bluuebunny/crossref_metadata_2025_split huggingface dataset:
The alternative NF-κB RelB subunit is a novel critical sensor of lipid metabolism to promote tumor development
Authors: Becquard* Thomas , Benatar* Clara , Bretot Charles , Eluard Baptiste , Bordereaux Didier , Thirouard Laura , Hammoutene Adel , Montagne Aurelie , Ransy Céline , Alves-Guerra Marie-Clothilde , Caradeuc Cédric , Cagnard Nicolas , Boissan Mathieu , Giraud Nicolas , Bouillaud Frédéric , Bertho Gildas , Paradis Valérie , Chiron David , Dentin Renaud , Prip-Buus Carina , Baud Véronique 
Published: October 2024

Abstract

Cancer cells reprogram their metabolism to fulfill their high energetic demand. Lipid metabolism is most often reprogrammed for cancer cell survival and tumor development. The role of alternative oncogenic NF-κB/RelB subunit in the reprogramming of lipid metabolism in cancer is unknown. Here we report that RelB plays a central role at the crossroads of lipid storage and liberation of fatty acids from the lipid droplets to feed the fatty acid oxidation (FAO) and mitochondrial energetic metabolism. High RelB expression defines a subset of hepatocellular carcinoma (HCC) patients and cell lines with a peculiar gene expression profile enriched in lipid catabolic-related genes, including lipases. Functional studies revealed that high RelB activation controls the expression of major lipolytic lipases including adipose triglyceride lipase (ATGL) and monoglyceride lipase (MAGL), and impacts on HCC cell survival, migration, and tumor development in vivo. Altogether, we uncovered that RelB is a central regulator of the lipid metabolism plasticity and an energy homeostasis sensor in cancer cells.

*Thomas Becquard and Clara Benatar are co-first authors.
Influence of growth temperature on development and yields in a medium late and late Scandinavian cultivar of potato
Authors: Mølmann Jørgen A.B. , Johansen Tor J. 
Published: October 2024

Abstract

With more available meteorological field-sensors for agriculture, there is an increasing need for local-adapted growth models. Especially for cultivation of crop cultivars in areas with marginal temperatures and varying light conditions. The temperature response was therefore studied in climate-controlled growth chambers under constant temperatures from 9 to 21°C under a natural 24 h photoperiod in greenhouse in Tromsø (69.7°N, 18.9°E), Norway, for the potato cultivars Gullauge (medium late) and Mandel (late). There was a strong response in both cultivars to temperature, with reduced developmental time from emergence and leaf formation to visible flower buds at increasing temperature intervals. Developmental rates were higher for Gullauge than for Mandel. Shoot dry matter weight per plant was highest at 12°C and 15°C for both cultivars, though with higher shoot biomass at harvest in Mandel than Gullauge. Tuber yields and tuber dry-matter percentages for both cultivars was higher for Gullauge than for Mandel. Fresh weight yields and dry matter percentage for both cultivars were highest at 15°C. Optimum temperature for above-ground vegetative development-rate was estimated to be 24.0°C and 22.6°C, for Gullauge and Mandel respectively. In contrast, the respective temperature optimums for developing tuber dry matter content were 16.6°C and 15.6°C. Lower temperature optimums for below-ground development makes potato a suitable crop for temperate and boreal climates with cooler autumn temperatures. Different temperature optimums for above- than below-ground development needs to be considered when developing temperature-based growth models for potato cultivars from emergence to tuber yields.
Optimized Intention-adaptive Graph Neural Network for Robust Failure Diagnosis of Microservice System Using Multimodal Data
Authors: Kumar N. Naveen , Suresh S. , Balamurugan S. , Kumar P. Seshu 
Published: October 2024

Abstract

Automatic failure diagnosis is critical for large-scale Microservice systems. Most failure detection methods available today only employ single-modal data (logs, traces, or metrics). It carries out an empirical study using actual failure scenarios to show that the integration of several data sources (multimodal data) leads to a more precise diagnosis. Effectively expressing these data and handling unequal failures remain challenging. The suggested paper, MD-RFD-MS-IGNN, proposes Multimodal Data for Robust Failure Diagnosis of Microservice System using Optimized Intention-adaptive Graph Neural Network. First, GAIA dataset is used to collect the input data. To implement this, the input data is pre-processed using Adaptive Kernel Learning Kalman Filtering (AKLKF) and it removes the N/A (not applicable) values or empty row from the collected data; then the Pre-processed data are feature extracted using Automated Tunable Q Wavelet Transform (ATQWT)is used to extract spatial features like strace, log, and metric. Subsequently, the gathered data is loaded into an Intention-adaptive Graph Neural Network (IGNN) to efficiently classify failure detection into categories such as file missing, system stuck, process crash, and access refused. Generally speaking, optimization algorithms that may be modified to get the optimal parameters for accurate failure classification are not expressed by IGNN. In order to optimize Intention-adaptive Graph Neural Networks, which precisely identify Microservice system failure, Human Evolutionary Optimization (HEO) was used. Next, the suggested MD-RFD-MS-IGNN is put into practice, and performance measures including F1-Score, Precision, and Recall are examined. When analysed through existing techniques such as Automated functional and robustness testing of microservice architectures (AFRT-MA-GCM), MTG_CD: multi-scale learnable transformation graph for fault classification and diagnosis in microservices (MTG-FCDM-GCN), and robust failure diagnosis of microservice system through multimodal data (RFD-MS-MD-GNN), the performance of the MD-RFD-MS-IGNN approach achieves 17.30%, 23.39%, and 32.41% higher F1-Score.
Sentence representation-based text matching algorithm using prompt learning and contrastive learning
Authors: Wu Liang , Li Xiang , Ma Ping , Hu Changhao 
Published: October 2024

Abstract

With the recent global popularity of ChatGPT (Chat Generative Pre-trained Transformer),it signifies that the era of natural language processing (NLP) technology flourishing in human society has arrived. As a fundamental yet indispensable component of NLP technology, text matching techniques have permeated various NLP application scenarios, such as search engines, dialogue systems, large-scale text deduplication, and recommendation systems. In the era of deep learning, particularly after the emergence of the pre-trained language model BERT (Bidirectional Encoder Representations from Transformers), representation-based text matching algorithms built on BERT and its variants have become one of the mainstream text matching algorithms. However, the effectiveness of these algorithms is limited by the low quality of sentence vectors generated by the original BERT. In 2021, the self-supervised contrastive learning algorithm SimCSE was introduced, which significantly improved the quality of sentence vectors encoded by BERT. However, the batch-wise negative sampling method used by SimCSE, due to its strong randomness, may lead to negative samples with low relevance, thereby limiting the amount of information the model can learn. This paper proposes a negative sample construction algorithm based on prompt learning, called PLNSC, which constructs a masked language modeling task. It leverages the powerful language capabilities of pre-trained language models to generate reasonable negative sample representations for each sentence, enabling the model to learn richer information during the contrastive learning process, thereby enhancing the sentence representation capability of the model. This approach improved the average performance of three baseline models across five datasets by 1.93, 0.49, and 0.63, respectively.
Polygenic proxies of age-related plasma protein levels reveal TIMP2 role in cognitive performance
Authors: Anastasi Federica , Genius Patricia , Rodriguez-Fernandez Blanca , Yang Chengran , Gorijala Priyanka , Timsina Jigyasha , Hernández-Villamizar Felipe , Lorenzini Luigi , Campo Marta del , Sanchez-Benavides Gonzalo , Minguillon Carolina , Navarro Arcadi , Cruchaga Carlos , Suárez-Calvet Marc , Vilor-Tejedor Natalia 
Published: October 2024

Abstract

Background While numerous studies have identified blood proteins that modulate brain aging in mice, the direct translation of these findings to human health remains a substantial challenge. Bridging this gap is critical for developing interventions that can effectively target human brain aging and associated diseases.Methods We first identified 12 proteins with aging or rejuvenating properties in murine brains through a systematic review. Using protein quantitative trait loci data for these proteins, we developed polygenic scores to predict plasma protein levels, which we then validated in two independent human cohorts. We employed association models to explore the association between these genetically predicted protein levels and cognitive performance, focusing specifically on their interaction with key genetic markers such as sex, APOE-ε4 and Aβ42 status.Results Predicted plasma levels of Tissue Inhibitor of Metalloproteinases 2 (TIMP2) were significantly associated with improved global cognition and memory performance in humans, also when the models were stratified by sex, APOE-ε4, and Aβ42 status.Conclusions This finding aligns with TIMP2's brain-rejuvenating role in murine models, suggesting it as a promising therapeutic target for brain aging and age-related brain diseases in humans.
Research on multi-section energy-saving operation control methods of urban rail train based on deep reinforcement learning
Authors: Chen Xiaoqiang , Yang Haobo , Meng Jianjun , Li Decang , Xu Ruxun , Guo Youmin 
Published: October 2024

Abstract

In order to improve the operation efficiency of urban rail transit and reduce energy consumption, a multi-objective train energy saving optimization model was established based on the train motion equation, parking accuracy and punctuality. On the basis of making full use of the stability of TD3 (Twin Delayed Deep Deterministic policy gradient) algorithm in processing the high-dimensional continuous action space and better adaptability in the face of complex environment, the maximum speed curve of the train is introduced. A reasonable action range is set to improve the efficiency of the algorithm and reduce the difficulty of solving the model. According to the train schedule, different single interval can be set up to allocate the running time and appropriate reward function within the allowable range of the total running time. The deep reinforcement learning method is used to optimize the train running curve under different running time in each interval by the agent choosing the action freely within a certain range. The simulation experiment is designed on the background of 5 stations and 4 sections of Beijing Metro Line 2, and the improved DDPG algorithm and TD3 algorithm are compared. The efficiency of the improved algorithm is verified by setting up three different energy-saving train running curves under the single interval distributable running time. The results show that the energy consumption of train traction can be reduced to a certain extent by adjusting the allotted time of the interval. However, the energy saving benefits brought by selecting too large allot time are not significant, and will greatly affect the normal operation of trains. Therefore, adjusting the running time of the train in a single interval can effectively save the overall traction energy consumption of the train in multiple intervals, and the maximum energy saving can reach 18.07% compared with the traditional control method. The research results can provide a feasible control method for the energy-saving operation of urban rail trains in multiple sections.
Transovarial transmission of Anaplasma phagocytophilum in Haemaphysalis ticks under field conditions
Authors: Ahn KyuSung , Alkathiri Badriah , Lee Seung-Hun , Lee Haeseung , Kwak Dongmi , Youn SoYoun , Yoo Mi-Sun , Kim Jaemyung , Cho Yun Sang , Shin SungShik 
Published: October 2024

Abstract

Background:
Anaplasmosis, a zoonotic tick-borne pathogen affecting both livestock, companion animals, and humans, exhibits 15 to 18% seropositivity among hunting dogs in Korea. The dominant tick species in Korea, Haemaphysalis longicornis can transmit this pathogen to both humans and animals. Given the limited understanding of transovarial transmission of Anaplasma spp., our study aimed to assess the prevalence of questing larval ticks containing Anaplasma DNA. Additionally, we aimed to gather data for establishing a nationwide forecasting and alert system on seasonal variation of tick developmental stages and tick-borne zoonotic pathogens.
Methods:
From March to October 2021 and again from March to October 2022, we collected a total of 36,912 unfed, questing ticks of Haemaphysalis spp. from 149 sites in Korea. Ticks were collected from herbaceous vegetation using the flagging method using a white flannel cloth. After species identification, one third of collected ticks underwent analysis for Anaplasma gene. Nymph ticks were pooled in groups of one to 10, larvae in groups of one to 50, while adults were examined individually. Nested PCR was performed to detect the genus Anaplasma by amplifying the 16S rRNA gene, followed by sequencing for species identification and phylogenetic analysis.
Results:
Of the 36,912 questing ticks collected, 13,082 (35.4%) were identified as nymphs and adults of H. longicornis and 3,850 (10.4%) as those of H. flava. The morphologically indistinguishable larval stage of Haemaphysalis predominated, with 19,980 (54.1%) collected primarily from July to October. From the 939 tick pools, 24 pools (2.6%) tested positive for Anaplasma, with the larval stage exhibiting the highest number of positive pools (16, 1.7%). Phylogenetic analysis revealed that 21 of the 24 Anaplasma-positive pools contained A. phagocytophilum-specific genes, while the remaining 1 was identified as Anaplasma sp. and 2 as A. bovis.
Conclusions:
Our study provides evidence of transovarial transmission of Anaplasma phagocytophilum in Haemaphysalis spp. larvae under field conditions, as A. phagocytophilum originates from their mother ticks in unengorged questing larval ticks. Additionally, our findings contribute significant data for establishing a nationwide forecasting and alert system on seasonal variation of tick developmental stages and tick-borne zoonotic pathogens.
Investigating Impact of Sound Frequencies and Light Exposure on Pepper Plant Growth
Authors: Abdeldaim Mariam Islam , Hamama Malak , Farha Omar , Gaafar Mostafa 
Published: October 2024

Abstract

Studies have indicated that acoustic waves and light wavelengths may influence plant development by improving enzymat-ic function, and hormonal regulation. This can lead to a decrease in reliance on fertilizers, which cause a significant number of deaths an-nually. Consequently, the primary objective of our experimental study is to examine the effects of red-light wavelength and sound frequen-cy on the growth of pepper plants. to prove the effectiveness of light and sound waves on plants four groups of pepper pots were estab-lished: one reference group with no influences, one with red light at a 660-nm wavelength, one with an auditory frequency of 500 Hz, and one in the presence of both red light and auditory frequency conditions. To attain the most precise outcome, a controlled environment was instituted for all the groups, the quantity of solar radiation that impinges upon the pots, the volume of irrigation, and the homogeneous soil composition. The data was gathered by taking photos of the growing process throughout three consecutive weeks Results obtained showed that the experimental set exposed to the 500 Hz sound frequency had a higher germination rate compared to other groups. The experi-mental group with red light showed a slight increase in growth from the control group. Specimens the combined treatment group demon-strated growth patterns more likely to the only sound-exposed group, indicating that the best result from the only sound group which exam-ines that there is a direct relation between the rate of growth of the plant and the frequency of sound waves depending on the type of plant and the hertz value that stimulate its enzymatic process. These results may serve as a significant determinant in optimizing agricultural methodologies.
Bio-Inspired Graphene Nanomaterials: Synthesis and Characterization for Ambient Microbial Destruction
Authors: Bartaria Vaishnav , Yadav Ishant , Jangid Ashok , Srivas Anupam , Kumar Ranjit 
Published: October 2024

Abstract

This study explores the conversion of carbon-rich coconut waste into bio-inspired graphene oxide (GO) and reduced graphene oxide (rGO) for application as antimicrobial agents, addressing air quality and public health concerns. Both GO and rGO demonstrated significant antimicrobial properties against a variety of airborne microorganisms. GO achieved nearly complete inhibition of bacteria, such as Staphylococcus spp., and fungi, including Aspergillus spp., at low concentrations. Similarly, rGO effectively inhibited Escherichia coli and Brucella spp., highlighting its broad-spectrum antimicrobial activity. The synthesis of GO was performed using an enhanced Hummers' method, producing nanomaterials with distinct surface functionalities, which were characterized by X-ray diffraction (XRD) and Field Emission Scanning Electron Microscopy (FESEM). XRD analysis revealed a notable shift from the sharp graphite peak at 2θ = 26.4°, indicating the (002) plane, to a broadened peak at 2θ = 11.15° in GO, signifying successful oxidation and exfoliation of graphite. For rGO, the disappearance of the 11.15° peak confirmed the partial restoration of graphitic structure. FESEM imaging displayed the nanosheet morphology of GO and rGO, along with microscopic analysis revealing the physical interaction of GO with bacterial and fungal cells, enabling precise identification of microorganisms. Moreover, the GO-coated surfaces exhibited higher surface roughness, promoting bacterial adhesion, which underscores the critical role of surface properties in antimicrobial efficacy. This research contributes to the Sustainable Development Goals (SDG 3: Good Health and Well-being, and SDG 11: Sustainable Cities and Communities) by providing an eco-friendly solution for mitigating microbial pollution in the ambient environment through the use of bio-based nanomaterials.
Prevalence and clinical features of gastroesophageal reflux disease in residents of the Makhachkala-Caspian agglomeration
Authors: Akhmedova P. N. , Chamsutdinov N. U. 
Published: October 2024

Introduction. Gastroesophageal reflux disease is the most common gastrointestinal disease. In recent decades, various questionnaires have been used to study the prevalence of gastroesophageal reflux disease: GERD-Q, QUEST, FSSG, RDQ, GERD-HRQL, the use of which has allowed us to establish a clear trend towards an increase in the frequency of gastroesophageal reflux disease throughout the world, including in Russia.Aim. To study the prevalence and features of the clinical course of gastroesophageal reflux disease in residents of the Makhachkala- Caspian agglomeration of the Republic of Dagestan.Materials and methods. A survey was conducted of 496 residents of the Republic of Dagestan (308 women and 188 men, average age ‒ 40 ± 0.7 years) using the GERD-Q and EFGERD (Epidemiology and Risk Factors for the Development of Gastroesophageal Reflux Disease in Dagestan) questionnaires.Results. The prevalence of gastroesophageal reflux disease in residents of the Makhachkala-C aspian agglomeration of the Republic of Dagestan according to the GERD-Q questionnaire was 11.8%, according to the EFGERD questionnaire – 26.2%. The most common residents of the Republic of Dagestan who suffer from gastroesophageal reflux disease are Dargins (39.8%), Avars (38.2%) and Kumyks (37.0%). Somewhat less common are Lezgins (30.7%) and mestizos (31.6%). Laks (12%) and Tabasarans (8.9%) suffer from gastroesophageal reflux disease much less frequently. It has been established that in addition to esophageal symptoms of the disease, patients with gastroesophageal reflux disease often have symptoms of gastrointestinal tract diseases, as well as otolaryngological, bronchopulmonary, cardiac and dental symptoms.Conclusion. The prevalence of gastroesophageal reflux disease in residents of the Makhachkala-C aspian agglomeration of the Republic of Dagestan according to the EFGERD questionnaire was 26.2%. At the same time, the EFGERD questionnaire has shown its high diagnostic value, which makes it possible to recommend it for the purpose of early diagnosis of gastroesophageal reflux disease and its extraesophageal manifestations.
The interrelation of cardiometabolic risk factors and metabolic dysfunction-associated steatotic liver disease subtypes
Authors: Ishchenko A. Yu. , Galushko M. Yu. , Bakulin I. G. 
Published: October 2024

Introduction. Metabolic dysfunction-associated steatotic liver disease is one of the leading causes of cirrhosis, liver cancer and extrahepatic complications. The number and various combinations of cardiometabolic risk factors, as well as the severity of liver steatosis, can affect the aggressiveness and prognosis of the disease. The article presents the results of own studies aimed at determining the prognostic significance of risk factors (individual and in different combinations) and steatometry.Objective. To determine the influence of individual cardiometabolic factors, their number and various combinations on the aggressiveness and prognosis of metabolic dysfunction-associated steatotic liver disease; to assess the prognostic value of steatometry for the disease.Material and methods. The examination results (steatometry, blood biochemistry tests, abdominal ultrasound) of patients diagnosed with metabolic dysfunction-associated steatotic liver disease were retrospectively analyzed. Statistical analysis: Student's t-test, Pearson's χ2-test, odds ratio, relative risk, Fisher's φ-test, ROC-analysis.Results. 146 patients were included. Cohort characteristics: average age 49.3 years, 84 men (57.5%). Aggressive forms (steatohepatitis, advanced steatosis) were associated with arterial hypertension, carbohydrate metabolism disorders (the most significant is insulin resistance), dyslipidemia, hyperuricemia. The severity of liver steatosis was less in patients who received therapy for lipid metabolism disorders comparing with those who didn’t. The presence of three or more cardiometabolic risk factors in a patient increased the risk of steatohepatitis and advanced steatosis. The combination of arterial hypertension and hyperuricemia significantly increased the risk of steatohepatitis. Advanced hepatic steatosis (≥310 dB/m2 by steatometry) was reliably associated with steatohepatitis.Conclusion. Individual risk factors, their number and combinations, contribute differently to the development of aggressive forms of the disease. Hyperuricemia should be considered as an additional cardiometabolic risk factor. Steatometry is capable of identifying advanced hepatic steatosis, being a risk factor for the development of steatohepatitis, which allows us to recommend it for the early diagnosis of metabolic dysfunction-associated steatotic liver disease.