bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Area-law of Molecular Entropy: Moving Beyond Harmonic Approximation
Authors: Venkatraman Vishwesh , Roy Amitava , Ali Tibra 
Published: April 2024

Abstract

Inspired by black hole thermodynamics, the area law that entropy is proportional to horizon area has been proposed in quantum entanglement entropy and has largely maintained its validity. This article shows that the area law is also valid for the thermodynamic entropy of molecules. We showed that the gas-phase entropy of molecules obeys the area law with our proposed correction for the different curvatures of the molecular surface. The coefficient for the ultraviolet cutoff for the molecular entropy, calculated from our curated experimental data, is tantalizingly close to the value $\frac{1}{4}$ proposed by Hawking [Hawking, 1976]. The ability to estimate gas-phase entropy by the area law also allows us to calculate molecular entropy faster and more accurately than currently popular methods of estimating molecular entropy with harmonic oscillator approximation. The speed and accuracy of our method will open up new possibilities for the explicit inclusion of entropy in various computational biology methods.
Identification of COVID-19 Infection from Features Extraction of Exhaled Breath Using Signal Processing Methods
Authors: Malarvili M. B. , Ramanathan Santheraleka , Manikam Rishya , Ramli Ahmad Adib , Ruslay Rafi’Uddin Radzi , Kamarudin Anhar , Tarmizi Aina Amalina Ahmad 
Published: April 2024

Abstract

The goal of the exhaled breath waveform used in ventilation monitoring is to improve COVID-19 illness detection. Using exhaled breath patterns to differentiate between COVID and non-COVID healthy individuals, an algorithm for valid exhaled breath waveform segmentation and feature computation is created to identify the COVID-19 infection. A two-minute exhaled breath pattern was recorded using a device and a nasal cannula sampling tube, resulting in the collection of exhaled breath waveforms from each subject. The developed algorithm is utilized to evaluate the valid exhaled breath waveforms and compute the features classified to distinguish between individuals who contracted COVID-19 infection and this who are healthy. Slope e2, activity e2 and intersection angle of expiration and inspiration phase showed p-value of 0.000, denoting the strong significance difference between COVID-19 patients and non-COVID healthy individuals. The statistical analyses revealed p-values of 0.039, 0.008, and 0.024 for area e2, mobility of e2 and complexity e3, indicating its significance in differentiating COVID-19 patients with non-COVID healthy individuals. The slope, area, and intersection angle as significant features showed good predictive power for compliance with p-value analysis with area under the receiver operating characteristic curve with 0.667, 0.693, and 0.775. Slope of e2, area of e2, intersection angle of expiration and inspiration phases are identified as the promising features to be chosen in discriminating COVID and non-COVID individuals.
Trial registration: Clinical trial approval by Medical Research and Ethics Committee (MREC), 53 Malaysia, NMRR-21-763-59692. Registered 9th June 2021 and valid till 8th June 2023.
Seasonal biases in fluorescence estimated chlorophyll-a derived from biogeochemical profiling floats
Authors: Long Jacqueline 
Published: April 2024

Abstract

Marine phytoplankton biomass and chlorophyll-a concentration are often estimated from pigment fluorescence measurements, which have become routine despite known variability in the fluorescent response for a given amount of chlorophyll-a. Here, we present a near-global, monthly climatology of chlorophyll-a fluorescence measurements from profiling floats combined with ocean color satellite estimates of chlorophyll-a concentration to illuminate seasonal biases in the fluorescent response and expand upon previously observed regional patterns in this bias. Global biases span over an order of magnitude, are highest in iron-limited zones, and can vary seasonally by a factor of 10. An independent estimate of chlorophyll-a from light attenuation shows similar global patterns in the chlorophyll-fluorescence bias when compared to biases derived from satellite estimates. Without accounting for these biases, studies or models using fluorescence-estimated chlorophyll-a will inherit the seasonal and regional biases described here.
Induction of systemic resistance through calcium signaling in Arabidopsis exposed to air plasma-generated dinitrogen pentoxide
Authors: Sasaki Shota , Iwamoto Hiroto , Takashima Keisuke , Toyota Masatsugu , Higashitani Atsushi , Kaneko Toshiro 
Published: April 2024

Abstract

Plasma technology, which can instantaneously transform air molecules into reactive species stimulating plants, potentially contributes to the development of a sustainable agricultural system with high productivity and low environmental impact. In fact, plant immunity activation by exposure to a reactive gas mainly consisting of dinitrogen pentoxide (N2O5) was recently discovered, while physiological responses to N2O5 are rarely known. Here, we demonstrate early physiological responses to N2O5 in Arabidopsis. Exposure to N2O5 gas induced an increase in cytosolic Ca2+ concentration within seconds in directly exposed leaves, followed by systemic long-distance Ca2+-based signaling within tens of seconds. In addition, jasmonic acid (JA)-related gene expression was induced within 10 minutes, and a significant upregulation of the defense-related gene PDF1.2 was observed after 1 day of exposure to N2O5 gas. These systemic resistant responses to N2O5 were found unique among air-plasma-generated species such as ozone (O3) and nitric oxide (NO)/nitrogen dioxide (NO2). Our results provide new insights into understanding of plant physiological responses to air-derived reactive species, in addition to facilitating development of plasma applications in agriculture.
Ultrasensitive piezoelectric sensor based on two-dimensional Na2Cl crystals with periodic atom vacancies
Authors: Chen Liang , Wang Tao , Fan Yan , Jiang Jie , Zhang Yangyang , Huang Yingying , Zhu Liuyuan , Zhan Haifei , Zhang Chunli , Peng Bingquan , Gu Zhen , Pan Qiubo , Wu Junjie , Chen Junlang , Li Pei , Zhang Lei , Lü Chaofeng , Fang Haiping 
Published: April 2024

Abstract

Pursuing ultrasensitivity in pressure sensors that offer precise mechano-electric transduction of tiny mechanical stimuli under complex ambient conditions, is of great importance and has been a long-standing goal1-10. The introduction of microstructures at micro- and nano-scale, can effectively improve the sensitivity, detection limit, and pressure-response range due to their structural asymmetries and compressibility5,6,11-19. However, numerous atom vacancies anticipated in sensors that are expected to exhibit giant asymmetries may make it difficult to maintain stability under ambient conditions. Here, we report a piezoelectric sensor that exhibits supreme pressure-sensing performance, including a peak sensitivity up to 3.5×106 kPa−1 in the pressure range of 1-100 mPa and a detection limit of less than 1 mPa, superior to the current state-of-the-art pressure sensors. These properties are attributed to the high percentage of periodic atom vacancies in the two-dimensional Na2Cl crystals formed within multilayered graphene oxide membrane in the sensor, which provides giant polarization with high stability. The sensor can even clearly detect the airflow fluctuations surrounding a flapping butterfly, which have long been the elusive tiny signals mentioned in the famous “butterfly effect”. The finding represents a step towards next-generation pressure sensors for various precision applications.
SARS-CoV-2 infected human ACE2 transgenic pigs develop severe COVID-19-like pathology.
Authors: Grey Finn , Chau Long Fung , Lillico Simon , Opriessnig Tanja , Blake Rose , Tardy Luc , Lee Chen-Hsuin , Maxwell Scott , Warren Claire , Thornton Elisabeth , McLaughlin Catherine , McLachlan Gerry , Tait-Burkard Christine , Fletcher Sarah , Anderson Stephen , Brown Sharon , Gibbard Louise , Tzelos Thomas , MacMillan Dawn , Dorward David , Baillie J , Griffiths David 
Published: April 2024

Abstract

COVID-19 continues to cause significant morbidity and mortality, with emerging strains rapidly spreading despite substantial immunity through vaccination and previous exposure. Animal models that accurately reflect COVID-19 are vital for testing mechanisms of disease, enabling development of improved vaccines and therapeutics. We have developed human ACE2 transgenic pigs that are highly susceptible to SARS-CoV-2 and display clinical signs, disease progression, and lung inflammation that faithfully replicate severe COVID-19 in humans.
It is Not a Small World for Psychiatric Patients
Authors: Akin Ata , Yorgancigil Emre , Ozturk Ozan , Sutcubasi Bernis , Kirimli Ceyhun , Kirimli Elcim Elgun , Dumlu Seda , Yukselen Gulnaz , Erdogan Sinem 
Published: April 2024

Abstract

Individuals suffering from Obsessive Compulsive Disorder (OCD) and Schizophrenia (SCZ) frequently exhibit symptoms of cognitive disassociations, which are linked to poor functional integration among brain regions. The loss of functional integration can be assessed using graph metrics computed from functional connectivity matrices (FCMs) derived from neuroimaging data. A healthy brain at rest is known to exhibit small-world features with high clustering coefficients and shorter path lengths in contrast to random networks. The aim of this study was to compare the small-world properties of prefrontal cortical functional networks of healthy subjects with OCD and SCZ patient groups by use of hemodynamic data obtained with functional near infrared spectroscopy (fNIRS). 13 healthy subjects and 47 patients who were clinically diagnosed with either OCD (N=21) or SCZ (N=26) completed a Stroop test while their prefrontal cortex (PFC) hemodynamics were monitored with fNIRS. The Stroop test had a block design consisting of neutral, congruent and incongruent stimuli. For each subject and stimuli type, FCMs were derived separately which were then used to compute small world features that included i) Global Efficiency (GE), ii) Clustering Coefficient (CC), iii) Modularity (Q), and iv) small-world parameter (σ). Small-world features of patients exhibited random networks which were indicated by higher GE and lower CC values when compared to healthy controls, implying a higher neuronal operational cost.
Development of the Technical Assistance Engagement Scale: A Modified Delphi Study
Authors: Scott Victoria , Temple Jasmine , Jilani Zara 
Published: April 2024

Abstract

Background: Technical assistance (TA) is a tailored approach to capacity building that is commonly used to support implementation of evidence based interventions. Despite its widespread applications, measurement tools for assessing critical components of TA are scant. In particular, the field lacks a robust measure for examining relationship quality between TA providers and recipients. TA relationships are central to TA and significantly associated with program implementation outcomes. The current study seeks to address the gap in TA measurement tools by providing a scale for assessing TA relationships.
Methods: We utilized a modified Delphi approach involving two rounds of Delphi surveys and a panel discussion with TA experts to garner feedback and consensus on the domains and items that compose the TA Engagement Scale.
Results: TA experts represented various U.S. organizations and TA roles (e.g., provider, recipient, researcher) with 25 respondents in the first survey and 26 respondents in the second survey. The modified Delphi process resulted in a scale composed of six domains and 22 items relevant and important TA relationships between providers and recipients.
Conclusion: The TA Engagement Scale is a formative evaluation tool intended to offer TA providers the ability to identify strengths and areas for growth in the provider-recipient relationship and to communicate about ongoing needs. As a standard measurement tool, it lends a step toward more systematic collection of TA data, the ability to generate a more coherent body of TA evidence, and enables comparisons of TA relationships across settings.
Study on the Prevalence and Influencing Factors of Anemia among Three Ethnic Minorities in the Southwest Border Area of China
Authors: Wang Chenlei , Yin Rongrong , Zhao Jiang , Liu Zhitao 
Published: April 2024

Abstract

Anemia is one of the most common nutritional deficiencies in the world. At present, anemia is still one of the serious nutrition-related diseases in China. This study aimed to conduct an exploratory investigation of the prevalence and factors related to anemia among three ethnic minorities in the Southwest Border Area of China. Multi-stage stratified cluster sampling method was used to conduct field questionnaire and physical examination for resident of ethnic minorities aged 15 years or above. Anemia was defined as a hemoglobin concentration level less than 120 g/L in non-pregnant women and less than 130 g/L in men. Participants over 55 had a higher risk of anemia than those aged 17–34. More than 7 hours of outdoor activities during the daytime in the winter was associated with a reduced risk of anemia. Alcohol drinking and central obesity was associated with increased hemoglobin and red blood cells.
Missing Evidence: Tracking Academic Data Use around the World
Authors: Stacy Brian , Kitzmüller Lucas , Wang Xiaoyu , Mahler Daniel , Serajuddin Umar 
Published: April 2024

Abstract

Data-driven research is key to producing evidence-based public policies, yet little is known about where data-driven research is lacking and how it can be expanded. We propose a method for tracking academic data use by country of subject, applying natural language processing to open-access research papers. The model’s predictions produce country estimates of the number of articles using data that are highly correlated with a human-coded approach, with a correlation of 0.99. Analyzing more than 1 million academic articles, we find that high-income countries are the subject of nearly 50% of all papers, despite only making up around 17% of the world’s population. Finally, we classify countries by whether they could most benefit from increasing their supply of or demand for data, with the former applying to many poorer countries and the latter to many wealthier countries.
AlexCapsNet: an integrated architecture for image classification with noise and many categories
Authors: Bao Muyi , Jin Nanlin , Xu Ming 
Published: April 2024

Abstract

Capsule network (CapsNet) is a pioneering architecture that can encode the image properties into vectors rather than scalars, addressing the limitations of traditional Convolutional Neural Networks (CNNs). This process is achieved by dynamic routing algorithms to maintain the spatial hierarchies. However CapsNet may decreases its efficacy in complex datasets, such as CIFAR-10. To address this problem, we propose AlexCapsNet architecture, in which the classic classification model AlexNet is used as the feature extraction layer. Such a layer allows CapsNet to capture deeper and more semantic features. Comprehensive evaluation with five datasets shows that AlexCapsNet has achieved improved performance compared to the baseline and other variants of CapsNet. In addition, we also introduce the Shallow-AlexNet module, revealing that deeper feature extraction layers are suitable for datasets with fewer categories and shallower feature extraction layers are more effective for datasets with many categories, owing to inherent category differences. Moreover, our investigation into the reconstruction module shows that noise in the datasets helps degrade the performance of the reconstruction module.
Unveiling the Microscopic World of Enamel: A Groundbreaking Study on Dental Remineralization
Authors: Zecca Piero Antonio , Carganico Andrea , Presti Doriana Lo , Reguzzoni Marcella , Scurati Eleonora Ivonne , Caccia Margherita , Levrini Luca 
Published: April 2024

Abstract
AIM: This study explores dental enamel remineralization, employing Scanning Electron Microscopy (SEM) to unveil the microstructural changes induced by various dental care products. Focusing on Mentadent Professional Resilience, the research investigates its impact on enamel surfaces at a microscopic level.
MATERIALS AND METHODS: The experimental approach involved precise tooth sectioning to preserve enamel integrity, followed by dehydration and SEM preparation. SEM analysis was conducted at multiple magnifications, revealing significant enamel texture and structure post-treatment variations.
RESULTS: The findings demonstrate distinct differences in enamel surfaces, ranging from rough and rugged textures indicative of demineralization to smooth, intact surfaces suggesting effective remineralization. These observations provide crucial insights into the mechanisms of action of dental care products and their potential to promote enamel health.
CONCLUSIONS: This study contributes to a deeper understanding of enamel remineralization processes and underscores the importance of microscopic analysis in evaluating and developing oral care products. The implications of these findings are significant for both dental research and clinical practice, offering a new perspective on the selection and efficacy of dental care products in enamel protection and caries prevention.
CLINICAL RELEVANCE: The current investigations suggest that Mentadent Professional Resilience could be beneficial as a remineralizing agent.
PH-SIFT with PSO Algorithm: A Novel Approach to Detecting Forgery in High- Resolution Images
Authors: Mishra Manuj , Jain Neelesh Kumar , Kumar Ajay 
Published: April 2024

Abstract

The use of machine learning for forgery detection in image forgeries poses a significant danger to modern multimedia technologies. These forgery detectors appear promising; however they are known to be vulnerable to evasion attacks. A "forgery" is a malicious user established by an attacker that may change its appearance while spreading; this is known as polymorphism and metamorphosis. Common behavioral characteristics showing the source and intended use of multimedia devices are displayed by image forgeries. However, the majority of novel forgery detection variations frequently elude detection by traditional forgery detection methods. This paper proposes a new approach to improve the utility of an existing framework. We need to create a unique model, PH-SIFT using PSO Algorithm, in order to improve the overall forgery analysis because the current deep learning algorithm forgery detection and analysis. Applying the PH-SIFT utilizing PSO Algorithm technique, which also aims to improve its accuracy and other metrics, achieves this. This method can get around the complexity and challenges presented by different concerns related to forgery detection and analysis. Here, the datasets from forgery detection and analysis are used to train the proposed algorithm. Following training, the datasets are pre-processed by reducing training mistakes. The PSO Algorithm-built PH-SIFT then carries out the detection procedure to anticipate any forgery activity.
Symmetric perfect state transfer on bipartite graphs
Authors: Li Xi , Chen Xiao , Hu Shouwei , Liu Zhihao 
Published: April 2024

Abstract
Continuous-time quantum walks (CTQW) becoming more significant for quantum information and quantum computation. The perfect state transfer (PST) on graphs is a specific phenomena of CTQW which is widely used in quantum communication of small system, and be found critical for quantum algorithm design recently, such as quantum approximate optimization algorithms and quantum search. If a network $G$ contains $2N$ vertices, and PST occurs between $N$ pairs of vertices at the same time $T$, then we say $G$ admits symmetric PST (SPST). In this work, we will study the SPST on bipartite graphs via spectra. And then, we will present how to set couplings of special bipartite graphs such as cycles, grids and chains to admit SPST is given. For cycles with 6, 8, 10, 12 vertices, we give the analytical method to obtain the couplings to admit SPST. Analogously, the couplings for chains and grids to admit SPST are analytical obtained. We also present a typical approach by using the graph tensor product to construct new quantum networks that admit SPST, in which the new quantum networks are derived from special kinds of integral graphs, such as complete graphs, strongly regular graphs and distance regular graphs.
An easy to implement strategy for improving organic electrochemical transistor stability: Combining chemical doping with solvent degassing.
Authors: Paterson Alexandra , Le Vianna , Baustert Kyle N. Baustert Kyle N. , Brown Megan , Bombile Joel , Flagg Lucas , Thorley Karl , Kousseff Christina , Solomeshch Olga , McCulloch Iain , Tessler Nir , Risko Chad , Graham Kenneth 
Published: April 2024

Abstract

Although p-type organic mixed ionic electronic conductors (OMIECs) are susceptible to oxidation, it has not yet been considered as to whether oxygen could behave as an uncontrolled p-dopant. Here, oxygen dissolved in solvents is shown to be behave as a p-dopant, that fills traps to enable more effective electrochemical doping in OMIECs and organic electrochemical transistors (OECTs). Yet the presence of oxygen also jeopardizes OECT stability. A two-step strategy is introduced to solve this contradictory problem, where first the solvent is degassed, and second the OMIEC is doped in a controlled manner using a chemical p-dopant. This strategy has a remarkable impact on OECT stability in air and water, while simultaneously increasing on-off ratio, tuning the threshold voltage, and enhancing the transconductance, mobility and the µC* product. This simple solution-processing technique is easily implemented, low-cost, highly effective in air and water, and effective in materials systems with different polymers, solvent and dopants. Overall, the data herein suggests that combining chemical doping with solvent degassing could be a broadly applicable technique to improve essential criteria needed to realize organic bioelectronics and more complex OMIEC circuitry.