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A nanoflower-like bimetallic FeP/CoP-1-350 heterostructure electrocatalyst with rich interfaces shows excellent OER electrocatalytic activity.

Intraventricular hemorrhage (IVH) is one of the most fatal types of intracerebral hemorrhage (ICH), especially when the third and the fourth ventricles are involved. The use of external ventricular drainage is limited for evacuation of hemorrhage in the lateral ventricles. Endoscopic surgery can provide visualized evacuation of the hemorrhage in the lateral and third ventricles. However, it is usually challenging to access the fourth ventricle using a routine endoscopic approach.We have reported 3 cases of severe IVH with cast fourth ventricles treated using an endoscopic-assisted trans-lateral ventricular transchoroidal fissure trans-aqueductal approach.The average preoperative Graeb score was 11, and the average IVH volume was 75.12 mL. The IVH evacuation rate was 97.5%-100%. The average Glasgow coma scale score had increased to 12 at discharge from 6.6 at admission. At 3 months after surgery, the average modified Rankin scale score was 3. No cerebrospinal fluid shunt had been required and no surgery-related complication had occurred in any patient.Our results have shown that the endoscopic-assisted trans-lateral ventricular transchoroidal fissure trans-aqueductal approach is a feasible and safe endoscopic option that can achieve one-off complete removal of clots in all 4 ventricles in patients with severe IVH.

A novel N-rimmed PAH molecule containing a dipleiadiene core (

We report the convenient synthesis of a benzobis(imidazolium)-embedded conjugated polyelectrolyte pBBI by a Cu-catalyzed direct C‒H arylation of a cationic benzobis(imidazolium) monomer with a diiodide comonomer. pBBI shows weak fluorescence in solution due to rotation of the repeat units in the conjugated backbone, and enhanced fluorescence when electrostatically interacting with a variety of anions to form aggregates. Specially, pBBI responds to the bisulfite anion with intensified unique deep-blue fluorescence easily discriminated by naked eye.

Guocan Yu,1,* Linhua Wang,2 Yanqin Shen,1 Likui Fang,1 Jun Yang,1 Bo Ye,1 Kan Xu,1 Fangming Zhong1,* 1Zhejiang Tuberculosis Diagnosis and Treatment Center, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China; 2Department of Hospital Infection, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Guocan Yu; Fangming Zhong, Zhejiang Tuberculosis Diagnosis and Treatment Center, Affiliated Hangzhou Chest Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, People’s Republic of China, Email dabaitwo@163.com; zhongfangm@126.comPurpose: We evaluated CapitalBio Mycobacterium RT-PCR assay diagnosing tuberculous pericarditis (TBP), performed a head-to-head comparison with Xpert MTB/RIF, and assessed the impact of a parallel test (positive result for either of these two tests).Methods: We reviewed suspected TBP patients with Xpert MTB/RIF, CapitalBio Mycobacterium RT-PCR assay, and Mycobacterium tuberculosis (MTB) culture. We analyzed sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC).Results: Seventy-four patients were included. Overall sensitivity, specificity, PPV, NPV, and AUC of CapitalBio Mycobacterium RT-PCR assay compared with culture were 50%, 91.1%, 64.3%, 85%, and 0.71, respectively. Overall sensitivity, specificity, PPV, NPV, and AUC of Xpert MTB/RIF for TBP were 61.1%, 91.1%, 68.8%, 87.9%, and 0.76. Parallel test values were 72.2%, 91.1%, 72.2%, 91.1%, and 0.82. The diagnostic accuracy of Xpert MTB/RIF was higher than CapitalBio Mycobacterium RT-PCR assay but was not significant (P > 0.05). The parallel test could improve diagnostic accuracy, but it was not significant compared to single tests (P > 0.05).Conclusion: CapitalBio Mycobacterium RT-PCR assay had a moderate diagnostic accuracy, similar to Xpert MTB/RIF. The parallel test maximized diagnostic efficacy, but differences were not significant. CapitalBio Mycobacterium RT-PCR assay and Xpert MTB/RIF for TBP could be an initial option for early diagnosis.Keywords: tuberculosis pericarditis, polymerase chain reaction, Xpert MTB/RIF, CapitalBio, accuracy

SummaryIn the era where transcriptome profiling moves towards single-cell and spatial resolutions, the traditional co-expression analysis lacks the power to fully utilize such rich information to unravel spatial gene associations. Here we present a Python package called Spatial Enrichment Analysis of Gene Associations using L-index (SEAGAL) to detect and visualize spatial gene correlations at both single-gene and gene-set levels. Our package takes spatial transcriptomics data sets with gene expression and the aligned spatial coordinates as input. It allows for analyzing and visualizing spatial correlations at both single-gene and gene-set levels. The output could be visualized as volcano plots and heatmaps with a few lines of code, thus providing an easy-yet-comprehensive tool for mining spatial gene associations.Availability and ImplementationThe Python package SEAGAL can be installed using pip:https://pypi.org/project/seagal/. The source code and step-by-step tutorials are available at:https://github.com/linhuawang/SEAGAL.Contactlinhuaw@bcm.edu

A series of cationic π-extended imidazolium salts were synthesized by a sequential Cu-catalyzed arylation/annulation and photocyclization strategy in a simple but efficient way. Among them, a nine-fused-ring compound with a doubly aza[5]helical geometry is by far the largest cationic polycyclic heteroaromatic with a central imidazolium core.

MotivationWith the development in single-cell multi-omics sequencing technology and data integration algorithms, we have entered the single-cell multi-omics era. Current multi-omics analysis algorithms failed to systematically dissect the heterogeneity within the datasets when inferring cis-regulatory events. Thus, there is a need for cis-regulatory element inferring algorithms that considers the cellular heterogeneity.ResultsHere, we propose scGREAT, a single-cell multi-omics regulatory state analysis Python package with a rapid graph-based correlation measurementL. The graph-based correlation method assigns each cell a localLindex, pinpointing specific cell groups of certain regulatory states. Such single-cell resolved regulatory state information enables the heterogeneity analysis equipped in the package. Applying scGREAT to the 10X Multiome PBMC dataset, we demonstrated how it could help subcluster cell types, infer regulation-based pseudo-time trajectory, discover feature modules, and find cluster-specific regulatory gene-peak pairs. Besides, we showed that global L index, which is the average of all local L values, is a better replacement for Pearson’s r in ruling out confounding regulatory relationships that are not of research interests.Availabilityhttps://github.com/ChaozhongLiu/scGREAT

Sequencing-based spatial transcriptomics (ST) techniques have been groundbreaking in dissecting cell-cell communications within tissues by profiling positional gene expression. However, the most widely used ST technique, Visium Spatial Gene Expression by 10x Genomics (Visium), does not provide single-cell resolution, making it difficult to profile cell type-level information. Many reference-based deconvolution methods have been developed to increase its resolution, but the platform and batch effects between the reference and ST data compromise their accuracy. Here, we propose a new approach,Region-based cellSorting (ReSort), that generates a pseudo-internal-reference to reduce these platform effects. By simulating ST datasets under various scenarios, we demonstrate that ReSort significantly improves the accuracy of six state-of-the-art reference-based deconvolution methods. Moreover, applying ReSort to a mouse breast cancer tumor bearing both epithelial and mesenchymal clones identifies the spatial differences of immune cells between the clones, providing important insights for understanding the relationship between epithelial-mesenchymal transition and immune infiltration in breast cancer.

With the growing availability of Alzheimer’s disease (AD) transcriptomic data, several studies have nominated new therapeutic targets. However, a major challenge is accounting for latent (hidden) factors which affect the discovery of therapeutic targets. Using unsupervised machine learning, we identified a latent factor in brain tissue, and we validated the factor in AD and normal samples, across multiple studies, and different brain tissues. Moreover, significant metabolic differences were observed due to the latent factor. The latent factor was found to reflect cell-type heterogeneity in the brain and after adjusting for it, we were able to identify new biological pathways. The changes observed at both transcriptomic and metabolomic levels support the importance of identifying any latent factors before pursuing downstream analysis to accurately identify biomarkers.

Osteoarthritis (OA) is a common degenerative disease with few treatments. In traditional Chinese medicine (TCM), Radix Angelicae biseratae (RAB) is commonly used to treat OA. Nodakenin (Nod) is one main coumarin active component in RAB and exhibits anti-inflammatory, anti-oxidative, and anti-apoptotic effects. Reactive oxygen species (ROS) produced by mitochondria play a vital role in the pathogenesis of OA. We hypothesized that Nod might ameliorate cartilage degradation and inflammatory responses by regulating the mitochondrial Drp1/ROS/NLRP3 axis. With this, the effects of Nod on a mouse model of knee OA and activated primary chondrocytes were assessed. The results showed that Nod intervention improved bone volume, lowered trabecular separation, and increased trabecular number in the subchondral bone. Nod decreased the Osteoarthritis Research Society International (OARSI) scores and increased collagen II-positive areas in the articular cartilage of the tibial plateau. Compared with OA mice, Nod-treated animals exhibited lower levels of inflammatory factors in the serum and synovitis of the knee joint. In vitro results indicated that Nod suppressed dynamin-related protein 1 (Drp1) phosphorylation and massive ROS production by Drp1-dependent mitochondrial fission in lipopolysaccharide-stimulated chondrocytes. Moreover, Nod inhibited the mRNA levels of inflammatory cytokines (COX 2, IL-1β, and TNF-α), nod-like receptor family pyrin domain-containing 3 (NLRP3) inflammasome, and matrix metalloproteinase 13 expression in activated chondrocytes. In conclusion, Nod attenuates cartilage degradation and inflammatory responses in mice with OA by regulating the mitochondrial Drp1/ROS/NLRP3 axis, suggesting its potential for OA therapy.

Motivation Integrating multimodal data represents an effective approach to predicting biomedical characteristics, such as protein functions and disease outcomes. However, existing data integration approaches do not sufficiently address the heterogeneous semantics of multimodal data. In particular, early and intermediate approaches that rely on a uniform integrated representation reinforce the consensus among the modalities but may lose exclusive local information. The alternative late integration approach that can address this challenge has not been systematically studied for biomedical problems. Results We propose Ensemble Integration (EI) as a novel systematic implementation of the late integration approach. EI infers local predictive models from the individual data modalities using appropriate algorithms and uses heterogeneous ensemble algorithms to integrate these local models into a global predictive model. We also propose a novel interpretation method for EI models. We tested EI on the problems of predicting protein function from multimodal STRING data and mortality due to coronavirus disease 2019 (COVID-19) from multimodal data in electronic health records. We found that EI accomplished its goal of producing significantly more accurate predictions than each individual modality. It also performed better than several established early integration methods for each of these problems. The interpretation of a representative EI model for COVID-19 mortality prediction identified several disease-relevant features, such as laboratory test (blood urea nitrogen and calcium) and vital sign measurements (minimum oxygen saturation) and demographics (age). These results demonstrated the effectiveness of the EI framework for biomedical data integration and predictive modeling. Availability and implementation Code and data are available at https://github.com/GauravPandeyLab/ensemble_integration. Supplementary information Supplementary data are available at Bioinformatics Advances online.

An efficient Pd-catalyzed double annulation reaction of 1-(2,6-dibromophenyl)-1H-pyrroles with arynes is developed to synthesize π-extended dibenzo[d,k]ullazines in good to excellent yields. For the first time, the parent dibenzo[d,k]ullazine core is obtained and characterized.

Background Single-cell omics technology is rapidly developing to measure the epigenome, genome, and transcriptome across a range of cell types. However, it is still challenging to integrate omics data from different modalities. Here, we propose a variation of the Siamese neural network framework called MinNet, which is trained to integrate multi-omics data on the single-cell resolution by using graph-based contrastive loss. Results By training the model and testing it on several benchmark datasets, we showed its accuracy and generalizability in integrating scRNA-seq with scATAC-seq, and scRNA-seq with epitope data. Further evaluation demonstrated our model's unique ability to remove the batch effect, a common problem in actual practice. To show how the integration impacts downstream analysis, we established model-based smoothing and cis-regulatory element-inferring method and validated it with external pcHi-C evidence. Finally, we applied the framework to a COVID-19 dataset to bolster the original work with integration-based analysis, showing its necessity in single-cell multi-omics research. Conclusions MinNet is a novel deep-learning framework for single-cell multi-omics sequencing data integration. It ranked top among other methods in benchmarking and is especially suitable for integrating datasets with batch and biological variances. With the single-cell resolution integration results, analysis of the interplay between genome and transcriptome can be done to help researchers understand their data and question.

Sepsis-induced myocardial dysfunction (SIMD), a deadly symptom in sepsis patients, is mainly caused by cardiovascular inflammation. However, it remains unclear how systemic inflammation triggers and aggravates cardiovascular inflammation in the pathogenesis of SIMD. This study found that proinflammatory cytokines and H2 O2 concentrations were significantly induced in SIMD-mice. In particular, a microarray analysis of CD63+ exosomes isolated from sham- and SIMD-monocytes revealed a significant induction of thioredoxin-interacting protein (TXNIP) and NLR family pyrin domain-containing 3 (NLRP3). We proved that oxidative stress caused the disassociation of the TXNIP-TRX2 (thioredoxin 2) complex and the assembly of the TXNIP-NLRP3 complex. In addition, this finding showed that the latter complex could be embedded into CD63+ exosomes and traffic from monocytes to the resident heart macrophages, where it activated caspase-1 and cleaved inactive interleukin 1β (IL-1β) and IL-18. Furthermore, using an amplified luminescent proximity homogeneous assay (Alpha) with GST-TXNIP and His-NLRP3, we obtained a small molecule named PSSM1443 that could disrupt the TXNIP-NLRP3 interaction in vitro, impairing NLRP3 downstream events. Of note, after administering PSSM1443 to the SIMD-mice, we found the small molecule could significantly suppress the activation of caspase-1 and the cleavage of pro-IL-1β and pro-IL-18, reducing inflammation in the SIMD-mice. Collectively, our results reveal that monocyte-derived exosomes harbor the overexpressed TXNIP-NLRP3 complex, which traffics from circulating monocytes to local macrophages and promotes the cleavage of inactive IL-1β and IL-18 in the macrophages, aggravating cardiovascular inflammation. PSSM1443 functions as an inhibitor of the TXNIP-NLRP3 complex and its administration can decrease inflammation in SIMD-mice.

Sepsis-induced myocardial dysfunction (SIMD) is a life-threatening complication caused by inflammation, but how it is initiated is still unclear. Several studies have shown that extracellular high mobility group box 1 (HMGB1), an important cytokine triggering inflammation, is overexpressed during the pathogenesis of SIMD, but the underlying mechanism regarding its overexpression is still unknown. Herein, we discovered that CUL4A (cullin 4A) assembled an E3 ligase complex with RBX1 (ring-box 1), DDB1 (DNA damage-binding protein 1), and DCAF8 (DDB1 and CUL4 associated factor 8), termed CRL4ADCAF8, which ubiquitinated and degraded NcoR1 (nuclear receptor corepressor 1) in an LPS-induced SIMD mouse model. The degradation of NcoR1 failed to form a complex with the SP1 transcription factor, leading to the upregulation of HMGB1. Mature HMGB1 functioned as an effector to induce the expression of proinflammatory cytokines, causing inflammation and resulting in SIMD pathology. Using an in vitro AlphaScreen technology, we identified three small molecules that could inhibit the CUL4A-RBX1 interaction. Of them, PSSM0332 showed the strongest ability to inhibit the ubiquitination of NcoR1, and its administration in SIMD mice exhibited promising effects on decreasing the inflammatory response. Collectively, our results reveal that the CRL4ADCAF8 E3 ligase is critical for the initiation of SIMD by regulating the expression of HMGB1 and proinflammatory cytokines. Our results suggest that PSSM0332 is a promising candidate to inhibit the inflammatory response in the pathogenesis of SIMD, which will provide a new option for the therapy of SIMD.

A Ni(ii)-catalyzed C–H hydroarylation of diarylacetylenes with imidazolium salts without adding any ligand was developed. It provides an easy and efficient access to (E)-2-(1,2-diarylvinyl)imidazolium salts.

Inflammatory pathways represented by TLR4/NF-κB (Toll-like receptor 4/Nuclear factor-κB) axis signaling are activated in the pathogenesis of endotoxin-induced myocardial dysfunction (EIMD). However, the underlying mechanism by which NF-κB coordinates with other transcriptional coactivators/corepressors to regulate the expression of proinflammatory cytokine genes remains unclear. We established an EIMD-mouse model by intraperitoneal injection of lipopolysaccharides (LPS), and we discovered that NCOA1 (nuclear receptor coactivator 1) assembled with CBP (CREB binding protein) and NF-κB subunits to form a transcriptional complex that specifically bound to promoters of proinflammatory cytokine genes to activate their expression. LPS treatment also inhibited DNMT1 (DNA methyltransferase 1) expression, thereby decreasing DNA methylation of a CpG island located on the promoter of NCOA1 and causing NCOA1 overexpression. Screening small molecules that abolished NCOA1-CBP interaction in a yeast system identified a compound PSSM2126 that effectively blocked the NCOA1-CBP interaction in vitro and in vivo. Administration of PSSM2126 to EIMD mice significantly alleviated the inflammation response and improved cardiac function. Collectively, our results reveal that an NCOA1-dependent transactivation mechanism can regulate proinflammatory cytokine expression, thereby improving our understanding of the activation of NF-κB targets. The promising inhibition of the NCOA1-CBP interaction by PSSM2126 may provide a new therapeutic option for EIMD.

We are pleased to introduce a first-of-its-kind tool that combines in-silico region detection and missing value estimation for spatially resolved transcriptomics. Spatial transcriptomics by 10X Visium (ST) is a new technology used to dissect gene and cell spatial organization. Analyzing this new type of data has two main challenges: automatically annotating the major tissue regions and excessive zero values of gene expression due to high dropout rates. We developed a computational tool—MIST—that addresses both challenges by automatically identifying tissue regions and estimating missing gene-expression values for each detected region. We validated MIST detected regions across multiple datasets using manual annotation on the histological staining images as references. We also demonstrated that MIST can accurately recover ST’s missing values through hold-out experiments. Furthermore, we showed that MIST could identify intra-tissue heterogeneity and recover spatial gene-gene co-expression signals. We therefore strongly encourage using MIST before downstream ST analysis because it provides unbiased region annotations and enables accurately denoised spatial gene-expression profiles.

Spatially resolved transcriptomics is a relatively new technique that maps transcriptional information within a tissue. Analysis of these datasets is challenging because gene expression values are highly sparse due to dropout events, and there is a lack of tools to facilitate in silico detection and annotation of regions based on their molecular content. Therefore, we develop a computational tool for detecting molecular regions and region-based Missing value Imputation for Spatially Transcriptomics (MIST). We validate MIST-identified regions across multiple datasets produced by 10x Visium Spatial Transcriptomics, using manually annotated histological images as references. We benchmark MIST against a spatial k-nearest neighboring baseline and other imputation methods designed for single-cell RNA sequencing. We use holdout experiments to demonstrate that MIST accurately recovers spatial transcriptomics missing values. MIST facilitates identifying intra-tissue heterogeneity and recovering spatial gene-gene co-expression signals. Using MIST before downstream analysis thus provides unbiased region detections to facilitate annotations with the associated functional analyses and produces accurately denoised spatial gene expression profiles.

We are pleased to introduce a first-of-its-kind algorithm that combines in-silico region detection and spatial gene-expression imputation. Spatial transcriptomics by 10X Visium (ST) is a new technology used to dissect gene and cell spatial organization. Analyzing this new type of data has two main challenges: automatically annotating the major tissue regions and excessive zero values of gene-expression due to high dropout rates. We developed a computational tool—MIST—that addresses both challenges by automatically identifying tissue regions and estimating missing gene-expression values for individual tissue regions. We validated MIST detected regions across multiple datasets using manual annotation on the histological staining images as references. We also demonstrated that MIST can accurately recover ST’s missing values through hold-out experiments. Furthermore, we showed that MIST could identify subtle intra-tissue heterogeneity and recover spatial gene-gene interaction signals. We therefore strongly encourage using MIST prior to downstream ST analysis because it provides unbiased region annotations and enables accurately de-noised spatial gene-expression profiles.

BackgroundPrediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth.MethodsAs a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset.ResultsApproximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06-0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p=5.23×10−5).ConclusionsWe demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.Key MessagesAs a first step in developing a youth diabetes risk screening tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with prediabetes/diabetes based on American Diabetes Association diagnostic biomarkers.In this cross-sectional study of youth, we found that the screening guideline correctly identified 43.1% of youth with prediabetes/diabetes, the performance of the guideline varied across demographic subgroups, and machine learning based classifiers performed comparably to or better than the screening guideline in identifying youth with prediabetes/diabetes.Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.

To enable efficient computations on rapidly growing big data, a variety of high-performance computing (HPC) platforms, such as traditional multi-processor systems, Hadoop and cloud computing systems, have been developed. On the analytics side of big data, several innovative machine learning methods have been developed to enable the extraction of accurate and actionable knowledge from large datasets. In particular, heterogeneous ensemble algorithms, which are designed to aggregate an unrestricted variety and number of analytical models, have performed well for a variety of prediction problems. However, the performance of these algorithms in terms of computational metrics, such as time requirement, disk space consumption and memory usage, on these HPC platforms has not been systematically examined yet. Here, we address this gap in knowledge by implementing these algorithms and systematically assessing their computational performance on traditional HPC and Hadoop platforms. Our results show that these implementations used the resources, especially disk space and memory, consistent with the respective designs of the platforms. Furthermore, due to the iterative nature of the heterogeneous ensemble computations, the traditional HPC system executed them faster than Hadoop, since an in-memory design is better suited for them than a disk-based one. Overall, our study sheds new light on the computational performance of ensemble algorithms and software frameworks on two prominent HPC platforms, and offers a systematic methodology for conducting similar assessments for other data analytics methods as well. Basic source code of our heterogeneous ensemble implementations, as well as the HPC performance assessments, are available at https://github.com/GauravPandeyLab/HPC-Ensemble.

Steel slag, a byproduct of the steel making process, has been adopted as a material to reduce non-point phosphorus (P) losses from agricultural land. Although substantial studies have been conducted on characterizing P removed by steel slag, few data are available on the removal of P under different conditions of P input, slag mass, and retention time (RT). The objective of this study was to investigate P removal efficiency as impacted by slag mass and RT at different physical locations through a horizontal steel slag column. Downstream slag segments were more efficient at removing P than upstream segments because they were exposed to more favorable conditions for calcium phosphate precipitation, specifically higher Ca2+ concentrations and pH. These results showed that P is removed in a moving front as Ca2+ and slag pH buffer capacity are consumed. In agreement with the calcium phosphate precipitation mechanism shown in previous studies, an increase in RT increased P removal, resulting in an estimated removal capacity of 61 mg kg&minus, 1 at a RT of 30 min. Results emphasized the importance of designing field scale structures with sufficient RT to accommodate the formation of calcium phosphate.

The increasingly fragile environment and associative nitrogen (N) biogeochemical cycle have become critical environmental and ecological issues in China's Loess Plateau. However, N flow and N source for typical catchments remain poorly understood in the Loess Plateau. In this study, we measured concentrations and isotopic signatures of N, hydrogen (H), and oxygen (O) in both rainfall and river water. Results showed that baseflow variation in total nitrogen (TN) concentrations ranged from 0.16 to 32.70 mg L⁻¹. The monthly TN deposition flux and monthly TN wet deposition concentration to river water were shown significant variations between rainy and dry seasons. The range of variation in δ²H values for rainfall and baseflow was from −90.0‰ to 19.8‰ and from −67.2‰ to −38.4‰, respectively, whereas δ¹⁸O–H₂O values ranged from −12.1‰ to +2.7‰ and from −9.3‰ to −3.6‰, respectively. The Local Meteoric Water Line in the check‐dam catchment was δ²H = 7.35 δ¹⁸O + 2.29 (R² = 0.93). Furthermore, NO₃⁻ δ¹⁵N and δ¹⁸O values in baseflow ranged from −2.0‰ to +20.5‰ and from +8.0‰ to +15.6‰, respectively. The results indicated that rainfall was affected by below‐cloud secondary evaporation and caused strong isotopic kinetic fractionation to occur during the precipitation process. The NO₃⁻ in runoff was mainly derived from the nitrification of soil organic matter and manure or sewage, for which the proportion of manure or sewage was from 50.5% to 83%.

Soil erosion on farmland is a critical environmental issue and the main source of sediment in the Yellow River, China. Thus, great efforts have been made to reduce runoff and soil loss by restoring vegetation on abandoned farmland. However, few studies have investigated runoff and soil loss from sloping farmland during crop growth season. The objective of this study was to investigate the effects of soil management on runoff and soil loss on sloping farmland during crop growth season. We tested different soybean growth stages (i.e., seedling stage (R1), initial blossoming stage (R2), full flowering stage (R3), pod bearing stage (R4), and initial filling stage (R5)) and soil management practice (one plot applied hoeing tillage (HT) before each rainfall event, whereas the other received no treatment (NH)) by applying simulated rainfall at an intensity of 80 mm/h. Results showed that runoff and soil loss both decreased and infiltration amount increased in successive soybean growth stages under both treatments. Compared with NH plot, there was less runoff and higher infiltration amount from HT plot. However, soil loss from HT plot was larger than that from NH plot in R1–R3, but lower in R4 and R5. In the early growth stages, hoeing tillage was effective for reducing runoff and enhancing rainfall infiltration. By contrast, hoeing tillage enhanced soil and water conservation during the late growth stages. The total soil loss from HT plot (509.0 g/m2) was 11.1% higher than that from NH plot (457.9 g/m2) in R1–R5. However, the infiltration amount from HT plot (313.9 mm) was 18.4% higher than that from NH plot (265.0 mm) and the total runoff volume from HT plot was 49.7% less than that from NH plot. These results indicated that crop vegetation can also act as a type of vegetation cover and play an important role on sloping farmland. Thus, adopting rational soil management in crop planting on sloping farmland can effectively reduce runoff and soil loss, as well as maximize rainwater infiltration during crop growth period.

The response of runoff and erosion to soil crusts has been extensively investigated in recent decades. However, there have been few attempts to look at the effects of spatial configuration of different soil crusts on erosion processes. Here we investigated the effects of different spatial distributions of physical soil crusts on runoff and erosion in the semi-arid Loess Plateau region. Soil boxes (1.5 m long × 0.2 m wide) were set to a slope of 17.6% (10°) and simulated rainfall of 120 mm h−1 (60 minutes). The runoff generation and erosion rates were determined for three crust area ratios (depositional crust for 20%, 33%, and 50% of the total slope) and five spatial distribution patterns (depositional crust on the lower, lower-middle, middle, mid-upper, and upper slope) of soil crusts. The reduction in sediment loss (‘sediment reduction’) was calculated to evaluate the effects of different spatial distributions of soil crusts on erosion. Sediment yield was influenced by the area ratio and spatial position of different soil crusts. The runoff rate reached a steady state after an initial trend of unsteadily increasing with increasing rainfall duration. Sediment yield was controlled by detachment limitation and then transport limitation under rainfall. The shifting time of erosion from a transport to detachment-limiting regime decreased with increasing area of depositional crust. No significant differences were observed in the total runoff among treatments, while the total sediment yield varied under different spatial distributions. At the same area ratio, total sediment yield was the largest when the depositional crust was on the upper slope, and it was smallest when the crust was deposited on the lower slope. The sediment reduction of structural crust (42.5–66.5%) was greater than that of depositional crust (16.7–34.3%). These results provide a mechanistic understanding of how different spatial distributions of soil crusts affect runoff and sediment production. Copyright © 2017 John Wiley & Sons, Ltd.