bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Demographics: National Amyotrophic Lateral Sclerosis Registry and Clinical Trials Data
Authors: Han Moon , Raymond Jaime , Larson Theodore C. , Mehta Paul , Horton D. Kevin
Published: July 2024

Objective

To characterize the participant demographics in the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database compared with the web-portal National Amyotrophic Lateral Sclerosis (ALS) Registry (the Registry).

Methods

Demographics and ALS symptom information were compared between the self-reported registrant data in the Registry web portal (2010–2021) and the latest available PRO-ACT data (updated August 2022), which is a collection of clinical trials data.

Results

Greater percentages of younger (≤ 59 years old) but smaller percentages of older (60 + years old) participants were represented in PRO-ACT compared to Registry. Enrollment for minority race groups was greater in the Registry portal data, but race information was largely missing/unknown in PRO-ACT database. Median age at the time of diagnosis and age at the time of symptom onset were significantly higher for Registry enrollees compared to the participants of PRO-ACT. Symptom onset sites were similarly reported, but duration between self-noted symptom onset and diagnosis was slight, but significantly longer for the Registry enrollees (11 vs. 9 months). Hispanic were as likely as non-Hispanic to participate in research studies, based on the Registry data.

Conclusion

There was a notable difference in the age distribution and minority representation of enrollees between the PRO-ACT and Registry study populations. Age distribution in the PRO-ACT database skewed to a younger and less diverse cohort. Despite the clinical heterogeneity and complex disease mechanism of ALS, identifying the underrepresented demographic niche in the PRO-ACT and Registry study populations can help improve patient participation and criteria for patient selection to enhance generalizability.
Tourniquets
Authors: Johnston Luke R. , Parker William J. , Walker Patrick , Bradley Matthew J.
Published: July 2024

Purpose of Review

Tourniquets have a longstanding history in combat casualty care, but only became widely accepted in the last 20 years as effective and safe. This review seeks to examine the history of tourniquets in combat casualty care and analyze their translation from military to civilian trauma care.

Recent Findings

The most recent research focusing on prehospital tourniquet use has focused on the expansion of tourniquets to the civilian trauma patient population. Research has shown that tourniquet use can be effectively taught to civilians without medical training, and that placement of tourniquets in the prehospital environment can reduce blood loss and shock at admission. Other recent research has established the effectiveness of tourniquets in unique military environments such as in cold weather gear and when chemical, biological, radiological, and nuclear (CBRN) gear is worn.

Tourniquets have become a standard piece of equipment for deployed servicemembers for their lifesaving potential. Evidence is building that tourniquets can play a similar role in civilian trauma, but more work is needed to demonstrate the cost effectiveness and mortality benefit of widespread civilian tourniquet training and application.
Multi-view subspace clustering based on multi-order neighbor diffusion
Authors: Long Yin , Xu Hongbin , Xiang Yang , Du Xiyu , Yang Yanying , Zhao Xujian
Published: July 2024

Multi-view subspace clustering (MVC) intends to separate out samples via integrating the complementary information from diverse views. In MVC, since the structural information in the graph is crucial to the graph learning, most of the existing algorithms construct the superficial graph from the original data by directly measuring the similarity between the first-order complementary nearest neighbors. However, the information provided by the superficial graph structure would be influenced by contaminated or absent samples. To address this problem, in the proposed method, the higher-order complementary neighbor graphs are exploited to discover the latent structural information between the samples, and fusing the latent structural information across different orders to achieve the MVC. Specifically, the higher-order neighbor graphs under different views are leveraged to estimate the missing samples. Then, to integrate the neighbor graphs of different orders, the multi-order neighbor diffusion fusion is proposed. Nevertheless, the above problem of diffusion fusion is an intractable non-convex issue. Thus, to address it, the multi-order neighbor diffusion fusion is considered as a combination problem of the solution under different order, and the heuristic algorithm is leveraged to solve it. In this way, not only the data representation under different view and also the neighbor structure under different order can be diffused under a joint optimization framework, thus the consistency and integral information among various perspectives and orders can be utilized effectively and simultaneously. Experiments on both incomplete and complete multi-view dataset demonstrate the convincingness of the high-order neighborhood structure based subspace clustering scheme by comparing with the existing approaches.
Improving the reliability of nanosatellite swarms by adopting blockchain technology
Authors: Ibrahim Hussein A. , Shouman Marwa A. , El-Fishawy Nawal A. , Ahmed Ayman
Published: July 2024

Satellite swarm networks have occupied a prominent position in many modern applications due to their low cost, simplicity of design, and flexibility. Reliability is an influential factor in the design of satellite networks with different structures. Usually, small satellites are based on COST components, which may reduce continues operability due to the lack of using backup system on board the sagecraft. Any failure in one subsystem means a complete loss of the function and data stored in this subsystem; hence the need for a reliable and applicable solution for this matter is a crucial topic. Using the redundancy strategy in satellite swarm networks increases reliability and availability. Blockchain is characterized by using a distributed ledger which enables the database to be replicated across nodes in the network and results in increasing transparency, security, and trust. This paper suggests adoption of blockchain technology in distributed multi-satellite mission swarm networks to provide a high level of reliability and availability of the entire system; the blockchain is usually used to secure system transactions in multilayer approach by storage of the key parameters in more than one node; here we suggest the adoption of this approach not only to secure satellite network transaction, but also to increase system reliability so that failure of one node can be recovered by other nodes. We compared this approach with similar traditional networks that do not use blockchain. The results show a higher reliability efficiency of 95.3% for applying blockchain technology compared to 64.3% without the use of blockchain, as well as a higher availability of 99% compared to 91%.
A granularity data method for power frequency electric and electromagnetic fields forecasting based on T–S fuzzy model
Authors: Nie Peng , Yu Qiang , Li Zhenkun , Yuan Xiguo
Published: July 2024

The impact of electromagnetic radiation generated by signal transmission base stations and power stations to meet the needs of communication equipment and energy consumption on the environment has caused people concerns. Monitoring and prediction of electric and magnetic fields have become critical tasks for researchers. In this paper, we propose a granularity data method based on T–S (Takagi–Sugeno) fuzzy model, named fuzzy rule-based model, which utilizing finite rules that are determined by the deviations between the predicted values and the true values after the data goes through a granulation-degranulation mechanism, to predict the intensity of power frequency electric field and electromagnetic field. A series of experiments show that fuzzy rule-based models have better robustness and higher prediction accuracy in comparison with several existing prediction models. The improvement of the performance of the fuzzy rule-based model quantified in terms of Root Mean Squared Error is 20.86%, 51.91%, 62.28%, 65.10%, and 71.92% in comparison with that of the Ridge model, Lasso model, and a family of support vector machine model with different kernel functions, including linear kernel (SVM-linear), radial basis function (SVM-BRF), polynomial kernel (SVM-polynomial) respectively, on the electromagnetic field testing data, and 37.42%, 55.16%, 58.79%, 59.28%, 64.27% lower than that of the Ridge model, Lasso model, SVM-linear model, SVM-BRF model and SVM-polynomial model on the power frequency electric field testing data.
A multi-period intuitionistic fuzzy consensus reaching model for group decision making problem in social network
Authors: Yang Wei , Zhang Luxiang
Published: July 2024

A new intuitionistic fuzzy consensus reaching model is developed with multi-period public opinions and expert evaluation values in social network environment. First, the public opinions are obtained by using the crawler software and sentiment analysis technology is used to transform public opinions into intuitionistic fuzzy decision matrix in each period. Attribute weights are calculated by using the time attenuation factor and changes in public opinion. Second, the social trust relationship is modeled and incomplete social trust relationships are completed by using Archimedean t-norm. The expert weights are calculated by using the dynamic trust degree and similarity degree. Third, a consensus framework is proposed for multiple-period decision making problem, which coordinates conflicts between experts through dual feedback paths. The collective opinion scores are calculated by using weights of periods and attribute weights obtained from the word frequency of public opinions. The tourism attraction recommendation method is used to illustrate the proposed method.
In Situ Synchrotron Diffraction Assessment of Reversibility of the Martensitic Transformation in Single-Crystalline Co–Ni–Ga Shape Memory Alloy Under Torsion
Authors: Lauhoff C. , Degener S. , Bolender A. , Liehr A. , Hübner L. , Frenck J.-M. , Molotnikov A. , Niendorf T.
Published: July 2024

Heusler-type Co–Ni–Ga shape memory alloys attracted significant attention due to their excellent functional properties in single-crystalline state under both compressive and tensile loading. The present study investigates the superelastic deformation behavior under torsion. Using a newly installed torsion testing setup, in situ synchrotron diffraction was carried out on single-crystalline material in order to investigate the martensitic phase transformation. Incremental deformation experiments reveal a fully reversible martensitic transformation under torsional loading at room temperature, leading to excellent strain recovery after deformation to 6.5% shear strain. Furthermore, relevant aspects towards the analysis of powder diffraction data obtained for single-crystalline material in transmission mode under torsional loading are presented and critically discussed.
Improving Irrigation Performance of Raised Bed Furrow Using WinSRFR Model
Authors: Shah Muhammad Ayub , Waseem Muhammad , Iqbal Mudassar , Nabi Ghulam , Tariq Muhammad Atiq Ur Rehman , Arshed Abu Bakar , Sultan Umar , Laraib Muhammad , Abbas Khawar
Published: July 2024

Agricultural productivity is intricately tied to efficient water management strategies, with raised bed furrow systems being a prevalent method for irrigation. However, the optimization of these systems remains a critical area of exploration. The border irrigation method is commonly employed in developing countries for irrigation and leads to significant water loss, reduced irrigation efficiency, and increased irrigation durations. In contrast, raised bed furrow irrigation represents an improved surface irrigation technique that optimizes water usage in irrigated systems. This study seeks to assess the irrigation performance of raised bed furrows, encompassing deep percolation loss, distribution uniformity, adequacy, and application efficiency. The evaluation will be conducted for both existing conditions and an optimized scenario achieved through the application of the WinSRFR model. Field data facilitated the numerical simulation and the model was calibrated to reflect the existing irrigation system dynamics accurately. The performance of the model was assessed by utilizing the statistical indicator of root mean square error (RMSE) and revealed good agreement between advance and recession time. Results revealed that existing raised bed furrow irrigation exhibited up to 40% deep percolation loss, 80% distribution uniformity, and 60% application efficiency. Increasing furrow length had adverse effects; decreased application efficiency and distribution uniformity; and increased deep percolation losses. In contrast, reducing the furrow length and cutoff time by up to 33% and 40%, respectively, and increasing the width and inflow rate by up to 55% and 100%, respectively, enhanced the application efficiency and distribution uniformity, and minimized deep percolation loss. Overall, improved raised bed furrow irrigation provides a more efficient option and is encouraged to adopt for irrigation.
From time series to networks in R with the ts2net package
Authors: Ferreira Leonardo N.
Published: July 2024

Network science established itself as a prominent tool for modeling time series and complex systems. This modeling process consists of transforming a set or a single time series into a network. Nodes may represent complete time series, segments, or single values, while links define associations or similarities between the represented parts. is one of the main programming languages used in data science, statistics, and machine learning, with many packages available. However, no single package provides the necessary methods to transform time series into networks. This paper presents a detailed revision of the main transformation methods in the literature and their implementation in the package in . The package provides time series distance functions that can be easily computed in parallel and in supercomputers to process larger data sets and methods to transform distance matrices into networks. also provides methods to transform a single time series into a network, such as recurrence networks, visibility graphs, and transition networks. Together with other packages, permits the use of network science and graph mining tools to extract information from time series.
Analyzing breast cancer comorbidities: a network approach using community detection algorithms
Authors: Permana Angga A. , Yaputra Reynard M.
Published: July 2024

Breast cancer is a prominent global health concern, as the data from the International Agency for Research on Cancer (IARC) shows that breast cancer is the leading cancer type with new cases in 2020 and among the Top 5 cancer types with the most deaths. To help improve the current breast cancer comorbidity identification by medical personnel and ultimately, lower the number of death cases from breast cancer comorbidity, this research aims to discover the breast cancer comorbidity community, do modularity and similarity-based evaluation, suggest the best semantic similarity measurement and threshold value, and validate the data of breast cancer comorbidities with several data from research papers. The Wang algorithm, with a threshold value of 0.5, is chosen to build the network. Leiden, Louvain, RBER Pots, RB Pots, and Walktrap are the best five community detection algorithms. Similarity measurements with the best three fitness functions (edges inside, scaled density, and size) suggest that the Leiden–Louvain algorithm and RBER Pots-RB Pots algorithm are two pairs of algorithms with similar results. Other similarity measurements with the V-measure heatmap suggest that Louvain–Leiden (0.99), RB Pots–Leiden (0.97), and RB Pots–RBER Pots (0.96) results are similar. Comorbidity is then evaluated using the best five community detection algorithms and four centrality algorithms. As a result, fourteen diseases are agreed upon by the best five community detection algorithms, five diseases are agreed by four algorithms, two diseases are agreed by three algorithms, a disease is agreed by two algorithms, and ten diseases are agreed by an algorithm.
Dynamics of economic growth in ASEAN-5 countries: a panel ARDL approach
Authors: Hidthiir Mohammad Helmi bin , Ahmad Zaki , Junoh Mohd Zukime Mat , Yusof Mohd Faizal Bin
Published: July 2024

This research explores economic growth dynamics in five ASEAN countries (1980–2020), analyzing the short and long-term impact of financial development (FD), oil prices, investment (INV), and inflation on GDP growth. Panel ARDL analysis reveals a transient positive link between FD and long-term GDP growth, necessitating a holistic approach for short-term stability. Similarly, oil prices exhibit long-term volatility, urging diversification strategies. In contrast, consistent and significant relationships exist between INV and GDP growth in both time frames, underscoring investment's pivotal role. The study emphasizes managing inflation for sustained growth, offering vital insights for policymakers, economists, and analysts in fostering ASEAN's economic stability.
Risk of Cardiovascular Disease Hospitalization After Common Psychiatric Disorders: Analyses of Disease Susceptibility and Progression Trajectory in the UK Biobank
Authors: Han Xin , Zeng Yu , Shang Yanan , Hu Yao , Hou Can , Yang Huazhen , Chen Wenwen , Ying Zhiye , Sun Yajing , Qu Yuanyuan , Wang Junren , Zhang Wei , Fang Fang , Valdimarsdóttir Unnur , Song Huan
Published: July 2024

Whether associations between psychiatric disorders and hospitalization for cardiovascular diseases (CVDs) can be modified by disease susceptibility and the temporal pattern of these associated CVDs remain unknown. In our study, we conducted a matched cohort study of the UK Biobank including 44,430 patients with common psychiatric disorders (anxiety, depression, and stress-related disorders) between 1997 and 2019, together with 222,150 sex-, Townsend deprivation index-, and birth year- individually matched unexposed individuals. The hazard ratios (HRs) for CVD hospitalization associated with a prior psychiatric disorder were derived from Cox models, adjusted for multiple confounders. We then stratified the analyses by self-reported family history of CVD and CVD polygenic risk score (PRS) calculated based on summary statistics of independent genome-wide association studies. We further conducted disease trajectory analysis and visualized the temporal pattern of CVDs after common psychiatric disorders. During a mean follow-up of 12.28 years, we observed an elevated risk of CVD hospitalization among patients with psychiatric disorders, compared with matched unexposed individuals (hazard ratios [HRs] = 1.20, 95% confidence interval [CI]: 1.18–1.23), especially during the first six months of follow-up (1.72 [1.55–1.91]). The stratification analyses by family history of CVD and by CVD PRS obtained similar estimates between subgroups with different susceptibilities to CVD. We conducted trajectory analysis to visualize the temporal pattern of CVDs after common psychiatric disorders, identifying primary hypertension, acute myocardial infarction, and stroke as three main intermediate steps leading to further increased risk of other CVDs. In conclusion, the association between common psychiatric disorders and subsequent CVD hospitalization is not modified by predisposition to CVD. Hypertension, acute myocardial infarction, and stroke are three initial CVDs linking psychiatric disorders to other CVD sequelae, highlighting a need of timely intervention on these targets to prevent further CVD sequelae among all individuals with common psychiatric disorders.
Local population decline of the threatened Lesser Grey Shrike Lanius minor is linked to the modernisation of the rural landscape
Authors: Krištín Anton , Hoi Herbert , Kaňuch Peter
Published: July 2024

Landscape changes and the intensification of agriculture in recent centuries were largely responsible for the dramatic decline in the biodiversity of farmlands. Rural settlements have also been subject to radical changes due to modernisation, but their impact on bird populations is poorly quantified. The Lesser Grey Shrike Lanius minor is a threatened farmland bird and already extinct in many areas. We monitored a population of this long-distance migrant in a traditional farming area in the Poľana Mountains (central Slovakia) in three breeding seasons (1996, 2016, and 2021). We analysed the impact of the increase in number of modern habitations and the decrease in traditional farmsteads on the population decline. The number of breeding territories decreased from 73 in 1996 to 38 in 2016 and 22 in 2021. As the population has declined, the breeding area has also shrunk significantly. While there were no modern homesteads in the breeding territories in 1996, by 2021 their number had increased to the number of traditional farmsteads. Building a single modern house in a territory reduced the probability of nesting to about 6%, and this effect was also seen when one or two farms were still present (17% and 40%, respectively). An additional modern homestead in the territory reduced the nesting probability to almost zero, even if a farmstead was already present. In this long-term empirical study, we identified these changes as a local threat factor for the species studied. The results presented can help in the design and implementation of conservation measures in traditional farming landscapes.
Integrating a Water Tracer Model Into WRF‐Hydro for Characterizing the Effect of Lateral Flow in Hydrologic Simulations
Authors: Hu Huancui , Leung L. Ruby , Dominguez Francina , Gochis David , Chen Xingyuan , Good Stephen , Dugger Aubrey , Larsen Laurel , Barlage Michael
Published: July 2024

Most current land models approximate terrestrial hydrological processes as one‐dimensional vertical flow, neglecting lateral water movement from ridges to valleys. Such lateral flow is fundamental at catchment scales and becomes crucial for finer‐scale land models. To test the effect of incorporating lateral flow toward three‐dimensional representations of hydrological processes in the next generation land models, we integrate a water tracer model into the WRF‐Hydro framework to track water movement from precipitation to discharge and evapotranspiration. This hydrologic‐tracer integrated system allows us to identify the key mechanisms by which lateral flow affects the flow paths and transit times in WRF‐Hydro. By comparing modeling experiments with and without lateral routing in two contrasting catchments, we determine the impacts of lateral flow on the transit times of precipitation event‐water. Results show that with limited hydrologic connectivity, lateral flow extends the transit times by reducing (increasing) event‐water drainage loss (accumulation) in ridges (valleys) and allowing reinfiltration of infiltration‐excess flow, which is missing in most land models. On the contrary with high hydrologic connectivity, lateral flow can effectively accelerate the water release to streams and reduce the transit time. However, the transit times are substantially underestimated by the model compared with isotope‐derived estimates, indicating model limitations in representing flow paths and transit times. This study provides some insights on the fundamental differences in terrestrial hydrology simulated by land models with and without lateral flow representation.
Insights on Source Lithology and Pressure‐Temperature Conditions of Basalt Generation Using Machine Learning
Authors: Cheng Lilu , Yang Zongfeng , Costa Fidel
Published: July 2024

Identifying the origin and conditions of basalt generation is a crucial yet formidable task. To tackle this challenge, we introduce an innovative approach leveraging machine learning. Our methodology relies on a comprehensive database of approximately one thousand major element concentrations derived from glass samples generated through experiments encompassing a wide range of source lithologies, pressure (from 0.28 to 20 GPa) and temperature (850–2100°C). We first applied the XGBoost classification models to assess the compositional characteristics of melts from three principal mantle source categories: peridotitic, transitional, and mafic sources. We obtained an accuracy of approximately 96% on the test data set. Furthermore, we also employ an XGBoost regression model to predict the pressure and temperature conditions of generation of basalts from diverse lithologic sources. Our predictions of temperature and pressure exhibit remarkable precisions, of about 49°C and 0.37 GPa, respectively. To enhance accessibility of our model, we have implemented a user‐friendly web browser application, available at (https://huggingface.co/spaces/lilucheng/sourcedetection). The web application allows users to swiftly recover the source lithology as well as pressure and temperature conditions governing basalt generation for a broad array of samples within a matter of seconds.
MAOOA‐Residual‐Attention‐BiConvLSTM: An Automated Deep Learning Framework for Global TEC Map Prediction
Authors: Wang Haoran , Liu Haijun , Yuan Jing , Le Huijun , Shan Weifeng , Li Liangchao
Published: July 2024

The high‐precision prediction of total ionospheric electron content (TEC) is of great significance for improving the accuracy of global navigation satellite systems. There are two problems with the current prediction of TEC: (a) The existing TEC prediction models mainly based on stacked structure, which has insufficient predictive ability when the network has fewer layers, and loss of fine‐grained features when there are more layers, resulting in a decrease in predictive performance; (b) The existing research on ionospheric TEC prediction mainly focuses on building deep learning prediction models, while there is little research on optimizing the hyper‐parameters of TEC prediction models. Optimization can help find a better quasi‐optimal hyperparameter combination and improve the performance of the model. This paper proposed an automatic deep learning framework for global TEC map prediction, named MAOOA‐Residual‐Attitude‐BiConvLSTM. This framework includes a TEC prediction model, Residual‐Attention‐BiConvLSTM, which can simultaneously consider both coarse‐grained and fine‐grained spatiotemporal features. It also includes an optimization algorithm, MAOOA, for optimizing the hyper‐parameters of the model. We conducted comparative experiments between our framework and C1PG, ConvLSTM, ConvGRU, and ED‐ConvLSTM during high solar activity years, low solar activity years, and a magnetic storm event. The results indicate that in all cases, the framework proposed in this paper outperforms the comparative models.
Lidar DEM and Computational Mesh Grid Resolutions Modify Roughness in 2D Hydrodynamic Models
Authors: Prior Elizabeth M. , Michaelson Nathan , Czuba Jonathan A. , Pingel Thomas J. , Thomas Valerie A. , Hession W. Cully
Published: July 2024

Topography and the computational mesh grid are fundamental inputs to all two‐dimensional (2D) hydrodynamic models, however their resolutions are often arbitrarily selected based on data availability. With the increasing use of drone technology, the end user can collect topographic data down to centimeter‐scale resolution. With this advancement comes the responsibility of choosing a resolution. In this study, we investigated how the choice of mesh grid and digital elevation model (DEM) resolutions affect 2D hydrodynamic modeling results, specifically water depths, velocities, and inundation extent. We made pairwise comparisons between simulations from a 2D HEC‐RAS model with varying mesh grid resolutions (1 and 2 m) and drone‐based lidar DEM resolutions (0.1, 0.25, 0.5, 1, and 2 m) over a 1.5 km reach of Stroubles Creek in Blacksburg, Virginia. The model was rerun for up to ±4% change in floodplain roughness to determine how the DEM and mesh grid changes relate to an equivalent change in roughness. We found that the modeled differences from resolution change were equivalent to altering floodplain roughness by up to 12% for depths and 44% for velocities. The largest differences in velocity were concentrated at the channel‐floodplain interface, whereas differences in depth occurred laterally throughout the floodplain and were not correlated with lidar ground point density. We also found that the inundation boundary is dependent on the DEM resolution. Our results suggest that modelers should carefully consider what resolution best represents the terrain while also resolving important riparian topographic features.
Synchronization bandwidth enhancement induced by a parametrically excited oscillator
Authors: Song Jiahao , Xu Yutao , Yang Qiqi , Huan Ronghua , Wei Xueyong
Published: July 2024

The synchronization phenomenon in nature has been utilized in sensing and timekeeping fields due to its numerous advantages, including amplitude and frequency stabilization, noise reduction, and sensitivity improvement. However, the limited synchronization bandwidth hinders its broader application, and few techniques have been explored to enhance this aspect. In this paper, we conducted theoretical and experimental studies on the unidirectional synchronization characteristics of a resonator with phase lock loop oscillation. A novel enhancement method for the synchronization bandwidth using a parametrically excited MEMS oscillator is proposed, which achieves a remarkably large synchronization bandwidth of 8.85 kHz, covering more than 94% of the hysteresis interval. Importantly, the proposed method exhibits significant potential for high-order synchronization and frequency stabilization compared to the conventional directly excited oscillator. These findings present an effective approach for expanding the synchronization bandwidth, which has promising applications in nonlinear sensing, fully mechanical frequency dividers, and high-precision time references.
The combination of temozolomide and perifosine synergistically inhibit glioblastoma by impeding DNA repair and inducing apoptosis
Authors: Zhao Wenpeng , Zhou Liwei , Zhao Wentao , Yang Huiying , Lu Zhenwei , Zhang Liang , Zhang Yaya , Xie Yuanyuan , Lu Hanwen , Han Wanhong , He Jiawei , Qiu Xiansheng , Jia Fang , Zhao Wujie , Zhang Bingchang , Wang Zhanxiang
Published: July 2024

Temozolomide (TMZ) is widely utilized as the primary chemotherapeutic intervention for glioblastoma. However, the clinical use of TMZ is limited by its various side effects and resistance to chemotherapy. The present study revealed the synergistic inhibition of glioblastoma through the combined administration of TMZ and perifosine. This combination therapy markedly diminished BRCA1 expression, resulting in the suppression of DNA repair mechanisms. Furthermore, the combination of TMZ and perifosine elicited caspase-dependent apoptosis, decreasing glioblastoma cell viability and proliferation. The observed synergistic effect of this combination therapy on glioblastoma was validated in vivo, as evidenced by the substantial reduction in glioblastoma xenograft growth following combined treatment with TMZ and perifosine. In recurrent glioma patients, higher BRCA1 expression is associated with worse prognosis, especially the ones that received TMZ-treated. These findings underscore the potent antitumor activity of the AKT inhibitor perifosine when combined with TMZ and suggest that this approach is a promising strategy for clinical glioblastoma treatment.
Computational gastronomy: capturing culinary creativity by making food computable
Authors: Bagler Ganesh , Goel Mansi
Published: July 2024

Cooking, a quintessential creative pursuit, holds profound significance for individuals, communities, and civilizations. Food and cooking transcend mere sensory pleasure to influence nutrition and public health outcomes. Inextricably linked to culinary and cultural heritage, food systems play a pivotal role in sustainability and the survival of life on our planet. Computational Gastronomy is a novel approach for investigating food through a data-driven paradigm. It offers a systematic, rule-based understanding of culinary arts by scrutinizing recipes for taste, nutritional value, health implications, and environmental sustainability. Probing the art of cooking through the lens of computation will open up a new realm of possibilities for culinary creativity. Amidst the ongoing quest for imitating creativity through artificial intelligence, an interesting question would be, ‘Can a machine think like a Chef?’ Capturing the experience and creativity of a chef in an AI algorithm presents an exciting opportunity for generating a galaxy of hitherto unseen recipes with desirable culinary, flavor, nutrition, health, and carbon footprint profiles.
Co-evolutionary dynamics of mammalian brain and body size
Authors: Venditti Chris , Baker Joanna , Barton Robert A.
Published: July 2024

Despite decades of comparative studies, puzzling aspects of the relationship between mammalian brain and body mass continue to defy satisfactory explanation. Here we show that several such aspects arise from routinely fitting log-linear models to the data: the correlated evolution of brain and body mass is in fact log-curvilinear. This simultaneously accounts for several phenomena for which diverse biological explanations have been proposed, notably variability in scaling coefficients across clades, low encephalization in larger species and the so-called taxon-level problem. Our model implies a need to revisit previous findings about relative brain mass. Accounting for the true scaling relationship, we document dramatically varying rates of relative brain mass evolution across the mammalian phylogeny, and we resolve the question of whether there is an overall trend for brain mass to increase through time. We find a trend in only three mammalian orders, which is by far the strongest in primates, setting the stage for the uniquely rapid directional increase ultimately producing the computational powers of the human brain.
Towards carbon-neutral and clean propulsion in heavy-duty transportation with hydroformylated Fischer–Tropsch fuels
Authors: Voelker Simon , Groll Niklas , Bachmann Marvin , Mueller Leonard , Neumann Marcel , Kossioris Theodoros , Muthyala Paul , Lehrheuer Bastian , Hofmeister Marius , Vorholt Andreas , Schmitz Katharina , Pischinger Stefan , Leitner Walter , Bardow André
Published: July 2024

Clean transport requires tailored energy carriers. For heavy-duty transportation, synthetic fuels are promising but must fulfil the key challenges of achieving carbon neutrality while reducing air pollution and ensuring scalability through compatibility with existing infrastructure. Here we show that hydroformylated Fischer–Tropsch (HyFiT) fuels composed of optimized alkane–alcohol blends simultaneously address these challenges. First, the design of the HyFiT fuel process flexibly closes the carbon cycle by employing biomass or carbon dioxide as feedstock, while being scalable through mature technologies. Second, fuel testing shows that HyFiT fuels comply with global fuel standards. Material compatibility is demonstrated for two standard sealing materials, enabling the retrofit of today’s vehicle fleets. Third, vehicle testing shows that HyFiT fuels substantially reduce combustion-induced particulate matter and nitrogen oxides. Fourth, a well-to-wheel life cycle assessment finds that HyFiT fuels enable the transition to net-zero greenhouse gas emissions, showing simultaneously a favourable profile in other environmental parameters. HyFiT fuels can thus complement electrification for heavy-duty transportation.
AI-based selection of individuals for supplemental MRI in population-based breast cancer screening: the randomized ScreenTrustMRI trial
Authors: Salim Mattie , Liu Yue , Sorkhei Moein , Ntoula Dimitra , Foukakis Theodoros , Fredriksson Irma , Wang Yanlu , Eklund Martin , Azizpour Hossein , Smith Kevin , Strand Fredrik
Published: July 2024

Screening mammography reduces breast cancer mortality, but studies analyzing interval cancers diagnosed after negative screens have shown that many cancers are missed. Supplemental screening using magnetic resonance imaging (MRI) can reduce the number of missed cancers. However, as qualified MRI staff are lacking, the equipment is expensive to purchase and cost-effectiveness for screening may not be convincing, the utilization of MRI is currently limited. An effective method for triaging individuals to supplemental MRI screening is therefore needed. We conducted a randomized clinical trial, ScreenTrustMRI, using a recently developed artificial intelligence (AI) tool to score each mammogram. We offered trial participation to individuals with a negative screening mammogram and a high AI score (top 6.9%). Upon agreeing to participate, individuals were assigned randomly to one of two groups: those receiving supplemental MRI and those not receiving MRI. The primary endpoint of ScreenTrustMRI is advanced breast cancer defined as either interval cancer, invasive component larger than 15 mm or lymph node positive cancer, based on a 27-month follow-up time from the initial screening. Secondary endpoints, prespecified in the study protocol to be reported before the primary outcome, include cancer detected by supplemental MRI, which is the focus of the current paper. Compared with traditional breast density measures used in a previous clinical trial, the current AI method was nearly four times more efficient in terms of cancers detected per 1,000 MRI examinations (64 versus 16.5). Most additional cancers detected were invasive and several were multifocal, suggesting that their detection was timely. Altogether, our results show that using an AI-based score to select a small proportion (6.9%) of individuals for supplemental MRI after negative mammography detects many missed cancers, making the cost per cancer detected comparable with screening mammography. ClinicalTrials.gov registration: NCT04832594.
VCP/p97-associated proteins are binders and debranching enzymes of K48–K63-branched ubiquitin chains
Authors: Lange Sven M. , McFarland Matthew R. , Lamoliatte Frederic , Carroll Thomas , Krshnan Logesvaran , Pérez-Ràfols Anna , Kwasna Dominika , Shen Linnan , Wallace Iona , Cole Isobel , Armstrong Lee A. , Knebel Axel , Johnson Clare , De Cesare Virginia , Kulathu Yogesh
Published: July 2024

Branched ubiquitin (Ub) chains constitute a sizable fraction of Ub polymers in human cells. Despite their abundance, our understanding of branched Ub function in cell signaling has been stunted by the absence of accessible methods and tools. Here we identify cellular branched-chain-specific binding proteins and devise approaches to probe K48–K63-branched Ub function. We establish a method to monitor cleavage of linkages within complex Ub chains and unveil ATXN3 and MINDY as debranching enzymes. We engineer a K48–K63 branch-specific nanobody and reveal the molecular basis of its specificity in crystal structures of nanobody-branched Ub chain complexes. Using this nanobody, we detect increased K48–K63-Ub branching following valosin-containing protein (VCP)/p97 inhibition and after DNA damage. Together with our discovery that multiple VCP/p97-associated proteins bind to or debranch K48–K63-linked Ub, these results suggest a function for K48–K63-branched chains in VCP/p97-related processes.
Identification of plant based potential antifungal compounds against BMK-1 protein of Bipolaris oryzae using molecular docking approach
Authors: Bhat Sheeba , Rather Mariya , Gani Saima , Nabi Asha , Ganai Shabir Ahmad , Shah Mehraj D. , Sofi Parvaze , Jeelani Fehim , Hussain Arif , Ashraf Sabiha , Anwar Ali , Iqbal Iram , Nisa Tawkeer Un , Summuna Baby , Banday Saba
Published: July 2024

Rice brown spot is an important disease of rice worldwide that inflicts substantial yield losses. The antimicrobial potential of methanol, acetone and dimethyl sulfoxide (DMSO) extracts of different medicinal plants, viz., Syzygium aromaticum, Saussurea costus, Acorus calamus, Bergenia ciliate, Geranium pratense, Mentha longifolia, Inula racemosa, Podophyllum hexandrum, Heracleum candicans and Picrorhiza kurroa, against the brown spot pathogen Bipolaris oryzae in vitro was evaluated via mycelial growth inhibition and spore germination inhibition assays. Among the plant extracts tested, 100% mycelial inhibition was observed for the methanol extract of Syzygium aromaticum at all three concentrations (2000 ppm, 3000 ppm and 4000 ppm), followed by the methanol extract of Inula racemosa (90.33%) at 4000 ppm. A maximum conidial germination inhibition of 83.54% was exhibited by the Heracleum candicans leaf extract. Phytochemical profiling of Syzygium aromaticum and Inula racemosa through liquid chromatography and mass spectrometry (HR-LCMS) revealed the presence of several compounds, such as eugenol, ursolic acid, quercetin, chlorogenic acid, and noscapine. A molecular docking approach was used to identify key inhibitory molecules against B. oryzae. Among the compounds detected in S. aromaticum and Inula racemosa, ursolic acid and noscapine were found to have the greatest binding affinity for the Big Mitogen Activated Protein Kinase (BMK-1) enzyme present in B. oryzae. In conclusion, S. aromaticum and Inula racemosa are potent compounds that could serve as lead compounds for drug discovery in the future.
Extremozymes and compatible solute production potential of halophilic and halotolerant bacteria isolated from crop rhizospheric soils of Southwest Saurashtra Gujarat
Authors: Reang Likhindra , Bhatt Shraddha , Tomar Rukam Singh , Joshi Kavita , Padhiyar Shital , Bhalani Hiren , Kheni JasminKumar , Vyas U. M. , Parakhia M. V.
Published: July 2024

Halophiles are one of the classes of extremophilic microorganisms that can flourish in environments with very high salt concentrations. In this study, fifteen bacterial strains isolated from various crop rhizospheric soils of agricultural fields along the Southwest coastline of Saurashtra, Gujarat, and identified by 16S rRNA gene sequencing as Halomonas pacifica, H. stenophila, H. salifodinae, H. binhaiensis, Oceanobacillus oncorhynchi, and Bacillus paralicheniformis were investigated for their potentiality to produce extremozymes and compatible solute. The isolates showed the production of halophilic protease, cellulase, and chitinase enzymes ranging from 6.90 to 35.38, 0.004–0.042, and 0.097–0.550 U ml−1, respectively. The production of ectoine-compatible solute ranged from 0.01 to 3.17 mg l−1. Furthermore, the investigation of the ectoine-compatible solute production at the molecular level by PCR showed the presence of the ectoine synthase gene responsible for its biosynthesis in the isolates. Besides, it also showed the presence of glycine betaine biosynthetic gene betaine aldehyde dehydrogenase in the isolates. The compatible solute production by these isolates may be linked to their ability to produce extremozymes under saline conditions, which could protect them from salt-induced denaturation, potentially enhancing their stability and activity. This correlation warrants further investigation.
Preferential evaluation of coal filling mining scheme under building pressure based on improved grey target decision-making model of game theory
Authors: Ma Junpeng , Ma Chaoqun , Liu Tao , Zhu Xianxiang , Li Song , Jin Lei
Published: July 2024

The problem of coal compression under buildings is common in underground mining of coal mines in China. The selection of traditional mining programme is subjective and lacks scientific rationality. In order to solve this problem, this paper studies the evaluation index system and model applicable to the selection of coal filling mining scheme under buildings. A multi-objective evaluation index system integrating economic, technical and adaptive factors is constructed. And an integrated optimization model is established, which is based on the traditional grey target model, combining the game theory optimal combination of weights with the hierarchical analysis method, entropy weight method, Critic method to determine the weights, and then introducing the TOPSIS model and the Mars distance to establish an improved grey target decision-making model. The validity of the evaluation index system and model is verified by taking the example of coal mining under pressure of buildings in five mining areas of a coal mine, which provides technical support for decision makers. This study helps to scientifically and reasonably carry out the preferred mining scheme of coal filling under building pressure.
Metagenomic comparison of intestinal microbiota between normal and liver fibrotic rhesus macaques (Macaca mulatta)
Authors: Wei Yuankui , Li Junhui , Zhu Baoqiang , Hu Qi , Lan Ming , Zhou Jia , Luo Jianbo , Zhu Wanlong , Lai Yong , Long Enwu , Zhou Liang
Published: July 2024

Liver fibrosis is an important pathological process in chronic liver disease and cirrhosis. Recent studies have found a close association between intestinal microbiota and the development of liver fibrosis. To determine whether there are differences in the intestinal microbiota between rhesus macaques with liver fibrosis (MG) and normal rhesus macaques (MN), fecal samples were collected from 8 male MG and 12 male MN. The biological composition of the intestinal microbiota was then detected using 16S rRNA gene sequencing. The results revealed statistically significant differences in ASVs and Chao1 in the alpha-diversity and the beta-diversity of intestinal microbiota between MG and MN. Both groups shared Prevotella and Lactobacillus as common dominant microbiota. However, beneficial bacteria such as Lactobacillus were significantly less abundant in MG (P = 0.02). Predictive functional analysis using PICRUSt2 gene prediction revealed that MG exhibited a higher relative abundance of functions related to substance transport and metabolic pathways. This study may provide insight into further exploration of the mechanisms by which intestinal microbiota affect liver fibrosis and its potential future use in treating liver fibrosis.
Calligraphic interdigitated capacitive sensors for green electronics
Authors: Thakur Abhay Singh , Srivatava Vinit , Park Hyeong Kwang Benno , Kebaili Imen , Boukhris Imed , Joo Yun Hwan , Sung Tae Hyun , Kumar Anuruddh , Vaish Rahul
Published: July 2024

This study presents a novel approach to fabricating interdigitated capacitive (IDC) touch sensors using graphite-based pencils on a wood substrate. The sensors were designed to detect touches and pressure variations, offering a cost-effective and environmentally friendly solution for sensor fabrication. The fabrication process involved abrasion of graphite pencils on a wooden substrate to create conductive traces, followed by the integration of interdigitated electrode structures. Capacitance variations resulting from touch interactions were investigated to calibrate sensor responses for tailored tasks. The sensitivity of the sensor was found to be 1.2 pF/kPa, highlighting its responsiveness to pressure variations. Additionally, the sensors were interfaced with an Arduino Uno microcontroller board to demonstrate practical applications, such as replicating arrow key functionality. Additionally, the sensors exhibit sensitivity to environmental factors, with the relative change in capacitance increasing from 0.1 to 0.65 as relative humidity ranges from 30 to 90%. Furthermore, variations in temperature from 30 to 60ºC result in a relative change in capacitance increasing to approximately 0.5. The results indicate the feasibility and versatility of using wood-based substrates and graphite-based pencils for fabricating IDC touch sensors, offering promising prospects for sustainable and accessible sensor technology.