A suitable sampling strategy for the detection of African swine fever virus in living and deceased pigs in the field: a retrospective study.

African swine fever (ASF) is a fatal disease that threatens the health status of the swine population and thus can impact the economic outcome of the global pig industry. Monitoring the ASF virus (ASFV) is of utmost concern to prevent and control its distribution. This study aims to identify a suitable sampling strategy for ASFV detection in living and deceased pigs under field conditions. A range of samples, comprising tissues obtained from deceased pigs, as well as serum and tonsil swab samples from live pigs, were gathered and subjected to detection using the qPCR method. The findings revealed that the mandibular lymph nodes demonstrated the highest viral loads among superficial tissues, thereby indicating their potential suitability for detecting ASFV in deceased pigs. Additionally, the correlations between virus loads in various tissues have demonstrated that tonsil swab samples are a viable specimen for monitoring live pigs, given the strong associations observed with other tissues. These findings indicated two dependable sample types for the detection of ASFV: mandibular lymph nodes for deceased pigs and tonsil swabs for live pigs, which supply some references for the development of efficacious preventive measures against ASFV.

Opportunities for fundamental physics research with radioactive molecules.

Molecules containing short-lived, radioactive nuclei are uniquely positioned to enable a wide range of scientific discoveries in the areas of fundamental symmetries, astrophysics, nuclear structure, and chemistry. Recent advances in the ability to create, cool, and control complex molecules down to the quantum level, along with recent and upcoming advances in radioactive species production at several facilities around the world, create a compelling opportunity to coordinate and combine these efforts to bring precision measurement and control to molecules containing extreme nuclei. In this manuscript, we review the scientific case for studying radioactive molecules, discuss recent atomic, molecular, nuclear, astrophysical, and chemical advances which provide the foundation for their study, describe the facilities where these species are and will be produced, and provide an outlook for the future of this nascent field.

Online Active Continual Learning for Robotic Lifelong Object Recognition.

In real-world applications, robotic systems collect vast amounts of new data from ever-changing environments over time. They need to continually interact and learn new knowledge from the external world to adapt to the environment. Particularly, lifelong object recognition in an online and interactive manner is a crucial and fundamental capability for robotic systems. To meet this realistic demand, in this article, we propose an online active continual learning (OACL) framework for robotic lifelong object recognition, in the scenario of both classes and domains changing with dynamic environments. First, to reduce the labeling cost as much as possible while maximizing the performance, a new online active learning (OAL) strategy is designed by taking both the uncertainty and diversity of samples into account to protect the information volume and distribution of data. In addition, to prevent catastrophic forgetting and reduce memory costs, a novel online continual learning (OCL) algorithm is proposed based on the deep feature semantic augmentation and a new loss-based deep model and replay buffer update, which can mitigate the class imbalance between the old and new classes and alleviate confusion between two similar classes. Moreover, the mistake bound of the proposed method is analyzed in theory. OACL allows robots to select the most representative new samples to query labels and continually learn new objects and new variants of previously learned objects from a nonindependent and identically distributed (i.i.d.) data stream without catastrophic forgetting. Extensive experiments conducted on real lifelong robotic vision datasets demonstrate that our algorithm, even trained with fewer labeled samples and replay exemplars, can achieve state-of-the-art performance on OCL tasks.

Complete pathological response and negative postoperative ctDNA were not predictive of discontinuation of adjuvant crizotinib therapy in a patient with locally advanced MET ex14 skipping mutation-positive non-small cell lung cancer: a case report.

Neoadjuvant targeted therapy is an alternative treatment for locally advanced non-small cell lung cancer (NSCLC) patients with driver gene mutation. MET ex14 mutation is considered a driver gene, and crizotinib is the first oral tyrosine kinase inhibitor (TKI) for metastatic MET ex14 mutation-positive NSCLC patients. Here, we reported a case of a locally advanced NSCLC patient harboring MET ex14 mutation who achieved pathological complete response following neoadjuvant crizotinib therapy but developed rapid metastasis due to discontinuation of short-term postoperative adjuvant crizotinib therapy. Although no driver gene mutation was found via next-generation sequencing (NGS) with blood samples before discontinuation of adjuvant crizotinib, the patient was given crizotinib rechallenge. Fortunately, the patient achieved durable complete response. This suggested that neither pathological complete response nor negative circulating tumor DNA (ctDNA) could be an effective predictor for discontinuation of adjuvant targeted therapy. This case report demonstrated the potential of crizotinib as neoadjuvant therapy in MET ex14 mutation-positive NSCLC patients as well as the importance of long-term postoperative therapy even with negative ctDNA in blood.

Infection characteristics of porcine circovirus type 2 in different herds from intensive farms in China, 2022.

Introduction: Porcine circovirus type 2 (PCV2) is the primary etiological agent of porcine circovirus diseases (PCVD), which are widespread in most pig herds, causing huge economic losses in the global pig industry. Therefore, it is critical to assess the infection characteristics of PCV2 in different swine herds to develop effective strategies against PCVD. Methods: In this study, routine diagnostic and monitoring protocols were used to collect 12,714 samples from intensive farms in China, and PCV2 was tested for by qPCR to determine positivity rates and viral loads in samples from different herds and materials. Results: PCV2 was found to be prevalent throughout China, and fattening farms had higher positivity rates than breeding farms. The PCV2 positivity rates in breeding farms in Southern China were higher than those in Northern China. Growing-finishing pigs demonstrated the highest positivity rate in the tested samples, while pre-weaning piglets and adult sows had the lowest. Meanwhile, samples with viral loads exceeding 106 copies/mL in growing-finishing pigs had 27.2% positivity, compared to 1.9% and 3.3% in sows and piglets, respectively. The results of the viral loads in the serum samples followed a similar trend. Discussion: The findings reveal that PCV2 circulates in different herds from intensive farms, with positivity increasing from pre-weaning to growing-finishing herds. It is urgent to develop effective strategies to reduce PCV2 positivity in growing-finishing herds and prevent viral circulation among pigs.

Probing fundamental particles with molecules.

Molecular spectroscopy constrains the size of the electron's electric dipole moment.

Evidence of aerosol transmission of African swine fever virus between two piggeries under field conditions: a case study.

African swine fever (ASF) is a devastating and economically significant infectious disease that has caused enormous losses in the commercial pig sector in China since 2018. The primary transmission routes of the African swine fever virus (ASFV), the causative agent of ASF, are direct pig-to-pig contact or indirect contact with virus-contaminated objects. While aerosol transmission of ASFV has been previously reported under experimental conditions, no reports have described it under field conditions. In this case study, aerosol-associated samples were collected over a monitoring period of 24 days in an ASFV-positive farm. A complete and clear chain of ASFV transmission through aerosols was observed: pigs in Room A on Day 0-aerosol in Room A on Day 6-dust of air outlets in Room A on Day 9-outdoor aerosols on Day 9-dust of air inlets in Room B on Day 15-aerosols/pigs in Room B on Day 21. Furthermore, a fluorescent powder experiment confirmed the transmission of dust from Room A to Room B. This study represents the first report providing evidence of aerosol transmission of ASFV under field conditions. Further research is needed to study the laws of aerosol transmission in ASFV and develop effective strategies such as air filtration or disinfection to create a low-risk environment with fresh air for pig herds.

Feature-guided attention network for medical image segmentation.

BACKGROUND: U-Net and its variations have achieved remarkable performances in medical image segmentation. However, they have two limitations. First, the shallow layer feature of the encoder always contains background noise. Second, semantic gaps exist between the features of the encoder and the decoder. Skip-connections directly connect the encoder to the decoder, which will lead to the fusion of semantically dissimilar feature maps. PURPOSE: To overcome these two limitations, this paper proposes a novel medical image segmentation algorithm, called feature-guided attention network, which consists of U-Net, the cross-level attention filtering module (CAFM), and the attention-guided upsampling module (AUM). METHODS: In the proposed method, the AUM and the CAFM were introduced into the U-Net, where the AUM learns to filter the background noise in the low-level feature map of the encoder and the CAFM tries to eliminate the semantic gap between the encoder and the decoder. Specifically, the AUM adopts a top-down pathway to use the high-level feature map so as to filter the background noise in the low-level feature map of the encoder. The AUM uses the encoder features to guide the upsampling of the corresponding decoder features, thus eliminating the semantic gap between them. Four medical image segmentation tasks, including coronary atherosclerotic plaque segmentation (Dataset A), retinal vessel segmentation (Dataset B), skin lesion segmentation (Dataset C), and multiclass retinal edema lesions segmentation (Dataset D), were used to validate the proposed method. RESULTS: For Dataset A, the proposed method achieved higher Intersection over Union (IoU) ( 67.91 ± 3.82 % $67.91\pm 3.82\%$ ), dice ( 79.39 ± 3.37 % $79.39\pm 3.37\%$ ), accuracy ( 98.39 ± 0.34 % $98.39\pm 0.34\%$ ), and sensitivity ( 85.10 ± 3.74 % $85.10\pm 3.74\%$ ) than the previous best method: CA-Net. For Dataset B, the proposed method achieved higher sensitivity (83.50%) and accuracy (97.55%) than the previous best method: SCS-Net. For Dataset C, the proposed method had highest IoU ( 83.47 ± 0.41 % $83.47\pm 0.41\%$ ) and dice ( 90.81 ± 0.34 % $90.81\pm 0.34\%$ ) than those of all compared previous methods. For Dataset D, the proposed method had highest dice (average: 81.53%; retina edema area [REA]: 83.78%; pigment epithelial detachment [PED] 77.13%), sensitivity (REA: 89.01%; SRF: 85.50%), specificity (REA: 99.35%; PED: 100.00), and accuracy (98.73%) among all compared previous networks. In addition, the number of parameters of the proposed method was 2.43 M, which is less than CA-Net (3.21 M) and CPF-Net (3.07 M). CONCLUSIONS: The proposed method demonstrated state-of-the-art performance, outperforming other top-notch medical image segmentation algorithms. The CAFM filtered the background noise in the low-level feature map of the encoder, while the AUM eliminated the semantic gap between the encoder and the decoder. Furthermore, the proposed method was of high computational efficiency.

Effect of different reduction methods on Pd/Al2O3 for o-xylene oxidation at low temperature.

Pd/Al2O3 was pretreated by CO, H2 and NaBH4 reduction, respectively. The reduced catalysts were tested for o-xylene oxidation and characterized by power X-ray diffraction (XRD), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and temperature-programmed decomposition of palladium hydride (TPDH). The characterizations indicate the pretreatments lead to distinct Pd particle sizes and amount of surface activated oxygen species, which are responsible for the catalytic performance. Compared with H2 and NaBH4 reduction methods, CO reduction shows a strong interaction between Pd and Al2O3 with smaller Pd particle size and more surface activated oxygen. It exhibited excellent catalytic performance, complete oxidation of 50 ppmV o-xylene at 85°C with a WHSV of 60,000 mL/(g∙hr).

Inguinal lymph node sample collected by minimally invasive sampler helps to accurately diagnose ASF in dead pigs without necropsy.

African swine fever (ASF) is a highly contagious hemorrhagic and transboundary animal disease, and it threatens global food security. A full necropsy to harvest the sample matrices for diagnosis in the farm may lead to contamination of the premises and directly threaten to the herds. In the present study, we compared the ASFV loads of the common samples that can be collected without necropsy. The unmatched nasal, throat, rectal samples were randomly taken using cotton swabs, and inguinal lymph node samples were collected by the minimally invasive samplers from the dead pigs of an ASF field outbreak farm. The ASFV loads of the samples were detected by qPCR and the results suggested that the overall ASFV nucleic acids levels of inguinal lymph node samples were higher than the swabs. What's more, sets of matched nasal swabs, rectal swabs, throat swabs, inguinal lymph nodes, serums, spleens and lungs samples were collected from 15 dead ASFV naturally infected pigs. Similarly, the results showed that inguinal lymph node samples, together with serum, spleen and lungs samples, contained more ASFV nucleic acids than the swabs. Our findings demonstrated that the inguinal lymph node collected by minimally invasive sampler is an ideal tissue for diagnosing ASFV infection in dead pigs without necropsy.

Development of an indirect ELISA to detect PEDV specific IgA antibody based on a PEDV epidemic strain.

BACKGROUND: Porcine epidemic diarrhea (PED), a swine epidemic disease caused by porcine epidemic diarrhea virus (PEDV), is characterized by severe watery diarrhea, vomiting, dehydration and high mortality in piglets, and has caused serious economic losses to the global porcine industry. The level of PEDV IgA antibody is a key marker to assess the extent of passive immunity of the resistance against PEDV infection. However, current commercial structure proteins-based kits for detection of PEDV antibody are not affordable, and those kits require complicated antigen preparation procedures, which cannot meet the scope of economic benefits of many large-scale pig companies in China. Therefore, there is an urgent need to develop an accurate, simple, and economical method for IgA detection in clinical samples. In this study, an indirect ELISA (i-ELISA) method was developed based on a purified PEDV epidemic strain (NH-TA2020). RESULTS: The results show that optimal working dilution ratios of PEDV antigen and HRP anti-swine IgA are at 1: 1000 and 1:15000 respectively. The sensitivity of this method is high with the maximum dilution of samples up to 1:160, and coefficients of variation (CV) of both the intra assays and inter assays were no more than 15%. In addition, the relative sensitivities of the i-ELISA were above 90% compared with values from commercial kits in both serum and oral fluid samples. CONCLUSIONS: Our results suggested that the i-ELISA developed in this study was an accurate, simple, and economical method for PEDV-IgA detection in clinical samples.

KMT2C deficiency promotes small cell lung cancer metastasis through DNMT3A-mediated epigenetic reprogramming.

Small cell lung cancer (SCLC) is notorious for its early and frequent metastases, which contribute to it as a recalcitrant malignancy. To understand the molecular mechanisms underlying SCLC metastasis, we generated SCLC mouse models with orthotopically transplanted genome-edited lung organoids and performed multiomics analyses. We found that a deficiency of KMT2C, a histone H3 lysine 4 methyltransferase frequently mutated in extensive-stage SCLC, promoted multiple-organ metastases in mice. Metastatic and KMT2C-deficient SCLC displayed both histone and DNA hypomethylation. Mechanistically, KMT2C directly regulated the expression of DNMT3A, a de novo DNA methyltransferase, through histone methylation. Forced DNMT3A expression restrained metastasis of KMT2C-deficient SCLC through repressing metastasis-promoting MEIS/HOX genes. Further, S-(5'-adenosyl)-L-methionine, the common cofactor of histone and DNA methyltransferases, inhibited SCLC metastasis. Thus, our study revealed a concerted epigenetic reprogramming of KMT2C- and DNMT3A-mediated histone and DNA hypomethylation underlying SCLC metastasis, which suggested a potential epigenetic therapeutic vulnerability.

Chirality of Perylene Diimides: Design Strategies and Applications.

Chirality is a particularly important concept in nature and exists at all length scales, ranging from the molecular level to the supramolecular level. Over the last two decades, various design strategies have been developed to construct chiral materials based on perylene diimides (PDIs) and to mimic the chiral assembly process in biological systems, but applications of these chiral aggregates are still at an early stage. This Minireview summarizes recent progress in the synthesis and properties of chiral PDIs. The chirality in PDI-based materials can be generated by three different approaches: from the twisted planes of PDIs, the chiral substituents of PDIs, and the co-assembly of achiral PDIs and chiral guests. A comprehensive understanding of the applications of chiral PDIs as well as potential future developments is also provided.

Online Semisupervised Active Classification for Multiview PolSAR Data.

Polarimetric synthetic aperture radar (PolSAR) data are sequentially acquired and have multiple views obtained from different feature extractors or multiple frequency bands. The fast and accurate classification of PolSAR data in dynamically changing environments is a critical and challenging task. Online learning can handle this task by learning a classifier incrementally from a stream of samples. In this article, we propose an online semisupervised active learning framework for multiview PolSAR data classification, called OSAM. First, a novel online active learning strategy is designed based on the relationships among multiple views and a randomized rule, which allows to only query the labels of some informative incoming samples. Then, in order to utilize both the incoming labeled and unlabeled samples to update the classifiers, a novel online semisupervised learning model is proposed based on co-regularized multiview learning and graph regularization. In addition, the proposed method can deal with the dynamic large-scale multifeature or multifrequency PolSAR data where not only the amount of data but also the number of classes gradually increases in the learning process. Moreover, the mistake bound of the proposed method is derived rigorously. Extensive experiments are conducted on real PolSAR data to evaluate the performance of our algorithm, and the results demonstrate the effectiveness of the proposed method.

Adaptive Data Structure Regularized Multiclass Discriminative Feature Selection.

Feature selection (FS), which aims to identify the most informative subset of input features, is an important approach to dimensionality reduction. In this article, a novel FS framework is proposed for both unsupervised and semisupervised scenarios. To make efficient use of data distribution to evaluate features, the framework combines data structure learning (as referred to as data distribution modeling) and FS in a unified formulation such that the data structure learning improves the results of FS and vice versa. Moreover, two types of data structures, namely the soft and hard data structures, are learned and used in the proposed FS framework. The soft data structure refers to the pairwise weights among data samples, and the hard data structure refers to the estimated labels obtained from clustering or semisupervised classification. Both of these data structures are naturally formulated as regularization terms in the proposed framework. In the optimization process, the soft and hard data structures are learned from data represented by the selected features, and then, the most informative features are reselected by referring to the data structures. In this way, the framework uses the interactions between data structure learning and FS to select the most discriminative and informative features. Following the proposed framework, a new semisupervised FS (SSFS) method is derived and studied in depth. Experiments on real-world data sets demonstrate the effectiveness of the proposed method.

Effects of different concentrations of metformin on osteoclast differentiation and apoptosis and its mechanism.

This study aimed to investigate the effect of metformin on osteoclast differentiation and apoptosis. Low concentration of metformin inhibited osteoclast differentiation and downregulated the expression of TRAP, RANK, Cathepsink, NFATC-1, MMP-9 and TRAF-6. High concentration of metformin promoted osteoclast apoptosis and upregulated the expression of Bax/Bcl-2 and caspase-3; BV/TV, BS/TV, Tb.N and BMD were increased while Tp.Sp decreased in the group of intraperitoneal metformin+femoral intramedullary osteoclast injection (Met+OC) compared with the control group, 1 nM metformin downregulated Akt, p44/42 MAPK, JNK, p38 MAPK phosphorylation, 5 nM metformin down regulated ERK and Akt phosphorylation. These results suggest that a low concentration of metformin inhibits osteoclast differentiation through PI3K/Akt and MAPK/ERK signaling pathway; high concentrations of metformin promote osteoclast apoptosis through PI3K/Akt and ERK signaling pathway.

Lipolymph node presenting as a pulmonary nodule: A case report.

A 53-year-old man was admitted to our hospital with a history of a right lung nodule which had gradually increased in size. Wedge resection of the right middle lobe using video-assisted thoracoscopic surgery (VATS) was performed and revealed a yellowish, soft, well circumscribed nodule. Histological analysis confirmed the diagnosis of an uncommon lipolymph node. The patient recovered well from surgery, and there has been no recurrence in the lung for over one-year of follow-up. To the best of our knowledge, this is the first report of a lipolymph node in the lung.

The sequence of pulmonary vessels ligation during lobectomy for non-small cell lung cancer: A systematic review and meta-analysis.

OBJECTIVES: The effects of ligating the pulmonary vein first or pulmonary artery first during lobectomy on the long-term survival of patients with non-small cell lung cancer (NSCLC) remain controversial. We conducted the first systematic review and meta-analysis to determine the association between different sequences of vessel ligation during lobectomy and the prognosis of patients with NSCLC. METHODS: Literature retrieval was performed by systematically searching Embase, PubMed and Web of Science to identify relevant articles published from the inception of each database to November 2020. The overall survival (OS) and disease-free survival (DFS) of patients treated with vein-first ligation versus those treated with artery-first ligation during lobectomy were analyzed. A standard fixed-effect model test (Mantel-Haenszel method) was used to calculate pooled hazard ratios (HRs) and 95% confidence intervals (CIs). Heterogeneity was assessed using the Q-test and I2-test. Sensitivity analysis was performed to further examine the stability of pooled HRs. RESULTS: Five studies with a total of 1109 patients receiving lobectomy, including one randomized controlled trial and four retrospective studies, were included in this meta-analysis. The results showed that patients with vein-first ligation had a significantly better OS (HR 1.25, 95% CI 1.03-1.50; P = 0.02) and DFS (HR 1.54, 95% CI 1.16-2.04; P = 0.003) than those with artery-first ligation during lobectomy. Significant heterogeneity and publication bias were not observed during analysis. CONCLUSION: Our meta-analysis indicates that vein-first ligation may improve the prognosis of NSCLC patients receiving lobectomy. Therefore, vein-first ligation is recommended during lobectomy for patients with non-small cell lung cancer whenever possible.

Self-Paced Clustering Ensemble.

The clustering ensemble has emerged as an important extension of the classical clustering problem. It provides an elegant framework to integrate multiple weak base clusterings to generate a strong consensus result. Most existing clustering ensemble methods usually exploit all data to learn a consensus clustering result, which does not sufficiently consider the adverse effects caused by some difficult instances. To handle this problem, we propose a novel self-paced clustering ensemble (SPCE) method, which gradually involves instances from easy to difficult ones into the ensemble learning. In our method, we integrate the evaluation of the difficulty of instances and ensemble learning into a unified framework, which can automatically estimate the difficulty of instances and ensemble the base clusterings. To optimize the corresponding objective function, we propose a joint learning algorithm to obtain the final consensus clustering result. Experimental results on benchmark data sets demonstrate the effectiveness of our method.

Top-k Feature Selection Framework Using Robust 0-1 Integer Programming.

Feature selection (FS), which identifies the relevant features in a data set to facilitate subsequent data analysis, is a fundamental problem in machine learning and has been widely studied in recent years. Most FS methods rank the features in order of their scores based on a specific criterion and then select the k top-ranked features, where k is the number of desired features. However, these features are usually not the top- k features and may present a suboptimal choice. To address this issue, we propose a novel FS framework in this article to select the exact top- k features in the unsupervised, semisupervised, and supervised scenarios. The new framework utilizes the l0,2 -norm as the matrix sparsity constraint rather than its relaxations, such as the l1,2 -norm. Since the l0,2 -norm constrained problem is difficult to solve, we transform the discrete l0,2 -norm-based constraint into an equivalent 0-1 integer constraint and replace the 0-1 integer constraint with two continuous constraints. The obtained top- k FS framework with two continuous constraints is theoretically equivalent to the l0,2 -norm constrained problem and can be optimized by the alternating direction method of multipliers (ADMM). Unsupervised and semisupervised FS methods are developed based on the proposed framework, and extensive experiments on real-world data sets are conducted to demonstrate the effectiveness of the proposed FS framework.