A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles.

We previously piloted the concept of a Connectivity Map (CMap), whereby genes, drugs, and disease states are connected by virtue of common gene-expression signatures. Here, we report more than a 1,000-fold scale-up of the CMap as part of the NIH LINCS Consortium, made possible by a new, low-cost, high-throughput reduced representation expression profiling method that we term L1000. We show that L1000 is highly reproducible, comparable to RNA sequencing, and suitable for computational inference of the expression levels of 81% of non-measured transcripts. We further show that the expanded CMap can be used to discover mechanism of action of small molecules, functionally annotate genetic variants of disease genes, and inform clinical trials. The 1.3 million L1000 profiles described here, as well as tools for their analysis, are available at https://clue.io.

Efficient prediction of peptide self-assembly through sequential and graphical encoding.

In recent years, there has been an explosion of research on the application of deep learning to the prediction of various peptide properties, due to the significant development and market potential of peptides. Molecular dynamics has enabled the efficient collection of large peptide datasets, providing reliable training data for deep learning. However, the lack of systematic analysis of the peptide encoding, which is essential for artificial intelligence-assisted peptide-related tasks, makes it an urgent problem to be solved for the improvement of prediction accuracy. To address this issue, we first collect a high-quality, colossal simulation dataset of peptide self-assembly containing over 62 000 samples generated by coarse-grained molecular dynamics. Then, we systematically investigate the effect of peptide encoding of amino acids into sequences and molecular graphs using state-of-the-art sequential (i.e. recurrent neural network, long short-term memory and Transformer) and structural deep learning models (i.e. graph convolutional network, graph attention network and GraphSAGE), on the accuracy of peptide self-assembly prediction, an essential physiochemical process prior to any peptide-related applications. Extensive benchmarking studies have proven Transformer to be the most powerful sequence-encoding-based deep learning model, pushing the limit of peptide self-assembly prediction to decapeptides. In summary, this work provides a comprehensive benchmark analysis of peptide encoding with advanced deep learning models, serving as a guide for a wide range of peptide-related predictions such as isoelectric points, hydration free energy, etc.

Dual-Mode Multicolor Display Based on Structural and Fluorescent Color CdS Photonic Crystal Hydrogel.

In this study, we prepared a multicolor structural-fluorescent CdS-PEGDA photonic crystal hydrogel (SFC-CPH) with a dual display mode, which has two different optical states: structural color mode and fluorescent color mode. SFC-CPH displays structural color mode under visible light and fluorescent color mode under ultraviolet light. Initially, monodisperse CdS colloidal particles were synthesized via a hydrothermal method, leading to the self-assembly of a photonic crystal template. The high refractive index of CdS contributes to the photonic crystals' low-angle dependence and vivid structural colors. Then, a variety of fluorescent molecules were doped into poly(ethylene glycol) diacrylate (PEGDA) hydrogel and combined with photonic crystals with distinct structural colors to prepare three distinct colors of SFC-CPH. We also investigated the optical characteristics and surface properties of these photonic crystal hydrogels. Based on these dual-mode display characteristics, we designed several dual-mode display patterns and a method for information encoding. The unique property of this photonic crystal hydrogel material suggests its substantial potential for applications in information storage, security, and encoding, offering innovative avenues in the realm of information display.

Operational encoding enhances action knowledge integration: insights from event-related potential analysis.

In this study, we conducted an examination of knowledge integration concerning action information and assessed the impact of operational on this process. Additionally, we delved into the underlying mechanisms of how operational encoding influences the processing of knowledge integration of action information, utilizing the event-related potential technique. The results of our investigation revealed that operational encoding, encompassing the observed operational encoding and the imagined operational encoding, exhibited superior performance in the integration of action knowledge compared to verbal encoding. This distinction may be attributed to the greater efficiency of operant encoding in activating motor cortical areas, thereby inducing more robust brain activity. These findings suggest the potential advantages of operational encoding in facilitating the integration of knowledge related to movement information at both cognitive and neural levels, underscoring its significant role in the processing of such information. Future studies can further explore the applications of operational encoding in domains, such as motor learning, skill training, and rehabilitation therapy. Such investigations may offer novel insights into enhancing human behavior and motor control.

The quality of guidelines on the pancreatic perioperative enhanced recovery after surgery: a systematic quality appraisal using AGREE II instrument.

PURPOSE: To evaluate the quality of guidelines on the pancreatic perioperative enhanced recovery after surgery both domestically and internationally, providing reference and reference for clinical practice. METHODS: Systemically retrieved in the guideline websites, professional association websites and databases, such as up to date, BMJ Best Practice, PubMed, Embase, The Cochrane Library, Web of Science, China National Knowledge Infrastructure (CNKI), Wan Fang Data, China Science and Technology Journal Database(VIP), China Biology Medicine disc (CBMdisc), Medlive, Guidelines International Network(GIN), National Guideline Clearinghouse(NGC), National Institute for Health and Care Excellence(NICE), Registered Nurses Association of Ontario(RNAO), Scottish Intercollegiate Guidelines Network(SIGN), Joanna Briggs Institute Library(JBI), including guidelines and expert consensus on enhanced postsurgical recovery in pancreatic surgery published as of December 20, 2023. The Appraisal of Guidelines for Research and Evaluation II(AGREE II) tool was applied to evaluate the quality of the guidelines by four assessors. RESULTS: This study included seven guidelines, all of which were rated as Grade B in terms of quality, with ICC coefficients ranging from 0.752 to 0.884, indicating a high level of consistency. CONCLUSION: When formulating guidelines in the future, it is recommended to use AGREE II as a reference, emphasizing the standardization of the guideline development process and methods, fully considering patients' values and preferences, focusing on the applicability of the guidelines, and striving to create high-quality evidence-based recommendations.

Dual depletion of myeloid-derived suppressor cells and tumor cells with self-assembled gemcitabine-celecoxib nano-twin drug for cancer chemoimmunotherapy.

Myeloid-derived suppressor cells (MDSCs) have played a significant role in facilitating tumor immune escape and inducing an immunosuppressive tumor microenvironment. Eliminating MDSCs and tumor cells remains a major challenge in cancer immunotherapy. A novel approach has been developed using gemcitabine-celecoxib twin drug-based nano-assembled carrier-free nanoparticles (GEM-CXB NPs) for dual depletion of MDSCs and tumor cells in breast cancer chemoimmunotherapy. The GEM-CXB NPs exhibit prolonged blood circulation, leading to the preferential accumulation and co-release of GEM and CXB in tumors. This promotes synergistic chemotherapeutic activity by the proliferation inhibition and apoptosis induction against 4T1 tumor cells. In addition, it enhances tumor immunogenicity by immunogenic cell death induction and MDSC-induced immunosuppression alleviation through the depletion of MDSCs. These mechanisms synergistically activate the antitumor immune function of cytotoxic T cells and natural killer cells, inhibit the proliferation of regulatory T cells, and promote the M2 to M1 phenotype repolarization of tumor-associated macrophages, considerably enhancing the overall antitumor and anti-metastasis efficacy in BALB/c mice bearing 4T1 tumors. The simplified engineering of GEM-CXB NPs, with their dual depletion strategy targeting immunosuppressive cells and tumor cells, represents an advanced concept in cancer chemoimmunotherapy.

Short-term personality development and early career success: Two longitudinal studies during the post-graduation transition.

OBJECTIVE: Investigate short-term personality development during the post-graduation transition. BACKGROUND: Prior research indicates that long-term personality development matters for employment outcomes. However, this evidence is primarily limited to multi-year longitudinal studies. This research switches the focus to personality changes during a shorter, impactful life transition. METHOD: We examined how short-term personality development during the 14-month post-graduation transition relates to early career outcomes among two diverse samples of graduates from universities (N = 816) and community colleges (N = 567). We used latent growth curve models to examine associations between career outcomes measured 14 months after graduation with initial personality levels and personality changes. RESULTS: Results revealed that mean-level changes in personality were small and mostly negative. Moreover, individual differences in personality changes were not associated with career outcomes. However, initial levels of conscientiousness, emotional stability, and extraversion positively related to both subjective and objective career success. Initial levels of agreeableness were also positively related to subjective (but not objective) success. CONCLUSIONS: Findings indicate that individual differences in personality trait levels at graduation are stronger predictors of early career success compared to short-term personality changes during the post-graduation transition. Taken together, these results help define the time sequence through which personality changes relate to career outcomes.

Effect of size on the thermal noise and acoustic response of viscous-driven microbeams.

A study is presented of the thermal-mechanical noise and response to sound of microphones that are designed to be driven by the viscous forces in air rather than by sound pressure. Virtually all existing microphone designs are intended to respond to sound pressure. The structures examined here consist of thin, micro-scale, cantilever beams. The viscous forces that drive the beams are proportional to the relative velocity between the beams and fluid medium. The beams' movement in response to sound is similar to that of the air in a plane acoustic wave. The thermal-mechanical noise of these beams is found to be a very weak function of their width and length; the size of the sensing structure does not appear to significantly affect the performance. This differs from the well-known importance of the size of a pressure-sensing microphone in determining the pressure-referred noise floor. Creating microphones that sense fluid motion rather than pressure could enable a significant reduction in the size of the sensing element. Calculated results are revealed to be in excellent agreement with the measured pressure-referred thermal noise.

Role of ventrolateral part of ventromedial hypothalamus in post-myocardial infarction cardiac dysfunction induced by sympathetic nervous system.

Psychological stress has been recognized as a contributing factor to worsened prognosis in patients with cardiac failure following myocardial infarction (MI). Although the ventrolateral part of the ventromedial hypothalamus (VMHVL) has been implicated in emotional distress, its involvement in post-MI cardiac dysfunction remains largely unexplored. This study was designed to investigate the effect of the VMHVL activation in the MI rat model and its underlying mechanisms. Our findings demonstrate that activation of VMHVL neurons enhances the activity of the cardiac sympathetic nervous system through the paraventricular nucleus (PVN) and superior cervical ganglion (SCG). This activation leads to an elevation in catecholamine levels, which subsequently modulates myosin function and triggers the release of anti-inflammatory factors, to exacerbate the post-MI cardiac prognosis. The denervation of the superior cervical ganglion (SGN) effectively blocked the cardiac sympathetic effects induced by the VMHVL activation, and ameliorated the cardia fibrosis and dysfunction. Therefore, our study identified the role of the "VMHVL-PVN-SCG" sympathetic pathway in the post-MI heart, and proposed SGN as a promising strategy in mitigating cardiac prognosis in stressful rats.

Boosting Sensitivity and Durability of Pressure Sensors Based on Compressible Cu Sponges by Strengthening Adhesion of "Rigid-Soft" Interfaces.

The interface adhesion plays a key role between rigid metal and elastomer in compressible and stretchable conductors. However, the poor interfacial adhesion hinders their wide applications. To strengthen the interface adhesion, herein, a combination strategy of structure interlocking and polymer bridging is designed by introducing a method of subsurface-initiated atom transfer radical polymerization (sSI-ATRP). This method can make polymer brush root in polydimethylsiloxane (PDMS) subsurface, on this basis, metals further grow from subsurface to surface of PDMS via electroless deposition. As a result, the adhesive strength (≈2.5 MPa) between metal layer and PDMS elastomer is 4 times higher than that made by common polymer modification. As a demonstration, pressure sensor is constructed by using as-prepared compressible 3D Cu sponge as a top electrode and paper-based interdigited metal electrode as a bottom electrode. The device sensitivity can reach up to 961.2 kPa-1 and the durability can arrive at 3 000 cycles without degradation. Thus, this proposed interface-enhancement strategy for rigid-soft materials can significantly promote the performance of piezoresistive pressure sensors based on 3D conductive sponge. In the future, it would also be expanded to the fabrication of stretchable conductors and extensively applied in other flexible and wearable electronics.

Highly efficient enhancement and extension of focusing ability for ring Pearcey beam by means of dual-region parabolic trajectory offset modulation.

It is a highly significant area of research to investigate how to effectively enhance the focusing ability of abruptly auto-focusing beams (AAFBs) while extending the focal length. We introduce a dual-region parabolic trajectory offset modulation to auto-focusing ring Pearcey beams (RPBs), presenting a novel, to the best of our knowlege, approach to extend the focal length while greatly enhancing their auto-focusing capabilities. Unlike directly introducing a linear chirp, which inevitably shortens the focal length to enhance the auto-focusing ability and allows only single focusing in the RPBs, our scheme can achieve a multi-focusing effect. Furthermore, we have experimentally generated such a beam, verifying our theoretical predictions. Our findings offer promising possibilities for generating optical bottles, trapping multiple particles periodically, and enhancing free-space optical communication capabilities.

Heat shock protein 90 inhibitors induce cell differentiation via the ubiquitin-dependent aurora kinase A degradation in a MPLW515L mouse model of primary myelofibrosis.

Primary myelofibrosis (PMF) is characterized by immature megakaryocytic hyperplasia, splenomegaly, extramedullary hematopoiesis and bone marrow fibrosis. Our preclinical study had demonstrated that aurora kinase A (AURKA) inhibitor MLN8237 reduced the mutation burden of PMF by inducing differentiation of immature megakaryocytes. However, it only slightly alleviated splenomegaly, reduced tissue fibrosis, and normalized megakaryocytes in PMF patients of the preliminary clinical study. So enhancing therapeutic efficacy of PMF is needed. In this study, we found that AURKA directly interacted with heat shock protein 90 (HSP90) and HSP90 inhibitors promoted the ubiquitin-dependent AURKA degradation. We demonstrated that HSP90 inhibitors 17-allylamino-17-demethoxygeldanamycin (17-AAG) and 17-dimethylaminoethylamino-17-demethoxygeldanamycin (17-DMAG), normalized peripheral blood counts, improved splenomegaly, attenuated extramedullary hematopoiesis, decreased tissue fibrosis and reduced mutant burden in a MPLW515L mouse model of PMF. Importantly, both 17-AAG and 17-DMAG treatment at effective doses in vivo did not influence on hematopoiesis in healthy mice. Collectively, the study demonstrates that HSP90 inhibitors induce cell differentiation via the ubiquitin-dependent AURKA and also are safe and effective for the treatment of a MPLW515L mouse model of PMF, which may provide a new strategy for PMF therapy. Further, we demonstrate that combined therapy shows superior activity in acute megakaryocytic leukemia mouse model than single therapy.

Identification and validation of major-effect quantitative trait locus QMS-5B associated with male sterility in photo-thermo-sensitive genic male sterile wheat.

KEY MESSAGE: QMS-5B, a major QTL for photo-thermo-sensitive genic male sterility in wheat, was fine mapped in a 2.15 Mb region harboring a serine/threonine protein kinase gene TraesCS5B03G0887500, which was the most likely candidate gene. Genic male sterility is an essential trait in the utilization of heterosis and hybrid seed production for wheat. Currently, genic male sterile genes have been reported in wheat mutants, but the sterile genes controlling photo-thermo-sensitive genic male sterility in wheat have not been studied systematically. Here, 235 doubled haploid lines derived from a cross between photo-thermo-sensitive genic male sterile line BS462 and its restorer line CP279 were used to map male sterile gene by GenoBaits® Wheat 100 K Panel, bulked segregant exome sequencing (BSE-Seq) and wheat 660 K array. As a result, the major stable QTL on chromosome 5B, QMS-5B, was identified in all four environments, accounting for 7.3-36.4% of the phenotypic variances. Ulteriorly, QMS-5B was delimited to an approximate 2.15 Mb physical interval between KASP-5B5 and KASP-5B6 using kompetitive allele-specific PCR (KASP) markers. Within the interval, twenty-nine high-confidence genes were predicted according to Chinese Spring RefSeq v2.1. TraesCS5B03G0887500, encoding a serine/threonine protein kinase, was identified as the most likely candidate gene for QMS-5B based on weighted gene co-expression network analysis. Expression analysis confirmed that TraesCS5B03G0887500 was significantly differentially expressed in anthers of BS462 and CP279 at different stages under fertile and sterile environments. In addition, flanking KASP marker KASP-5B6 can effectively genotype male sterile lines and restorer lines, and can be used for molecular marker-assisted selection. This study provides insights into for exploring the mechanism of photo-thermo-sensitive genic male sterility in wheat.

Increased sympathetic outflow induced by emotional stress aggravates myocardial ischemia-reperfusion injury via activation of TLR7/MyD88/IRF5 signaling pathway.

BACKGROUND AND OBJECTIVE: Emotional stress substantially increases the risk of ischemic cardiovascular diseases. Previous study indicates that sympathetic outflow is increased under emotional stress. We aim to investigate the role of increased sympathetic outflow induced by emotional stress in myocardial ischemia-reperfusion (I/R) injury, and explore the underlying mechanisms. METHODS AND RESULTS: We used Designer Receptors Exclusively Activated by Designer Drugs technique to activate the ventromedial hypothalamus (VMH), a critical emotion-related nucleus. The results revealed that emotional stress stimulated by VMH activation increased sympathetic outflow, enhanced blood pressure, aggravated myocardial I/R injury, and exacerbated infarct size. The RNA-seq and molecular detection demonstrated that toll-like receptor 7 (TLR7), myeloid differentiation factor 88 (MyD88), interferon regulatory factor 5 (IRF5), and downstream inflammatory markers in cardiomyocytes were significantly upregulated. Emotional stress-induced sympathetic outflow further exacerbated the disorder of the TLR7/MyD88/IRF5 inflammatory signaling pathway. While inhibition of the signaling pathway partially alleviated myocardial I/R injury aggravated by emotional stress-induced sympathetic outflow. CONCLUSION: Increased sympathetic outflow induced by emotional stress activates TLR7/MyD88/IRF5 signaling pathway, ultimately aggravating I/R injury.

Physical adsorption and oxidation of ultra-thin MoS2crystals: insights into surface engineering for 2D electronics and beyond.

The oxidation mechanism of atomically thin molybdenum disulfide (MoS2) plays a critical role in its nanoelectronics, optoelectronics, and catalytic applications, where devices often operate in an elevated thermal environment. In this study, we systematically investigate the oxidation of mono- and few-layer MoS2flakes in the air at temperatures ranging from 23 °C to 525 °C and relative humidities of 10%-60% by using atomic force microscopy (AFM), Raman spectroscopy and x-ray photoelectron spectroscopy. Our study reveals the formation of a uniform nanometer-thick physical adsorption layer on the surface of MoS2, which is attributed to the adsorption of ambient moisture. This physical adsorption layer acts as a thermal shield of the underlying MoS2lattice to enhance its thermal stability and can be effectively removed by an AFM tip scanning in contact mode or annealing at 400 °C. Our study shows that high-temperature thermal annealing and AFM tip-based cleaning result in chemical adsorption on sulfur vacancies in MoS2, leading to p-type doping. Our study highlights the importance of humidity control in ensuring reliable and optimal performance for MoS2-based electronic and electrochemical devices and provides crucial insights into the surface engineering of MoS2, which are relevant to the study of other two-dimensional transition metal dichalcogenide materials and their applications.

Nanofiltration Membranes with Salt-Responsive Ion Valves for Enhanced Separation Performance in Brackish Water Treatment: A Battle against the Limitation of Salt Concentration.

Utilizing brackish water resources has imposed a high requirement on the design and construction of nanofiltration membranes. To overcome the limitation of high salt concentration on the nanofiltration separation performance resulting from the weakened Donnan effect, a nanofiltration membrane with the effect of salt-responsive ion valves was developed by incorporating zwitterionic nanospheres into the polyamide layer (PA-ZNs). The interaction between the nanospheres and membranes at high salinity was revealed through a combination analysis from the perspectives of water transport model, positron annihilation spectroscopy, and solute rejection, contributing to the formation of the valve effect. The PA-ZNs membrane presented a breakthrough in overcoming the limitation of increased salt concentrations on nanofiltration separation performance, achieving a high selectivity of 105 for mono/multivalent anions. To reveal the role of the ion valve effect in ion transport through the membrane, the membrane conductance was determined at different salt concentrations, confirming channel-controlled transport at low salinity and ion valve-controlled transport at high salinity. Moreover, the main membrane separation mechanisms were systematically studied. The concept of salt-responsive ion valves may contribute to expanding the application of nanofiltration in brackish water treatment.

Association of at-home and out-of-home eating frequency with the estimated 10-year arteriosclerotic cardiovascular disease risk in rural population: the Henan Rural Cohort Study.

PURPOSE: Insufficient evidence currently exists regarding the relationship between eating frequency and arteriosclerotic cardiovascular disease (ASCVD). Thus, the objective of this study was to explore the association of at-home eating (AHE) and out-of-home eating (OHE) frequency with 10-year ASCVD risk. METHODS: A total of 23,014 participants were included from the Henan Rural Cohort Study. A face-to-face questionnaire was used to acquire data on the frequency of OHE and AHE. The relationship of OHE and AHE frequency with 10-year ASCVD risk was evaluated by logistic regression. Mediation analysis was conducted to evaluate whether BMI mediated the association of OHE and AHE frequency with 10-year ASCVD risk. RESULTS: The adjusted OR and 95% CI of 10-year ASCVD risk for participants who ate out 7 or more times a week was 2.012 (1.666, 2.429) compared with participants who had OHE 0 times. Compared to those who had AHE ≤ 11 times, the adjusted OR and 95% CI for the participants eating every meal at home (21 times) was 0.611 (0.486, 0.769). The relationship of OHE and AHE frequency with 10-year ASCVD risk was mediated by BMI, and the proportion of BMI explained was 25.3% and 36.6%. CONCLUSIONS: The OHE frequency was associated with increased 10-year ASCVD risk, while AHE was related to decreased 10-year ASCVD risk, and BMI may play a partial mediating role in the relationship. Implementing health promotion strategies that promote AHE and discourage frequent OHE may prove to be an effective approach to preventing and controlling ASCVD. TRIAL REGISTRATION NUMBER: ChiCTR-OOC-15006699 (2015-07-06).

A novel deep learning ensemble model based on two-stage feature selection and intelligent optimization for water quality prediction.

Accurate prediction of effluent total nitrogen (E-TN) can assist in feed-forward control of wastewater treatment plants (WWTPs) to ensure effluent compliance with standards while reducing energy consumption. However, multivariate time series prediction of E-TN is a challenge due to the complex nonlinearity of WWTPs. This paper proposes a novel prediction framework that combines a two-stage feature selection model, the Golden Jackal Optimization (GJO) algorithm, and a hybrid deep learning model, CNN-LSTM-TCN (CLT), aiming to effectively capture the nonlinear relationships of multivariate time series in WWTPs. Specifically, convolutional neural network (CNN), long short-term memory (LSTM), and temporal convolutional network (TCN) combined to build a hybrid deep learning model CNN-LSTM-TCN (CLT). A two-stage feature selection method is utilized to determine the optimal feature subset to reduce the complexity and improve the accuracy of the prediction model, and then, the feature subset is input into the CLT. The hyperparameters of the CLT are optimized using GJO to further improve the prediction performance. Experiments indicate that the two-stage feature selection model learns the optimal feature subset to predict best, and the GJO-CLT achieves the best performance for different backtracking windows and prediction steps. These results demonstrate that the prediction system excels in the task of multivariate water quality time series prediction of WWTPs.

Ensemble water quality forecasting based on decomposition, sub-model selection, and adaptive interval.

The prediction of effluent quality for wastewater treatment plants (WWTPs) has caused widespread concern due to its essential role in ensuring water quality standards and reducing energy consumption. However, the complex nonlinearity of WWTPs leads to difficulties in forecasting and less attention to forecast uncertainty. A novel ensemble water quality forecasting (EWQF) system is proposed that incorporates data preprocessing, point prediction and interval prediction. The system provides an accurate prediction of effluent quality and analyses this uncertainty, for enabling feed-forward control of WWTPs. Specifically, the original water quality data is decomposed into subsequences containing more information and less noise based on improved variational modal decomposition (IVMD). The optimal sub-model for each sub-series is selected from six prediction models based on the sub-model selection strategy, and the point prediction results for water quality are obtained by combining the prediction results of the sub-models. Robust and reliable prediction interval construction based on adaptive kernel density estimation. The results demonstrate that the EWQF achieves optimal point prediction results (R2 = 0.955). The EWQF interval prediction achieves the optimal coverage width criterion (CWC) for different confidence intervals and decision objectives. These results demonstrate that EWQF systems can perform excellent point and interval prediction.

Identify nitrogen transport paths and sources contribution in karst valley depression area using isotopic approach.

Karst groundwater provides drinking water for a quarter of Earth's population. However, in intensive agricultural regions worldwide, karst water is commonly polluted by nitrate (NO3-), particularly in the valley depression areas with well hydrological connectivity. The valley depression aquifers are particularly vulnerable to anthropogenic pollution because their pipes and sinkholes respond quickly to rainfall events and anthropogenic inputs. Identifying nitrate sources and transport paths in the valley depression areas is key to understanding the nitrogen cycle and effectively preventing and controlling NO3- pollution. This study collected high-resolution samples at four sites including one surface stream-SS, two sinkholes-SH and reservoir-Re, during the wet season in the headwater sub-catchment. The chemical component concentrations and stable isotopes (δ15N-NO3- and δ18O-NO3-) were analyzed. The stable isotope analysis model in R language (SIAR) was used to quantitatively analyze the contribution rate of NO3- sources. The results showed that the down section site (Re) has the highest [NO3--N], followed by SH and the lowest SS. The sources calculation of SIAR demonstrated that, during the non-rainfall period, soil organic nitrogen was the primary source of the down section site, followed by fertilizer and the upper reaches sinkholes. During the rainfall period, fertilizer was the primary source of the down section site, followed by soil organic nitrogen and from upper reaches sinkholes. Rainfall events accelerated fertilizer-leaching into the groundwater. Slight denitrification may have occurred at the sampling sites but the assimilation of Re and SH could not occur. In conclusion, agricultural activities were still the primary influencing factor of [NO3--N] in the study area. Therefore, the focus of NO3- prevention and control in the valley depression areas should consider the methods and timing of fertilization and the spatial distribution of sinkholes. To reduce nitrogen flux in the valley depression area, effective management policy should consider, e.g., prolongation of water residence time by wetland, and blocking nitrogen loss paths by sinkholes.