bluuebunny/crossref_metadata_2025_split huggingface dataset:
Wideband ratiometric measurement of tonic and phasic dopamine release in the striatum
Authors: Hamid Arif , Gottschalk Amy , Menees Haley , Bogner Celine , Zewde Semele , Jibin Joanna , Gamam Asma , Flink Dylan , Mosissa Meea , Bonneson Faith , Wehelie Hibo , Alonso-Caraballo Yanaira 
Published: November 2024

Abstract

Reward learning, cognition, and motivation are supported by changes in neurotransmitter levels across multiple timescales. Current measurement technologies for various neuromodulators (such as dopamine and serotonin) do not bridge timescales of fluctuations, limiting the ability to define the behavioral significance, regulation, and relationship between fast (phasic) and slow (tonic) dynamics. To help resolve longstanding debates about the behavioral significance of dopamine across timescales, we developed a novel quantification strategy, augmenting extensively used carbon-fiber Fast Scan Cyclic Voltammetry (FSCV). We iteratively engineered the FSCV scan sequence to rapidly modify electrode sensitivity within a sampling window and applied ratiometric analysis for wideband dopamine measurement. This allowed us to selectively eliminate artifacts unrelated to electrochemical detection (i.e., baseline drift), overcoming previous limitations that precluded wideband dopamine detection from milliseconds to hours. After extensive characterization and validation in vitro and in vivo with simultaneous microdialysis, pharmacological, reward, and optogenetic manipulations, we demonstrate the utility of ratiometric FSCV to resolve key questions about how phasic DA transients contribute to setting striatal DA tone. We identify specific DA frequency bands that correlate with canonical microdialysis-defined “tonic” DA levels while also uncovering a paradoxical constraint on the accumulation of phasic DA into tonic levels. Our approach can extend to additional analytes, including serotonin, empowering the emperical testing of contrasting predictions from theories about neuromodulator circuit and computaional mechanisms that support behavioral flexibility.
Secure Novel Hybrid PUF Design For Key Generation and Authentication Applications Of IoT
Authors: Narayanapuram Bhagyalakshmi , Panda Jeebananda 
Published: November 2024

Abstract

This paper presents proposed multi mode memory PUF design for high speed 5G IOT nodes. Since in conventional IOT nodes keys are used to be stored in non volatile memory, This will cause the side channel vulnerability and compromise the integrity. Silicon PUFs are used as feasible solution for this. However there is vulnerabilities in existed PUF architectures. To overcome these a new multi mode memory PUF was designed. This new PUF can be operated in two modes i.e Weak PUF(W) mode Strong(S) PUF mode. Authentication by Strong mode and weak Mode are discussed. The performance evolution metrics of proposed PUFs are close to ideal responses. The vulnerability analysis and possible side channel attacks such as data remanance attack and modeling attacks already performed in the existed memory PUFS were discussed and counter measures are used to design novel PUF of this work. The work is implemented on Artix-7 FPGA The authentication by S mode PUF and W mode PUF for IOT is implemented. The key generation by using W mode is implemented. The propose PUF has uniqueness is 49.96 %, uniformity is 49.87%, BER is 0.41
Biphasic Lubrication Theory of Hydrogels: Transient Response and Sample Size Effects
Authors: Yamaguchi Tetsuo , Sawae Yoshinori 
Published: November 2024

Abstract

Hydrogels are soft and wet materials with remarkable properties. Among such properties, low friction has attracted much attention from scientists and engineers. While many studies investigated the correlation between material properties and frictional behavior, little attention has been paid to other effects, such as sample sizes. In this study, we developed a theoretical model based on the biphasic lubrication theory to describe the frictional behavior of hydrogels against a glass substrate in the rotational shear test. Consequently, we obtained analytical solutions for the time evolutions of friction coefficient, hydrostatic pressure, and elastic stress. To validate our model, we prepared cylindrical PVA (Polyvinyl alcohol) hydrogels with different radii and thicknesses and conducted friction experiments. We confirmed reasonable agreements with theoretical predictions for the transient responses and their size dependences. Interestingly, we found that the friction coefficient at the initial phase decreased drastically with decreasing thickness. Our results indicate the fundamental importance of bulk transport in surface friction of hydrogels, and the controllability of friction by varying the geometry of hydrogels.
Quantitation of the Interplay between Economic Growth and Atmospheric Environment in China's Transition to Green Economy
Authors: Zhang Dan , Li Ziwei , Hu Hao , Wu Jiayuan , Wu Chaohui 
Published: November 2024

Abstract

Quantifying the interplay between economic growth and atmospheric environment (EG-AE) is crucial for the transition of green economy in China. The Aerosol Optical Depth (AOD) variation under GDP growth index (AVGGI) is suggested to measure the air pollutant emissions per unit of economic growth. Geographically weighted regression (GWR) model is used to analyze the spatio-temporal patterns of EG-AE. The results show that: (1) Taking the Hu line as the boundary, AVGGI values present obvious spatio-temporal differences. High AVGGI values are mainly distributed in the Beijing-Tianjin-Hebei urban agglomeration and the Central Plains urban agglomeration. (2) The local R2 shows a striped distribution in the east of the Hu line. There are two distinct symmetrical U-shapes in the region of R2 > 0.6. The local R2 in the west is a low-value zone with poor correlation between GDP and AOD. (3) In 2007 - 2017, the trajectory of the barycenter shift of high-value, medium-value, and low-value zones in China's EG-AE showed a trend of moving westward. China's economic growth pattern has affected the dynamic change of atmospheric environment over the past decade. Therefore, policymakers should formulate urban economic growth patterns which helps reduce urban air pollution, to achieve green economy development.
Gonadal miRNomes and transcriptomes in infected fish reveal sexually dimorphic patterns of the immune response
Authors: Gelderen Tosca A. van , Debnath Pinky , Joly Silvia , Bertomeu Edgar , Duncan Neil , Furones Dolors , Ribas Laia 
Published: November 2024

Abstract

Background
Fish disease outbreaks caused by bacterial burdens are responsible for decreasing productivity in aquaculture. Unraveling the molecular mechanisms activated in the gonads after infections is pivotal for enhancing husbandry techniques in fish farms, ensuring disease management, and selecting the most resistant phenotype.
Methods
Here, an experiment with European sea bass (Dicentrarchus labrax), an important commercial species in Europe, was conducted to study the miRNome and transcriptome through sequencing analysis 48 hours after an intraperitoneal infection with Vibrio anguillarum.
Results
The findings indicate that following infection, testes exhibited more pronounced alterations in both the miRNome and transcriptome. Specifically, males showed approximately 26% more differentially expressed genes in testicular genes compared to females (2,624 vs. 101 DEGs). Additionally, four miRNAs (miR-183-5p, miR-191-3p, miR-451-5p, and miR-724-5p) were significantly expressed post-infection in males, while none were identified in females. Interestingly, upon deep analysis of sexual dimorphic gene modules, a larger number of miRNAs were identified in infected females targeting genes related to the immune system compared to infected males. These results suggest that fish ovaries demonstrate greater resilience in response to infections by suppressing genes related to the immune system through a post-transcriptional mechanism performed by miRNAs. In contrast, testes activate genes related to the immune system and repress genes related to cellular processes to cope with the infection. In particular, the crosstalk between the miRNome and transcriptome in infected males revealed a pivotal gene, namely, insulin-like growth factor binding protein (igfbp), acting as a gene network hub in which miR-192-3p was connected.
Conclusions
The current study elucidated the need to comprehend the basic immune regulatory responses associated with miRNAs and gene regulation networks that depend on fish sex. The data reveal the importance of considering sex as a factor in interpreting the immune system in fish to generate efficient protocols to prevent outbreaks in fish farms.
AEJaya+DE: A Hybrid Feature Selection Methodfor Machine Learning-Based Network IntrusionDetection on Cybersecurity Datasets
Authors: Albasheer Fawzia Omer , Kalaimannan Ezhil , Agarwal Mayank 
Published: November 2024

Abstract

The exponential growth of network traffic in recent years has intensified the need for robust and sustainable security measures. Intrusion Detection Systems (IDS) play a crucial role in maintaining network security but face significant challenges due to the increasing volume and complexity of data. The high dimensionality of network traffic data makes it difficult to efficiently identify and process the most relevant features for effective intrusion detection. To address these challenges and enhance IDS performance, we propose AEJaya+DE, an adaptive optimization technique that combines the Adaptive Enhanced Jaya Algorithm (AEJaya) with Differential Evolution (DE) for effective feature selection in IDS. Unlike the basic Jaya algorithm, which can get trapped in local optima due to its single learning strategy, AEJaya improves local search ability by utilizing local attractors for exploitation and historical population for enhanced global exploration. AEJaya+DE incorporates dynamic parameter adjustment and adaptive probabilistic strategy selection to maintain a balanced exploration and exploitation process. We evaluated the method on two benchmark datasets: UNSW-NB15 and NSL-KDD. AEJaya+DE reduced the feature set from 42 to 21 on UNSW-NB15, achieving 96.10% accuracy with the XGBoost classifier, and from 41 to 24 on NSL-KDD, achieving 99.93% accuracy with the CatBoost classifier. These results demonstrate AEJaya+DE’s ability to significantly enhance IDS performance by selecting a minimal yet highly informative feature set, improving both accuracy and computational efficiency. This novel approach offers a robust and efficient solution for intrusion detection across diverse network environments, contributing to advancements in the field of network security.
Towards Efficient, Fair and Interpretable Neural Dynamic Data Valuation
Authors: Liang Zhangyong , Gao Huanhuan , Zhang Ji 
Published: November 2024

Abstract

In the modern data economy, data has emerged as a valuable form of property, driving the growth of data marketplaces and elevating the importance of data valuation. While existing methods have made progress in this field, they often struggle to simultaneously achieve efficiency, fairness, and interpretability. To address these challenges, we introduce a novel approach called Neural Dynamic Data Valuation (NDDV), which leverages the principles of optimal control theory. NDDV offers a comprehensive framework that accounts for the dynamic nature of data value and the intricate interactions between data points. By reformulating classical valuation criteria through the lens of stochastic optimal control, our method provides a fresh perspective on quantifying data worth. At its core, NDDV employs trajectory learning to accurately identify data value, supported by robust theoretical interpretations. We enhance the method's fairness through a data re-weighting strategy that captures unique features of individual data points, facilitating interactions between individual data states and the weighted mean-field state. Our results demonstrate NDDV's ability to unveil the dynamic process of data valuation while extending to an interpretable model. Importantly, NDDV achieves significant computational efficiency by requiring only a single training session to estimate the value of all data points. This advancement represents a substantial improvement over existing methods, potentially revolutionizing how we approach data valuation in complex, real-world scenarios.
IvoryOS: an interoperable operating system for flexible self-driving laboratories
Authors: Hein Jason , Zhang Wenyu , Hao Lucy , Reish Matthew , Greenwood Rebekah , Depner Noah , Min Jiyoon , El-khawldeh Rama , Prieto Paloma , Trushina Ekaterina 
Published: November 2024

Abstract

Graphical User Interfaces (GUIs) are essential for providing a user-friendly experience to chemists employing Self-Driving Laboratories (SDLs). However, building an interoperable GUI is challenging due to the diverse layouts, frameworks and functionalities of SDLs, which often limit the use of existing GUIs on these SDLs. In this work, we introduce ivoryOS, a configuration-free software that automatically generates a snapshot of an SDL Python script, capturing all device instances, functionalities and argument information. This snapshot allows for the automatic creation of interactive forms that enable visual programming for control and workflow design across any SDLs. We demonstrate an example use case with the Hein Group Purification Platform Optimizing Solubility based Experimentation (PurPOSE) platform. This plug-and-play operating system for SDLs streamlines robot and lab hardware interaction, democratizing access to advanced SDLs for a broader range of scientists.
Biogenesis, molecular characterization and dye degradation efficacy of alkaline protease from an estuarine associated actinobacterium Streptomyces variabilis using in-silico method
Authors: Balamurugan Pandiselvi , Tamilselvan Silambarasan 
Published: November 2024

Abstract

In this study, biogenesis, statistical optimization and molecular modeling of alkaline protease from an estuarine associated actinobacterium Streptomyces variabilis was carried out by Box-Behnken design. Initially, the biogenesis of alkaline protease from the selected actinobacterium was attained through submerged condition. Simultaneously, the actinobacterial mediated biogenesis of alkaline protease was statistically optimized through ‘one factor at a time approach’ using Box-Behnken design in a basal medium constitutes 2.5% w/v of NaCl concentration with pH 8.0, temperature 55°C and 2.50% of inoculum size for 94h of incubation. The analysis of variance (ANOVA) exhibited a maximum level of coefficient (R2 = 0.9720) with more significance (P < 0.0001). In purification step, the alkaline protease expressed 21.93% of recovery with 2.93 of purification fold at the last stage using Sephadex G-100 chromatography. Followed by, the molecular mass of the enzyme was calculated as 35kDa on 10% of SDS-PAGE. The three dimensional structure of purified alkaline protease was predicted with the encoded total amino acid content 481. The maximum stability range of purified alkaline protease was denoted at pH 8, temperature 60°C and the fermentation medium constituted with 1mM of Mg2+, 3.5% of NaCl and 2.5% of casein. The kinetic parameters like Km and Vmax of purified alkaline protease showed 5.158mg/ml and 484.90 ± 2.04µg/min/mg, respectively. Further, the degradation efficacy and the interaction between the alkaline protease as well as dye molecules like acridine orange and erythrosine pink were assessed by in-silico docking method using online Swiss modeling software tool. The decolouration of dyes were evaluated through first order kinetic study with the R2 values 0.9987 & 0.9953 respectively. By keeping this view, this study could be validated that the selected actinobacterium is a potent strain for the production of alkaline protease and also used as dye decoulouring agent.
Microclimate and dry years interfere with landscape structure effects on intraspecific trait variation
Authors: Jacome Santiago Ordonez , Deák Balázs , Valkó Orsolya , Szász Vivien , Neumann Krisztina Verbényiné , Csergő Anna Mária 
Published: November 2024

Abstract

Context
Predicting how changes in weather patterns and land use jointly impact populations is a pressing task in ecology. Microclimate may play a key role in species’ local persistence by modulating regional weather effects. We lack sufficient empirical evidence to understand the relative effects of landscape structure and habitat conditions on intraspecific trait variation.
Objectives
Using a spatially and temporally replicated demographic dataset, we tested the relative effect of landscape structure (area and connectivity of remnant habitat fragments), microclimate (heat load), and fluctuation in weather conditions (study year) on intraspecific plant trait variation, and we investigated whether the local heat load modulated the weather effects on the studied traits.
Methods
We performed repeated measurements of stem height, leaf area, number of stems, main inflorescence length and number of primary side inflorescences of 569 permanently marked individuals of the grassland specialist Salvia nemorosa L. We sampled 13 populations encompassing microhabitats exposed to different heat load levels, over three consecutive years.
Results
Mature individuals had fewer stems in isolated, and taller stems in small habitat fragments. High levels of heat load and dry years affected negatively all measured traits, and the negative effects of exposure to high heat load were generally exacerbated in dry years.
Conclusions
Exposure to strong environmental stressors could ultimately obscure the real effect of human impact on plant populations. Landscape planning for conservation of dry grassland species should ensure not only improved habitat connectivity but also high-quality habitats with heterogeneous microclimates able to buffer weather extremes.
Non-invasive prediction of KRAS mutation in rectal cancer using hybrid intravoxel incoherent motion and diffusion kurtosis model
Authors: YUAN Jie , Wang Ziyuan , Tan Wenli , Zhang Yun , Yan Huamei , Liu Mengxiao , Gong Hangjun , Zhan Songhua 
Published: November 2024

Abstract

Purpose: This study seeks to evaluate the efficacy of the hybrid intravoxel incoherent motion and diffusion kurtosis imaging (IVIM-DKI) model in predicting Sarcoma Viral Oncogene Homologue (KRAS) mutation status in rectal cancer patients.
Materials and Methods: Rectal cancer patients received hybrid IVIM-DKI MRI, surgery, and KRAS mutation status was assessed. The parameters derived from the hybrid IVIM-DKI model, including the apparent diffusion coefficient (ADC), true diffusion coefficient (D), diffusion kurtosis (K), perfusion fraction (f), and pseudo-diffusion coefficient (D*), were compared between the KRAS mutation group and wild-type group. The diagnostic performance was evaluated using the receiver operating characteristic (ROC) curve. The hybrid IVIM-DKI parameters and their association with clinicopathological features were also explored.
Results: In this prospective study, 73 patients (mean age, 66 ± 11 years) of 50 men and 23 women were included. Significant differences were observed between the KRAS mutation and wild-type groups for ADC, D, and K values (p < 0.05). The K value derived from the IVIM-DKI model demonstrated the highest area under the ROC curve (AUC = 0.779) in characterizing KRAS mutation status, with a sensitivity of 88.1% and specificity of 70.3%. The ADC value also showed satisfactory diagnostic performance (AUC = 0.702). Specific IVIM-DKI parameters, such as f and K, were associated with various clinicopathological features, suggesting their potential as imaging biomarkers.
Conclusion: The hybrid IVIM-DKI model, especially the K value, shows promise as a non-invasive tool for predicting KRAS mutation status in rectal cancer patients, potentially improving personalized treatment strategies.
Impact of comorbidity on the association between socioeconomic factors and long-term mortality of intensive care patients
Authors: Dannewitz Anna Aronsson , Svennblad Bodil , Michaëlsson Karl , Lipcsey Miklos , Gedeborg Rolf 
Published: November 2024

Abstract

Purpose

We aimed to estimate the association between socioeconomic status and long-term mortality after intensive care, with a particular focus on the impact of comorbidity.

Methods

First admissions to the ICU, registered in the national Swedish intensive care register between 2009 to 2012, were linked to information on education and income. We estimated the association between these socioeconomic factors and mortality, using Cox regression with follow-up until 2016. Comorbidity was measured with a multi-dimensional comorbidity measure based on hospital discharge diagnoses.

Results

We identified 101 745 ICU patients ≥30 years old. The group with only elementary school had a higher mortality rate than the group with the highest educational level (adjusted HR, 1.20; 95% CI 1.16-1.23). The association was not notably influenced by adjustment for comorbidity. In a landmark analysis, the association was weaker during the first year after ICU admission (adjusted HR, 1.14; 95% CI 1.09-1.18) than after the first year (adjusted HR, 1.28; 95% CI 1.23-1.35). The associations were stronger in patients with lower comorbidity burden than in patients with more severe comorbidity. The associations were largely consistent when income was used to indicate socioeconomic status.

Conclusion

Low educational level was associated with an increased long-term mortality rate after ICU admission, but the association was not notably related to comorbidity. The association was stronger after the first year of follow-up, suggesting that it may be more related to unmeasured background characteristics such as lifestyle factors that are not reflected in measurable comorbidity rather than the ICU admission.
Farmer’s Awareness on Climate Change and Needs for Adaptation. Case Study in Dinajpur and Feni District, Bangladesh
Authors: Chowdhury Rumman Mowla , Mahmud Chowdhury Arman , Alminara Mosammat 
Published: November 2024

Abstract

This study aims to analyze the farmer’s perception of weather pattern changes and their capability to change crop production according to climate alteration. This is one of the leading challenges affecting the performance of agriculture and the livelihood of farmers. They play a major role in the agricultural economy and face many problems during cultivation time mainly caused by the increasing climate change. Farmers are known how to take appropriate steps to cope with this situation and when they perceive change will have to be taken. In three different agroclimatic zones in Bangladesh identifying the main differences of temperature, rainfall, humidity, wind speed varies a lot and discusses with farmers to know their opinion and analyze the impact of these changes in crop production. Primary data collection was undertaken by a questionary survey with individual farmers, focused group discussions and key informant interviews. Analyzing the variance, secondary data of different weather parameters were collected from Bangladesh Meteorological Department (BMD), to cross-check according to farmer's perception and BMD data. Farmers perceived changes in temperature, rainfall, wind rate, frequent flood, drought, type of soil and quality of irrigation water affecting crop production. By analyzing primary data and secondary data, how farmers' perception and weather parameters hugely affect the agricultural system.