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Object detection is crucial for remote sensing image processing, yet the detection of small objects remains highly challenging due to factors such as image noise and cluttered backgrounds. In response to this challenge, this paper proposes an improved network, named SED-YOLO, based on YOLOv5s. Firstly, we leverage Switchable Atrous Convolution (SAC) to replace the standard convolutions in the original C3 modules of the backbone network, thereby enhancing feature extraction capabilities and adaptability. Additionally, we introduce the Efficient Multi-Scale Attention(EMA) mechanism at the end of the backbone network to enable efficient multi-scale feature learning, which reduces computational costs while preserving crucial information. In the Neck section, an adaptive Concat method is designed to dynamically adjust the feature fusion strategy according to image content and object characteristics, strengthening the model’s ability to handle diverse objects. Lastly, the three-scale feature detection head is expanded to four by adding a small object detection layer, and incorporating the Dynamic Head(DyHead) module. This enhances the detection head’s expressive power by dynamically adjusting attention weights in feature maps. Experimental results demonstrate that this improved network achieves an mean Average Precision (mAP) of 71.6% on the DOTA dataset, surpassing the original YOLOv5s by 2.4%, effectively improving the accuracy of small object detection.

Richardson-Lucy (RL) deconvolution optimizes the likelihood of the object estimate for an incoherent imaging system. It can offer an increase in contrast, but converges poorly, and shows enhancement of noise as the iteration progresses. We have discovered the underlying reason for this problematic convergence behaviour using a Cramér Rao Lower Bound (CRLB) analysis. An analytical expression for the CRLB diverges for spatial frequency components that approach the diffraction limit from below. The resulting mean noise variance per pixel diverges for large images. These results imply that a regular optimum of the likelihood does not exist, and that RL deconvolution is necessarily ill-convergent. Computer algorithms for contrast and resolution enhancement of microscopic images can become derailed. The authors here provide proof is offered that the Richardson-Lucy algorithm turns out to amplify noise in the initial data, wrecking the final outcome.

In this study, three homogeneous fractions, PSP-N-b-1, PSP-N-b-2, and PSP-N-c-1, were obtained from an aqueous extract of Polygonatum using DEAE cellulose column chromatography, CL-6B agarose gel chromatography, and Sephadex G100 chromatography. Their monosaccharide compositions and molecular weights were analyzed. The results revealed that PSP-N-b-1, PSP-N-b-2, and PSP-N-c-1 are primarily composed of six monosaccharides: Man (mannose), GlcA (glucuronic acid), Rha (rhamnose), GalA (galacturonic acid), Glc (glucose), and Ara (arabinose), with molecular weights of 6.3 KDa, 5.78 KDa, and 3.45 KDa, respectively. Furthermore, we observed that Polygonatum polysaccharides exhibited protective effects against CCL4-induced liver damage in HepG2 cells in vitro, operating through both anti-oxidant and anti-inflammatory mechanisms. Our research findings suggest that Polygonatum polysaccharides may emerge as a promising option in the development of hepatoprotective drugs or functional foods with anti-inflammatory and antioxidant properties.

Brain-computer interfaces (BCIs) have attracted considerable attention in motor and language rehabilitation. Most devices use cap-based non-invasive, headband-based commercial products or microneedle-based invasive approaches, which are constrained for inconvenience, limited applications, inflammation risks and even irreversible damage to soft tissues. Here, we propose in-ear visual and auditory BCIs based on in-ear bioelectronics, named as SpiralE, which can adaptively expand and spiral along the auditory meatus under electrothermal actuation to ensure conformal contact. Participants achieve offline accuracies of 95% in 9-target steady state visual evoked potential (SSVEP) BCI classification and type target phrases successfully in a calibration-free 40-target online SSVEP speller experiment. Interestingly, in-ear SSVEPs exhibit significant 2 nd harmonic tendencies, indicating that in-ear sensing may be complementary for studying harmonic spatial distributions in SSVEP studies. Moreover, natural speech auditory classification accuracy can reach 84% in cocktail party experiments. The SpiralE provides innovative concepts for designing 3D flexible bioelectronics and assists the development of biomedical engineering and neural monitoring. In-ear visual and auditory brain-computer interfaces typically have issues with poor interfacial adhesion or user irritation. Here, Wang et al. presents an in-ear hollow bioelectronic device that adaptively conforms to the ear canal, under electrothermal actuation, for electroencephalogram sensing.

Traditionally, lactate has been considered a ‘waste product’ of cellular metabolism. Recent findings have shown that lactate is a substance that plays an indispensable role in various physiological cellular functions and contributes to energy metabolism and signal transduction during immune and inflammatory responses. The discovery of lactylation further revealed the role of lactate in regulating inflammatory processes. In this review, we comprehensively summarize the paradoxical characteristics of lactate metabolism in the inflammatory microenvironment and highlight the pivotal roles of lactate homeostasis, the lactate shuttle, and lactylation (‘lactate clock’) in acute and chronic inflammatory responses from a molecular perspective. We especially focused on lactate and lactate receptors with either proinflammatory or anti-inflammatory effects on complex molecular biological signalling pathways and investigated the dynamic changes in inflammatory immune cells in the lactate-related inflammatory microenvironment. Moreover, we reviewed progress on the use of lactate as a therapeutic target for regulating the inflammatory response, which may provide a new perspective for treating inflammation-related diseases.

Object detection, as a crucial component of remote sensing image processing, has become one of the primary methods with the maturation of deep learning technologies. Nonetheless, detecting small objects in remote sensing images remains a significant challenge. Addressing this issue, this study proposes an enhanced network model based on You Only Look Once YOLOv5, aimed at improving the detection capabilities for small objects in remote sensing images. The model employs a novel backbone network, ODCSP-Darknet53, to enhance feature extraction efficiency, and incorporates the small object enhancement bi-directional feature pyramid network (STEBIFPN) structure in the neck region of the network for optimized scaling of small object information. Additionally, we have designed two distinct weighted fusion strategies to further boost the model’s performance in detecting small objects. In the detection head portion of the model, a four-head detection network specialized for small objects is constructed, and adaptively spatial feature fusion (ASFF) technology is introduced to optimize the recognition capabilities for small objects. Experiments conducted on the DOTA and DIOR datasets demonstrate that our model achieves an average precision mean of 75.9% and 80.5%, respectively, with the model’s parameters and computational requirements amounting to 13.4M and 30.2 GFLOPs, respectively. Compared to the original YOLOv5s model, our model exhibits significant performance improvements in detecting typical small objects such as Bridge and Ship. Thus, this research provides an effective solution for object detection in the field of remote sensing image processing.

The developmental trajectory of exercise self-efficacy refers to the course of change in an individual's belief in their capability to successfully perform exercise-related behaviors over time. This study aims to explore the developmental trajectory of exercise self-efficacy in patients with multivessel coronary artery disease, and analyze the predictors of various trajectory subgroups. Between September 2023 to October 2024, 297 patients with multivessel coronary artery disease were recruited from three tertiary hospitals in Tangshan, China. Exercise self-efficacy was measured using the Multidimensional Self-Efficacy for Exercise Scale on the third day of admission (T1), one month after discharge (T2), three months after discharge (T3), and six months after discharge (T4). The latent class growth model was employed to identify the developmental trajectory of exercise self-efficacy in patients with multivessel coronary artery disease. Multinomial logistic regression was adopted to determine the predictors of trajectory subgroups. Three distinct trajectories of exercise self-efficacy were identified: the \"Low-Efficacy Decline Group\" (22%), the \"High-Efficacy Ascending-Stable Group\" (34%), and the \"Moderate-Efficacy Continuous Increase Group\" (44%). Multinomial logistic regression analysis revealed significant determinants of distinct exercise self-efficacy trajectories, when compared with the moderate-efficacy continuous increase group, the predictors for the low-efficacy decline group included having diabetes, a lack of exercise habits, low social support, and anxiety. In contrast, the predictors for the high-efficacy ascending-stable group were high average monthly household income, established exercise habits, and strong social support. The study revealed heterogeneous trajectories of exercise self-efficacy among patients with multivessel coronary artery disease, highlighting the necessity for personalized intervention strategies. These findings offer a valuable opportunity for early prevention and targeted interventions aimed at enhancing exercise self-efficacy.

Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a new “hardware friendly” coal gangue image recognition algorithm, RRBM-YOLO, which is combined with dark light enhancement. Specifically, coal gangue image samples were customized in two scenarios: normal lighting and simulated underground lighting with poor lighting conditions. The images were preprocessed using the dim light enhancement algorithm Retinexformer, with YOLOv8 as the backbone network. The lightweight module RepGhost, the repeated weighted bi-directional feature extraction module BiFPN, and the multi-dimensional attention mechanism MCA were integrated, and different datasets were replaced to enhance the adaptability of the model and improve its generalization ability. The findings from the experiment indicate that the precision of the proposed model is as high as 0.988, the mAP@0.5(%) value and mAP@0.5:0.95(%) values increased by 10.49% and 36.62% compared to the original YOLOv8 model, and the inference speed reached 8.1GFLOPS. This indicates that RRBM-YOLO can attain an optimal equilibrium between detection precision and inference velocity, with excellent accuracy, robustness, and industrial application potential.

AbstractObjectiveTo synthesise results of mental health outcomes in cohorts before and during the covid-19 pandemic.DesignSystematic review.Data sourcesMedline, PsycINFO, CINAHL, Embase, Web of Science, China National Knowledge Infrastructure, Wanfang, medRxiv, and Open Science Framework Preprints.Eligibility criteria for selecting studiesStudies comparing general mental health, anxiety symptoms, or depression symptoms assessed from 1 January 2020 or later with outcomes collected from 1 January 2018 to 31 December 2019 in any population, and comprising ≥90% of the same participants before and during the covid-19 pandemic or using statistical methods to account for missing data. Restricted maximum likelihood random effects meta-analyses (worse covid-19 outcomes representing positive change) were performed. Risk of bias was assessed using an adapted Joanna Briggs Institute Checklist for Prevalence Studies.ResultsAs of 11 April 2022, 94 411 unique titles and abstracts including 137 unique studies from 134 cohorts were reviewed. Most of the studies were from high income (n=105, 77%) or upper middle income (n=28, 20%) countries. Among general population studies, no changes were found for general mental health (standardised mean difference (SMD)change 0.11, 95% confidence interval −0.00 to 0.22) or anxiety symptoms (0.05, −0.04 to 0.13), but depression symptoms worsened minimally (0.12, 0.01 to 0.24). Among women or female participants, general mental health (0.22, 0.08 to 0.35), anxiety symptoms (0.20, 0.12 to 0.29), and depression symptoms (0.22, 0.05 to 0.40) worsened by minimal to small amounts. In 27 other analyses across outcome domains among subgroups other than women or female participants, five analyses suggested that symptoms worsened by minimal or small amounts, and two suggested minimal or small improvements. No other subgroup experienced changes across all outcome domains. In three studies with data from March to April 2020 and late 2020, symptoms were unchanged from pre-covid-19 levels at both assessments or increased initially then returned to pre-covid-19 levels. Substantial heterogeneity and risk of bias were present across analyses.ConclusionsHigh risk of bias in many studies and substantial heterogeneity suggest caution in interpreting results. Nonetheless, most symptom change estimates for general mental health, anxiety symptoms, and depression symptoms were close to zero and not statistically significant, and significant changes were of minimal to small magnitudes. Small negative changes occurred for women or female participants in all domains. The authors will update the results of this systematic review as more evidence accrues, with study results posted online (https://www.depressd.ca/covid-19-mental-health).Review registrationPROSPERO CRD42020179703.

Exosomes are a type of membrane vesicles secreted by living cells. Recent studies suggest exosome-like nanovesicles (ELNVs) from fruits and vegetables are involved in tissue renewal process and functional regulation against inflammatory diseases or cancers. However, there are few reports on ELNVs derived from medicinal plants. ELNVs derived from (Lour.) Merr. (ACNVs) were isolated and characterized. Cytotoxicity, antiproliferative and apoptosis-inducing capacity of ACNVs against hepatoma carcinoma cell were assessed. The endocytosis mechanism of ACNVs was evaluated on Hep G2 cells in the presence of different endocytosis inhibitors. In vivo distribution of ACNVs was detected in healthy and tumor-bearing mice after scavenger receptors (SRs) blockade. PEG engineering of ACNVs was achieved through optimizing the pharmacokinetic profiles. In vivo antitumor activity and toxicity were evaluated in Hep G2 cell xenograft model. ACNVs were isolated and purified using a differential centrifugation method accompanied by sucrose gradient ultracentrifugation. The optimized ACNVs had an average size of about 119 nm and showed a typical cup-shaped nanostructure containing lipids, proteins, and RNAs. ACNVs were found to possess specific antitumor cell proliferation activity associated with an apoptosis-inducing pathway. ACNVs could be internalized into tumor cells mainly via phagocytosis, but they were quickly cleared once entering the blood. Blocking the SRs or PEGylation decoration prolonged the blood circulation time and increased the accumulation of ACNVs in tumor sites. In vivo antitumor results showed that PEGylated ACNVs could significantly inhibit tumor growth without side effects. This study provides a promising functional nano platform derived from edible that can be used in antitumor therapy with negligible side effects.