bluuebunny/crossref_metadata_2025 huggingface dataset:
Proteome-wide analysis of lysine β-hydroxybutyrylation in the myocardium of diabetic rat model with cardiomyopathy
Authors: Luo Weiguang , He Mei , Luo Qizhi , Li Yi
Published: January 2023

Lysine ß-hydroxybutyrylation (kbhb), a novel modification of lysine residues with the ß-hydroxybuty group, is associated with ketone metabolism in numerous species. However, its potential role in diabetes, especially in diabetic cardiomyopathy (DCM), remains largely unexplored. In this study, using affinity enrichment and liquid chromatography-mass spectrometry (LC-MS/MS) method, we quantitatively analyze the kbhb residues on heart tissues of a DCM model rat. A total of 3,520 kbhb sites in 1,089 proteins were identified in this study. Further analysis showed that 336 kbhb sites in 143 proteins were differentially expressed between the heart tissues of DCM and wild-type rats. Among them, 284 kbhb sites in 96 proteins were upregulated, while 52 kbhb sites in 47 proteins were downregulated. Bioinformatic analysis of the proteomic results revealed that these kbhb-modified proteins were widely distributed in various components and involved in a wide range of cellular functions and biological processes (BPs). Functional analysis showed that the kbhb-modified proteins were involved in the tricarboxylic acid cycle, oxidative phosphorylation, and propanoate metabolism. Our findings demonstrated how kbhb is related to many metabolic pathways and is mainly involved in energy metabolism. These results provide the first global investigation of the kbhb profile in DCM progression and can be an essential resource to explore DCM’s pathogenesis further.
Efficacy of tolvaptan on the short and mid-term prognosis in elderly patients with acute heart failure coexisting with oliguria: A retrospective cohort study
Authors: Liu Yang , Zhang Yabin , Chen Hongyu , Zhao Jiahui , Ma Qiang , Yang Guang , Wang Xiaohua , Wu Zhen , Hou Jiebin , Cheng Qingli , Ao Qiangguo
Published: January 2023

Background

In patients with acute heart failure (AHF) coexisting with oliguria, high doses of loop diuretics are often ineffective in increasing urine output and may adversely affect the patient's prognosis, especially in elderly patients. We investigated the efficacy of adding tolvaptan (TLV) on improving the prognosis in elderly patients with AHF coexisting with oliguria.

Methods

All data for this retrospective cohort study were extracted from the electronic medical record system of the Second Medical Center of Chinese PLA General Hospital from January 2018 to December 2020. Patients diagnosed with AHF coexisting with oliguria were enrolled in this study and were divided into TLV and non-TLV groups based on the use of TLV. The primary outcome was all-cause mortality at 7 and 90-day. The secondary outcomes were the remission of AHF within 7 and 30 days or continued progression of AHF, and new-onset chronic kidney disease (CKD) after 90 days. Cox proportional hazards regression was used to assess the relationships between all-cause mortality and diuretic regimens, demographics, laboratory parameters, comorbidities, and medications.

Results

A total of 308 patients met the study criteria for the final statistical analysis, and they had a median age of 91 years (88, 95). The results showed that the addition of TLV was associated with a decreased risk of the 7 and 90-day all-cause mortality in patients with AHF with oliguria [adjusted HR, 95% CI: 0.60 (0.37, 0.98), p = 0.042; 0.56 (0.41, 0.75), p < 0.001, respectively]. Adding TLV significantly increased urine output and decreased N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels in 7 days, and alleviated the progression of AHF within 30 days. There were no statistically significant differences between the patients with or without TLV in terms of the occurrence of hypernatremia, the development of hepatic impairment within 30 days, and new-onset CKD after 90 days.

Conclusions

This study demonstrated that the addition of TLV was clinically effective in increasing urine output, and had favorable effects on alleviating AHF progression and may reduce the risk of all-cause mortality at 7 and 90-day in elderly patients with AHF with oliguria, and TLV had a good safety profile.

Trial registration

http://www.chictr.org.cn/showprojen.aspx?proj=148046, identifier: ChiCTR2200055518.
Antiplatelet therapy in patients with myocardial infarction with non-obstructive coronary arteries: A clinical perspective
Authors: Chen Wenjie , Jiang Yufeng , Chen Tan , Zhou Yafeng
Published: January 2023

Myocardial infarction with non-obstructive coronary arteries (MINOCA) is a heterogeneous group of diseases with different pathological mechanisms, and it is uncertain whether the classical secondary prevention and treatment strategies for myocardial infarction in obstructive coronary artery disease (MI-CAD) are appropriate for patients with MINOCA. Therefore the choice of antiplatelet agents and the therapeutic effect may vary depending on the etiology and pathophysiological mechanisms of MINOCA. This requires our clinical and scientific researchers to properly design prospective studies to explore the pathophysiology of MINOCA and its corresponding etiology in greater depth, so as to understand the effectiveness and safety of medical therapies for different etiologies of MINOCA. Although the current observational studies do not show an obvious beneficial effect of antiplatelet therapy on MINOCA. We are eager to conduct specific prospective randomized controlled trials of antiplatelet agents to assess the specificity, efficacy and safety of different types of antiplatelet agents in patients with MINOCA of different etiologies.
Develop ment and validation of a prognostic dynamic nomogram for in-hospital mortality in patients with Stanford type B aortic dissection
Authors: Yang Lin , Wang Yasong , He Xiaofeng , Liu Xuanze , Sui Honggang , Wang Xiaozeng , Wang Mengmeng
Published: January 2023

Background

This study aimed to identify the risk factors for in-hospital mortality in patients with Stanford type B aortic dissection (TBAD) and develop and validate a prognostic dynamic nomogram for in-hospital mortality in these patients.

Methods

This retrospective study involved patients with TBAD treated from April 2002 to December 2020 at the General Hospital of Northern Theater Command. The patients with TBAD were divided into survival and non-survival groups. The data were analyzed by univariate and multivariate logistic regression analyses. To identify independent risk factors for in-hospital mortality, multivariate logistic regression analysis, least absolute shrinkage, and selection operator regression were used. A prediction model was constructed using a nomogram based on these factors and validated using the original data set. To assess its discriminative ability, the area under the receiver operating characteristic curve (AUC) was calculated, and the calibration ability was tested using a calibration curve and the Hosmer-Lemeshow test. Clinical utility was evaluated using decision curve analysis (DCA) and clinical impact curves (CIC).

Results

Of the 978 included patients, 52 (5.3%) died in hospital. The following variables helped predict in-hospital mortality: pleural effusion, systolic blood pressure ≥160 mmHg, heart rate >100 bpm, anemia, ischemic cerebrovascular disease, abnormal cTnT level, and estimated glomerular filtration rate <60 ml/min. The prediction model demonstrated good discrimination [AUC = 0.894; 95% confidence interval (CI), 0.850–0.938]. The predicted probabilities of in-hospital death corresponded well to the actual prevalence rate [calibration curve: via 1,000 bootstrap resamples, a bootstrap-corrected Harrell’s concordance index of 0.905 (95% CI, 0.865–0.945), and the Hosmer–Lemeshow test (χ2 = 8.3334, P = 0.4016)]. DCA indicated that when the risk threshold was set between 0.04 and 0.88, the predictive model could achieve larger clinical net benefits than “no intervention” or “intervention for all” options. Moreover, CIC showed good predictive ability and clinical utility for the model.

Conclusion

We developed and validated prediction nomograms, including a simple bed nomogram and online dynamic nomogram, that could be used to identify patients with TBAD at higher risk of in-hospital mortality, thereby better enabling clinicians to provide individualized patient management and timely and effective interventions.
Bibliometric evaluation of 2011–2021 publications on hydrogen sulfide in heart preservation research
Authors: Chen Mingcong , Zhou Qian , Wu Fei , Sun Fang , Meng Yang , Zhang Yang , Zhao Mingyi
Published: January 2023

Background

Hydrogen sulfide (H2S) is known for its unpleasant odor and severe toxicity. However, an in-depth study of H2S showed that it can be used as an important messenger, which can play important physiological and pathological roles in vitro and in vivo. In recent years, the application of H2S in the field of cardiac preservation has attracted the interest and attention of scholars worldwide. H2S plays an effective and protective role in cardiac ischemia/reperfusion injury through antioxidant, anti-inflammatory, and antiapoptotic mechanisms.

Objective

The purpose of this study is to analyze the current scientific achievements on the application of H2S in the field of cardiac preservation and to provide new ideas for further research.

Methods

TS = (“hydrogen sulfide” OR “hydrogen sulfide”) AND TS = (“cardiac” OR “heart” OR “myocardium” OR “hearts”) AND TS = (“reperfusion” or “transplantation” or “implanted” or “transplant” or “implantation” or “migration” or “preservation” or “grafting” OR “ischemia” OR “perfusion” or “conservation” or “preserve” or “reservation”) AND DT = (Article OR Review) AND LA = (English) were used as search strategies for data collection from the Science Citation Index-Expanded database of the Web of Science Core Collection. CiteSpace 5.8. R3 and Microsoft Office Excel 2019 were used for data analysis.

Results

A total of 429 related articles were included, and the total number of articles showed a fluctuating upward trend. We used CiteSpace 5.8. R3 and Microsoft Excel 2019 to evaluate and visualize the results, analyzing institutions, countries, journals, authors, co-cited references, and keywords.

Conclusions

As increasing evidence shows that H2S plays an indispensable role in the field of cardiac preservation, its mechanistic research and clinical application may become the main focus of future research.
Determination of nucleoside DOT1L inhibitors’ residence times by τRAMD simulations
Authors: Flores-León Carlos D. , Colorado-Pablo Luis Fernando , Santos-Contreras Miguel Á. , Aguayo-Ortiz Rodrigo
Published: January 2023

Human epigenetic enzyme disruptor of telomeric silencing 1-like (DOT1L) is a key drug target for treating acute myeloid leukemia. Several nucleoside and non-nucleoside DOT1L inhibitors have been developed to inhibit its histone methyltransferase activity. Non-mechanism-based nucleoside DOT1L inhibitors have shown good inhibitory activity and high on-target residence times. Previous computational studies have explored the dynamic behavior of this group of molecules on DOT1L to design compounds with enhanced binding affinities. Nevertheless, it is well known that drug-target kinetics also plays a crucial role in the discovery of new drugs. Therefore, we performed τ-Random Acceleration Molecular Dynamics (τRAMD) simulations to estimate the residence times of nucleoside DOT1L inhibitors. The high correlation between the calculated and experimental residence times suggested that the method can reliably estimate the residence time of nucleoside DOT1L inhibitors when modifications are made to those substituents that occupy the buried hydrophobic pocket of the active site, exhibit hydrophobic interactions with F245 or that form H-bonds with D161 and G163. Overall, this study will be a step toward understanding the binding kinetics of nucleoside DOT1L inhibitors for the treatment of acute myeloid leukemia.
Monitoring the impact of the COVID-19 lockdown on air quality in Lanzhou: Implications for future control strategies
Authors: Liu Hui , Yu Ye , Ma Xiaoyi , Liu Xinying , Dong Longxiang , Xia Dunsheng
Published: January 2023

China implemented a one-month lockdown after the 2020 Spring Festival to prevent the spread of COVID-19. The closure measures provide a rare opportunity to understand the resulting changes in air pollution levels and to test the effectiveness of previous environmental protection measures. We used the time series decomposition method to quantify the air pollution in Lanzhou during the closure period. The results showed that during the epidemic lockdown period, although the concentration of SO2 in Lanzhou decreased substantially, there was a significant increase in the concentration of O3 (by 19.14%), followed by a gradual return to the normal level. Most of the changes during the COVID-19 lockdown were within the range of fluctuations over the past five years. The trend of decreasing SO2 and CO in 2020 was less than that during 2015–2019, and the continuous decline of the PM10 concentration exceeded expectations. NO2, PM2.5 and O3 maintained the trend of the previous five years. Our results show that temporary social closure measures have a limited effect on improving air quality in Lanzhou, and they emphasize the importance of reducing the O3 concentration in the future.
Optimizing construction parameters for fractured horizontal wells in shale oil
Authors: Liu Xuewei , Li Dongping , Jia Yunpeng , Liyong Yang , Xiaoting Gou , Tao Zhao , Ziwei Chen , Mao Li , Juan Wang , Xiangyun Sui , Donghua Zhao , Hongxia Tang , Yulin Li , Yu Zhang
Published: January 2023

Shale oil is mainly extracted by fracturing. However, it is difficult to determine the optimum construction parameters to obtain maximum productivity. In this paper, a fuzzy comprehensive production evaluation model for fractured shale oil horizontal wells based on random forest algorithm and coordinated principal component analysis is proposed. The fracturing parameters of the target wells are optimized by combining this model with an orthogonal experimental design. The random forest algorithm was used to calculate the importance of data sample factors. The main controlling factors of the production of fractured horizontal wells in shale oil were obtained. To reduce the noise of the sample data, principal component analysis was used to reduce the dimensions of the main control factors. Furthermore, the random forest algorithm was used to determine the weight of the principal components after reducing the dimensionality. The membership function of the main control factors after reducing dimensionality was established by combining the fuzzy statistics and assignment methods. In addition, the membership matrix of the effect prediction of fractured horizontal wells in shale oil was determined. The fuzzy comprehensive evaluation method is used to score and evaluate the effect of fractured horizontal wells. Combined with the orthogonal experimental design method, the optimized parameter design of a fractured horizontal well considering the comprehensive action of multiple parameters is realized. After construction according to the optimized parameters, production following fracturing increases significantly. This verifies the rationality of the optimization method that is proposed in this paper.
Experimental study on creep failure characteristics of coal mass under multistage stress and construction of prediction model
Authors: Yang Yiran , Li Yanzhong , Yuan Kekuo , Lu Zidong , Li Gang
Published: January 2023

The creep and failure mode of stratified coal is complex and challenging to predict. This is highly attributed to the internal stratified structure generated during coal forming. In this study, the inner stratified structure of coal during creep failure was explored. The evolution relationship between the key creep parameters and the inner stratified structure was evaluated through creep tests. The creep failure characteristics of coal under complex stress were determined. A neural network model was established to predict the creep failure modes, and the output results were subjected to smoothing and tracking to reduce the error. The results showed distinct creep characteristics of coal with various types of stratified structure. Key parameters such as creep variable, steady creep state and creep rate exhibited a nonlinear relationship with stress. The dual control neural network model constructed in the study showed high accuracy and stability. This model can be used as a guidance and reference for the study of creep failure mechanism and elucidation of creep behavior prediction of stratified coal.
Cassiterite U-Pb dating and micro-XRF analysis constraint on the formation of Xinlu Sn-Zn deposit, South China
Authors: Xiao Chang-Hao , Yu Ping-Ping , Liu Xiao-Hu , Liu Xiang-Chong , Tang Yan-Wen , Wang Wen-Lei , Zhang Yu
Published: January 2023

This study presents rock slab and thin section reflection mapping, element mapping by Micro-XRF (μ-XRF) analysis, and in situ cassiterite U-Pb laser ablation-inductively coupled plasma mass spectrometer (LA-ICP-MS) ages of the Xinlu Sn-Zn deposit, Nanling Sn-W belt, South China. The new results provided constraints on the age and processes of Sn mineralization and thus further discussed the role of the Paleo-Pacific plate in the formation of Sn-W mineralization in the Nanling Sn-W belt. Based on mineralogy and μ-XRF analysis, four mineralization episodes are distinguished in the Baimianshan segment, Xinlu deposit, including 1) prograde skarn stage characterized by the mineral assemblages of garnet, pyroxene, marble, hornfels; 2) retrograde skarn-sulfide composed of massive pyrrhotite, sphalerite with parts of epidote, cassiterite; 3) quartz sulfide vein stage consisting of arsenopyrite, pyrite, quartz, and 4) barren calcite crosscutting the former episodes. The time of retrograde skarn-sulfide mineralization is dated by LA-ICP-MS cassiterite U-Pb at 168.7 ± 1.3 Ma. These U-Pb ages, combined with previous geochronological studies, support one Sn mineralization event associated with the 159.5–168.7 Ma magmatism in the Guposhan-Huashan district. The Xinlu Sn-Zn mineralization and the Middle-Late Jurassic Sn-W mineralization in the Nanling belt, South China is genetically related to asthenospheric upwelling and crust-mantle interaction caused by the subduction of the Paleo-Pacific plate. Our results demonstrate that thin section reflection and μ-XRF element mapping are powerful tools for determining paragenesis in ore deposits and are highly effective to guide the selection of analytical positions for cassiterite U-Pb dating. This method may also be applicable to in-situ geochronology of other minerals.
Stable downward continuation of the gravity potential field implemented using deep learning
Authors: Li Honglei , Chen Shi , Li Yongbo , Zhang Bei , Zhao Ming , Han Jiancheng
Published: January 2023

Downward continuation (DC) of the gravity potential field is an important approach used to understand and interpret the density structure and boundary of anomalous bodies. It is widely used to delineate and highlight local and shallow anomalous sources. However, it is well known that direct DC transformation in the frequency domain is unstable and easily affected by high-frequency noise. Recent deep learning applications have led to the development of image recognition and resolution enhancement using the convolutional neural network technique. A similar deep learning architecture is also suitable for training a model for the DC problem. In this study, to solve the problems in existing DC methods, we constructed a dedicated model called DC-Net for the DC problem. We fully trained the DC-Net model on 38,400 pairs of gravity anomaly data at different altitudes using a convolutional neural network. We conducted several experiments and implemented a real-world example. The results demonstrate the following. First, several validation data subset and test data prediction results indicate that the DC-Net model was sufficiently trained. Moreover, it performed better than the traditional strategy in refining the upscaling of low-resolution images. Second, we performed tests on test datasets with changing levels of noise and demonstrated that the DC-Net model is noise-resistant and robust. Finally, we used the proposed model in a real-world example, which demonstrates that the DC-Net model is suitable for solving the DC problem and delineating the detailed gravity anomaly feature near the field source. For real data processing, noise in the gravity anomaly should be reduced in advance. Additionally, we recommend noise quantification of the gravity anomaly before network training.
Decadal variation of the summer extreme high temperature days in northern Eurasia during 1960–2018
Authors: Fan Yi , Yao Xiaona , Zhou Botao , Li Huixin , Liu Mei
Published: January 2023

Unprecedented heat waves have been demonstrated sweeping across much of the Northern Hemisphere in recent summers. However, this study reveals that for northern Eurasia (30°–70°N, 10°–130°E), significant increases in summer extreme high temperature days (EHTDs) have already commenced since the mid-1990s, with the peaks centered on the surrounding areas of the Caspian Sea and Lake Baikal. Results indicate that compared with the period of 1960–1994 (P1), during 1995–2018 (P2) high-pressure and anticyclonic anomalies occupy the areas around the Caspian Sea and Lake Baikal, thus suppressing local cloud cover and precipitation, enhancing the solar radiation and high-temperature anomalies. The anticyclonic anomaly over Lake Baikal shows a close relationship with the increasing trend of global temperature, and the anomalous anticyclone over the Caspian Sea is under the influence of the warm North Atlantic and anomalous upper-troposphere jet stream during P2. The warmer than normal North Atlantic leads to the high-pressure anomaly over the Caspian Sea by modulating the Rossby wave activity. In addition, during P2, the temperatures in the middle North Atlantic and Western Europe show greater increments than those over higher latitudes, and thus the temperature gradient enhances the westerly thermal wind, which therefore leads to an anomalous upper-level jet stream to the North of the Caspian Sea. Under this circumstance, the Caspian Sea is located to the right side of the anomalous jet exit, inducing the suppressed upward motion over there and favoring the occurrence of more EHTDs after the mid-1990s.
Research on basin effect based on 2-D geophysical prospecting profile
Authors: Wang Chong , Qi Wenhao , Bo Jingshan , Wang Fuyun , Wang Shuaijun , Xin Hailiang
Published: January 2023

The basin effects alter the frequency content, amplitude and duration of seismic waves with different depth and shape basin. In this paper, we selected 2-D geophysical profiles spanning the Weihe Basin with large area and the Luoquanli Basin with small area. Based on the velocity structure data of the two geophysical profiles, 2-D finite element models were established to analyze the influences of the sedimentary layer in a broad basin and the basement geometry of a small basin on the basin effects using the spectral element method. The results showed that the sedimentary deposits in the basin could significantly amplify the ground motion, and the diffraction waves generated by the lateral inhomogeneity of the basin could prolong the duration of the ground motion. The highlight of this paper, our results showed that the amplification characteristics were controlled by the velocity mean in the vertical direction of sedimentary deposits (vp < 4.5 km/s) for a broad basin with small depth-to-width ratio as Weihe Basin, while the amplification characteristics were dominated by basement geometry of the small basin with large depth-to-width ratio as Luoquanli Basin. In this study, a high-resolution 2-D geophysical refraction profile was directly used to construct a finite element model to study the basin effects, which provided a new method of studying the basin effects.
Discovery and significance of tempestites and storm deposits in the Qingshankou Formation of the Gulong Sag, northeastern China
Authors: He Wenyuan , Zhong Jianhua , Sun Ningliang
Published: January 2023

The Qingshankou Formation in the Gulong Sag has attracted significant attention due to its rich shale oil reserves. However, several fundamental geological problems associated with shale from the Qingshankou Formation remain unresolved. The Qingshankou Formation in the Gulong Sag is a set of deep-lake and semi-deep-lake fine-grained deposits primarily composed of mudstone and shale. Recent studies have shown that tempestites and storm deposits commonly occur in the Qingshankou Formation. Fine liquefaction veins often occur at the bottom of the tempestite, indicating that storm-shaking liquefaction is expected in the swale structure. Meanwhile, the mudstone and shales primarily have a sand structure with fine grain size, indicating that the environment is exposed to an undercurrent activity, with a water flow velocity of 20–55 cm s−1. Unlike previous studies, we posit that the ancient water depth was only 20–30 m or shallower. These phenomena have significance for understanding the formation environment and diagenetic process of shale in the first member of the Qingshankou Formation, which provides a reference for oil and gas exploration and development.
Enhancing writing analytics in science education research with machine learning and natural language processing—Formative assessment of science and non-science preservice teachers’ written reflections
Authors: Wulff Peter , Westphal Andrea , Mientus Lukas , Nowak Anna , Borowski Andreas
Published: January 2023

Introduction

Science educators use writing assignments to assess competencies and facilitate learning processes such as conceptual understanding or reflective thinking. Writing assignments are typically scored with holistic, summative coding rubrics. This, however, is not very responsive to the more fine-grained features of text composition and represented knowledge in texts, which might be more relevant for adaptive guidance and writing-to-learn interventions. In this study we examine potentials of machine learning (ML) in combination with natural language processing (NLP) to provide means for analytic, formative assessment of written reflections in science teacher education.

Methods

ML and NLP are used to filter higher-level reasoning sentences in physics and non-physics teachers’ written reflections on a standardized teaching vignette. We particularly probe to what extent a previously trained ML model can facilitate the filtering, and to what extent further fine-tuning of the previously trained ML model can enhance performance. The filtered sentences are then clustered with ML and NLP to identify themes and represented knowledge in the teachers’ written reflections.

Results

Results indicate that ML and NLP can be used to filter higher-level reasoning elements in physics and non-physics preservice teachers’ written reflections. Furthermore, the applied clustering approach yields specific topics in the written reflections that indicate quality differences in physics and non-physics preservice teachers’ texts.

Discussion

Overall, we argue that ML and NLP can enhance writing analytics in science education. For example, previously trained ML models can be utilized in further research to filter higher-level reasoning sentences, and thus provide science education researchers efficient mean to answer derived research questions.
Engaging in writing to learn – Increasing the motivation during a long-term self-regulated learning training
Authors: Udvardi-Lakos Nina , Endres Tino , Glogger-Frey Inga , Renkl Alexander
Published: January 2023

Introduction

University students should be able to self-regulate their learning to gain the most from their studies. Extended self-regulated-learning trainings can help students to reach that goal. A frequent problem of such trainings is that students’ motivation decreases over the course of training. To avoid this decrease, we combined a learning-journal-based, self-regulated-learning training with a learning environment teaching conditional knowledge about learning strategies, a guided peer-feedback system, and an optional intervention for students reporting low levels of motivation from the start of the semester.

Methods and Results

Four motivational variables were repeatedly measured over the course of 10 weeks. Students (N = 103) in our training showed an increase in their self-efficacy and intrinsic value, and a decrease in cost associated with learning-journal-writing throughout the semester. The optional intervention led to an increase in the intrinsic value associated with the learning journals for students with low motivation.

Discussion

Our study suggests that this expanded self-regulated-learning training can help students maintain their motivation during a long-term training.
Gut bacteriome, mycobiome and virome alterations in rheumatoid arthritis
Authors: Dagar Sonali , Singh Jagdeep , Saini Aastha , Kumar Yashwant , Chhabra Seema , Minz Ranjana Walker , Rani Lekha
Published: January 2023

Rheumatoid arthritis (RA) is a chronic destructive autoimmune disease of the joints which causes significant pain, functional disability, and mortality. Although aberrant immune cell activation induced by the imbalance between T helper Th1/Th17 and Treg cells is implicated in the RA development, its etiopathogenesis remains unclear. The presence of mucosal inflammation and systemic IgA-isotype-autoantibodies (anti-citrullinated peptide antibodies and rheumatoid factor) in pre-clinical RA supports the mucosal origin hypothesis involving altered microbiota in disease development. The gut microbiota comprises diverse bacteria, fungal and viral components, which are critical in developing host immunity. Alterations in microbial abundance are known to exacerbate or attenuate immune responses in the gut microenvironment subsequently affecting the joints. Further, these changes can provide biomarkers for disease activity and outcome in RA. Most of the research till date has been focused on describing gut bacterial components in RA. Studies on gut mycobiome and virome components in RA are relatively new and burgeoning field. Given the paucity of mycobiome or virome specific studies in RA, this review, discusses the recent findings on alterations in gut bacterial, fungal, and viral components as well as their role in regulating the spectrum of immune-pathogenic events occurring in RA which might be explored in future as a potential therapeutic target. Further, we provide an overview on inter-kingdom interactions between bacteria, fungi, and viruses in RA. The current understanding on gut microbiota modulation for managing RA is also summarised.
Nitric oxide and thyroid carcinoma: A review
Authors: Huang Yu , Suguro Rinkiko , Hu Wei , Zheng Jiayu , Liu Yawen , Guan Mingxin , Zhou Na , Zhang Xin
Published: January 2023

Thyroid carcinoma is the most common endocrine cancer in the world, and its incidence has been steadily increasing in recent years. Despite its relatively good prognosis, therapies have not improved greatly in recent years. Therefore, exploring new therapies for thyroid carcinoma represents an unmet need. Nitric oxide (NO) is a short-term endogenous signaling molecule that plays a vital role in various physiological and pathological processes and is synthesized by nitric oxide synthase (NOS). Many studies have been conducted over the past decades to explain its correlation to cancer. NO exerts a wide range of effects on cancer, involving angiogenesis, apoptosis, cell cycle, invasion, and metastasis. It also serves a dual function by promoting and halting tumor development simultaneously. The relationship between NO and thyroid carcinoma has been intensively studied and discussed. This paper reviews the role and molecular mechanism of NO in thyroid carcinoma and discusses potentials of prevention and treatment of thyroid carcinoma.
Regulation of ovarian function by growth hormone: Potential intervention of ovarian aging
Authors: Han Lei , Tian Hongcheng , Guo Xiaoxiao , Zhang Lei
Published: January 2023

Growth hormone (GH) is mainly secreted by eosinophils of anterior pituitary gland. GH plays an important role in regulating the growth and development of many tissues and cells, so it is used in the treatment of many diseases. In recent years, the regulation of GH on ovarian function has attracted much attention. GH has been applied in controlled ovarian hyperstimulation, particularly in the patients with advanced age, diminished ovarian reserve (DOR) and poor ovarian response (POR). GH can directly bind to the growth hormone receptor (GHR) on the ovary to promote the growth, maturation and ovulation of follicles, as well as to inhibit follicular atresia. GH so as to promote the occurrence of early follicles, enhance the sensitivity of follicles to gonadotropins, accelerate the maturation of oocyte nucleus, improve mitochondrial activity and the quality of oocytes through the insulin-like growth factor (IGF) system, which is an indirect regulation. The deep-seated effects of GH on human reproduction and ovarian aging need further basic research and clinical practice.
Diabetic retinopathy: Looking forward to 2030
Authors: Tan Tien-En , Wong Tien Yin
Published: January 2023

Diabetic retinopathy (DR) is the major ocular complication of diabetes mellitus, and is a problem with significant global health impact. Major advances in diagnostics, technology and treatment have already revolutionized how we manage DR in the early part of the 21st century. For example, the accessibility of imaging with optical coherence tomography, and the development of anti-vascular endothelial growth factor (VEGF) treatment are just some of the landmark developments that have shaped the DR landscape over the last few decades. Yet, there are still more exciting advances being made. Looking forward to 2030, many of these ongoing developments are likely to further transform the field. First, epidemiologic projections show that the global burden of DR is not only increasing, but also shifting from high-income countries towards middle- and low-income areas. Second, better understanding of disease pathophysiology is placing greater emphasis on retinal neural dysfunction and non-vascular aspects of diabetic retinal disease. Third, a wealth of information is becoming available from newer imaging modalities such as widefield imaging systems and optical coherence tomography angiography. Fourth, artificial intelligence for screening, diagnosis and prognostication of DR will become increasingly accessible and important. Fifth, new pharmacologic agents targeting other non-VEGF-driven pathways, and novel therapeutic strategies such as gene therapy are being developed for DR. Finally, the classification system for diabetic retinal disease will need to be continually updated to keep pace with new developments. In this article, we discuss these major trends in DR that we expect to see in 2030 and beyond.
Convergence characteristics and acceleration of the transient fixed source equation solved by Monte Carlo method
Authors: Guo Xiaoyu , Wang Guanbo , Wang Kan
Published: January 2023

The safety analysis of nuclear systems such as nuclear reactors requires transient calculation. The Monte Carlo (MC) method has grown rapidly in recent years because of its high-fidelity modelling and simulation capability. The predictor-corrector quasi-static (PCQS) MC method has been investigated for kinetic calculation. However, the approach to shorten the computational time required to solve the transient fixed source equation (TFSE) is still under development. The convergence characteristic of the neutron source iteration algorithm of the PCQS MC method is analyzed in this study with a simplified model. It is found that the convergence rate of the iteration algorithm is governed by the effective spectral radius (ESR). The lower the ESR is, the faster the convergence is. In order to reduce the ESR, the asymptotic superhistory method (ASM) is developed for the PCQS MC method in the RMC code. The performance of ASM is evaluated by the C5G7-TD benchmark. Results show that the reduction in the number of inactive cycles is more than 85%, and over 15% of computational time including active cycles is saved. It is demonstrated how ASM speeds up the iterations using the Wasserstein distance measure.
Robust dispatching model of active distribution network considering PV time-varying spatial correlation
Authors: Ma Xin , Wu Han , Yuan Yue
Published: January 2023

With a high proportion of photovoltaic (PV) connected to the active distribution network (ADN), the correlation and uncertainty of the PV output will significantly affect the grid dispatching operation. Therefore, this paper proposes a novel robust ADN dispatching model, which considers the dynamic spatial correlation and power uncertainty of PV. First, the dynamic spatial correlation of PV output is innovatively modeled by dynamic conditional correlation (DCC) generalized autoregressive conditional heteroskedasticity (DCC-GARCH) model. DCC can accurately represent and forecast the spatial correlation of the PV output and reflect its time-varying characteristics. Second, a time-varying ellipsoidal uncertainty set constructed using the DCC, is introduced to bound the uncertainty of the PV outputs. Subsequently, the original mixed integer linear programming (MILP) model is transformed into the mixed integer robust programming (MIRP) model to realize robust optimal ADN dispatching. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method.
Shaping energy cost management in process industries through clustering and soft sensors
Authors: Lu Yu , Fang Gang , Huang Daoping , Cai Baoping , Chen Hongtian , Liu Yiqi
Published: January 2023

With the ever-increasing growth of energy demand and costs, process monitoring of operational costs is of great importance for process industries. In this light, both financial budget management and local operational optimization supposed to be guaranteed properly. To achieve this goal, a support vector machine recursive feature elimination (SVM-RFE) method together with clustering algorithm was developed to extract features while serving as importance measurements of each input variable for the sequential prediction model construction. Then, the four variants of autoregressive and moving average (ARMA), i.e., ARMA with exogenous input (ARMAX) based on recursive least squares algorithm (RLS), ARMAX based on recursive extended least squares algorithm (RELS), nonlinear auto-regressive neural network (NARNN) and nonlinear auto-regressive neural network with exogenous input (NARXNN), were applied, respectively, to predict the costs incurred in the daily production for process industries. The methods were validated in the Benchmark Simulation Model No.2-P (BSM2-P) and a practical data set about steel industry energy consumption from an open access database (University of California, Irvine (UCI)), respectively. The nonlinear model, NARXNN, was validated to achieve better performance in terms of mean square error (MSE) and correlation coefficient (R), when used for multi-step prediction of the aforementioned datasets with strong nonlinear and coupled characteristics.
A field-scale evaluation of the removal of odorous gases by a plant material-based deodorant
Authors: Wu Jun , Yang Jing , Wang Jian , Fang Liben , Fang Jingjing
Published: January 2023

The composting process emits various volatile chemicals, including hydrocarbons, nitrogen compounds, sulfur compounds, and oxygenated compounds. These gases must be removed to avoid atmospheric pollution. Spraying extracted materials from plants, such as tea tree oil, onto the surface of a windrow is a novel odor-reduction technique for the treatment of gaseous emissions from composting facilities. In this study, a plant material-based deodorant from a mixture of coconut, lemon, and tea tree essential oils was sprayed directly onto the surface of a windrow with a spraying frequency of three times a day. Odorous compounds were measured before and after spraying to evaluate the performance of the plant material-based deodorant. After spraying, the ammonia concentration decreased from 10,000 to 7,000 μg/m3. There was a large decline in alcohols, aromatics, and carbonyls from 30,612, 12,270, and 5,138 μg/m3 to 4,965, 4,669, and 2,253 μg/m3, achieving efficiencies of 83%, 62%, and 56%, respectively. The removal efficiencies for terpenes, volatile fatty acids, sulfur compounds, and ammonia were lower than the former, falling within the range of 24%–36%. The theoretical total odor concentration was decreased from 23,757 μg/m3 at the beginning to 13,056 μg/m3 in the end, revealing that most of the bad smell had been eliminated.
Corporate digitalization, application modes, and green growth: Evidence from the innovation of Chinese listed companies
Authors: Jiang Shaoxiang , Li Yafei , You Nicole
Published: January 2023

Digitalization is one of the main ways for enterprise growth in the digital economy era. However, the existing literature on digital technology application models and their impact on corporate green growth is rare. By using the green innovation data of Chinese A-share listed companies from 2008 to 2020, this paper empirically investigates the association between enterprise digitalization and green innovation. The empirical results show that digitalization has significantly improved enterprises’ substantive green innovation level, which is valid after conducting a series of endogenous and robustness tests. Further results show that digital technology application, intelligent manufacturing application, and modern information system application are the three main models of digitalization to promote green innovation of enterprises, while internet business model application cannot significantly promote corporate green innovation. In addition, the mechanism analysis results indicate that the increase in government subsidy and corporate own R&D investment contribute to the incentive effect mentioned above, while the loss of governance efficiency and fluctuation of the external environment offset this effect. This incentive effect is more obvious in non-state-owned, high-tech, and lower-polluted industry enterprises. Our paper reveals the mode and mechanisms for enterprises to realize innovative green growth by applying digital technology in the digital economy era, which is of great significance to relevant theoretical research and policy formulation.
Scaling migrations to communities: An empirical case of migration network in the Arctic
Authors: Moisan Louis , Gravel Dominique , Legagneux Pierre , Gauthier Gilles , Léandri-Breton Don-Jean , Somveille Marius , Therrien Jean-François , Lamarre Jean-François , Bêty Joël
Published: January 2023

Seasonal migrants transport energy, nutrients, contaminants, parasites and diseases, while also connecting distant food webs between communities and ecosystems, which contributes to structuring meta-communities and meta-ecosystems. However, we currently lack a framework to characterize the structure of the spatial connections maintained by all migratory species reproducing or wintering in a given community. Here, we use a network approach to represent and characterize migratory pathways at the community level and provide an empirical description of this pattern from a High-Arctic terrestrial community. We define community migration networks as multipartite networks representing different biogeographic regions connected with a focal community through the seasonal movements of its migratory species. We focus on the Bylot Island High-Arctic terrestrial community, a summer breeding ground for several migratory species. We define the non-breeding range of each species using tracking devices, or range maps refined by flyways and habitat types. We show that the migratory species breeding on Bylot Island are found across hundreds of ecoregions on several continents during the non-breeding period and present a low spatial overlap. The migratory species are divided into groups associated with different sets of ecoregions. The non-random structure observed in our empirical community migration network suggests evolutionary and geographic constraints as well as ecological factors act to shape migrations at the community level. Overall, our study provides a simple and generalizable framework as a starting point to better integrate migrations at the community level. Our framework is a far-reaching tool that could be adapted to address the seasonal transport of energy, contaminants, parasites and diseases in ecosystems, as well as trophic interactions in communities with migratory species.
Influence of grazing on the activity pattern and temporal niche of two dominant rodent species in Alxa desert
Authors: Li Xin , Yuan Shuai , Li Linlin , Zhang Haoting , Jin Yonglin , Liu Lina , Zhang Rong , Bu Fan , Sun Shanshan , Fu Heping , Wu Xiaodong
Published: January 2023

Grazing by large herbivores can potentially affect interspecific interactions between small herbivores by reducing the ecological fitness of animals. Desert rodents are important components in desert ecosystems and indicators of environmental change. Grazing reduces food resources, but rodents can decrease interspecific niche overlap by adaptive behavior. However, the key factors driving rodent behavioral activities and coexistence in the Alxa desert remains unstudied. We monitored population density and behavioral activities of Midday gerbil (Meriones meridianus) and northern three-toed jerboa (Dipus sagitta) in a grazing exclusion experiment in Alxa desert, Inner Mongolia, China, in 2017. We assessed the relationship between environmental factors (such as plant height, density, coverage, rainfall and temperature) and the behavioral activities of two coexisting rodent species. The results showed that: (1) In summer, grazing significantly reduced the activity time of gerbil and jerboa compared to that in grazing exclusion areas (gerbil: F = 5.98, p < 0.05, η2 = 0.22; jerboa: F = 8.57, p < 0.01, η2 = 0.28). Grazing reduced the temporal niche overlap with an obvious shifting of activity peaks between two species. (2) Grazing exclusion enhanced the temporal niche overlap between the two rodent species due to greater food availability which relieved inter-specific competition in each season. (3) Grazing strengthened the sensitivity of rodents to environmental changes in all seasons. These results indicated that grazing affected competition between the rodent species by altering vegetation conditions, which in turn affected the temporal niche and activity patterns of rodents.