bluuebunny/crossref_metadata_2025 huggingface dataset:
Chalcone-derivative L6H21 attenuates the OVA-induced asthma by targeting MD2
Authors: Ge Xiangting , Xu Tingting , Wang Meiyan , Gao Lijiao , Tang Yue , Zhang Ningjie , Zheng Rui , Zeng Weimin , Chen Gaozhi , Zhang Bing , Dai Yuanrong , Zhang Yali
Published: January 2024

Asthma represents a significant global challenge that affects individuals across all age groups and imposes substantial social and economic burden. Due to heterogeneity of the disease, not all patients obtain benefit with current treatments. The objective of this study was to explore the impact of MD2 on the progression of asthma using L6H21, a novel MD2 inhibitor, to identify potential targets and drug candidates for asthma treatment. To establish an asthma-related murine model and evaluate the effects of L6H21, ovalbumin (OVA) was used to sensitize and challenge mice. Pathological changes were examined with various staining techniques, such as H&E staining, glycogen staining, and Masson staining. Inflammatory cell infiltration and excessive cytokine secretion were evaluated by analyzing BALF cell count, RT-PCR, and ELISA. The TLR4/MD2 complex formation, as well as the activation of the MAPK and NF-кB pathways, was examined using western blot and co-IP. Treatment with L6H21 demonstrated alleviation of increased airway resistance, lung tissue injury, inflammatory cell infiltration and excessive cytokine secretion triggered by OVA. In addition, it also ameliorated mucus production and collagen deposition. In the L6H21 treatment group, inhibition of MAPK and NF-кB activation was observed, along with the disruption of TLR4/MD2 complex formation, in contrast to the model group. Thus, L6H21 effectively reduced the formation of the MD2 and TLR4 complex induced by OVA in a dose-dependent manner. This reduction resulted in the attenuation of MAPKs/NF-κB activation, enhanced suppression of inflammatory factor secretion, reduced excessive recruitment of inflammatory cells, and ultimately mitigated airway damage. MD2 emerges as a crucial target for asthma treatment, and L6H21, as an MD2 inhibitor, shows promise as a potential drug candidate for the treatment of asthma.
Rapid weather changes are associated with daily hospital visitors for atrial fibrillation accompanied by abnormal ECG repolarization: a case-crossover study
Authors: Wu Shanmei , Guo Jingyi , Chen Xin , Wang Jie , Zhao Gang , Ma Shixin , Hao Tianzheng , Tan Jianguo , Li Yongguang
Published: January 2024

Background

Atrial fibrillation (AF) is highly prevalent in the population, yet the factors contributing to AF events in susceptible individuals remain partially understood. The potential relationship between meteorological factors and AF, particularly with abnormal electrocardiograph (ECG) repolarization, has not been adequately studied. This case-crossover study aims to investigate the association between meteorological factors and daily hospital visits for AF with abnormal ECG repolarization in Shanghai, China.

Methods

The study cohort comprised 10,325 patients with ECG-confirmed AF who sought treatment at Shanghai Sixth People's Hospital between 2015 and 2018. Meteorological and air pollutant concentration data were matched with the patient records. Using a case-crossover design, we analyzed the association between meteorological factors and the daily count of hospital visitors for AF with abnormal ECG repolarization at our AF center. Lag analysis models were applied to examine the temporal relationship between meteorological factors and AF events.

Results

The analysis revealed statistically significant associations between AF occurrence and specific meteorological factors. AF events were significantly associated with average atmospheric pressure (lag 0 day, OR 0.9901, 95% CI 0.9825–0.9977, P < 0.05), average temperature (lag 1 day, OR 0.9890, 95% CI 0.9789–0.9992, P < 0.05), daily pressure range (lag 7 days, OR 1.0195, 95% CI 1.0079–1.0312, P < 0.01), and daily temperature range (lag 5 days, OR 1.0208, 95% CI 1.0087–1.0331, P < 0.01). Moreover, a significant correlation was observed between daily pressure range and daily temperature range with AF patients, particularly those with abnormal ECG repolarization, as evident in the case-crossover analysis.

Conclusion

This study highlights a significant correlation between meteorological factors and daily hospital visits for AF accompanied by abnormal ECG repolarization in Shanghai, China. In addition, AF patients with abnormal ECG repolarization were found to be more vulnerable to rapid daily changes in pressure and temperature compared to AF patients without such repolarization abnormalities.
Bioinformatics analysis identifies GLUD1 as a prognostic indicator for clear cell renal cell carcinoma
Authors: Liu Shuang
Published: January 2024

Background

Renal cell carcinoma (RCC) is a common primary tumor of the kidney and is divided into three major subtypes, of which clear cell renal cell carcinoma (ccRCC) has the highest incidence. Glutamate dehydrogenase 1 (GLUD1) encodes glutamate dehydrogenase 1, which catalyzes the oxidative deamination of glutamate.

Methods

We analyzed TCGA data using R language software and used multiple online databases to explore the relationship of GLUD1 with signaling pathways and drug sensitivity as well as GLUD1 protein expression and methylation.

Results

The results showed that GLUD1 mRNA expression was reduced in tumor tissues and correlated with the progression of ccRCC. Univariate and multivariate Cox analysis showed that GLUD1 could be used as a prognostic marker for ccRCC. GLUD1 expression in ccRCC was associated with immune cells infiltration and multiple classical signaling pathways. In addition, GLUD1 mRNA expression was related to drug sensitivity.

Conclusions

These findings provide new ideas for finding new prognostic molecular markers and therapeutic targets for ccRCC.
Prone positioning effect on tracheal intubation rate, mortality and oxygenation parameters in awake non-intubated severe COVID-19-induced respiratory failure: a review of reviews
Authors: Tekantapeh Sepideh Tahsini , Nader Nader D. , Ghojazadeh Morteza , Fereidouni Fatemeh , Soleimanpour Hassan
Published: January 2024

Background

Prone positioning (PP) is a low-cost method with minimal risk to the patient that improves the oxygenation of patients with acute hypoxic respiratory failure (AHRF) due to COVID-19 pneumonia, thereby reducing their need for tracheal intubation (TI) and transferring to the intensive care unit (ICU). We aimed to overview the results of all previous systematic reviews and meta-analyses to examine the net effect of PP on oxygenation, the rate of TI and mortality in COVID-19 patients.

Methods

We searched PubMed, Scopus, Web of Science, Google Scholar, and Cochrane Library databases from December 2019 through 2022 without publication language restriction for systematic reviews and meta-analysis studies on PP vs. supine position (SP) in conscious patients with hypoxic respiratory failure COVID-19. After study selection, data were extracted from published meta-analyses and pooled by comprehensive meta-analysis (CMA) software version 2.2.064 to achieve effect sizes. They were analyzed for TI and mortality rates dichotomous variables, and the results were shown as pooled odds ratios (OR) with a 95% confidence interval (CI). Continuous variables such as oxygenation indices (PaO2/FiO2 and SpO2) were also analyzed, and the data were shown as mean differences (MD) with lower and upper CI. The level of statistical significance was set at p ≤ 0.05.

Results

Twelve systematic reviews and meta-analyses with 19,651 patients and six systematic reviews with 2,911 patients were included in this Review of Reviews (total: 22,562). PP treatment significantly reduced the rate of TI (OR = 0.639, %95 CI (0.492, 0.829); P-value = 0.001) and decreased mortality (OR = 0.363, %95 CI (0.240, 0.549), P-value < 0.001). There was no difference in PaO2/FiO2 (MD = 3.591[− 40.881, 48.062]; P-value = 0.874) and SpO2 percent (MD = 1.641[− 4.441, 7.723]; P-value = 0.597).

Conclusion

Prone positioning can be recommended in conscious ICU patients with COVID-19 pneumonia to reduce mortality and intubation.

Systematic review registration: PROSPERO registration number: CRD42022326951. Registered 25 April 2022.

Graphical Abstract
Skeletal muscle index together with body mass index is associated with secondary osteoporosis in patients with rheumatoid arthritis
Authors: Chu Yi-ran , Xu Yue-chen , Ma Ling-li , Wang Jian-xiong , Zong He-xiang , Tong Wan-qiu , Wang Xi-le , Zhao Xu , Xu Sheng-qian
Published: January 2024

Objective

The objective of this study was to explore the associations of body mass index (BMI), fat mass index (FMI), skeletal mass index (SMI) and secondary osteoporosis (OP) in patients with rheumatoid arthritis (RA).

Methods

The bone mineral density (BMD) at sites of the femur neck (Neck), total hip (Hip) and lumbar vertebrae 1–4 (L1-4) was measured by dual-energy X-ray absorptiometry. The skeletal muscle index, body fat percentage and mineral content were measured by biological electrical impedance for calculating BMI, FMI and SMI.

Results

A total of 433 patient with RA and 158 healthy controls were enrolled. The BMDs at each site of the RA patients were lower compared with those of the healthy controls (p < 0.0001), and the prevalence of OP (36.1%, 160/443) and sarcopenia (65.2%, 288/443) in the RA patients were higher than those in the controls (12.7%, 20/158, p < 0.0001; 9.0%, 14/156, p < 0.0001). Significant differences in the BMD, FMI, SMI, mineral content, body fat percentage and skeletal muscle mass were found among the RA patients in the different BMI groups (p < 0.05). In RA patients with BMI < 18.5 kg/m2, the prevalence of OP in the RA patients with sarcopenia was similar to that in those without sarcopenia (44.4% vs. 66. 7%, χ2 = 0. 574, p = 0.449). In the RA patients with a normal BMI or who were overweight or obese, prevalence of OP in the RA patients with sarcopenia was significantly higher than that in the RA patients without sarcopenia (42.8% vs. 21.7%, χ2 = 10.951, p = 0.001; 61.1% vs. 13.0%, χ2 = 26.270, p < 0.0001). In the RA patients without sarcopenia, the prevalence of OP in the RA patients in the different BMI groups was different (p = 0.039). In the RA patients with sarcopenia, there was no significant difference in the prevalence of OP among the RA patients in the different BMI groups (p = 0. 128). The linear correlation analysis showed that the SMI in RA patients was positively correlated with the BMD of each site measured and BMI and FMI (p < 0.0001). However, there was a negative linear correlation between SMI and disease duration (p = 0.048). The logistic regression analysis found that SMI (OR = 0.569, p = 0.002, 95% CI 0.399–0.810), BMI (OR = 0.884, p = 0.01, 95% CI 0.805–0.971) and gender (1 = female, 2 = male) (OR = 0.097, p < 0.0001, 95% CI 0.040–0.236) were protective factors for OP in RA, while age (OR = 1.098, p < 0.0001, 95% CI 1.071–1.125) was the risk factor.

Conclusion

BMI and SMI are associated with the occurrence of OP in RA patients, and both SMI and BMI are important protective factors for OP secondary to RA.
A tripartite bacterial-fungal-plant symbiosis in the mycorrhiza-shaped microbiome drives plant growth and mycorrhization
Authors: Zhang Changfeng , van der Heijden Marcel G. A. , Dodds Bethany K. , Nguyen Thi Bich , Spooren Jelle , Valzano-Held Alain , Cosme Marco , Berendsen Roeland L.
Published: January 2024

Background

Plant microbiomes play crucial roles in nutrient cycling and plant growth, and are shaped by a complex interplay between plants, microbes, and the environment. The role of bacteria as mediators of the 400-million-year-old partnership between the majority of land plants and, arbuscular mycorrhizal (AM) fungi is still poorly understood. Here, we test whether AM hyphae-associated bacteria influence the success of the AM symbiosis.

Results

Using partitioned microcosms containing field soil, we discovered that AM hyphae and roots selectively assemble their own microbiome from the surrounding soil. In two independent experiments, we identified several bacterial genera, including Devosia, that are consistently enriched on AM hyphae. Subsequently, we isolated 144 pure bacterial isolates from a mycorrhiza-rich sample of extraradical hyphae and isolated Devosia sp. ZB163 as root and hyphal colonizer. We show that this AM-associated bacterium synergistically acts with mycorrhiza on the plant root to strongly promote plant growth, nitrogen uptake, and mycorrhization.

Conclusions

Our results highlight that AM fungi do not function in  isolation and that the plant-mycorrhiza symbiont can recruit beneficial bacteria that support the symbiosis.
Dual roles of BK Polyomavirus in promoting urothelial carcinoma progression via regulating CLDN1
Authors: Xu Cuidi , Chen Siyue , Chen Juntao , Wang Jina , Niu Xinhao , Rong Ruiming , Zhu Tongyu , Zeng Yigang
Published: January 2024

Uncontrolled productive infection of BK polyomaviruses (BKV) in immunocompromised patients was reported to result in serious diseases, especially renourinary malignancies. However, the mechanism of BKV as a role of human carcinogen is still unknown. In this study, we showed that there is a significant association between BKV infection and metastasis of urothelial carcinoma (UCA). BKV-infected tumor tissues exhibit invasive histologic phenomena with vascular invasion and myometrial invasion. Then we identified that BKV promotes UCA invasion in a mode of dual regulation of tumor cells (TCs) invasion and endothelial cells (ECs) adhesion by encoding miRNAs. In cancer cells, BKV-B1-miR-5p promotes cell motility and invasiveness by directly targeting CLDN1. Moreover, exosomal-BKV-B1-miR-3p derived from BK-infected BC cells would be transferred to ECs and increase its adhesion to tumor cells by switching on the CLDN1 enhancer, which subsequently destroyed endothelial monolayers and increased permeability. In a human urothelial cancer metastasis mouse model, BK-inoculated cells exhibited higher incidence of vascular leakage and liver colonization. However, the vascular leakage and liver metastasis could be reduced when knocking down miRNAs in BK-inoculated cells. Our research delineates the bifunctional impact of BKV-encoded microRNAs on the expression of CLDN1 within both TCs and ECs, which orchestrates the establishment of a pre-metastatic niche in UCA.
The dilemma of the trigger timing in IVF: a review
Authors: Maghraby Hassan , Saleh Hesham , Fourtia Ismail L. , Rasheed Salah , Elmahdy Mohamed , Abdelbadie Amr S. , Di Guardo Federica , Drakopoulos Panagiotis , Midassi Habib , Shoukry Ahmed
Published: January 2024

Background

Triggering final oocyte maturation is a pivotal step in modern patient-tailored IVF/ICSI treatment, securing the optimal number of mature oocytes retrieved without compromising fertilization, embryo development, and live birth. Several factors need to be considered when deciding the time of the trigger: the size of the leading follicles, distribution of the follicular cohort, the duration of stimulation, the protocol used for stimulation, and ovarian response status.

Main body

The current narrative review aims to appraise all available evidence for determining the proper time for inducing final oocyte maturation following IVF treatment. Moreover, it discusses the impact of the stimulation protocol, follicular size, and magnitude of ovarian response on choosing the proper timing for trigger. Comprehensive literature search of all available articles and relevant articles studying the criteria for timing of final oocyte maturation trigger in IVF/ICSI cycles were included in this review. It was found that leading follicles size of 16–22 mm is associated with the optimum oocyte maturation ratio, size of the remaining cohort of follicles should be ≥ 14 mm, 10–12 days of minimum length of stimulation should be auspicated in normal responders before trigger, and the timing of trigger administration should not depend solely on hormonal levels.

Conclusion

In conclusion, the timing of triggering of final oocyte maturation in ICSI cycles should be individualized on a case-by-case basis.
The impact of long non-coding RNA H19 on metabolic features and reproductive phenotypes of Egyptian women with polycystic ovary syndrome
Authors: Rashad Nearmeen M. , Elnagar Walid Mohamed , Issa Dina Rasheed , Hussien Marwa H. S. , Atef Rehab M. , Afifi Hoda
Published: January 2024

Background

Polycystic ovary syndrome (PCOS) is known as the most common endocrine/metabolic disorder in women of reproductive age. Long non-coding RNAs (lncRNAs) regulate a wide range of physiological and pathological processes. We designed this study to evaluate lncRNA H19 relative expression in patients with PCOS and to evaluate its impact on metabolic features and reproductive phenotypes of Egyptian women with polycystic ovaries.

Material and methods

The case–control study enrolled 50 control groups and 50 patients, with PCOS. The selection of patients with PCOS depended on the diagnosis according to the Rotterdam Consensus (2004). The lncRNA H19 were measured by real-time quantitative polymerase chain reaction (RT-qPCR).

Results

The lncRNA H19 level was significantly higher in the PCOS group (1.71 ± 0.48) compared to controls (0.924 ± 0.081). Furthermore, lncRNA H19 levels were significantly positively correlated with anthropometric and metabolic parameters including BMI, waist/hip ratio, TC, TG, LDL, FPG, FSI, HbA1c, and HOMA-IR. Regarding reproductive phenotypes features, hirsutism score, and AFC levels were significantly positively correlated with lncRNA H19 levels. The linear regression test revealed that BMI and AFC were the only parameters independently associated with lncRNA H19 among other studied parameters. Interestingly, receiver operating characteristic curve (ROC) analysis detected that the area under the curve (AUC) for the lncRNA H19 was 0.925 (95% CI = 0.856–0.955) with sensitivity = 96.4%, specificity = 96%, and the cutoff values (1.08). Thus, the predictive power of lncRNA H19 of PCOS was highly sensitive and specific.

Conclusion

PCOS patients had significantly higher lncRNA H19 levels than controls. lncRNA H19 levels were significantly positively correlated with metabolic risk factors as well as clinical and laboratory features of PCOS.
Safety, risk stratification, and cost of ERCP in patients with cirrhosis: a prospective controlled study
Authors: Attia Ahmed
Published: January 2024

Introduction

Given that ERCP has major procedure-related complications in non-cirrhotic patients, the complications and the cost of therapeutic ERCP were not studied in cirrhotic patients.

Aim

We aimed to study the complications and cost of ERCP compared to patients without cirrhosis.

Patients and methods

Outcome and complications of therapeutic ERCP were prospectively studied in patients with cirrhosis and compared to patients without cirrhosis undergoing ERCP. Patients with cirrhosis were evaluated using the Child-Pugh classification, MELD, MELD Na, and APRI scores. Safety was assessed up to 30 days following the procedure.

Results

Pancreatitis, perforation, bleeding, and cardiopulmonary complications were not different in both groups (8% vs. 9.3, 0% vs. 2%, 3.3% vs. 4%, and 4% vs. 2% respectively), while cholangitis occurred more frequently in cirrhosis (13.3% vs. 1.3% respectively). Hospital stay was longer, and mortality and costs were significantly higher among patients with cirrhosis. Patients with Child C cirrhosis developed more complications and had higher mortality. A MELD score cut-off of ≥ 11.5 separated all mortalities. Similarly, MELD-Na and APRI separated patients with cirrhosis who had more frequent complications and mortalities.

Conclusions

Patients with cirrhosis experienced more complications and costs, and cirrhotic patients who developed moderate to severe complications were more likely to die.
Motor activity of children with disabilities in conditions of mixed education as scientific and pedagogical problem
Authors: Kurivskyi Y. , Iedynak G.
Published: January 2024

The key challenges of the educational process in the conditions of war are limited ways of organizing motor activity, reduced motivation to use various forms of such activity and deterioration of the psycho-emotional state of participants in the educational process. Goal of research is to identify pedagogical problems in ensuring the appropriate level of motor activity of children with disabilities in the context of mixed education. Research methods used: theoretical analysis, systematization, comparison of different views on the investigated problem, generalization of data from scientific and methodological and special literature. The results. It was found that motor activity is considered as a means of achieving the highest possible positive result in the specified indicators of the state of functioning of body systems. The leading place in this process is given to expanding the potential of PE in the process of getting an education. The main pedagogical problems that require a systematic and comprehensive solution in the process of inclusive physical education of children with disabilities, which is implemented in a mixed format, are highlighted: ensuring the implementation of the fundamentals of inclusive pedagogy; flexibility in planning and modification of activities aimed at ensuring the optimal level of motor activity of children with disabilities; a comprehensive approach to problem-solving; permanent monitoring and implementation of new domestic and foreign practices for the implementation of high-quality remote physical education, development of motivation in children with disabilities for systematic physical activity; ensuring independent motor activity of children with disabilities in conditions of mixed education; a differentiated approach in the implementation of motor activity; implementation of a multisensory strategy. Conclusions. The possibilities of mixed learning in the implementation of physical education for children with disabilities as a means of realizing the appropriate level of their motor activity have been clarified. The identified problematic aspects lead to the need to develop a new model of the inclusive physical education process for a system-wide solution. This approach is based on interdisciplinary integration processes of using distance technologies and innovative technologies of mixed learning to solve the issues.
Probiotic supplementation as a supportive treatment in inflammatory bowel diseases - literature overview
Authors: Grabarczyk Alicja , Grabowska-Szczurek Martyna , Rybak Jakub , Magiera Karol , Magiera Barbara , Bator Piotr , Razik Michał , Rozwadowska Patrycja
Published: January 2024

Introduction and purpose: The human body hosts a diverse ecosystem of commensal microbes, known as the human microbiome. This review aims to asses the current state of knowledge regarding the effectiveness, mechanisms of action, and potential benefits of probiotic supplementation in patients with inflammatory bowel diseases.

Materials and methods: A literature review was conducted using the “PubMed” and “Google Scholar” databases with the keywords “Probiotics”, “Gut Microbiome”, “Probiotics and the treatment of Crohns Disease”, “Probiotics and the treatment of Colitis Ulcerosa”.

State of knowledge: Dysbiosis is known to disrupt the function of the intestinal mucosis and has been associated with chronic tissue inflammation. The use of probiotics is a promising method to improve the state of gut microbiota. It is known that the gut microbiome is disrupted in inflammatory bowel diseases. 

Summary: The use of probiotics in Ulcerative Colitis is supported by a significant amount of evidence supporting its effectiveness. In the case of Crohn’s Disease there is a lack of evidence to definitively determine whether the use of probiotics yields therapeutic benefits. Therefore there is a need for further research in this area.
When should we think about Fabry disease?
Authors: Chojnacka Natalia , Cymer Radosław , Jurasz Karolina , Podgórska Dominika , Rzeska Ewa , Sanecki Miłosz , Tomczyk Karolina , Klarycki Jakub
Published: January 2024

According to the European Union definition, a rare disease is a disease that occurs with a frequency of less than 5 per 10,000. Rare diseases pose a major diagnostic problem for physicians. Due to the often uncharacteristic symptoms and rarity of the disease, it can take a long time before a correct diagnosis and treatment is made. Fabry disease is classified as a rare disease. It is an X-linked hereditary syndrome caused by alpha-galactosidase A deficiency. This leads to accumulation of glycosphingolipids in tissues and dysfunction of many organs. The clinical picture is variable and dependent on residual alpha-galactosidase A activity. The classic form of the disease occurs most often in males due to the presence of only one X chromosome. When alpha-galactosidase A activity is partially preserved, a non-classical form develops, which is more commonly seen in the female sex. The most common clinical manifestations reported by patients are angiokeratoma, anhidrosis, diarrhoea or left ventricular hypertrophy. To diagnose Fabry disease, alpha-galactosidase A activity can be measured in whole blood - dry blood drop test (DBS), plasma or leukocytes. Fabry disease can be treated effectively, but treatment must last for life. The treatments reimbursed in Poland are agalsidase alfa and beta preparations and migalastat.
Clinical management and supervision of Influenza – systematic review
Authors: Głodzik Michał , Miśkiewicz Marek , Łęcka Martyna , Ogiegło-Kowalczyk Aleksandra , Samborska Julia , Stelmaszak Karina , Stencel Katarzyna , Więckowiak Paweł , Wojtasik Monika , Żak Katarzyna
Published: January 2024

The main aim of the following study is to review current knowledge on Influenza.

Influenza, also known as the flu, is an acute, infectious disease which attacks the respiratory system. The disease is caused by the influenza virus, of which there are 3 types. The most common is type A of influenza virus. It is transmitted between people by the droplet route, with cases occurring particularly frequently during seasonal epidemics. This is why the most important form of prevention is maintaining personal hygiene and isolation of sick people. Another form of prevention is a vaccine for the influenza virus developed with new variants every year. The flu causes a variety of symptoms from many systems but can also be asymptomatic. Diagnosis of the illness is based on clinical criteria and the results of diagnostic tests for which there are established criteria of legitimacy. Most common and sufficient curation for an illness is a symptomatic treatment but there is also a causal treatment for influenza. However certain conditions are required for its implementation. Influenza is usually a mild disease, but there are present cases of serious complications. Groups particularly exposed to severe and complicated courses of the disease are pregnant women, children and patients with multimorbidities. Currently, the co-occurrence of influenza with Covid-19 infection is particularly dangerous, which constitutes a new challenge for the global health care system.
Toxicity of Ocimum basilicum L. leaf extract against Spodoptera exigua Hübner (Lepidoptera: Noctuidae)
Authors: Sofia Siti Sa'adah Nadya , Sukirno Sukirno , Rini Nuringtyas Tri , Nugroho Laurentius Hartanto
Published: January 2024

The beet armyworm Spodoptera exigua Hübner (Lepidoptera: Noctuidae) is one of the most significant shallot pests worldwide, which affects agricultural production by approximately 100% in Indonesia. The growing awareness of sustainable agriculture encourages the search for natural alternatives to synthetic pesticides that promote resistance and contaminate the environment. Therefore, this study aims to evaluate the efficacy of basil (Ocimum basilicum L) leaf extract against the 2nd and 3rd instar larvae of S. exigua. The extracts were prepared using maceration and hydrodistillation and the pathogenicity was carried out against the 2nd and 3rd instar of S. exigua using three replicates with several doses of extract ranging from 0- 2.0%. The extracts were applied to the larvae using contact and topical methods. The results showed that maceration extraction yields a greater extract with different types compared to those of the hydrodistillation method. Maceration extract of basil leaves with the contact application method to 2nd instar showed better results than the topical application with the highest mortality rate of 75% (F=24.464; P< 0.001) and LC50 of 0.007%. This indicated that basil leaf extract with the contact application method has great potential to be developed as a botanical insecticide to control S. exigua in the field as part of Integrated Pest Management (IPM).
RAGraph: A Region-Aware Framework for Geo-Distributed Graph Processing
Authors: Yao Feng , Tao Qian , Yu Wenyuan , Zhang Yanfeng , Gong Shufeng , Wang Qiange , Yu Ge , Zhou Jingren
Published: January 2024

In many global businesses of multinational enterprises, graph-structure data is usually geographically distributed in different regions to support low-latency services. Geo-distributed graph processing suffers from the Wide Area Networks (WANs) with scarce and heterogeneous bandwidth, thus essentially differs from traditional distributed graph processing. In this paper, we propose RAGraph, aRegion-Aware framework for geo-distributed graph processing.At the core of RAGraph, we design a region-aware graph processing framework that allows advancing inefficient global updates locally and enables sensible coordination-free message interactions. RAGraph also contains an adaptive hierarchical message interaction engine to switch interaction modes adaptively based on network heterogeneity and fluctuation, and a discrepancy-aware message filtering strategy to filter important messages. Finally, the experiments show that RAGraph can achieve 1.69X - 40.53X speedup and 20.9% - 97% WAN cost reduction compared with state-of-the-art systems.
Blocker and Matcher Can Mutually Benefit: A Co-Learning Framework for Low-Resource Entity Resolution
Authors: Wu Shiwen , Wu Qiyu , Dong Honghua , Hua Wen , Zhou Xiaofang
Published: January 2024

Entity resolution (ER) approaches typically consist of a blocker and a matcher. They share the same goal and cooperate in different roles: the blocker first quickly removes obvious non-matches, and the matcher subsequently determines whether the remaining pairs refer to the same real-world entity. Despite the state-of-the-art performance achieved by deep learning methods in ER, these techniques often rely on a large amount of labeled data for training, which can be challenging or costly to obtain. Thus, there is a need to develop effective ER systems under low-resource settings. In this work, we propose an end-to-end iterative Co-learning framework for ER, aimed at jointly training the blocker and the matcher by leveraging their cooperative relationship. In particular, we let the blocker and the matcher share their learned knowledge with each other via iteratively updated pseudo labels, which broaden the supervision signals. To mitigate the impact of noise in pseudo labels, we develop optimization techniques from three aspects: label generation, label selection and model training. Through extensive experiments on benchmark datasets, we demonstrate that our proposed framework outperforms baselines by an average of 9.13--51.55%. Furthermore, our analysis confirms that our framework achieves mutual benefits between the blocker and the matcher.
TSGBench: Time Series Generation Benchmark
Authors: Ang Yihao , Huang Qiang , Bao Yifan , Tung Anthony K. H. , Huang Zhiyong
Published: January 2024

Synthetic Time Series Generation (TSG) is crucial in a range of applications, including data augmentation, anomaly detection, and privacy preservation. Although significant strides have been made in this field, existing methods exhibit three key limitations: (1) They often benchmark against similar model types, constraining a holistic view of performance capabilities. (2) The use of specialized synthetic and private datasets introduces biases and hampers generalizability. (3) Ambiguous evaluation measures, often tied to custom networks or downstream tasks, hinder consistent and fair comparison.

To overcome these limitations, we introduce TSGBench, the inaugural Time Series Generation Benchmark, designed for a unified and comprehensive assessment of TSG methods. It comprises three modules: (1) a curated collection of publicly available, real-world datasets tailored for TSG, together with a standardized preprocessing pipeline; (2) a comprehensive evaluation measures suite including vanilla measures, new distance-based assessments, and visualization tools; (3) a pioneering generalization test rooted in Domain Adaptation (DA), compatible with all methods. We have conducted comprehensive experiments using TSGBench across a spectrum of ten real-world datasets from diverse domains, utilizing ten advanced TSG methods and twelve evaluation measures. The results highlight the reliability and efficacy of TSGBench in evaluating TSG methods. Crucially, TSGBench delivers a statistical analysis of the performance rankings of these methods, illuminating their varying performance across different datasets and measures and offering nuanced insights into the effectiveness of each method.
Maximum Balanced (
k
,
ϵ
)-Bitruss Detection in Signed Bipartite Graph
Authors: Chung Kai Hiu , Zhou Alexander , Wang Yue , Chen Lei
Published: January 2024

Signed bipartite graphs represent relationships between two sets of entities, including both positive and negative interactions, allowing for a more comprehensive modeling of real-world networks. In this work, we focus on the detection of cohesive subgraphs in signed bipartite graphs by leveraging the concept of balanced butterflies. A balanced butterfly is a cycle of length 4 that is considered stable if it contains an even number of negative edges. We propose a novel model called the balanced (
k
, ϵ)-bitruss, which provides a concise representation of cohesive signed bipartite subgraphs while enabling control over density (
k
) and balance (ϵ). We prove that finding the largest balanced (
k
, ϵ)-bitruss is NP-hard and cannot be efficiently approximated to a significant extent. Furthermore, we extend the unsigned butterfly counting framework to efficiently compute both balanced and unbalanced butterflies. Based on this technique, we develop two greedy heuristic algorithms: one that prioritizes followers and another that focuses on balanced support ratios. Experimental results demonstrate that the greedy approach based on balanced support ratios outperforms the follower-based approach in terms of both efficiency and effectiveness.
Missing Value Imputation for Multi-Attribute Sensor Data Streams via Message Propagation
Authors: Li Xiao , Li Huan , Lu Hua , Jensen Christian S. , Pandey Varun , Markl Volker
Published: January 2024

Sensor data streams occur widely in various real-time applications in the context of the Internet of Things (IoT). However, sensor data streams feature missing values due to factors such as sensor failures, communication errors, or depleted batteries. Missing values can compromise the quality of real-time analytics tasks and downstream applications. Existing imputation methods either make strong assumptions about streams or have low efficiency. In this study, we aim to accurately and efficiently impute missing values in data streams that satisfy only general characteristics in order to benefit real-time applications more widely. First, we propose a message propagation imputation network (MPIN) that is able to recover the missing values of data instances in a time window. We give a theoretical analysis of why MPIN is effective. Second, we present a continuous imputation framework that consists of data update and model update mechanisms to enable MPIN to perform continuous imputation both effectively and efficiently. Extensive experiments on multiple real datasets show that MPIN can outperform the existing data imputers by wide margins and that the continuous imputation framework is efficient and accurate.
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
Authors: Chen Yuhang , Zhang Chaoyun , Ma Minghua , Liu Yudong , Ding Ruomeng , Li Bowen , He Shilin , Rajmohan Saravan , Lin Qingwei , Zhang Dongmei
Published: January 2024

Anomaly detection in multivariate time series data is of paramount importance for large-scale systems. However, accurately detecting anomalies in such data poses significant challenges due to the need for precise data modeling capability. Existing forecasting and reconstruction-based methods struggle to address these challenges effectively. To overcome these limitations, we propose a novel anomaly detection framework named ImDiffusion, which combines time series imputation and diffusion models to achieve accurate and robust anomaly detection. The imputation-based approach employed by ImDiffusion leverages the information from neighboring values in the time series, enabling precise modeling of temporal and inter-correlated dependencies, reducing uncertainty in the data, thereby enhancing the robustness of the anomaly detection process. ImDiffusion further leverages diffusion models as time series imputers to accurately capture complex dependencies. We leverage the step-by-step denoised outputs generated during the inference process to serve as valuable signals for anomaly prediction, resulting in improved accuracy and robustness of the detection process.

We evaluate the performance of ImDiffusion via extensive experiments on benchmark datasets. The results demonstrate that our proposed framework significantly outperforms state-of-the-art approaches in terms of detection accuracy and timeliness. ImDiffusion is further integrated into the real production system in Microsoft and observes a remarkable 11.4% increase in detection F1 score compared to the legacy approach. To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.
A Shapelet-Based Framework for Unsupervised Multivariate Time Series Representation Learning
Authors: Liang Zhiyu , Zhang Jianfeng , Liang Chen , Wang Hongzhi , Liang Zheng , Pan Lujia
Published: January 2024

Recent studies have shown great promise in unsupervised representation learning (URL) for multivariate time series, because URL has the capability in learning generalizable representation for many downstream tasks without using inaccessible labels. However, existing approaches usually adopt the models originally designed for other domains (e.g., computer vision) to encode the time series data and rely on strong assumptions to design learning objectives, which limits their ability to perform well. To deal with these problems, we propose a novel URL framework for multivariate time series by learning time-series-specific shapelet-based representation through a popular contrasting learning paradigm. To the best of our knowledge, this is the first work that explores the shapelet-based embedding in the unsupervised general-purpose representation learning. A unified shapelet-based encoder and a novel learning objective with multi-grained contrasting and multi-scale alignment are particularly designed to achieve our goal, and a data augmentation library is employed to improve the generalization. We conduct extensive experiments using tens of real-world datasets to assess the representation quality on many downstream tasks, including classification, clustering, and anomaly detection. The results demonstrate the superiority of our method against not only URL competitors, but also techniques specially designed for downstream tasks. Our code has been made publicly available at https://github.com/real2fish/CSL.
Fast and Space-Efficient Parallel Algorithms for Influence Maximization
Authors: Wang Letong , Ding Xiangyun , Gu Yan , Sun Yihan
Published: January 2024

Influence Maximization (IM) is a crucial problem in data science. The goal is to find a fixed-size set of highly influentialseedvertices on a network to maximize the influence spread along the edges. While IM is NP-hard on commonly used diffusion models, a greedy algorithm can achieve (1 - 1/e)-approximation by repeatedly selecting the vertex with the highestmarginal gainin influence as the seed. However, we observe two performance issues in the existing work that prevent them from scaling to today's large-scale graphs: space-inefficient memorization to estimate marginal gain, and time-inefficient seed selection process due to a lack of parallelism.

This paper significantly improves the scalability of IM using two key techniques. The first is asketch-compressiontechnique for the independent cascading model on undirected graphs. It allows combining the simulation and sketching approaches to achieve a time-space tradeoff. The second technique includes new data structures for parallel seed selection. Using our new approaches, we implementedPaC-IM: Parallel and Compressed IM.

We comparePaC-IMwith state-of-the-art parallel IM systems on a 96-core machine with 1.5TB memory.PaC-IMcan process the ClueWeb graph with 978M vertices and 75B edges in about 2 hours. On average, across all tested graphs, our uncompressed version is 5--18x faster and about 1.4x more space-efficient than existing parallel IM systems. Using compression further saves 3.8x space with only 70% overhead in time on average.
TERI: An Effective Framework for Trajectory Recovery with Irregular Time Intervals
Authors: Chen Yile , Cong Gao , Anda Cuauhtemoc
Published: January 2024

The proliferation of trajectory data has facilitated various applications in urban spaces, such as travel time estimation, traffic monitoring, and flow prediction. These applications require a substantial volume of high-quality trajectories as the prerequisite to achieve effective performance. Unfortunately, a large number of real-world trajectories are inevitably collected in unsatisfactory quality due to device constraints. To address this issue, previous studies have proposed numerous trajectory recovery methods to augment the quality of such trajectories, thereby ensuring the performance of related applications. However, these methods all assume the awareness of the recovery positions in advance, which is a condition not always available in practice. In this paper, we discard this strong assumption and focus on trajectory recovery with irregular time intervals as a more prevalent setting in downstream scenarios. We propose a novel framework, called TERI, to tackle trajectory recovery without prior information in a two-stage process, where recovery positions are first detected, followed by the imputation of the missing data points. In each stage, TERI framework deploys a model named RETE, which is based on Transformer encoder architecture enhanced by novel designs to boost the performance for the new problem setting. Specifically, RETE features a learnable Fourier encoding module to better model spatial and temporal correlations, and integrates collective transition pattern learning and trajectory contrastive learning to effectively capture sequential transition patterns. Extensive experiments on three real-world datasets demonstrate that TERI consistently outperforms all the baselines by a significant large margin.
Caerus: Low-Latency Distributed Transactions for Geo-Replicated Systems
Authors: Hildred Joshua , Abebe Michael , Daudjee Khuzaima
Published: January 2024

Distributed deterministic database systems achieve high transaction throughput for geographically replicated data. Supporting transactions with ACID guarantees requires deterministic databases to order transactions globally to dictate execution order. In a geographically distributed environment, ordering transactions globally can take multiple wide-area network (WAN) round trips of messaging, which adds significant latency to transaction response times, leading to poor user experiences. To improve the response time of transactions in deterministic databases, we propose an ordering protocol that can include a transaction in the global order in a single WAN round trip to the primary regions of the data items within the transaction's read and write set. The protocol reduces the cost of determining the global order for all transactions by leveraging deterministic merging of partial sequences of transactions per geographic region. We implement the protocol in Caerus, our geo-replicated deterministic database system that serializably commits and replicates transactions after a delay of only a single WAN round trip of messaging. Using popular workload benchmarks over geographically replicated data in Azure, we show that Caerus outperforms state-of-the-art comparison systems to deliver low-latency transaction execution.
FormaT5: Abstention and Examples for Conditional Table Formatting with Natural Language
Authors: Singh Mukul , Cambronero José , Gulwani Sumit , Le Vu , Negreanu Carina , Nouri Elnaz , Raza Mohammad , Verbruggen Gust
Published: January 2024

Formatting is an important property in tables for visualization, presentation, and analysis. Spreadsheet software allows users to automatically format their tables by writing data-dependent conditional formatting (CF) rules. Writing such rules is often challenging for users as it requires understanding and implementing the underlying logic. We present FormaT5, a transformer-based model that can generate a CF rule given the target table and a natural language description of the desired formatting logic. We find that user descriptions for these tasks are often under-specified or ambiguous, making it harder for code generation systems to accurately learn the desired rule in a single step. To tackle this problem of under-specification and minimise argument errors, FormaT5 learns to predict placeholders though an abstention objective. These placeholders can then be filled by a second model or, when examples of rows that should be formatted are available, by a programming-by-example system. To evaluate FormaT5 on diverse and real scenarios, we create an extensive benchmark of 1053 CF tasks, containing real-world descriptions collected from four different sources. We release our benchmarks to encourage research in this area. Abstention and filling allow FormaT5 to outperform 8 different neural approaches on our benchmarks, both with and without examples. Our results illustrate the value of building domain-specific learning systems.
The Art of Latency Hiding in Modern Database Engines
Authors: Huang Kaisong , Wang Tianzheng , Zhou Qingqing , Meng Qingzhong
Published: January 2024

Modern database engines must well use multicore CPUs, large main memory and fast storage devices to achieve high performance. A common theme is hiding latencies such that more CPU cycles can be dedicated to "real" work, improving overall throughput. Yet existing systems are only able to mitigate the impact of individual latencies, e.g., by interleaving memory accesses with computation to hide CPU cache misses. They still lack the joint optimization of hiding the impact of multiple latency sources.

This paper presents MosaicDB, a set of latency-hiding techniques to solve this problem. With stackless coroutines and carefully crafted scheduling policies, we explore how I/O and synchronization latencies can be hidden in a well-crafted OLTP engine that already hides memory access latency, without hurting the performance of memory-resident workloads. MosaicDB also avoids oversubscription and reduces contention using the coroutine-to-transaction paradigm. Our evaluation shows MosaicDB can achieve these goals and up to 33x speedup over prior state-of-the-art.