Essential Number of Principal Components and Nearly Training-Free Model for Spectral Analysis.

Learning-enabled spectroscopic analysis, promising for automated real-time analysis of chemicals, is facing several challenges. Firstly, a typical machine learning model requires a large number of training samples that physical systems can not provide. Secondly, it requires the testing samples to be in range with the training samples, which often is not the case in the real world. Further, a spectroscopy device is limited by its memory size, computing power, and battery capacity. That requires highly efficient learning models for on-site analysis. In this paper, by analyzing multi-gas mixtures and multi-molecule suspensions, we first show that orders of magnitude reduction of data dimension can be achieved as the number of principal components that need to be retained is the same as the independent constituents in the mixture. From this principle, we designed highly compact models in which the essential principal components can be directly extracted from the interrelations between the individual chemical properties and principal components; and only a few training samples are required. Our model can predict the constituent concentrations that have not been seen in the training dataset and provide estimations of measurement noises. This approach can be extended as an effectively standardized method for principle component extraction.

The impact of China's carbon emissions trading policy on corporate investment expenditures: Evidence from carbon-intensive industries.

The carbon emissions trading (CET) policy internalises the cost of carbon emission reductions borne by companies, which will affect the companies' investment and management decisions. From a micro perspective, this paper analyzes the impact on company investment expenditure and its transmission mechanism by implementing the CET policy. Based on panel data of China's A-share listed companies from eight carbon-intensive industries spanning 2010 to 2020, the time-varying difference-in-difference model and its extended model are used to evaluate the impact of the policy in the pilot areas. The results show that: first, based on the cost effect and legality theories, CET policy can reduce the investment expenditure of the companies by 71.95%. Second, CET policy reduces corporate investment expenditures by increasing corporate debt financing costs. When debt financing costs increase by 120.25%, the investment expenditures will reduce by 2.56% indirectly while the intermediary effect of equity financing costs is not significant. Finally, with the implementation of CET policy, the inhibitory effect on corporate investment expenditures has gradually increased. CET policy has a more significant inhibitory effect on investment expenditures of nonstate-owned companies and small-scale companies. The results have passed the robustness test and provide evidence for the policy-maker to balance microeconomic entity development and carbon reduction, and for companies to make optimization investment and financing decisions in response to policy shocks effectively.

Radiomics as a tool for prognostic prediction in transarterial chemoembolization for hepatocellular carcinoma: a systematic review and meta-analysis.

INTRODUCTION: Transarterial chemoembolization (TACE) is one of the predominant locoregional therapeutic modalities for addressing hepatocellular carcinoma (HCC). However, achieving precise prognostic predictions and effective patient selection remains a challenging pursuit. The primary objective of this systematic review and meta-analysis is to evaluate the efficacy of radiomics in forecasting the prognosis associated with TACE treatment. METHODS: A comprehensive exploration of pertinent original studies was undertaken, encompassing databases of PubMed, Web of Science and Embase. The studies' quality was meticulously evaluated employing the quality assessment of diagnostic accuracy studies 2 (QUADAS-2), the radiomics quality score (RQS) and the METhodological RadiomICs Score (METRICS). Pooled statistics, along with 95% confidence intervals (95% CI), were computed for sensitivity, specificity, positive likelihood ratio (PLR), and negative likelihood ratio (NLR). Additionally, a summary receiver operating characteristic curve (sROC) was generated. To discern potential sources of heterogeneity, meta-regression and subgroup analyses were performed. RESULTS: The systematic review incorporated 29 studies, comprising a total of 5483 patients, with 14 studies involving 2691 patients qualifying for inclusion in the meta-analysis. The assessed studies exhibited commendable quality with regard to bias risk, with mean RQS of 12.90 ± 5.13 (35.82% ± 14.25%) and mean METRICS of 62.98% ± 14.58%. The pooled sensitivity was 0.83 (95% CI: 0.78-0.87), specificity was 0.86 (95% CI: 0.79-0.92), PLR was 6.13 (95% CI: 3.79-9.90), and NLR was 0.20 (95% CI: 0.15-0.27). The area under the sROC was 0.90 (95% CI: 0.87-0.93). Significant heterogeneity within all the included studies was observed, while meta-regression and subgroup analyses revealed homogeneous and promising findings in subgroups where principal methodological variables such as modeling algorithms, imaging modalities, and imaging phases were specified. CONCLUSION: Radiomics models have exhibited robust predictive capabilities concerning prognosis subsequent to TACE, thereby presenting promising prospects for clinical translation.

Study on Pharmacological Activities and Mechanisms of the Essential Oil of the Flowers of Hemerocallis citrina Baroni (EOFHCB) in the Treatment of Anxiety Disorders by GC-MS, Network Pharmacology, and Molecular Docking.

BACKGROUND: Hemerocallis citrina Baroni (Huanghuacai), a plant of the genus Hemerocallis in the family Asphodelaceae, is widely planted in China. Based on our survey results, the chemical compounds in the essential oil of the flowers of Hemerocallis citrina Baroni (EOFHCB) and relevant pharmacological activities have never been studied systematically. OBJECTIVE: To preliminarily decipher the pharmacological activities and mechanisms of EOFHCB in the treatment of anxiety disorders by GC-MS, Network Pharmacology, and Molecular docking. METHODS: EOFHCB compositions were identified using GC-MS, and their targets were predicted using Swiss Target Prediction databases. The targets of anxiety disorders were obtained by GeneCards, DisGeNET, and OMIM databases. The STRING database was used to construct the protein-protein interaction networks, and the DAVID database was used to carry out GO enrichment and KEGG pathway enrichment analysis. The EOFHCB-components-targetspathways- anxiety disorders network was constructed by Cytoscape software (Version 3.10.0). Finally, the result was verified by molecular docking. RESULTS: 28 chemical components were identified by GC-MS, including 3-furanmethanol (28.43%), 2-methyl-1-butanol (27.13%), nerolidol (10.62%), and so on, which correspond to 241 potential targets. Several 2440 biological processes, 187 cellular compositions, and 311 molecular functions were enriched by GO enrichment analysis and 174 pathways by KEGG enrichment analysis. The key targets are PTGS 2, SRC, DRD 2, ESR 1, MAOB, and SLC6A4. The most important pathway is the neuroactive ligand-receptor interaction. CONCLUSION: EOFHCB exerts its therapeutic effects on anxiety disorders through multicomponents, multi-targets, and multi-pathways, which provided new ideas and methods for the in-depth research of aromatic Chinese medicine in the treatment of anxiety disorders.

Prognostic prediction and comparison of three staging programs for patients with advanced (T2-T4) esophageal squamous carcinoma after radical resection.

Purpose: Lymph node-based staging protocols are frequently employed to evaluate the prognosis of esophageal cancer, yet their accuracy remains contentious. The present study was conducted to assess the prognostic significance of three lymph node staging systems, namely N stage, lymph node rate (LNR), and log odds of positive lymph nodes (LODDS), in patients diagnosed with advanced (T2-T4) esophageal squamous cell carcinoma (ESCC). Methods: This cohort comprised 319 eligible patients, with an additional 409 individuals retrieved from the Surveillance, Epidemiology, and End Results (SEER) database, forming the validation cohort. Differences in overall survival (OS) of patients between groups were assessed using the log-rank test. Prognostic independent risk variables were identified, and lymph nodes (LN) prognostic models were built using multivariate Cox regression analysis. Besides, the predictive accuracy of each model was evaluated utilizing the (-2) log-likelihood ratio (-2LLR), the likelihood ratio χ2 score (LRχ2), the Akaike information criterion (AIC), and Harrell's concordance index (C-index). To further evaluate the potential superiority of the model, a nomogram was constructed for comparison with the conventional Tumor Node Metastasis (TNM) staging approach. Results: Independent prognostic factors for advanced ESCC include the N stage, LNR, and LODDS. Herein, LODDS presented higher values for C-index and LRχ2, and lower values for AIC and -2LLR in OS compared to the others. Consequently, a nomogram was constructed based on LODDS. Calibration curves exhibited strong agreement, and assessment through C-index, receiver operating characteristic (ROC) curves, and clinical decision curve analysis (DCA) demonstrated promising clinical applicability. Conclusion: LODDS emerges as a promising future prognostic indicator. After surgery, the proposed model holds the potential to provide valuable treatment recommendations for patients with advanced ESCC.

Research Progress on Mechanical Strength of Rice Stalks.

As one of the most important food crops in the world, rice yield is directly related to national food security. Lodging is one of the most important factors restricting rice production, and the cultivation of rice varieties with lodging resistance is of great significance in rice breeding. The lodging resistance of rice is directly related to the mechanical strength of the stalks. In this paper, we reviewed the cell wall structure, its components, and its genetic regulatory mechanism, which improved the regulatory network of rice stalk mechanical strength. Meanwhile, we analyzed the new progress in genetic breeding and put forward some scientific problems that need to be solved in this field in order to provide theoretical support for the improvement and application of rice breeding.

Glycomol: A pervasive tool for structure predication of natural saponin products basing on MS data.

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Modeling the dynamic impacts of maritime network blockage on global supply chains.

Recent phenomena such as pandemics, geopolitical tensions, and climate change-induced extreme weather events have caused transportation network interruptions, revealing vulnerabilities in the global supply chain. A salient example is the March 2021 Suez Canal blockage, which delayed 432 vessels carrying cargo valued at $92.7 billion, triggering widespread supply chain disruptions. Our ability to model the spatiotemporal ramifications of such incidents remains limited. To fill this gap, we develop an agent-based complex network model integrated with frequently updated maritime data. The Suez Canal blockage is taken as a case study. The results indicate that the effects of such blockages go beyond the directly affected countries and sectors. The Suez Canal blockage led to global losses of about $136.9 ($127.5-$147.3) billion, with India suffering 75% of these losses. Global losses show a nonlinear relationship with the duration of blockage and exhibit intricate trends post blockage. Our proposed model can be applied to diverse blockage scenarios, potentially acting as an early-alert system for the ensuing supply chain impacts. Furthermore, high-resolution daily data post blockage offer valuable insights that can help nations and industries enhance their resilience against similar future events.

Binding sites and design strategies for small molecule GLP-1R agonists.

Glucagon-like peptide-1 receptor (GLP-1R) is a pivotal receptor involved in blood glucose regulation and influencing feeding behavior. It has received significant attention in the treatment of obesity and diabetes due to its potent incretin effect. Peptide GLP-1 receptor agonists (GLP-1RAs) have achieved tremendous success in the market, driving the vigorous development of small molecule GLP-1RAs. Currently, several small molecules have entered the clinical research stage. Additionally, recent discoveries of GLP-1R positive allosteric modulators (PAMs) are also unveiling new regulatory patterns and treatment methods. This article reviews the structure and functional mechanisms of GLP-1R, recent reports on small molecule GLP-1RAs and PAMs, as well as the optimization process. Furthermore, it combines computer simulations to analyze structure-activity relationships (SAR) studies, providing a foundation for exploring new strategies for designing small molecule GLP-1RAs.

Two Cases of False Elevation of MCHC.

BACKGROUND: Mean corpuscular hemoglobin concentration (MCHC) is one of the parameters detected by blood cell analyzers, often used together with mean corpuscular volume (MCV) and mean corpuscular hemoglobin content (MCH) as diagnostic indicators for anemia classification. It has important clinical value in early detection of the cause of anemia and the underlying etiology of anemia. Therefore, the accuracy of MCHC results is of great significance for the diagnosis and treatment of diseases. METHODS: We reported two cases of false elevation of MCHC. Considering the possibility of cold agglutination and lipid blood interference detection, we used 37℃ water bath and plasma exchange to correct for interference on the sample. RESULTS: After correcting the interference, MCHC returned to normal, consistent with the patient's disease status. Therefore, the two cases of abnormal elevation of MCHC are considered to be pseudo elevation caused by interference. CONCLUSIONS: For specimens with abnormally elevated MCHC levels, experimenters should first analyze possible interfering factors and choose effective methods to correct different interferences, providing accurate testing reports for doctors and patients.

[Bibliometrics Analysis of Studies on Hypertrophic Cardiomyopathy From 2018 to 2022].

Objective To analyze the research progress and hot topics in hypertrophic cardiomyopathy from 2018 to 2022.Methods The publications in the field of hypertrophic cardiomyopathy from January 1,2018 to December 31,2022 were retrieved from Web of Science core collection database and included for a bibliometric analysis.Results A total of 6355 publications were included,with an average citation frequency of 7 times.The year 2021 witnessed the most publications (1406).The analysis with VOSviewer showed that the research on sudden death related to hypertrophic cardiomyopathy,especially the predictive value of late gadolinium-enhanced cardiac MRI in sudden death,was a hot topic.In addition,gene detection and the new drug mavacamten became hot research topics.The United States was the country with the largest number of publications and the highest citation frequency in this field.Chinese scholars produced the second largest number of publications,which,however,included few high-quality research results.Conclusions Risk stratification and prevention of sudden death is still an important and hot research content in the field of hypertrophic cardiomyopathy.Chinese scholars should carry out multi-center cooperation in the future to improve the research results.

The Multifunction of TRIM26: From Immune Regulation to Oncology.

Ubiquitination, a crucial post-translational modification, plays a role in nearly all physiological processes. Its functional execution depends on a series of catalytic reactions involving numerous proteases. TRIM26, a protein belonging to the TRIM family, exhibits E3 ubiquitin ligase activity because of its RING structural domain, and is present in diverse cell lineages. Over the last few decades, TRIM26 has been documented to engage in numerous physiological and pathological processes as a controller, demonstrating a diverse array of biological roles. Despite the growing research interest in TRIM26, there has been limited attention given to examining the protein's structure and function in existing reviews. This review begins with a concise overview of the composition and positioning of TRIM26 and then proceeds to examine its roles in immune response, viral invasion, and inflammatory processes. Simultaneously, we demonstrate the contribution of TRIM26 to the progression of various diseases, encompassing numerous malignancies and neurologic conditions. Finally, we have investigated the potential areas for future research on TRIM26.

Multigenerational Adaptation Can Enhance the Pathogen Resistance of Plants via Changes in Rhizosphere Microbial Community Assembly.

Plants withstand pathogen attacks by recruiting beneficial bacteria to the rhizosphere and passing their legacy on to the next generation. However, the underlying mechanisms involved in this process remain unclear. In our study, we combined microbiomic and transcriptomic analyses to reveal how the rhizosphere microbiome assembled through multiple generations and defense-related genes expressed in Arabidopsis thaliana under pathogen attack stress. Our results showed that continuous exposure to the pathogen Pseudomonas syringae pv tomato DC3000 led to improved growth and increased disease resistance in a third generation of rps2 mutant Arabidopsis thaliana. It could be attributed to the enrichment of specific rhizosphere bacteria, such as Bacillus and Bacteroides. Pathways associated with plant immunity and growth in A. thaliana, such as MAPK signaling pathways, phytohormone signal transduction, ABC transporter proteins, and flavonoid biosynthesis, were activated under the influence of rhizosphere bacterial communities. Our findings provide a scientific basis for explaining the relationship between beneficial microbes and defense-related gene expression. Understanding microbial communities and the mechanisms involved in plant responses to disease can contribute to better plant management and reduction of pesticide use.

Baseline hepatobiliary MRI for predicting chemotherapeutic response and prognosis in initially unresectable colorectal cancer liver metastases.

PURPOSE: To evaluate the performance of hepatobiliary MRI parameters as predictors of clinical response to chemotherapy in patients with initially unresectable colorectal cancer liver metastases (CRLM). METHODS: Eighty-five patients with initially unresectable CRLM were retrospectively enrolled from two hospitals and scanned using gadobenate dimeglumine-enhanced MRI before treatment. Therapy response was evaluated based on the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. Conventional parameters (i.e., signal intensity [SI]) and radiomics features of portal venous phase (PVP) and hepatobiliary phase (HBP) images were analyzed between the responders and non-responders. Next, the combined model was constructed, and the area under the receiver operating characteristic (ROC) curve (AUC) was calculated. The relationship between the combined model and progression-free survival (PFS) was analyzed using Cox regression. RESULTS: Of the 85 patients from two hospitals, 42 were in the response group, and 43 were in the non-response group. Upon conducting five-fold cross-validation, the normalized relative enhancement (NRE) of CRLM during the PVP yielded an AUC of 0.625. Additionally, a radiomics feature derived from the tumor area in the HBP achieved an AUC of 0.698, while a separate feature extracted from the peritumoral region in the HBP recorded an AUC of 0.709. The model that integrated these three features outperformed the individual features, achieving an AUC of 0.818. Furthermore, the combined model exhibited a significant correlation with PFS (P < 0.001). CONCLUSION: The combined model, based on baseline hepatobiliary MRI, aids in predicting chemotherapeutic response and PFS in patients with initially unresectable CRLM.

ScribSD+: Scribble-supervised medical image segmentation based on simultaneous multi-scale knowledge distillation and class-wise contrastive regularization.

Despite that deep learning has achieved state-of-the-art performance for automatic medical image segmentation, it often requires a large amount of pixel-level manual annotations for training. Obtaining these high-quality annotations is time-consuming and requires specialized knowledge, which hinders the widespread application that relies on such annotations to train a model with good segmentation performance. Using scribble annotations can substantially reduce the annotation cost, but often leads to poor segmentation performance due to insufficient supervision. In this work, we propose a novel framework named as ScribSD+ that is based on multi-scale knowledge distillation and class-wise contrastive regularization for learning from scribble annotations. For a student network supervised by scribbles and the teacher based on Exponential Moving Average (EMA), we first introduce multi-scale prediction-level Knowledge Distillation (KD) that leverages soft predictions of the teacher network to supervise the student at multiple scales, and then propose class-wise contrastive regularization which encourages feature similarity within the same class and dissimilarity across different classes, thereby effectively improving the segmentation performance of the student network. Experimental results on the ACDC dataset for heart structure segmentation and a fetal MRI dataset for placenta and fetal brain segmentation demonstrate that our method significantly improves the student's performance and outperforms five state-of-the-art scribble-supervised learning methods. Consequently, the method has a potential for reducing the annotation cost in developing deep learning models for clinical diagnosis.

Metagenomic and culture-dependent analysis of Rhinopithecius bieti gut microbiota and characterization of a novel genus of Sphingobacteriaceae.

Culture-dependent and metagenomic binning techniques were employed to gain an insight into the diversification of gut bacteria in Rhinopithecius bieti, a highly endangered primate endemic to China. Our analyses revealed that Bacillota_A and Bacteroidota were the dominant phyla. These two phyla species are rich in carbohydrate active enzymes, which could provide nutrients and energy for their own or hosts' survival under different circumstances. Among the culturable bacteria, one novel bacterium, designated as WQ 2009T, formed a distinct branch that had a low similarity to the known species in the family Sphingobacteriaceae, based on the phylogenetic analysis of its 16S rRNA gene sequence or phylogenomic analysis. The ANI, dDDH and AAI values between WQ 2009T and its most closely related strains S. kitahiroshimense 10CT, S. pakistanense NCCP-246T and S. faecium DSM 11690T were significantly lower than the accepted cut-off values for microbial species delineation. All results demonstrated that WQ 2009T represent a novel genus, for which names Rhinopithecimicrobium gen. nov. and Rhinopithecimicrobium faecis sp. nov. (Type strain WQ 2009T = CCTCC AA 2021153T = KCTC 82941T) are proposed.

Vision-Based Estimation of Force Balance of Near-Suspended Melt Pool for Drooping and Collapsing Prediction.

Wire-arc additive manufacturing (WAAM) is favored by the industry for its high material utilization rate and low cost. However, wire-arc additive manufacturing of lattice structures faces problems with forming accuracy such as broken rod and surface morphology defects, which cannot meet the industrial demand. This article innovatively combines the melt pool stress theory with visual perception algorithms to visually study the force balance of the near-suspended melt pool to predict the state of the melt pool. First, the method for melt pool segmentation was studied. The results show that the optimized U-net achieved high accuracy in melt pool segmentation tasks, with accuracies of 98.18%, MIOU 96.64%, and Recall 98.34%. In addition, a method for estimating melt pool force balance and predicting normal, sagging, and collapsing states of the melt pool is proposed. By combining experimental testing with computer vision technology, an analysis of the force balance of the melt pool during the inclined rod forming process was conducted, showing a prediction rate as high as 90% for the testing set. By using this method, monitoring and predicting the state of the melt pool is achieved, preemptively avoiding issues of broken rods during the printing process. This approach can effectively assist in adjusting process parameters and improving welding quality. The application of this method will further promote the development of intelligent unmanned WAAM and provide some references for the development of artificial intelligence monitoring systems in the manufacturing field.

Association between systemic immunity-inflammation index and psoriasis among outpatient US adults.

Background: Psoriasis is a chronic dermatological condition characterized by a complex pathogenesis that impacts approximately 3% of adults in the United States and brings enormous social burdens. For many diseases, the systemic immune-inflammatory index (SII), defined as neutrophils × platelets/lymphocytes, has been recognized as a prognostic indicator. Therefore, we conducted a cross-sectional study to assess the association between SII and psoriasis among outpatient US adults. Methods: In this cross-sectional study, we used data on the US adults 20 to 59 years of age from the National Health and Nutrition Examination Survey (NHANES) spanning 2003-2006 and 2009-2014. Sample-weighted logistic regression and stratified analysis of subgroups were used. Results: Among the 16,831 adults, there were 8,801 women and 8,030 men, with a psoriasis prevalence rate of 3.0%. A fully adjusted model revealed a positive association between a SII higher than 479.15 × 109/L and a high risk of psoriasis. According to subgroup analysis and interaction testing (p for interaction > 0.05), age, sex, alcohol drinking status, marital status, and body mass index (BMI) were not significantly correlated with this positive association. Conclusion: Our findings suggested that SII higher than 479.15 × 109/L was positively associated with a high risk of psoriasis among outpatient US adults. To the best of our knowledge, this is the first cross-sectional study using NHANES data focused on the risk of higher SII on psoriasis among outpatient US adults. The outcomes of this cross-sectional serve to supplement previous research, indicating a need for larger-scale prospective cohorts for further validation.

The Effects of Different Durations of Night-Time Supplementary Lighting on the Growth, Yield, Quality and Economic Returns of Tomato.

To achieve higher economic returns, we employ inexpensive valley electricity for night-time supplementary lighting (NSL) of tomato plants, investigating the effects of various durations of NSL on the growth, yield, and quality of tomato. Tomato plants were treated with supplementary light for a period of 0 h, 3 h, 4 h, and 5 h during the autumn-winter season. The findings revealed superior growth and yield of tomato plants exposed to 3 h, 4 h, and 5 h of NSL compared to their untreated counterparts. Notably, providing lighting for 3 h demonstrated greater yields per plant and per trough than 5 h exposure. To investigate if a reduced duration of NSL would display similar effects on the growth and yield of tomato plants, tomato plants received supplementary light for 0 h, 1 h, 2 h, and 3 h at night during the early spring season. Compared to the control group, the stem diameter, chlorophyll content, photosynthesis rate, and yield of tomatoes significantly increased upon supplementation with lighting. Furthermore, the input-output ratios of 1 h, 2 h, and 3 h NSL were calculated as 1:10.11, 1:4.38, and 1:3.92, respectively. Nonetheless, there was no detectable difference in yield between the 1 h, 2 h, and 3 h NSL groups. These findings imply that supplemental LED lighting at night affects tomato growth in the form of light signals. Night-time supplemental lighting duration of 1 h is beneficial to plant growth and yield, and its input-output ratio is the lowest, which is an appropriate NSL mode for tomato cultivation.

Unveiling the Nature and Strength of Selenium-Centered Chalcogen Bonds in Binary Complexes of SeO2 with Oxygen-/Sulfur-Containing Lewis Bases: Insights from Theoretical Calculations.

Among various non-covalent interactions, selenium-centered chalcogen bonds (SeChBs) have garnered considerable attention in recent years as a result of their important contributions to crystal engineering, organocatalysis, molecular recognition, materials science, and biological systems. Herein, we systematically investigated π-hole-type Se∙∙∙O/S ChBs in the binary complexes of SeO2 with a series of O-/S-containing Lewis bases by means of high-level ab initio computations. The results demonstrate that there exists an attractive interaction between the Se atom of SeO2 and the O/S atom of Lewis bases. The interaction energies computed at the MP2/aug-cc-pVTZ level range from -4.68 kcal/mol to -10.83 kcal/mol for the Se∙∙∙O chalcogen-bonded complexes and vary between -3.53 kcal/mol and -13.77 kcal/mol for the Se∙∙∙S chalcogen-bonded complexes. The Se∙∙∙O/S ChBs exhibit a relatively short binding distance in comparison to the sum of the van der Waals radii of two chalcogen atoms. The Se∙∙∙O/S ChBs in all of the studied complexes show significant strength and a closed-shell nature, with a partially covalent character in most cases. Furthermore, the strength of these Se∙∙∙O/S ChBs generally surpasses that of the C/O-H∙∙∙O hydrogen bonds within the same complex. It should be noted that additional C/O-H∙∙∙O interactions have a large effect on the geometric structures and strength of Se∙∙∙O/S ChBs. Two subunits are connected together mainly via the orbital interaction between the lone pair of O/S atoms in the Lewis bases and the BD*(OSe) anti-bonding orbital of SeO2, except for the SeO2∙∙∙HCSOH complex. The electrostatic component emerges as the largest attractive contributor for stabilizing the examined complexes, with significant contributions from induction and dispersion components as well.