This study aims to investigate the influences of maternal dietary patterns on the triacylglycerol (TAG) composition, particular medium- and long-chain triacylglycerol (MLCT) in human breast milk. A total of 180 Chinese human breast milk samples were collected and divided into three dietary patterns groups accounting to semi-quantitative food intake frequency questionnaire and a 24-hour dietary recall. Using high performance liquid chromatography combined with quadrupole time-of-flight mass spectrometry, 108 TAGs were identified and quantified. In breast milk samples, MLCTs accounted for 25.76 % (median of 24.63%; 95% CI: 24.60, 26.92) of the total TAGs. In human breast milk, the main type of MLCT is TAGs esterified with one medium-chain fatty acid and two long-chain fatty acids (MLL type) which accounted for ~81% of total MLCTs. Among the molecular species of MLCT, O-P-La, O-L-La, and O-L-M are the three most abundant configurations in MLCT (2.28%, 2.17%, and 2.01% of total TAGs, respectively). MLCT in breast milk was positively correlated with the energy supply ratio of carbohydrates (P = 0.205), which was significantly higher than that of the other two groups (P = −0.150 and −0.232).

Objective To investigate the value of quantitative computed tomography (QCT) body composition parameters in assessing the prognosis of patients with resectable rectal cancer (RC). Methods A total of 94 patients with colorectal adenocarcinoma from 2016 to 2021 were retrospectively collected. All patients underwent QCT scan before operation. Skeletal muscle cross-sectional area, total fat area, subcutaneous fat area and visceral fat area were measured, and skeletal muscle index (SMI), total fat tissue index (TATI), subcutaneous fat index (SATI) and visceral fat index (VATI) were calculated. TATI, SATI, and VATI truncation values were obtained to predict postoperative outcomes in RC patients, and sarcopenia (SA) was evaluated by SMI. Results The cut off values of TATI, SATI, and VATI were 106.39 cm 2/m 2, 47.33 cm 2/m 2, and 42.61 cm 2/m 2. The overall incidence of SA was 30.0% (28/94). Tumor N-stage, SA, and VATI were independent risk factors for postoperative outcomes in RC patients ( P < 0.05). The AUC of the nomogram model was 0.844 (95% CI: 0.755 - 0.911). Conclusion Tumor N-stage, SA, and VATI correlated with prognosis in RC patients undergoing surgery. The nomogram model established in this study has some value for evaluating the prognosis of patients with resectable RC.

Background Neuroblastoma (NB) is a childhood cancer of the sympathetic nervous system, and its prognosis is poor. NB undergoes transcription changes to use aerobic glycolysis as its primary metabolic pathway. Aerobic glycolysis provides an immediate source of ATP in metastasis proliferation, and therefore may be exploited in dietary therapies, such as the ketone diet. Procedure In this study, the expression of glycolytic and ketolytic genes in the context of MYCN oncogene amplification, tumor staging, and Kaplan-Meier survivability was investigated using the R2: Genomics analysis and visualisation platform (http://r2.amc.nl). The R2 database is a platform for gene expression and prognostic data for primary tumour samples. Six NB genomics datasets were accessed and further analysed in GraphPad Prism to investigate the relationships between glycolytic and ketolytic gene expression and prognosis. Results Glycolytic gene expression is increased in MYCN amplified, metastatic tumours, and is associated with worse event free survival. Ketolytic gene expression is lower in metastatic tumours and is associated with better event free survivability. The glycolytic gene expression profile of NB suggests that elevated levels correlate with tumorigenicity and low probability of survival. Ketolytic gene expression patterns suggest a decreased reliance for ketolytic energy, which may be exploited to slow tumorigenic growth. Conclusions This study validates the glycolytic gene expression profiles in metastatic and MYCN-amplified NB tumours and suggest the potential use of these genes in prognosis prediction. Furthermore, the study highlights the reliability and utility of the R2 database as an oncogenomic tool for NB research.

Background: There has been an increase in the number of patients with lung cancer who had previously contracted SARS-CoV-2 and currently require surgery. This study was to share the early postoperative outcomes in patients who underwent radical lung cancer surgery after early recovery of COVID-19 infection. Methods We retrospectively collected data for 99 patients who had undergone lung cancer surgery in our hospital during January 2022 and January 2023 (including the peak of the COVID-19 crisis）. The patients were divided into two groups according to the inclusion criteria and exclusion criteria. One of these included individuals with a history of SARS-CoV-2 infection. Perioperative and follow-up data at 30-day and 90-day were recorded. Results There were no statistical differences between groups (p > 0.05) in terms of their postoperative complications or 30-day and 90-day postoperative readmission rates. However, there were significant differences between groups (p < 0.05) in terms of their tumor sizes, pathological stages, total drainage volumes, drainage diversion times, and hospital stays. Conclusions The results of the present trial suggested that it is safe to implement radical curative lung cancer surgery in patients without pulmonary impairment and in the early stages (2–4 weeks) of recovery from SARS-CoV-2 infection.

Influenza-A-virus (IAV) is one of the common threats to humankind since 1918. The viral proteome is frequently substituted leading to new strains and recurrent pandemics. Despite knowing the effects of single amino acid substitutions on individual viral proteins, effects of collective substitutions on viral infection remain elusive. Here, we addressed the question whether the “consistent amino substitutions” occur in consortium on functional domains and protein-protein interaction (PPI) sites, impacting overall viral infection and host immune responses. By definition, “consistent substitutions” occur on “all” the IAV strains isolated in a particular year. Big protein data (563370 sequences and 9824 PPI) and bioinformatics techniques were exploited to address this question. Total one-hundred-and-five “consistent substitutions” were mapped on IAV proteome. Fifty of those emerged on viral functional domains and PPIs, engaged in the specific stages of IAV infection, namely, i) cell surface entry and exit, ii) nuclear import, vRNP assembly and nuclear export and iii) antagonizing immune responses. The study for the first time showed that consortium of “consistent substitutions” emerged on protein functional domains and PPIs, impacting specific stages of viral infection, rather than a single protein, and presumably navigate viral escape from human immune response.

Objectives: To investigate the oral symptoms of COVID-19 ’s “positive” group in Suining City after the comprehensive liberalization of epidemic prevention and control measures in China. To explore the related influencing factors of symptom differences and provide reference for the treatment of patients in the post-epidemic era. Methods: An online survey based on a random sample of 25 questions. Results: 1768 valid questionnaires were collected. The data showed that 363 people had oral problems after an infection with novel coronavirus. The oral problems after SARS‐CoV‐2 infection mainly focused on gingiva swelling or bleeding (38.57%), toothache (37.74%), bad breath (27.55%), oral ulcer (30.30%), abnormal taste (24.24%) and some infected people had a variety of oral symptoms. A small number of infected people also have other oral problems. The incidence of oral symptoms was significantly correlated with sex, age, educational level, place of residence, smoking, basic diseases, long-term saline gargle and vaccination of COVID-19 vaccine. In addition, the age, place of residence, smoking history, basic diseases, and vaccination also showed significant differences in prognosis. Conclusions: In the post-epidemic era, the most common oral problems are gingiva swelling or bleeding, toothache, bad breath, oral ulcer, and abnormal taste. And maintaining good living habits is the basis for promoting disease recovery and oral health.

In this paper, we consider the predefined-time extended state observer (PTESO) design for a class of high-order strict-feedback systems subject to Hölder continuous nonlinearities and multiple disturbances. By developing an adaptive mechanism and using time-varying functions, a class of adaptive PTESOs is designed, in which the convergence time can be tightly and explicitly preset by only one parameter, irrelevant to the initial conditions. The adaptive mechanism is used to improve the transient performance under wide-range lumped disturbance. Moreover, thanks to the design of the time-varying gains, the PTESO could realize fast observation with trivial peaking observation errors. Finally, simulation results are provided to illustrate the effectiveness of the proposed observer.

Background: Human papillomaviruses (HPV) of the genus Betapapillomavirus can infect both cutaneous and mucosal sites, but research on its natural history at mucosal sites remains scarce. We examined the risk factors and co-detection patterns of HPVs of the Betapapillomavirus and Alphapapillomavirus genera in cervical samples of the Ludwig-McGill cohort study. Methods: We assessed a subset of 505 women from the Ludwig-McGill cohort study from São Paulo, Brazil. Cervical samples over the first year of follow-up were tested for DNA of over 40 alphapapillomavirus types and 43 betapapillomavirus types using a type-specific multiplex genotyping PCR assay. We assessed the risk factors for prevalent and incident betapapillomavirus type detection, and whether types were detected more frequently together than expected assuming independence using permutation tests, logistic regression, and Cox regression. Results: We observed significant within-genus clustering but not cross-genus clustering. Multiple betapapillomavirus types were co-detected in the same sample 2.24 (95%CI: 1.65-3.29) times more frequently than expected. Conversely, co-detections of alphapapillomavirus and betapapillomavirus types in the same sample occurred only 0.64 (95%CI: 0.51-0.83) times as often as expected under independence. In prospective analyses, positivity to one HPV genus was associated with a non-significant lower incidence of detection of types in the other genus. Lifetime number of sex partners and new sex partner acquisition were associated with lower risks of prevalent and incident betapapillomavirus detection. Conclusion: Betapapillomaviruses are commonly found in the cervicovaginal tract. Results suggest potentially different mechanisms of transmission for betapapillomavirus genital infections other than vaginal sex.

The information on the microbiome’s human pathways and active members that can affect SARS-CoV-2 susceptibility and pathogenesis in the salivary proteome is very scarce. Here, we studied samples collected from April to June 2020 from unvaccinated patients. We compared 10 infected and hospitalized patients with severe (n=5) and moderate (n=5) Coronavirus Disease (COVID-19) with 10 uninfected individuals, including Non-COVID but susceptible individuals (n=5) and Non-COVID and non-susceptible healthcare workers with repeated high-risk exposures (n=5). By performing high-throughput proteomic profiling in saliva samples, we detected 226 unique differentially expressed (DE) human proteins between groups (q-value ≤0.05) out of 2721 unambiguously identified proteins (false discovery rate ≤1%). Major differences were observed between the Non-COVID vs the non-susceptible groups. Bioinformatics analysis of DE proteins revealed human proteomic signatures related to inflammatory responses, central cellular processes, and antiviral activity associated with saliva of SARS-CoV-2 infected patients (p-value ≤0.0004). Discriminatory biomarker signatures from human saliva include cystatins, protective molecules present in the oral cavity, calprotectins, involved in cell cycle progression, and histones, related to nucleosome functions. The expression level of two human proteins related to protein transport in the cytoplasm, named DYNC1 (p-value, 0.0021) and MAPRE1 (p-value, 0.047), correlated with angiotensin-converting enzyme 2 (ACE2) plasma activity. Finally, the proteomes of microorganisms present in the saliva samples showed 4 main microbial functional features related to ribosome functioning that are overrepresented in the infected group. Our study explores potential candidates involved in pathways implicated in SARS-CoV-2 susceptibility although further studies in larger cohorts will be necessary.

Abstract Objective: Evaluate surgical outcomes of Carotid body tumors (CBT), especially postoperative complications (PC) and the influence of cranial nerve injuries (CNI) on patients’ quality of life (QoL). Design: A prospective follow-up study (2004-2020) utilizing a database from Charité University Hospital and follow-up questionnaires EORTC QLQ-C30 and QLQ-H&N43. Setting: A single-institution study. Participants: Patients with histopathologically confirmed CBT and an ICD code 44.6. Main Outcome Measures: Tumor characteristics, complications, and postoperative QoL (EORTC QLQ-C30, QLQ-H&N43) Results: From 40 identified CBT patients, 55% were females with an average age of 51.1. 47.5% tumors were right-sided; 70% presented with painless neck swelling. 12.5% had a Succinate dehydrogenase mutation. Average tumor volume was 133 cm³. CNIs affected 45%. Preoperative embolization (PE) was done in 47.5%, not reducing PC (p=0.98). No association was found between tumor size and PC: gender (p=0.7), age (p=0.847), co-morbidities (p=0.13). Average stay was 8.07 days. Patients without complications had better QoL (M=92.59 vs. M=69.44, p=.019); those without CNIs also had better QoL (p=.068). Males had superior QoL scores (p=.05). PC affected functioning, especially social functioning (p = .05) and CNX injury (p= .041). Conclusion: CBT surgery significantly affects nerve function and QoL. The results present the need for effective postoperative care, monitoring and management of complications to boost patient outcomes. Gender differences in QoL after CBT surgery merit further investigation into possible physiological and environmental factors.

A historic challenge for shotgun proteomics has been the requirement for high quality, simple and nonredundant curated protein sequences in small .fasta text files. Due to the intrinsic informatic challenges and time required to assemble these files, proteomics has struggled to expand beyond the confines of a few model organisms. When considering post-translational modifications that may or may not be present on a specific peptide sequence, these factors inevitably compound. A study on how mangos continue to ripen on the shelf may not be the first thing you'd think of as proof of a scientific discipline shedding historic limitations. However, Bautiste-Valle et al., may be just that. These authors present a quantitative comparison of both peptide and glycopeptide alterations through the complexity of the fruit ripening process and in this we see the present state of a field that no longer needs to wait on genomics to obtain deep mechanistic insights.

Esophageal cancer is a prevalent malignant tumor in the digestive tract, ranking seventh in terms of incidence among all malignant tumors. Therefore, identifying new targets and prognostic markers is crucial for the successful treatment of this disease. In this study, we analyzed two GEO microarray dataset using the GOE2R tool to find differentially expressed genes. And then used DAVID, KEGG, ChEA, X2K, HMDB, and mirTarBase to analyze the differentially gene-associated gene ontology, signaling pathways, transcription factors, kinases, and miRNAs. The PPI interaction network was constructed by Spring. Hub gene was analyzed by Cytoscape and theirs expression level verified by GEPIA. The results showed that 307 genes were up-regulated, and 295 genes were down-regulated in the two datasets. GO and KEGG analysis revealed that the up-regulated genes were mainly involved in collagen fibril organization, extracellular matrix and cell division, KEGG showed that ECM-receptor interaction, cell cycle, and the IL-17 signaling pathway is the improtant pathway. The top 10 hub genes were CCNA2, CDK1, AURKA, MKI67, CDC6, KIF20A, AURKB, KIF14, BUB1, and ASPM. In addition to the AURKA gene, these genes were up-regulated in esophageal cancer tumor tissues according to the GEPIA database. The top five transcription factors identified were FOXM1, SOX2, E2F4, BACH1, and CEBPD. The most important kinases in esophageal cancer were CDK1, CSNK2A1, and MAPK14. miRNA analysis revealed that hsa-miR-193b-3p, hsa-miR-192-5p and hsa-miR-1-3p could target the most up-regulated genes. In summary, the core genes, transcription factors, and metabolites identified in esophageal cancer tissues could serve as potential biomarkers for the diagnosis and treatment of esophageal cancer in the future.

The 2022 global mpox outbreak swiftly introduced unforeseen diversity in the monkeypox virus (MPXV) population resulting in numerous Clade IIb sublineages. This propagation of new MPXV mutations warrants thorough re-investigation of previously recommended or validated primers designed to target MPXV genomes. In this study, we explored 18 PCR primer sets and examined their binding specificity against 5,210 MPXV genomes, representing all established MPXV lineages. Our results indicated that only five primer sets resulted in almost all perfect matches against the targeted MPXV lineages, and the remaining primer sets all contained 1-2 mismatches against almost all MPXV lineages. We further investigated mismatch primer-genome pairs and revealed that some of the primers overlaid with poorly sequenced and assembled regions of the MPXV genomes that are consistent across multiple lineages. However, we identified 173 99% genome-wide conserved regions across all 5,210 MPXV genomes representing 30 lineages/clades with at least 80% lineage-specific consensus for future primer development and primer binding evaluation. This exercise is crucial to ensure current detection schemes are robust and serves as a framework for primer evaluation in clinical testing development for other infectious diseases.

Colour polymorphism can be maintained by colour morph-specific benefits across environmental conditions. Currently, the amount and duration of snow cover during winter decreases especially in northern latitudes, which can alter the potential for camouflage of animals with light and dark morphs. Tawny owls, Strix aluco, are colour polymorphic avian predators with dark (brown) and light (grey) colour morphs, where the grey morph is presumed to enjoy camouflage benefits under snowy conditions. We studied the two tawny owls’ morphs’ camouflage potential using passerines’ probability to detect and mob in the wild during spring, autumn, and winter with and without snow. We find that grey tawny owls are both less likely to be detected and have a lower probability of being mobbed compared to brown tawny owls only during snowy winters. The two colour morphs therefore experience differential benefits across snow conditions, which may help to maintain colour morphs in the population, although further warming of winter climate will reduce the potential for camouflage for grey tawny owls in northern latitudes.

Reinforcement learning (RL), a subset of machine learning (ML), can potentially optimize and control biomanufacturing processes, such as improved production of therapeutic cells. Here, the process of CAR-T cell activation by antigen presenting beads and their subsequent expansion is formulated in-silico. The simulation is used as an environment to train RL-agents to dynamically control the number of beads in culture with the objective of maximizing the population of robust effector cells at the end of the culture. We make periodic decisions of incremental bead addition or complete removal. The simulation is designed to operate in OpenAI Gym which enables testing of different environments, cell types, agent algorithms and state-inputs to the RL-agent. Agent training is demonstrated with three different algorithms (PPO, A2C and DQN) each sampling three different state input types (tabular, image, mixed); PPO-tabular performs best for this simulation environment. Using this approach, training of the RL-agent on different cell types is demonstrated, resulting in unique control strategies for each type. Sensitivity to input-noise (sensor performance), number of control step interventions, and advantage of pre-trained agents are also evaluated. Therefore, we present a general computational framework to maximize the population of robust effector cells in CAR-T cell therapy production.

Background: Neutropenic fever is a definite side effect of intensive chemotherapy. It is essential to initiate rapid and effective treatment for febrile neutropenia. Restoring the adrenal reserve and maintaining its anti-inflammatory role is one of the treatment goals. Objective: to Study the benefit of adding hydrocortisone to cancer children with high -risk neutropenic fever and its effect on duration of hospitalization, clinical status, and patients’ outcome. Patients and methods: An interventional single blind randomized controlled study including 50 pediatric cancer patients. Patients were randomized into a standard group (A) following the standard of care for management of high -risk febrile neutropenia and interventional group (B) using hydrocortisone infusion in the first 48h of febrile neutropenia as an adjuvant therapy. Results: Neutropenic fever period and hospital admission days were shorter in group B than group A ( p =0.052 and 0.005 respectively). The need to change the empiric antibiotic was less for group B than group A ( p = 0.024). Systolic blood pressure was higher in group B than group A ( p = 0.040). Conclusion: The use of hydrocortisone was associated with shorter duration of neutropenic fever and hospital stay, less need for second line antibiotics and protective against hemodynamic instability.

Qinghai Province is an important part of the ‘ Third Pole of the World ’ Tibetan Plateau. Under the background of global warming, the climate and ecological environment of the Tibetan Plateau have undergone dramatic changes. Assessing the response of vegetation cover change to climate change in Qinghai Province in the 21st century and driving force analysis will help to understand the trend of climate change in the Tibetan Plateau and its impact on the ecological environment. It is of great significance to promote the ecological protection and high-quality development of the Tibetan Plateau. Based on Google Earth Engine cloud computing platform, MODIS MOD13Q1 data, land use type data, meteorological data, digital elevation model data and population density data, this paper uses pixel dichotomy model, trend analysis, coefficient of variation method and other methods to comprehensively analyze the temporal and spatial variation characteristics of vegetation cover in Qinghai Province from 2000 to 2020, and quantitatively analyzes the response characteristics of vegetation cover change to driving factors in the past 21 years through geodetector, and discusses the driving factors affecting vegetation cover change. Explanatory power, interaction type and mechanism of action. The Hurst index was used to predict the future trend of vegetation cover in Qinghai Province.

Coastal wetlands are crucial ecosystems at the interface between land and sea. In the context of economic development and urbanization, these wetlands face challenges such as reduction in area and fragmentation. Ecological networks can connect fragmented habitats, creating corridors for material, information, and energy transmission. This is vital for maintaining biological and landscape diversity and ensuring the healthy development of ecosystems. However, there is currently no research on the ecological networks in the Hebei-Tianjin coastal wetlands. In this study, the morphological spatial pattern analysis (MSPA) method is employed to identify wetlands sources, while the minimum cumulative resistance (MCR) model is used to extract potential ecological corridors. By combining these with existing river corridors, the ecological network of coastal wetlands in Hebei and Tianjin is constructed, and regional network characteristics are analyzed. Critical areas of ecological protection and restoration are determined, including important ecological corridors, ecological pinch points, and ecological breakpoints. The results showed that: (1) The ecological network of Hebei-Tianjin coastal wetlands consisted of 38 ecological sources, 171 potential ecological corridors, and 399 river corridors, with a total area of 851.31 km2. (2) Key ecological protection and restoration areas were proposed, including 35 crucial potential wetlands ecological corridors, 343 ecological pinch points, and 99 ecological breakpoints. Targeted restoration of these critical areas could significantly improve the connectivity of wetlands ecological networks. (3) At present, priority should be given to protecting critical ecological corridors and existing river corridors with high similarity to potential ecological corridors. The findings of this study can provide a scientific basis for the network construction and protection of Hebei-Tianjin coastal wetlands.

To enable personalized genetics and medicine, it is important yet highly challenging to accurately predict disease-causing mutations from the sequences alone at high throughput. To meet this challenge, we build upon recent progress in machine learning, network analysis, and protein language models, and develop a sequences-based variant site prediction workflow based on the protein residue contact networks: 1. We employ and integrate various methods of building protein residue networks using state-of-the-art coevolution analysis tools (e.g., RaptorX, DeepMetaPSICOV, and SPOT-Contact) powered by deep learning. 2. We use machine learning algorithms (e.g., Random Forest, Gradient Boosting, and Extreme Gradient Boosting) to optimally combine 13 network centrality scores (calculated by NetworkX) with 7 other network scores calculated from the contact probability matrices to jointly predict key residues as hot spots for disease mutations. 3. Using a dataset of 107 proteins rich in disease mutations, we rigorously evaluate the network scores individually and collectively in comparison with alternative structures-based network scores (using predicted structures by AlphaFold). By optimally combing three coevolution analysis methods and the resulting network scores by machine learning, we are able to discriminate deleterious and neutral mutation sites accurately (AUC of ROC ~ 0.84). Furthermore, by combining our method with a state-of-the-art predictor of the functional effects of sequence variations based on large protein language models, we have significantly improved the prediction of disease variant sites (AUC ~ 0.89). This work supports a promising strategy of combining an ensemble of network scores based on different coevolution analysis methods via machine learning to predict candidate sites of disease mutations, which will inform downstream applications of disease diagnosis and targeted drug design.

Diffuse large B cell lymphoma presenting with hypersomnia

Secure and reliable electricity supply is a prerequisite for the development of smart cities, and the trustworthy and efficient transmission of electrical data is the foundation for the safe and stable operation of the power grid. This paper introduces a real-time data transmission blockchain technique based on parallel proof of work algorithm. The new block generation progress of proposed blockchain is divided into five subroutines: hash pointer computation, real-time data pudding, signature value iteration, interruption, block header assembly. The real-time data pudding and signature value iteration are parallel processed, which brings the effect of decreasing energy loss of blockchain system, and upgrades the speed of new block generation and the bandwidth of data storing on blockchain. Computer simulation shows the proposed strategy can be effectively applied in real-time electrical data transmission application, raising the data transmission reliability with no harm to real-time data transfer function. This strategy provides a solution to guarantee data transmission safety in the digital conversion of power grid.

In order to ensure the power safety of important power customers, a new evaluation of power consumption status of important power customers based on the AHP(Analytic Hierarchy Process)-TOPSIS(Technique for Order Preference by Similarity to an Ideal Solution) algorithm is proposed by fully mining and applying the power big data. Firstly, a power consumption big data analysis platform based on the Hadoop architecture is built to provide a high-performance platform support for big data analysis. Secondly, nine evaluation indexes are constructed from the three dimensions of voltage, load and synthesis, which objectively and scientifically describes the power consumption status of important power customers. Finally, the AHP-TOPSIS algorithm is used to evaluate and analyze the voltage, load and comprehensive indicators respectively, thus, obtaining the evaluation values of three kinds of indicators. The power consumption status scores of important power customers are determined by the variable weight weighted summation. The rationality and feasibility of the method and algorithm are proved by example analysis and field verification. This method helps to promote the transformation from post fault emergency repair to warning beforehand. It has the multiple effects of ensuring safe power consumption, supporting accurate patrolling and active emergency repair serving.

N. Sahu1*, S.K. Mallick1,2, P. Das1,3, A. Saini4, T. Sayama5, A. Varun1, B. Pani6, A. Kumar1, and R. Kesharwani11Department of Geography, Delhi School of Economics, University of Delhi, New Delhi, 110007, India2Department of Geography, Shaheed Bhagat Singh College, University of Delhi, New Delhi,110017, India‘3Department of Geography, Shaheed Bhagat Singh Evening College, University of Delhi, New Delhi,110017, India4Delhi School of Climate Change & Sustainability, Institution of Eminence, University of Delhi, Delhi, New Delhi 110007, India5Disaster Prevent Research Institute, Innovative Disaster Prevent Technology and Policy Research Laboratory, Kyoto University, Gokasho, Uji City, Kyoto, Japan6Department of Chemistry (Environmental Science), Bhaskarcharya College of Applied Science, University of Delhi, New Delhi 110075, India*Corresponding author: Netrananda Sahu (babunsahu@gmail.com)ORCID: https://orcid.org/0000-0001-8505-7185Key Points:Spring water reduction in the upper HimalayasProlonged dry climatic condition reflects in spring water disappearanceEstablish the relationship between rainfall and surface runoffAbstractDue to climate change and human activity, the drop in spring-water discharge poses a serious issue in the Himalayan region, especially in the higher section of Himachal Pradesh. This study used topographic index assumptions, complex geological circumstances, and rainfall data from 1901 to 2021 to understand the decrease in spring-water discharge. It was determined which climate parameter was most closely correlated with spring discharge volumes using a multi-linear correlation plot. Based on 40 years of daily average rainfall data, a rainfall-runoff model was utilised to predict and assess trends in spring-water discharge using the MIKE 11 NAM hydrological model. The model’s effectiveness was effectively proved by the validation results (NSE=0.79, R2=0.944, RMSE=0.23, PBIAS=32%). Consequently, spring disappearance directly impacted hydropower electricity generation, the entire groundwater regime, and agriculture productivity as well as hampering livelihood in the prolonged dry season indirectly. A possible solution is to preserve rainwater in smaller reservoirs or water tanks during the rainy season, to build resilience for the dependent population. This study will help researchers and policymakers to think and work on the spring disappearance and water security issue in the Himalayan region.

A polar cubic equation of state (EOS) is developed by incorporating the dipolar theory of Jog and Chapman into the Soave-Redlich-Kwong (SRK) EOS. We propose simplifying assumptions in the dipolar term of Jog and Chapman to reduce the double and triple sums in the theory to single sums. The simplified version of the dipolar theory can significantly improve computational speed and can be used with either Cubic EOS or SAFT-based EOS. The proposed model, which we here call polar-SRK (P-SRK), is parametrized in a similar fashion to classical cubic EOS to exactly reproduce T_ci,P_ci,ω_i, and will self-consistently reduce to the base SRK EOS in the absence of polar interactions. Binary VLE data with a non-polar reference hydrocarbon is used to extract the polarity of the respective functional group. The model shows superior performance in capturing the phase behavior of polar mixtures compared to the base SRK.