Introducing hafnium to atomically small- and medium-sized tin clusters (HfSnn0/-/2- (n = 4-17)): A computational investigation of geometrical and growth behavior, spectral properties, electronic configuration and thermochemistry.

The global and local minimum configurations of single Hf atom doped Sn clusters are conducted via density function theory (DFT) combined with artificial bee colony algorithm (ABCluster). Furthermore, DFT method is also used to systematically investigate on their structural growth evolution, spectral and electronic information, thermochemical properties following the size of tin clusters doped Hf atom. Structurally, the ground-state geometries of neutral, anion and di-anion are discovered that, from n = 4, the number of Sn atoms in cluster, HfSnn0/-/2- adsorb additional Sn atom on the prior architecture one by one until forming n = 17 for HfSnn-10/-, as well as forming n = 16 for HfSnn-12-. And for the HfSn110/- and HfSn102- as beginning the species veritably develop sealed architectures. The strongest vibrational modes of sealed nanoclusters are stretching modes of Hf atom with infrared actives and breathing modes of the Sn cage framework with Raman actives, respectively. The natural population analysis (NPA) elucidates the stronger relationship between the Hf atoms and the tin frameworks in sealed clusters than that in unsealed clusters. The results of thermochemical properties, molecular orbital shell (MOs), adaptive natural density partitioning (AdNDP) and ultraviolet visible absorption spectrum (UV-Vis) indicate that, the HfSn16 with high symmetry of Td exhibits thermochemical stability and optoelectronic properties, which is utilized potentially as zero-dimensional unit of self-assembling fluorescent nanomaterials.

Association of dietary inflammatory index with constipation: Evidence from the National Health and Nutrition Examination Survey.

The association of dietary inflammatory index (DII) with constipation has not been well studied in general population. Therefore, the aim of this cross-sectional study was to investigate whether DII is associated with constipation in a large representative sample of the US population. Data were obtained from the 2005-2010 National Health and Nutrition Examination Survey (NHANES). A total of 12,308 participants aged ≥20 years were included in the analysis. DII was calculated based on a single 24-h dietary recall, and constipation was defined as having fewer than three bowel movements per week by the questionnaire on bowel health. Logistic regression analysis demonstrated a significant positive association between DII score and constipation, with each unit increase in DII score associated with a 20% increase in constipation risk (95% CI: 1.13-1.28). Subgroup analysis revealed high odds ratios (ORs) among individuals classified as "Other Race" (OR: 1.42, 95% CI: 1.12-1.80) and "Non-Hispanic White" (OR: 1.31, 95% CI: 1.12-1.54). In addition, RCS analysis indicated a nonlinear relationship between DII and constipation among individuals with a BMI less than 25 (OR: 1.17, 95% CI: 1.07-1.28), while the overall trend remained positive correlation (OR: 1.20, 95% CI: 1.10-1.31). Briefly, our study suggests that there may be a link between DII and constipation, which has implications for the development of dietary interventions aimed at preventing and managing constipation. However, this association was complex and variable depending on individual factors such as BMI and racial background and needed to establish longitudinal studies to confirm the underlying mechanisms between DII and constipation.

Structure characterization of polysaccharides from Cistanche deserticola and their neuroprotective effects against oxidative stress in slow transit constipation mice.

Oxidative stress-induced enteric neuropathy is an important factor in slow transit constipation (STC). Cistanche deserticola crude polysaccharides (CDCP) are natural antioxidants with various biological activities. We prepared CDCP through water-extract and alcohol-precipitation methods. The structural characteristics of CDCP were analyzed by infrared spectroscopy and methylation analysis. The results showed that CDCP was primarily composed of (1 â†’ 4)-linked glucans with minor amounts of pectic polysaccharides. Different doses of CDCP (100, 200, and 400 mg/kg) were administered to loperamide-induced STC mice to explore the therapeutic effects of CDCP. Compared with the untreated group, CDCP treatment significantly improved constipation symptoms, relevant gut-regulating peptides levels, colonic pathological damage, and colonic myenteric nerons injury. CDCP enhanced the antioxidant capacity by decreasing Malondialdehyde (MDA) content, increasing Superoxide Dismutase (SOD) activity and Reduced Glutathione (GSH) content. CDCP significantly reduced oxidative stress-induced injury by preserving mitochondrial function in the colonic myenteric plexus. Furthermore, the neuroprotective effects of CDCP might be associated with the Nrf2/Keap1 pathway. Thus, our findings first revealed the potential of CDCP to protect the colonic myenteric plexus against oxidative stress-induced damage in STC, establishing CDCP as promising candidates for natural medicine in the clinical management of STC.

An intelligent quantification system for fetal heart rhythm assessment: A multicenter prospective study.

BACKGROUND: The motion relationship and time intervals of the pulsed-wave Doppler (PWD) spectrum are essential for diagnosing fetal arrhythmia. However, few technologies currently are available to automatically calculate fetal cardiac time intervals (CTIs). OBJECTIVE: The purpose of this study was to develop a fetal heart rhythm intelligent quantification system (HR-IQS) for the automatic extraction of CTIs and establish the normal reference range for fetal CTIs. METHODS: A total of 6498 PWD spectrums of 2630 fetuses over the junction between the left ventricular inflow and outflow tracts were recorded across 14 centers. E, A, and V waves were manually labeled by 3 experienced fetal cardiologists, with 17 CTIs extracted. Five-fold cross-validation was performed for training and testing of the deep learning model. Agreement between the manual and HR-IQS-based values was evaluated using the intraclass correlation coefficient and Spearman's rank correlation coefficient. The Jarque-Bera test was applied to evaluate the normality of CTIs' distributions, and the normal reference range of 17 CTIs was established with quantile regression. Arrhythmia subset was compared with the non-arrhythmia subset using the Mann-Whitney U test. RESULTS: Significant positive correlation (P <.001) and moderate-to-excellent consistency (P <.001) between the manual and HR-IQS automated measurements of CTIs was found. The distribution of CTIs was non-normal (P <.001). The normal range (2.5th to 97.5th percentiles) was successfully established for the 17 CTIs. CONCLUSIONS: Using our HR-IQS is feasible for the automated calculation of CTIs in practice and thus could provide a promising tool for the assessment of fetal rhythm and function.

Structural Growth Pattern, Electronic Configurations, and Spectral and Thermochemistry Properties of ZrSnn0/-/2- (n = 4-17) Nanoscale Compounds: A Systematic Study Using Density Functional Theory.

By performing density functional theory (DFT) calculations for geometric optimization in conjunction with the artificial bee colony algorithm for cluster (ABCluster) global search approach, the ground-state structures of the neutral, anionic, and dianionic ZrSnn0/-/2- (n = 4-17) nanoscale compounds are obtained. Their structural growth evolution, spectral information, and electronic and thermochemical properties are investigated. Regarding the architectural evolution of the neutral, anion, and dianionic species, ZrSnn0/-/2- (n = 4-17) compounds possess two different stages of adsorption patterns in which, when n = 4-7 and n = 8-17, ZrSn40/-/2- and ZrSn80/-/2- compounds as the basic motif adsorb Sn atoms to become the larger clusters, respectively. The simulated photoelectron spectra (PES) of anionic compounds are in good agreement with the available experimental PES. The infrared and Raman spectra can be summarized as follows: under infrared vibrational modes, the sealed cages of ZrSnn0/-/2- compounds belong to the deformation mode, and under Raman vibrational modes, they belong to the breathing mode of the Sn cage framework. The density of states (DOS) spectra and natural population analysis (NPA) indicate that the interaction between the Zr atom and Snn frameworks of capsulated compounds has been developing stronger than for unsealed compounds. The results of thermochemical properties, molecular orbital shell (MOs) analysis, and ultraviolet-visible (UV-vis) absorption spectrum indicate that the neutral ZrSn16 nanoscale compound possesses not only both thermodynamic and chemical stability but also far-infrared sensing and optoelectronic properties and hence, is the best building block motif for new multipurpose nanoscale materials.

Sintilimab plus bevacizumab, oxaliplatin and capecitabine as first-line therapy in RAS-mutant, microsatellite stable, unresectable metastatic colorectal cancer: an open-label, single-arm, phase II trial.

Background: Microsatellite stable (MSS) and RAS-mutant metastatic colorectal cancer (mCRC) patients are characterized by an immunosuppressive microenvironment and a low response rate to immunotherapy. Chemotherapy and anti-angiogenesis therapy have been reported to potentially promote immunotherapy response. This study aims to assess the preliminary anti-tumor activity and safety of sintilimab plus bevacizumab, oxaliplatin and capecitabine as a treatment option for patients with RAS-mutant MSS mCRC. Methods: This study was an open-label, single-arm, phase II trial in China. Patients with unresectable, RAS-mutant and MSS metastatic colorectal adenocarcinoma received treatment by intravenous sintilimab (200 mg, day 1) plus bevacizumab (7.5 mg/kg, day 1), oxaliplatin (135 mg/m2, day 1) and oral capecitabine (1 g/m2, day 1-14) in each 21-day cycle. The primary endpoints included objective response rate (ORR) and adverse events. Biomarker analysis was performed to identify potential predictors of good response to treatment. This study is registered with ClinicalTrials.gov, number NCT04194359. Findings: Between April 2021 and December 2021, 25 patients were enrolled. Two (8%) patients showed complete response (CR), 19 (76%) had partial response (PR) and 4 (16%) presented with stable disease. ORR reached 84% (95% CI, 63.9-95.5) and the disease control rate was 100% (95% CI, 86.3-100). The median progression-free survival (PFS) was 18.2 months for the full analysis set. The most common treatment-related adverse events (TRAEs) in all grades were anemia (21/25, 84%), neutropenia (20/25, 80%), and hand-foot syndrome (14/25, 56%). The most frequent grade 3 or 4 TRAEs were neutropenia (3/25, 12%) and increased alanine transaminase (2/25, 8%). No grade 5 adverse events occurred. In the exploration of biomarkers, 5 patients could be characterized as TTN/OBSCN "double-hit" after treatment, and the copy number variants burden was significantly decreased in tumor tissues after treatment compared with the baseline. Nanostring panel RNA sequencing analysis indicated a better tumor immune microenvironment cell infiltration in CR/PR patients compared with non-CR/PR patients as well as the PFS-long (≥12.5 months) group compared with the PFS-short group. Interpretation: Combination treatment with sintilimab plus bevacizumab, oxaliplatin and capecitabine as first-line treatment demonstrated a promising antitumor activity and a manageable safety profile in RAS-mutant, MSS and unresectable mCRC. Exploratory biomarker assessment analysis showed that some RAS-mutant and MSS patients changed into "immune-hot" subtype after the treatment. Funding: This study was supported by the Key R&D Program of Zhejiang Province (2021C03125 to Ying Yuan), the National Natural Science Foundation of China (81872481 to Ying Yuan, 82072624 to Kefeng Ding), the Fundamental Research Funds for the Central Universities (No. 226-2022-00009 to Kefeng Ding), and the Zhejiang Provincial Natural Science Foundation of China (No. LY22H160024 to Hanguang Hu).

A novel approach to quantify calcifications of thyroid nodules in US images based on deep learning: predicting the risk of cervical lymph node metastasis in papillary thyroid cancer patients.

OBJECTIVE: Based on ultrasound (US) images, this study aimed to detect and quantify calcifications of thyroid nodules, which are regarded as one of the most important features in US diagnosis of thyroid cancer, and to further investigate the value of US calcifications in predicting the risk of lymph node metastasis (LNM) in papillary thyroid cancer (PTC). METHODS: Based on the DeepLabv3+ networks, 2992 thyroid nodules in US images were used to train a model to detect thyroid nodules, of which 998 were used to train a model to detect and quantify calcifications. A total of 225 and 146 thyroid nodules obtained from two centers, respectively, were used to test the performance of these models. A logistic regression method was used to construct the predictive models for LNM in PTCs. RESULTS: Calcifications detected by the network model and experienced radiologists had an agreement degree of above 90%. The novel quantitative parameters of US calcification defined in this study showed a significant difference between PTC patients with and without cervical LNM (p < 0.05). The calcification parameters were beneficial to predicting the LNM risk in PTC patients. The LNM prediction model using these calcification parameters combined with patient age and other US nodular features showed a higher specificity and accuracy than the calcification parameters alone. CONCLUSIONS: Our models not only detect the calcifications automatically, but also have value in predicting cervical LNM risk of PTC patients, thereby making it possible to investigate the relationship between calcifications and highly invasive PTC in detail. CLINICAL RELEVANCE STATEMENT: Due to the high association of US microcalcifications with thyroid cancers, our model will contribute to the differential diagnosis of thyroid nodules in daily practice. KEY POINTS: • We developed an ML-based network model for automatically detecting and quantifying calcifications within thyroid nodules in US images. • Three novel parameters for quantifying US calcifications were defined and verified. • These US calcification parameters showed value in predicting the risk of cervical LNM in PTC patients.

Impacts of sulfur fumigation on the chemistry and immunomodulatory activity of polysaccharides in ginseng.

Ginseng is widely regarded as a panacea in Oriental medicine mainly due to its immunomodulatory activity. We previously found that sulfur fumigation, a commonly used pesticidal and anti-bacterial processing practice, weakened the immunomodulatory activity of ginseng. However, if and how sulfur fumigation affects the polysaccharides in ginseng, the crucial components contributing to the immunomodulatory function, remain unknown. Here we report that polysaccharides extracted from sulfur-fumigated ginseng (SGP) presented different chemical properties with polysaccharides extracted with non-fumigated ginseng (NGP), particularly increased water extraction yield and decreased branching degree. SGP had weaker immunomodulatory activity than NGP in immunocompromised mice, as evidenced by less improved immunophenotypes involving body weight, immune organ indexes, white blood cells, lymphocyte cell populations and inflammation. The different immunomodulatory activities were accompanied by changes in the interaction between the polysaccharides and gut microbiota, in which SGP stimulated the growth of different bacteria but produced less SCFAs as compared to NGP. Fecal microbiota transplantation experiment suggested that gut microbiota played a central role in causing the weakened immunomodulatory activity in vivo. This study provides definite evidence that sulfur fumigation affects the chemistry and bioactivity of ginseng polysaccharides, thereby contributing to understanding how sulfur fumigation weakens the immunomodulatory activity of ginseng.

Sintilimab plus bevacizumab and CapeOx (BBCAPX) on first-line treatment in patients with RAS mutant, microsatellite stable, metastatic colorectal cancer: study protocol of a randomized, open-label, multicentric study.

BACKGROUND: Rat sarcoma viral oncogene homolog (RAS) gene mutation is a common molecular event in colorectal cancer (CRC). The prognosis of mCRC (metastatic colorectal cancer) patients with RAS mutation is poor and capecitabine and oxaliplatin (CapeOx) plus bevacizumab has shown to be one of the standard therapeutic regimens as first line for these patients with objective response rate (ORR) of ~ 50% and median progression-free survival (mPFS) of 8-9 months. Immunotherapy, especially anti-programmed death 1 (PD-1) monoclonal antibody has demonstrated ground-breaking results in deficient mismatch repair (dMMR) / microsatellite instability-high (MSI-H) mCRC patients. However, the response rate of in microsatellite stable (MSS) patients is extremely low. In addition, preclinical studies have demonstrated that anti-Vascular endothelial growth factor (VEGF) agents, such as bevacizumab, can induce tumor vascular normalization and enhance antitumor immunity. Previous study indicated the combination of chemotherapy, anti-VEGF agents (bevacizumab) with immune checkpoint inhibitors may have promising clinical activity in RAS mutant, MSS refractory mCRC patients. Based on these evidences, we will explore the combination of CapeOx with bevacizumab and sintilimab (anti-PD-1 monoclonal antibody) in RAS mutant, MSS mCRC patients as first-line therapy. METHODS: This is a randomized, open-label, multicentric clinical trial. In the sintilimab arm, patients will receive sintilimab in combination with CapeOx and bevacizumab. In the control arm, patients will receive CapeOx and bevacizumab. This trial will recruit 494 patients from 20 centers and randomly (1:1) disseminated into two groups. The primary endpoint is the PFS. The secondary endpoints include overall survival, safety, ORR, and disease control rate. DISCUSSION: This study may provide new ideas for optimizing oncology treatment planning for RAS mutant, MSS mCRC patients in the first-line set. TRIAL REGISTRATION: This study is short for BBCAPX and has been registered at clinicaltrials.gov registry with identifier NCT05171660.

Impact of Sulfur Fumigation on the Chemistry of Dioscoreae Rhizoma (Chinese Yam).

Dioscoreae Rhizoma (Chinese yam; derived from the rhizome of Dioscorea opposita Thunb.) (DR), commonly consumed as a food or supplement, is often sulfur-fumigated during post-harvest handling, but it remains largely unknown if and how sulfur fumigation impacts the chemistry of DR. In this study, we report the impact of sulfur fumigation on the chemical profile of DR and then the molecular and cellular mechanisms potentially involved in the chemical variations induced by sulfur fumigation. The results show that sulfur fumigation significantly and specifically changed the small metabolites (molecular weight lower than 1000 Da) and polysaccharides of DR at both qualitative and quantitative levels. Multifaceted molecular and cellular mechanisms involving chemical transformations (e.g., acidic hydrolysis, sulfonation, and esterification) and histological damage were found to be responsible for the chemical variations in sulfur-fumigated DR (S-DR). The research outcomes provide a chemical basis for further comprehensive and in-depth safety and functional evaluations of sulfur-fumigated DR.

MS-Net: Learning to assess the malignant status of a lung nodule by a radiologist and her peers.

BACKGROUND: Automatically assessing the malignant status of lung nodules based on CTscan images can help reduce the workload of radiologists while improving their diagnostic accuracy. PURPOSE: Despite remarkable progress in the automatic diagnosis of pulmonary nodules by deep learning technologies, two significant problems remain outstanding. First, end-to-end deep learning solutions tend to neglect the empirical (semantic) features accumulated by radiologists and only rely on automatic features discovered by neural networks to provide the final diagnostic results, leading to questionable reliability, and interpretability. Second, inconsistent diagnosis between radiologists, a widely acknowledged phenomenon in clinical settings, is rarely examined and quantitatively explored by existing machine learning approaches. This paper solves these problems. METHODS: We propose a novel deep neural network called MS-Net, which comprises two sequential modules: A feature derivation and initial diagnosis module (FDID), followed by a diagnosis refinement module (DR). Specifically, to take advantage of accumulated empirical features and discovered automatic features, the FDID model of MS-Net first derives a range of perceptible features and provides two initial diagnoses for lung nodules; then, these results are fed to the subsequent DR module to refine the diagnoses further. In addition, to fully consider the individual and panel diagnosis opinions, we propose a new loss function called collaborative loss, which can collaboratively optimize the individual and her peers' opinions to provide a more accurate diagnosis. RESULTS: We evaluate the performance of the proposed MS-Net on the Lung Image Database Consortium image collection (LIDC-IDRI). It achieves 92.4% of accuracy, 92.9% of sensitivity, and 92.0% of specificity when panel labels are the ground truth, which is superior to other state-of-the-art diagnosis models. As a byproduct, the MS-Net can automatically derive a range of semantic features of lung nodules, increasing the interpretability of the final diagnoses. CONCLUSIONS: The proposed MS-Net can provide an automatic and accurate diagnosis of lung nodules, meeting the need for a reliable computer-aided diagnosis system in clinical practice.

[Effects of partial substitution of chemical fertilizer with organic fertilizer on arbuscular mycorrhizal fungal community of Mangifera indica]. / æœ‰æœºè‚¥æ›¿ä»£éƒ¨åˆ†åŒ–è‚¥å¯¹èŠ’æžœä¸›æžèŒæ ¹çœŸèŒç¾¤è½çš„å½±å“.

Nutrient enrichment caused by fertilization would reduce the diversity of arbuscular mycorrhizal fungi (AMF). To explore whether partial substitution of chemical fertilizer with organic fertilizer would alleviate the negative effects of nutrient enrichment on AMF, we conducted a two-year mango (Mangifera indica) field experiment to examine the effects of different fertilization regimes on AMF communities in roots and rhizospheric soils by using high-throughput sequencing. The treatments included chemical-only fertilization (control), and two kinds of organic fertilizer (commercial organic fertilizer and bio-organic fertilizer) with replacing 12% (low) and 38% (high) chemical fertilizer. The results showed that under equivalent nutrient input, partial substitution of chemical fertilizer with organic fertilizer had positive effects on the yield and quality of mango. The application of organic fertilizer could effectively increase AMF richness. AMF diversity was significantly positively correlated with some indices of fruit quality. Compared with chemical-only fertilization, high replacement ratio of organic fertilizer could significantly change root AMF community, but did not affect AMF community in the rhizospheric soil. Bio-organic fertilizer could enrich more AMF species and form a more complex AMF co-occurrence network than commercial organic fertilizer. In all, replacing chemical fertilizer with a high proportion of organic fertilizer could improve the yield and quality of mango while maintain AMF richness. The changes of AMF community caused by organic fertilizer substitution pre-ferably occurred in roots rather than soils.

Development of a chemiluminescence assay for detection of Giardia lamblia in canine stool samples.

Our Giardia chemiluminescence assay (GCA) detected Giardia antigens in a dose-dependent manner with a limit of detection at 0.46 ng/mL and a signal-to-baseline ratio at 475. In a study of 30 clinic collected canine stool samples, samples were identified as Giardia positive or negative by a standard Giardia II ELISA (TechLab), the GCA had sensitivity of 93.8 % and specificity of 92.9 %. Study on the set of 16 Giardia positive samples showed that all samples displayed higher signal-to-baseline ratio in GCA than they did in a colorimetric ELISA. A dilution analysis of antigen titer showed that for all positive samples, antigen titers in GCA were equal or higher than those in ELISA. The GCA system of chemiluminescence has shown improved capability in detecting Giardia antigens and provided a valid alternative method for researchers and for laboratories.

A novel multi-attention, multi-scale 3D deep network for coronary artery segmentation.

Automatic segmentation of coronary arteries provides vital assistance to enable accurate and efficient diagnosis and evaluation of coronary artery disease (CAD). However, the task of coronary artery segmentation (CAS) remains highly challenging due to the large-scale variations exhibited by coronary arteries, their complicated anatomical structures and morphologies, as well as the low contrast between vessels and their background. To comprehensively tackle these challenges, we propose a novel multi-attention, multi-scale 3D deep network for CAS, which we call CAS-Net. Specifically, we first propose an attention-guided feature fusion (AGFF) module to efficiently fuse adjacent hierarchical features in the encoding and decoding stages to capture more effectively latent semantic information. Then, we propose a scale-aware feature enhancement (SAFE) module, aiming to dynamically adjust the receptive fields to extract more expressive features effectively, thereby enhancing the feature representation capability of the network. Furthermore, we employ the multi-scale feature aggregation (MSFA) module to learn a more distinctive semantic representation for refining the vessel maps. In addition, considering that the limited training data annotated with a quality golden standard are also a significant factor restricting the development of CAS, we construct a new dataset containing 119 cases consisting of coronary computed tomographic angiography (CCTA) volumes and annotated coronary arteries. Extensive experiments on our self-collected dataset and three publicly available datasets demonstrate that the proposed method has good segmentation performance and generalization ability, outperforming multiple state-of-the-art algorithms on various metrics. Compared with U-Net3D, the proposed method significantly improves the Dice similarity coefficient (DSC) by at least 4% on each dataset, due to the synergistic effect among the three core modules, AGFF, SAFE, and MSFA. Our implementation is released at https://github.com/Cassie-CV/CAS-Net.

Efficient potassium (K) recycling and root carbon (C) metabolism improve K use efficiency in pear rootstock genotypes.

To investigate K absorption and transport mechanisms by which pear rootstock genotypes respond to low-K stress, seedlings of a potassium-efficient pear rootstock, Pyrus ussuriensis, and a potassium-sensitive rootstock, Pyrus betulifolia, were supplied with different K concentrations in solution culture. Significant differences in the absorption rate, Vmax and Km between the genotypes indicate that P. ussuriensis acclimatizes more readily to low-K stress by regulating its absorption and internal cycling. We also found that the K content in the leaves of P. betulifolia was significantly lower than that of P. ussuriensis, and the proportion of K that was returned to root from shoot, relative to K that was transported from root to shoot, was greater in P. ussuriensis, which suggests that P. ussuriensis more efficiently recycles and reuses K. When the transcriptomes of the two genotypes were compared, we found that photosynthetic genes such as CABs (Chlorophyll a/b-binding proteins), Lhcbs (Photosystem II-related proteins), and Psas (Photosystem Ⅰ associated proteins) displayed lower expression in leaves of P. betulifolia under no-K conditions, but not in P. ussuriensis. However, in the root of P. ussuriensis, carbon metabolism-related genes SS (Sucrose Synthase), HK (HexoKinase) and SDH (Sorbitol Dehydrogenase) and components of the TCA cycle (Tricarboxylic Acid cycle) were differentially expressed, indicating that changes in C metabolism may provide energy for increased K+ cycling in these plants, thereby allowing it to better adapt to the low-K environment. In addition, exogenous supply of various sugars to the roots influenced K+ influx, supporting the conclusion that sugar metabolism in roots significantly affects K+ absorption in pear.

Learning from dermoscopic images in association with clinical metadata for skin lesion segmentation and classification.

Automatic segmentation and classification of lesions are two clinically significant tasks in the computer-aided diagnosis of skin diseases. Both tasks are challenging due to the nonnegligible lesion differences in dermoscopic images from different patients. In this paper, we propose a novel pipeline to efficiently implement skin lesions' segmentation and classification tasks, which consists of a segmentation network and a classification network. To improve the performance of the segmentation network, we propose a novel module of Multi-Scale Holistic Feature Exploration (MSH) to thoroughly exploit perceptual clues latent among multi-scale feature maps as synthesized by the decoder. The MSH module enables holistic exploration of features across multiple scales to more effectively support downstream image analysis tasks. To boost the performance of the classification network, we propose a novel module of Cross-Modality Collaborative Feature Exploration (CMC) to discover latent discriminative features by collaboratively exploiting potential relationships between cross-modal features of dermoscopic images and clinical metadata. The CMC module enables dynamically capturing versatile interaction effects among cross-modal features during the model's representation learning procedure by discriminatively and adaptively learning the interaction weight associated with each crossmodality feature pair. In addition, to effectively reduce background noise and boost the lesion discrimination ability of the classification network, we crop the images based on lesion masks generated by the best segmentation model. We evaluate the proposed pipeline on the four public skin lesion datasets, where the ISIC 2018 and PH2 are for segmentation, and the ISIC 2019 and ISIC 2020 are combined into a new dataset, ISIC 2019&2020, for classification. It achieves a Jaccard index of 83.31% and 90.14% in skin lesion segmentation, an AUC of 97.98% and an Accuracy of 92.63% in skin lesion classification, which is superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Last but not least, the new model for segmentation utilizes much fewer model parameters (3.3 M) than its peer approaches, leading to a greatly reduced number of labeled samples required for model training, which obtains substantially stronger robustness than its peers.

Bio-Organic Fertilizer Promotes Pear Yield by Shaping the Rhizosphere Microbiome Composition and Functions.

Bio-organic fertilizers (BOF) containing both organic amendments and beneficial microorganisms have been consistently shown to improve soils fertility and yield. However, the exact mechanisms which link amendments and yields remain disputed, and the complexity of bio-organic fertilizers may work in parallel in several ways. BOF may directly improve yield by replenishing soil nutrients or introducing beneficial microbial genes or indirectly by altering the soil microbiome to enrich native beneficial microorganisms. In this work, we aim to disentangle the relative contributions of direct and indirect effects on pear yield. We treated pear trees with either chemical fertilizer or organic fertilizer with/without the plant-beneficial bacterium Bacillus velezensis SQR9. We then assessed, in detail, soil physicochemical and biological properties (metagenome sequencing) as well as pear yield. We then evaluated the relative importance of direct and indirect effects of soil amendments on pear yield. Both organic treatments increased plant yield by up to 20%, with the addition of bacteria tripling the increase driven by organic fertilizer alone. This increase could be linked to alterations in soil physicochemical properties, bacterial community function, and metabolism. Supplementation of organic fertilizer SQR9 increased rhizosphere microbiome richness and functional diversity. Fertilizer-sensitive microbes and functions responded as whole guilds. Pear yield was most positively associated with the Mitsuaria- and Actinoplanes-dominated ecological clusters and with gene clusters involved in ion transport and secondary metabolite biosynthesis. Together, these results suggested that bio-organic fertilizers mainly act indirectly on plant yield by creating soil chemical properties which promote a plant-beneficial microbiome. IMPORTANCE Bio-organic fertilization is a widely used, eco-friendly, sustainable approach to increasing plant productivity in the agriculture and fruit industries. However, it remains unclear whether the promotion of fruit productivity is related to specific changes in microbial inoculants, the resident microbiome, and/or the physicochemical properties of rhizosphere soils. We found that bio-organic fertilizers alter soil chemical properties, thus manipulating specific microbial taxa and functions within the rhizosphere microbiome of pear plants to promote yield. Our work unveils the ecological mechanisms which underlie the beneficial impacts of bio-organic fertilizers on yield promotion in fruit orchards, which may help in the design of more efficient biofertilizers to promote sustainable fruit production.

Theoretical Insights into the Geometrical Evolution, Photoelectron Spectra, and Vibrational Properties of YGe n - (n = 6-20) Anions: From Y-Linked to Y-Encapsulated Structures.

The structural evolution behavior of germanium anionic clusters doped with the rare-earth metal yttrium, YGe n - (n = 6-20), has been investigated using a mPW2PLYP density functional scheme and an ABCluster structure searching technique. The results reveal that with increasing cluster size n, the structure evolution pattern is from the Y-linked framework (n = 10-14), where Y serves as a linker (the Y atom bridges two germanium subclusters), to the Y-encapsulated framework (n = 15-20), where the Y atom is located in the center of the Ge cage. The simulated PES spectra show satisfying agreement with the experimental PES spectra for n = 12-20, which reveals that the global minimum structures reported here are reliable. In particular, the anionic YGe16 - nanocluster is found to be the most stable structure in the size range of n = 6-20 through analyzes of the relative stability, highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap, spherical jellium model, and isochemical shielding surface. Moreover, spectral properties such as infrared and Raman spectra were reported. In addition, the UV-vis spectra of the YGe16 - nanocluster are in good agreement with solar energy distribution, showing that such substances serve as multifunctional building blocks to be potentially used in optoelectronic devices or solar energy converters.

WGCNA analysis revealing molecular mechanism that bio-organic fertilizer improves pear fruit quality by increasing sucrose accumulation and reducing citric acid metabolism.

It's been long known that the application of organic fertilizer (OF) and bio-organic fertilizer (BF) which containing beneficial microorganisms to pear trees can both significantly improve fruit quality and yield. In order to reveal the mechanism of BF and OF regulating fruit growth and quality in pear, the effects of BF and OF on the photosynthetic characteristics and the accumulation of major sugars and organic acids of the pear fruit were quantified compared with chemical fertilizer (CF). Additionally, the molecular mechanisms regulating pear fruit development and quality were studied through transcriptome analysis. The three treatments were conducted based on the same amounts of nitrogen supply. The results showed that compared with CF, BF and OF treatments increased the fruit yield, and also significantly improved the photosynthesis efficiency in pear. BF and OF both significantly increased the sucrose content but significantly decreased the fructose and glucose content within the pear fruit. The amount of malic acid was significantly higher in OF treatment. Compared with CF and OF, BF significantly increased the sugar-acid ratio and thus improved the fruit quality. Transcriptome analysis and weighted correlation network analysis (WGCNA) revealed that the sugar metabolism of fruits applied with the BF was enhanced compared with those applied with CF or OF. More specifically, the expression of SDH (Sorbitol dehydrogenase) was higher in BF, which converts sorbitol into fructose. For both of the OF and BF, the transcript abundance of sugar transporter genes was significantly increased, such as SOT (Sorbitol transporter), SUT14 (Sugar transport 14), UDP-GLUT4 (UDP-glucose transporter 4), UDP-SUT (UDP-sugar transporter), SUC4 (Sucrose transport 4), SUT7 (Sugar transporter 7), SWEET10 and SWEET15 (Bidirectional sugar transporter), which ensures sugar transportation. The genes involved in organic acid metabolism showed decreased transcripts abundance in both BF and OF treatments, such as VAP (Vesicle-associated protein) and cyACO (Cytosolic aconitase), which reduce the conversion from succinate to citric acid, and decrease the conversion from citric acid to malic acid in the TCA cycle (Tricarboxylic Acid cycle) through Pept6 (Oligopeptide transporter). In conclusion, the application of BF and OF improved fruit quality by regulating the expression of sugar and organic acid metabolism-related genes and thus altering the sugar acid metabolism. Both BF and OF promote sucrose accumulation and citric acid degradation in fruits, which may be an important reason for improving pear fruit quality. The possible mechanism of bio-organic fertilizer to improve fruit quality was discussed.

A polysaccharide from native Curcuma kwangsiensis and its mechanism of reversing MDSC-induced suppressive function.

A purified polysaccharide nCKAP-2 was prepared from Curcuma kwangsiensis and characterized. Structural analyses revealed that nCKAP-2 contains a high-branched arabinan composed of mono-substituted (O-5, 17.07 %) and di-substituted (O-2,5, 16.67 %) (1 â†’ 3)-α-Araf residues. Bioactive test showed that nCKAP-2 significantly reversed the suppression function of myeloid-derived suppressor cells (MDSCs) on T cells. Further study revealed that treatment of MDSCs with nCKAP-2 could induce apoptotic cell death at the G0/G1 phase via the intrinsic pathway as suggested from the up-regulation of cleaved caspase 3 and 9, cleaved PARP, and Bax and the down-regulation of Bcl-xl. This apoptotic process was mainly mediated by the TLR4-NF-κB signaling pathway. Additionally, the down-regulation of ROS level of MDSCs after nCKAP-2 treatment involved in this process. Summarily, we explain how nCKAP-2 reverses the MDSC-induced suppressive function on T cells, and provide a scientific basis for the clinical application and development of C. kwangsiensis.