squares, within the context of semi-supervised learning. We investigate when and how unlabeled data can enhance the estimation efficiency of a regression parameter functional. First, we demonstrate that a straightforward debiased estimator can only be more efficient than its supervised counterpart if the unknown conditional mean function can be consistently estimated at an appropriate rate. Otherwise, incorporating unlabeled data can actually be counterproductive. To address this vulnerability, we propose a novel estimator guaranteed to be at least as efficient as the supervised baseline, even when the conditional mean function is misspecified. This ensures the dependable use of unlabeled data for statistical inference. Finally, we extend our approach to the general M-estimation framework, and demonstrate the effectiveness of our methodology through comprehensive simulation studies and a real data application.