3D SHINKEI MR neurography in evaluation of traumatic brachial plexus.

3D SHINKEI neurography is a new sequence for imaging the peripheral nerves. The study aims at assessing traumatic brachial plexus injury using this sequence. Fifty-eight patients with suspected trauma induced brachial plexus injury underwent MR neurography (MRN) imaging in 3D SHINKEI sequence at 3 T. Surgery and intraoperative somatosensory evoked potentials or clinical follow-up results were used as the reference standard. MRN, surgery and electromyography (EMG) findings were recorded at four levels of the brachial plexus-roots, trunks, cords and branches. Fifty-eight patients had pre- or postganglionic injury. The C5-C6 nerve postganglionic segment was the most common (average 42%) among the postganglionic injuries detected by 3D SHINKEI MRN. The diagnostic accuracy (83.75%) and the specificity (90.30%) of MRN higher than that of EMG (p < 0.001). There was no significant difference in the diagnostic sensitivity of MRN compared with EMG (p > 0.05). Eighteen patients with brachial plexus injury underwent surgical exploration after MRN examination and the correlation between MRN and surgery was 66.7%. Due to the high diagnostic accuracy and specificity, 3D SHINKEI MRN can comprehensively display the traumatic brachial plexus injury. This sequence has great potential in the accurate diagnosis of traumatic brachial plexus injury.

Construction of an Angular Tricyclic Benzofuran Skeleton Using the C-H Activation Strategy.

A protocol for the construction of an angular tricyclic benzofuran skeleton based on the C-H activation strategy has been established. Different phthalide lactones on this skeleton can be easily assembled with various side chains by using C-H activation with aldehydes and subsequent reduction. This skeleton provides a versatile and crucial motif for the total synthesis of naturally occurring angular tricyclic benzofurans and their derivatives. Based on this protocol, the improved total syntheses of daldinin A and annullatin D were achieved in yields of 17.3 and 7.6%, respectively.

Mushroom Poisoning Outbreaks - China, 2023.

What is already known about this topic?: Mushroom poisoning poses a significant food safety concern in China, with a total of 196 species identified in poisoning incidents by the end of 2022. What is added by this report?: In 2023, the China CDC conducted an investigation into 505 cases of mushroom poisoning spanning 24 provincial-level administrative divisions. This investigation resulted in 1,303 patients and 16 deaths, yielding a case fatality rate of 1.23%. A total of 97 mushrooms were identified as the cause of 6 distinct clinical disease types, with 12 species newly documented as poisonous mushrooms in China. What are the implications for public health practice?: Close collaboration among CDC staff, physicians, and mycologists remains crucial for the control and prevention of mushroom poisoning in the future.

Investigation of acousto-optic interaction with momentum mismatching considering acoustic anisotropy in birefringent crystal.

In this work, the momentum mismatching based on which the acousto-optic (AO) transfer function and diffraction efficiency was acquired, was calculated considering the properties of AO crystals in AO interactions in acousto-optic tunable filter (AOTF). Transfer functions were obtained using a 4f optical system combined with AOTF and compared with theoretical calculations. It demonstrated the influence of acoustic energy shift on the AO interaction which should be considered in the design of AOTF.

Individual Differences in Volitional Social Self-Administration and Motivation in Male and Female Mice Following Social Stress.

BACKGROUND: A key challenge in developing treatments for neuropsychiatric illness is the disconnect between preclinical models and the complexity of human social behavior. We integrate voluntary social self-administration into a rodent model of social stress as a platform for the identification of fundamental brain and behavior mechanisms underlying stress-induced individual differences in social motivation. METHODS: Here, we introduced an operant social stress procedure in male and female mice composed of 3 phases: 1) social self-administration training, 2) social stress exposure concurrent with reinforced self-administration testing, and 3) poststress operant testing under nonreinforced and reinforced conditions. We used social-defeat and witness-defeat stress in male and female mice. RESULTS: Social defeat attenuated social reward seeking in males but not females, whereas witness defeat had no effect in males but promoted seeking behavior in females. We resolved social stress-induced changes to social motivation by aggregating z-scored operant metrics into a cumulative social index score to describe the spectrum of individual differences exhibited during operant social stress. Clustering does not adequately describe the relative distributions of social motivation following stress and is better described as a nonbinary behavioral distribution defined by the social index score, capturing a dynamic range of stress-related alterations in social motivation inclusive of sex as a biological variable. CONCLUSIONS: We demonstrated that operant social stress can detect stable individual differences in stress-induced changes to social motivation. The inclusion of volitional behavior in social procedures may enhance the understanding of behavioral adaptations that promote stress resiliency and their mechanisms under more naturalistic conditions.

Perovskite Oxide as A New Platform for Efficient Electrocatalytic Nitrogen Oxidation.

Electrocatalytic nitrogen oxidation reaction (NOR) offers an efficient and sustainable approach for conversion of widespread nitrogen (N2 ) into high-value-added nitrate (NO3 - ) under mild conditions, representing a promising alternative to the traditional approach that involves harsh Haber-Bosch and Ostwald oxidation processes. Unfortunately, due to the weak absorption/activation of N2 and the competitive oxygen evolution reaction, the kinetics of NOR process is extremely sluggish accompanied with low Faradaic efficiencies and NO3 - yield rates. In this work, an oxygen-vacancy-enriched perovskite oxide with nonstoichiometric ratio of strontium and ruthenium (denoted as Sr0.9 RuO3 ) was synthesized and explored as NOR electrocatalyst, which can exhibit a high Faradaic efficiency (38.6 %) with a high NO3 - yield rate (17.9 µmol mg-1 h-1 ). The experimental results show that the amount of oxygen vacancies in Sr0.9 RuO3 is greatly higher than that of SrRuO3 , following the same trend as their NOR performance. Theoretical simulations unravel that the presence of oxygen vacancies in the Sr0.9 RuO3 can render a decreased thermodynamic barrier toward the oxidation of *N2 to *N2 OH at the rate-determining step, leading to its enhanced NOR performance.

TestFit: A plug-and-play one-pass test time method for medical image segmentation.

Deep learning (DL) based methods have been extensively studied for medical image segmentation, mostly emphasizing the design and training of DL networks. Only few attempts were made on developing methods for applying DL models in test time. In this paper, we study whether a given off-the-shelf segmentation network can be stably improved on-the-fly during test time in an online processing-and-learning fashion. We propose a new online test-time method, called TestFit, to improve results of a given off-the-shelf DL segmentation model in test time by actively fitting the test data distribution. TestFit first creates a supplementary network (SuppNet) from the given trained off-the-shelf segmentation network (this original network is referred to as OGNet) and applies SuppNet together with OGNet for test time inference. OGNet keeps its hypothesis derived from the original training set to prevent the model from collapsing, while SuppNet seeks to fit the test data distribution. Segmentation results and supervision signals (for updating SuppNet) are generated by combining the outputs of OGNet and SuppNet on the fly. TestFit needs only one pass on each test sample - the same as the traditional test model pipeline - and requires no training time preparation. Since it is challenging to look at only one test sample and no manual annotation for model update each time, we develop a series of technical treatments for improving the stability and effectiveness of our proposed online test-time training method. TestFit works in a plug-and-play fashion, requires minimal hyper-parameter tuning, and is easy to use in practice. Experiments on a large collection of 2D and 3D datasets demonstrate the capability of our TestFit method.

An arginine-rich nuclear localization signal (ArgiNLS) strategy for streamlined image segmentation of single-cells.

High-throughput volumetric fluorescent microscopy pipelines can spatially integrate whole-brain structure and function at the foundational level of single-cells. However, conventional fluorescent protein (FP) modifications used to discriminate single-cells possess limited efficacy or are detrimental to cellular health. Here, we introduce a synthetic and non-deleterious nuclear localization signal (NLS) tag strategy, called 'Arginine-rich NLS' (ArgiNLS), that optimizes genetic labeling and downstream image segmentation of single-cells by restricting FP localization near-exclusively in the nucleus through a poly-arginine mechanism. A single N-terminal ArgiNLS tag provides modular nuclear restriction consistently across spectrally separate FP variants. ArgiNLS performance in vivo displays functional conservation across major cortical cell classes, and in response to both local and systemic brain wide AAV administration. Crucially, the high signal-to-noise ratio afforded by ArgiNLS enhances ML-automated segmentation of single-cells due to rapid classifier training and enrichment of labeled cell detection within 2D brain sections or 3D volumetric whole-brain image datasets, derived from both staining-amplified and native signal. This genetic strategy provides a simple and flexible basis for precise image segmentation of genetically labeled single-cells at scale and paired with behavioral procedures.

Unsupervised Fusion of Misaligned PAT and MRI Images via Mutually Reinforcing Cross-Modality Image Generation and Registration.

Photoacoustic tomography (PAT) and magnetic resonance imaging (MRI) are two advanced imaging techniques widely used in pre-clinical research. PAT has high optical contrast and deep imaging range but poor soft tissue contrast, whereas MRI provides excellent soft tissue information but poor temporal resolution. Despite recent advances in medical image fusion with pre-aligned multimodal data, PAT-MRI image fusion remains challenging due to misaligned images and spatial distortion. To address these issues, we propose an unsupervised multi-stage deep learning framework called PAMRFuse for misaligned PAT and MRI image fusion. PAMRFuse comprises a multimodal to unimodal registration network to accurately align the input PAT-MRI image pairs and a self-attentive fusion network that selects information-rich features for fusion. We employ an end-to-end mutually reinforcing mode in our registration network, which enables joint optimization of cross-modality image generation and registration. To the best of our knowledge, this is the first attempt at information fusion for misaligned PAT and MRI. Qualitative and quantitative experimental results show the excellent performance of our method in fusing PAT-MRI images of small animals captured from commercial imaging systems.

The maca protein ameliorates DSS-induced colitis in mice by modulating the gut microbiota and production of SCFAs.

Maca is a functional food with anti-inflammatory activity, and it is rich in protein. Currently, inflammatory bowel disease (IBD) is a common gastrointestinal disease. However, there is little research focusing on the effect of maca protein (MCP) on IBD. In this study, we extracted MCP from maca root and explored its effect and mechanism on improving dextran sodium sulfate (DSS)-induced IBD in mice. The results indicated that MCP intervention alleviated the clinical symptoms and colon tissue damage of mice with DSS-induced colitis and inhibited the expression of inflammatory factors. Moreover, it can modulate the gut microbiota composition in mice with DSS-induced colitis. The regulation is achieved by reducing the relative abundance of the IBD-exacerbating key bacterial genera: Lachnospiraceae_NK4A136_group, Bacteroides, Desulfovibrio, Prevotella, Helicobacter and Sutterella, while increasing the relative abundance of the IBD-alleviating key bacterial genera: norank_f_Muribaculaceae, Lactobacillus, Oscillospira, Akkermansia and Bifidobacterium. MCP can also promote the production of short-chain fatty acids (SCFAs). The further western blotting results indicated that MCP can regulate the Treg/Th17 immune balance in mice with colitis via the SCFAs-GPR41/43/HDAC1 signaling pathway. Overall, MCP can alleviate colitis by comprehensively regulating the gut microbiota and inflammatory response. It may be a promising functional component that reduces the risk of colitis by maintaining intestinal health.

Contact-Inhibited ERK Signaling is Determined by Cellular-Resolution Western Blotting.

Extracellular signal-regulated kinase (ERK) signaling is essential to regulated cell behaviors, including cell proliferation, differentiation, and apoptosis. The influence of cell-cell contacts on ERK signaling is central to epithelial cells, yet few studies have sought to understand the same in cancer cells, particularly with single-cell resolution. To acquire both phenotypic (cell-contact state) and proteomic profile (ERK phosphorylation) on the same HeLa cells, we prepend high-content, whole-cell imaging prior to endpoint cellular-resolution western blot analyses for hundreds of cancer cells cultured on chip. By indexing the phosphorylation level of ERK in each cell or cell-contact cluster to the imaged cell-contact state, we compare ERK signaling between isolated and in-contact cells. We observe attenuated (∼2×) ERK signaling in HeLa cells which are in contact versus isolated. Attenuation is sustained when the HeLa cells are challenged with hyperosmotic stress. The contact-dependent differential ERK-phosphorylation corresponds to the differential EGFR distribution on cell surfaces, suggesting the involvement of EGFRs in contact-inhibited ERK signaling. Our findings show the impact of cell-cell contacts on ERK activation with isolated and in-contact cells, hence providing a new tool into control and scrutiny of cell-cell interactions.

Neuralgic amyotrophy: sensitivity and specificity of magnetic resonance neurography in diagnosis: A retrospective study.

BACKGROUND: Neuralgic amyotrophy (NA) is a clinically acute or subacute disease. To study the characteristics of brachial plexus magnetic resonance neurography (MRN) in patients with NA, and to explore the clinical application value of MRN combined with electromyography (EMG) in the diagnosis of NA. METHODS: Brachial plexus MRN images of 32 patients with NA were retrospectively analyzed, and their characteristics were investigated. The accuracy, sensitivity and specificity of MRN, EMG, and the combination of the 2 methods for NA diagnosis were compared. RESULTS: Among the 32 patients with NA, 28 (87.5%) cases of unilateral brachial plexus involvement, 18 (56.3%) cases of multiple nerve roots involvement. In 10 cases, C5 nerve roots were involved alone, and in 9 cases, C5 to C6 nerve roots were involved together. The T2 signal intensity of the affected nerve increased, and 19 cases showed thickened and smooth nerve root edges. Twelve cases showed uneven thickening and segmental stenosis of the involved nerve roots. The diagnostic accuracy, sensitivity, and specificity of MRN for NA were higher than those of EMG. Combining MRN and EMG could improve the sensitivity and specificity of diagnosis. CONCLUSION: The main feature of MRN in patients with NA was that it was unilateral brachial plexus asymmetric involvement. The diagnostic effect of MRN was better than that of EMG. The combined diagnosis of MRN and EMG can help clinicians diagnose NA accurately.

Learning from algorithm-generated pseudo-annotations for detecting ants in videos.

Deep learning (DL) based detection models are powerful tools for large-scale analysis of dynamic biological behaviors in video data. Supervised training of a DL detection model often requires a large amount of manually-labeled training data which are time-consuming and labor-intensive to acquire. In this paper, we propose LFAGPA (Learn From Algorithm-Generated Pseudo-Annotations) that utilizes (noisy) annotations which are automatically generated by algorithms to train DL models for ant detection in videos. Our method consists of two main steps: (1) generate foreground objects using a (set of) state-of-the-art foreground extraction algorithm(s); (2) treat the results from step (1) as pseudo-annotations and use them to train deep neural networks for ant detection. We tackle several challenges on how to make use of automatically generated noisy annotations, how to learn from multiple annotation resources, and how to combine algorithm-generated annotations with human-labeled annotations (when available) for this learning framework. In experiments, we evaluate our method using 82 videos (totally 20,348 image frames) captured under natural conditions in a tropical rain-forest for dynamic ant behavior study. Without any manual annotation cost but only algorithm-generated annotations, our method can achieve a decent detection performance (77% in [Formula: see text] score). Moreover, when using only 10% manual annotations, our method can train a DL model to perform as well as using the full human annotations (81% in [Formula: see text] score).

Enlargement of the knowledge of Cortinarius section Anomali (Agaricales, Basidiomycota): introducing three new species from China.

Cortinarius is a globally distributed agaricoid genus that has been well studied in Europe and America with over 1,000 described species. However, as part of an ongoing effort to investigate the diversity of Cortinarius section Anomali in China, the resource investigation and classification research are still limited, and the species diversity has not been clarified by far. During the re-examination of the Chinese Cortinarius specimens, C. cinnamomeolilacinus, C. subclackamasensis, and C. tropicus, belonging to the sect. Anomali, were described in China as new to science based on morphological examination and phylogenetic analysis. The three new species are described and illustrated in detail according to the Chinese materials. The phylogenetic analysis based on internal transcribed spacer sequences confirmed the placement of the three species in the Cortinarius sect. Anomali clade. Phylogenetically related and morphologically similar species to these three new species are discussed.

Effects of Selenium Enrichment on Dough Fermentation Characteristics of Baker's Yeast.

In this research, the effect of selenium (Se) enrichment on dough fermentation characteristics of yeast and the possible mechanisms was investigated. Then, the Se-enriched yeast was used as starter to make Se-enriched bread, and the difference between Se-enriched bread and common bread was investigated. It was found Se enrichment increased CO2 production and sugar consumption rate of Saccharomyces cerevisiae (S. cerevisiae) in dough fermentation, and had positive impacts on final volume and rheological index of dough. The mechanism is possibly related to higher activity and protein expression of hexokinase (HK), phosphofructokinase (PFK), pyruvate kinase (PK), citrate synthase (CS), isocitrate dehydrogenase (ICD), and α-ketoglutarate dehydrogenase (α-KGDHC) in Se-enriched yeast. Moreover, Se-enriched bread (Se content: 11.29 µg/g) prepared by using Se-enriched yeast as starter exhibited higher overall acceptability on sensory, cell density in stomatal morphology, and better elasticity and cohesiveness on texture properties than common bread, which may be due to effect of higher CO2 production on dough quality. These results indicate Se-enriched yeast could be used as both Se-supplements and starter in baked-foods making.

Flexible Fusion Network for Multi-Modal Brain Tumor Segmentation.

Automated brain tumor segmentation is crucial for aiding brain disease diagnosis and evaluating disease progress. Currently, magnetic resonance imaging (MRI) is a routinely adopted approach in the field of brain tumor segmentation that can provide different modality images. It is critical to leverage multi-modal images to boost brain tumor segmentation performance. Existing works commonly concentrate on generating a shared representation by fusing multi-modal data, while few methods take into account modality-specific characteristics. Besides, how to efficiently fuse arbitrary numbers of modalities is still a difficult task. In this study, we present a flexible fusion network (termed F 2Net) for multi-modal brain tumor segmentation, which can flexibly fuse arbitrary numbers of multi-modal information to explore complementary information while maintaining the specific characteristics of each modality. Our F 2Net is based on the encoder-decoder structure, which utilizes two Transformer-based feature learning streams and a cross-modal shared learning network to extract individual and shared feature representations. To effectively integrate the knowledge from the multi-modality data, we propose a cross-modal feature-enhanced module (CFM) and a multi-modal collaboration module (MCM), which aims at fusing the multi-modal features into the shared learning network and incorporating the features from encoders into the shared decoder, respectively. Extensive experimental results on multiple benchmark datasets demonstrate the effectiveness of our F 2Net over other state-of-the-art segmentation methods.

How has China's industrial eco-efficiency been improved? Evidence from multi-scale countrywide study.

The process of industrialization often causes resource depletion and environmental pollution. To shed light on China's resource use and pollution trends in the context of the country's rapid industrial growth, this study analyzes the eco-efficiency of China's industry from 2000 to 2015. We quantify industrial eco-efficiency (IEE) for China and its provinces using data envelopment analysis (DEA) and analyze potential influencing factors at national and regional levels using Tobit regression. IEE in China and in most provinces shows a clear upward trend with some fluctuations, with national scores increasing from 0.394 to 0.704. There is strong regional disparity, with average IEE scores in eastern provinces (0.840) higher than those in central provinces (0.625), which are in turn higher than those in the northeast (0.537) and west (0.438). We next consider potential drivers. Economic development and foreign direct investment (FDI) are positively associated with IEE but appear to show diminishing returns. Environmental enforcement and market for technology are also positively associated with IEE, as expected. The impact of economic development, industrial sector structure, and investment in research and development (R&D) are modified by the stage of industrialization in each region. Targeted measures that can adjust industry structure, enhance environmental enforcement, attract FDI, and increase R&D investment may help further improve IEE in China.

Discovery of novel, potent, and orally bioavailable HDACs inhibitors with LSD1 inhibitory activity for the treatment of solid tumors.

Histone deacetylases (HDACs) and lysine-specific demethylase 1 (LSD1) are attractive targets for epigenetic cancer therapy. There is an intimate interplay between the two enzymes. HDACs inhibitors have shown synergistic anticancer effects in combination with LSD1 inhibitors in several types of cancer. Herein, we describe the discovery of compound 5e, a highly potent HDACs inhibitor (HDAC1/2/6/8; IC50 = 2.07/4.71/2.40/107 nM) with anti-LSD1 potency (IC50 = 1.34 µM). Compound 5e exhibited marked antiproliferative activity in several cancer cell lines. 5e effectively induced mitochondrial apoptosis with G2/M phase arrest, inhibiting cell migration and invasion in MGC-803 and HCT-116 cancer cells. It also showed good liver microsomal stability and acceptable pharmacokinetic parameters in SD rats. More importantly, orally administered compound 5e demonstrated higher in vivo antitumor efficacy than SAHA in the MGC-803 (TGI = 71.5%) and HCT-116 (TGI = 57.6%) xenograft tumor models accompanied by good tolerability. This study provides a novel lead compound with dual inhibitory activity against HDACs and LSD1 to further develop epigenetic drugs for solid tumor therapy. Further optimization is needed to improve the LSD1 activity to achieve dual inhibitors with balanced potency on LSD1 and HDACs.

LAGAN: Lesion-Aware Generative Adversarial Networks for Edema Area Segmentation in SD-OCT Images.

Large volume of labeled data is a cornerstone for deep learning (DL) based segmentation methods. Medical images require domain experts to annotate, and full segmentation annotations of large volumes of medical data are difficult, if not impossible, to acquire in practice. Compared with full annotations, image-level labels are multiple orders of magnitude faster and easier to obtain. Image-level labels contain rich information that correlates with the underlying segmentation tasks and should be utilized in modeling segmentation problems. In this article, we aim to build a robust DL-based lesion segmentation model using only image-level labels (normal v.s. abnormal). Our method consists of three main steps: (1) training an image classifier with image-level labels; (2) utilizing a model visualization tool to generate an object heat map for each training sample according to the trained classifier; (3) based on the generated heat maps (as pseudo-annotations) and an adversarial learning framework, we construct and train an image generator for Edema Area Segmentation (EAS). We name the proposed method Lesion-Aware Generative Adversarial Networks (LAGAN) as it combines the merits of supervised learning (being lesion-aware) and adversarial training (for image generation). Additional technical treatments, such as the design of a multi-scale patch-based discriminator, further enhance the effectiveness of our proposed method. We validate the superior performance of LAGAN via comprehensive experiments on two publicly available datasets (i.e., AI Challenger and RETOUCH).

Morphology and phylogeny identify two new species and one new subspecies of Podoscypha from Yunnan Province, Southwest China.

In this study, Podoscypha was taxonomically and phylogenetically evaluated. In total, five specimens collected from the tropical areas of Yunnan Province in Southwest China were studied. In combination with morphological observations and phylogenetic analyses based on ITS and LSU loci, two new species and one new subspecies, Podoscypha subinvoluta, P. tropica, and P. petalodes subsp. cystidiata, respectively, were discovered. The illustrated descriptions of the new species and subspecies are provided. Moreover, the main morphological differences between related species are discussed.