bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effects of extreme temperatures on public sentiment in 49 Chinese cities
Authors: Wang Chan , Bai Yi-Xiang , Li Xin-Wu , Lin Lu-tong 
Published: May 2024

AbstractThe rising sentiment challenges of the metropolitan residents may be attributed to the extreme temperatures. However, nationwide real-time empirical studies that examine this claim are rare. In this research, we construct a daily extreme temperature index and sentiment metric using geotagged posts on one of China's largest social media sites, Weibo, to verify this hypothesis. We find that extreme temperatures causally decrease individuals' sentiment, and extremely low temperature may decrease more than extremely high temperature. Heterogeneity analyses reveal that individuals living in high levels of PM2.5, existing new COVID-19 diagnoses and low-disposable income cities on workdays are more vulnerable to the impact of extreme temperatures on sentiment. More importantly, the results also demonstrate that the adverse effects of extremely low temperatures on sentiment are more minor for people living in northern cities with breezes. Finally, we estimate that with a one-standard increase of extremely high (low) temperature, the sentiment decreases by approximately 0.161 (0.272) units. Employing social media to monitor public sentiment can assist policymakers in developing data-driven and evidence-based policies to alleviate the adverse impacts of extreme temperatures.
Development of a new caged intein for multi-input conditional translation of synthetic mRNA
Authors: Yang Tingting , Nakanishi Hideyuki , Itaka Keiji 
Published: May 2024

AbstractmRNA medicines can be used to express therapeutic proteins, but the production of such proteins in non-target cells has a risk of adverse effects. To accurately distinguish between therapeutic target and nontarget cells, it is desirable to utilize multiple proteins expressed in each cell as indicators. To achieve such multi-input translational regulation of mRNA medicines, in this study, we engineered Rhodothermus marinus (Rma) DnaB intein to develop “caged Rma DnaB intein” that enables conditional reconstitution of full-length translational regulator protein from split fragments. By combining the caged Rma DnaB intein, the split translational regulator protein, and target protein-binding domains, we succeeded in target protein-dependent translational repression of mRNA in human cells. In addition, the caged Rma intein showed orthogonality to the previously reported Nostoc punctiforme (Npu) DnaE-based caged intein. Finally, by combining these two orthogonal caged inteins, we developed an mRNA-based logic gate that regulates translation based on the expression of multiple intracellular proteins. This study provides important information to develop safer mRNA medicines.
Association between maternal heavy metal exposure and Kawasaki Disease, the Japan Environment and Children’s Study (JECS)
Authors: Yanai Takanori , Yoshida Satomi , Takeuchi Masato , Kawakami Chihiro , Kawakami Koji , Ito Shuichi ,   the Japan Environment and Children’s Study Group, Kamijima Michihiro , Yamazaki Shin , Ohya Yukihiro , Kishi Reiko , Yaegashi Nobuo , Mori Chisato , Yamagata Zentaro , Inadera Hidekuni , Nakayama Takeo , Sobue Tomotaka , Shima Masayuki , Nakamura Hiroshige , Suganuma Narufumi , Kusuhara Koichi , Katoh Takahiko 
Published: May 2024

AbstractKawasaki disease (KD) is an acute systemic vasculitis primarily affecting young children, with an unclear etiology. We investigated the link between maternal heavy metal exposure and KD incidence in children using the Japan Environment and Children’s Study, a large-scale nationwide prospective cohort with approximately 100,000 mother–child pairs. Maternal blood samples collected during the second/third trimester were analyzed for heavy metals [mercury (Hg), cadmium (Cd), lead (Pb), selenium (Se), manganese (Mn)], divided into four quartiles based on concentration levels. KD incidence within the first year of life was tracked via questionnaire. Among 85,378 mother–child pairs, 316 children (0.37%) under one year were diagnosed with KD. Compared with the lowest concentration group (Q1), the highest (Q4) showed odds ratios (95% confidence interval) for Hg, 1.29 (0.82–2.03); Cd, 0.99 (0.63–1.58); Pb, 0.84 (0.52–1.34); Se, 1.17 (0.70–1.94); Mn, 0.70 (0.44–1.11), indicating no concentration-dependent increase. Sensitivity analyses with logarithmic transformation and extended outcomes up to age 3 yielded similar results. No significant association was found between maternal heavy metal levels and KD incidence, suggesting that heavy metal exposure does not increase KD risk.
Fracture propagation and pore pressure evolution characteristics induced by hydraulic and pneumatic fracturing of coal
Authors: Zhengzheng Cao , Xiangqian Yang , Zhenhua Li , Cunhan Huang , Feng Du , Wenqiang Wang , Xianjie Ni , Shuai Liu , Zhen Li 
Published: May 2024

AbstractA two-dimensional unsteady seepage model for coal using a finite element program is developed, and the temporal variations of key factors such as water pressure and hydraulic gradient are analyzed in this paper. Additionally, the triaxial rock mechanical experiment and utilized pneumatic fracturing equipment on raw coal samples to investigate both hydraulic and pneumatic fracturing processes are conducted. Through these experiments, the relationship between pressure and crack formation and expansion are examined. The analysis reveals that the pore pressure gradient at the coal inlet reaches its peak during rapid surges in water pressure but diminishes over time. Conversely, the pore pressure gradient at the outlet side exhibits a gradual increase. Hydraulic fracturing is most likely to occur at the water inlet during sudden increases in water pressure. Besides, as the permeability of coal decreases, the duration for seepage stabilization prolongs due to the intensified pore pressure gradient resulting from sudden increases in water pressure. Moreover, an extended period of high hydraulic gradient further increases the risk of hydraulic fracturing. The experimental findings indicate that coal samples initially experience tensile failure influenced by water and air pressure. Subsequently, mode I cracks form under pressure, propagating along the fracture surface and becoming visible. The main types of failure observed in hydraulic and pneumatic fracturing are diametrical tensile failure, and the development of fractures can be categorized into three distinct stages, which contains the initial stage characterized by slight volume changes while water pressure increases, the expansion stage when pressure reaches the failure strength, and the crack closure stage marked by little or even decreasing volume changes during pressure unloading. The acoustic emission signal accurately corresponds to these three stages.
Functional soft robotic composites based on organic photovoltaic and dielectric elastomer actuator
Authors: Abolhosen Ahmed Miguel Román , Lee Shinyoung , Fukuda Kenjiro , Someya Takao , González Leobardo Hernández , Shintake Jun 
Published: May 2024

AbstractImproving the energy efficiency of robots remains a crucial challenge in soft robotics, with energy harvesting emerging as a promising approach to address it. This study presents a functional soft robotic composite called OPV-DEA, which integrates flexible organic photovoltaic (OPV) and dielectric elastomer actuator (DEA). The composite can simultaneously generate electrostatic bending actuation and harvest energy from external lights. Owing to its simplicity and inherent flexibility, the OPV-DEA is poised to function as a fundamental building block for soft robots. This study aimed to validate this concept by initially establishing the fabrication process of OPV-DEA. Subsequently, experimental samples are fabricated and characterized. The results show that the samples exhibit a voltage-controllable bending actuation of up to 15.6° and harvested power output of 1.35 mW under an incident power irradiance of 11.7 mW/cm2. These performances remain consistent even after 1000 actuation cycles. Finally, to demonstrate the feasibility of soft robotic applications, an untethered swimming robot equipped with two OPV-DEAs is fabricated and tested. The robot demonstrates swimming at a speed of 21.7 mm/s. The power consumption of the robot is dominated by a high-voltage DC-DC converter, with a value approximately 1.5 W. As a result, the on-board OPVs cannot supply the necessary energy during locomotion simultaneously. Instead, they contribute to the overall system by charging a battery used for the controller on board. Nevertheless, these findings suggest that the OPV-DEA could pave the way for the development of an unprecedented range of functional soft robots.
A critical assessment of using ChatGPT for extracting structured data from clinical notes
Authors: Huang Jingwei , Yang Donghan M. , Rong Ruichen , Nezafati Kuroush , Treager Colin , Chi Zhikai , Wang Shidan , Cheng Xian , Guo Yujia , Klesse Laura J. , Xiao Guanghua , Peterson Eric D. , Zhan Xiaowei , Xie Yang 
Published: May 2024

AbstractExisting natural language processing (NLP) methods to convert free-text clinical notes into structured data often require problem-specific annotations and model training. This study aims to evaluate ChatGPT’s capacity to extract information from free-text medical notes efficiently and comprehensively. We developed a large language model (LLM)-based workflow, utilizing systems engineering methodology and spiral “prompt engineering” process, leveraging OpenAI’s API for batch querying ChatGPT. We evaluated the effectiveness of this method using a dataset of more than 1000 lung cancer pathology reports and a dataset of 191 pediatric osteosarcoma pathology reports, comparing the ChatGPT-3.5 (gpt-3.5-turbo-16k) outputs with expert-curated structured data. ChatGPT-3.5 demonstrated the ability to extract pathological classifications with an overall accuracy of 89%, in lung cancer dataset, outperforming the performance of two traditional NLP methods. The performance is influenced by the design of the instructive prompt. Our case analysis shows that most misclassifications were due to the lack of highly specialized pathology terminology, and erroneous interpretation of TNM staging rules. Reproducibility shows the relatively stable performance of ChatGPT-3.5 over time. In pediatric osteosarcoma dataset, ChatGPT-3.5 accurately classified both grades and margin status with accuracy of 98.6% and 100% respectively. Our study shows the feasibility of using ChatGPT to process large volumes of clinical notes for structured information extraction without requiring extensive task-specific human annotation and model training. The results underscore the potential role of LLMs in transforming unstructured healthcare data into structured formats, thereby supporting research and aiding clinical decision-making.
Forming cognitive maps for abstract spaces: the roles of the human hippocampus and orbitofrontal cortex
Authors: Qiu Yidan , Li Huakang , Liao Jiajun , Chen Kemeng , Wu Xiaoyan , Liu Bingyi , Huang Ruiwang 
Published: May 2024

AbstractHow does the human brain construct cognitive maps for decision-making and inference? Here, we conduct an fMRI study on a navigation task in multidimensional abstract spaces. Using a deep neural network model, we assess learning levels and categorized paths into exploration and exploitation stages. Univariate analyses show higher activation in the bilateral hippocampus and lateral prefrontal cortex during exploration, positively associated with learning level and response accuracy. Conversely, the bilateral orbitofrontal cortex (OFC) and retrosplenial cortex show higher activation during exploitation, negatively associated with learning level and response accuracy. Representational similarity analysis show that the hippocampus, entorhinal cortex, and OFC more accurately represent destinations in exploitation than exploration stages. These findings highlight the collaboration between the medial temporal lobe and prefrontal cortex in learning abstract space structures. The hippocampus may be involved in spatial memory formation and representation, while the OFC integrates sensory information for decision-making in multidimensional abstract spaces.
Restoration of non-Hermitian bulk-boundary correspondence by counterbalancing skin effect
Authors: Xiao Yi-Xin , Zhang Zhao-Qing , Chan C. T. 
Published: May 2024

AbstractIn systems exhibiting the non-Hermitian skin effect (NHSE), the bulk spectrum under open boundary conditions (OBC) significantly differs from that of its periodic counterpart. This disparity renders the conventional bulk-boundary correspondence (BBC) inapplicable. Here we propose an intuitive approach called doubling and swapping to restore the BBC, using the non-Hermitian Su-Schrieffer-Heeger model as an example. Explicitly, we construct a modified system free of NHSE by swapping the asymmetric intracell hoppings in every second primitive unit cell. Importantly, this change does not alter the OBC spectrum. As a result, the modified periodic system can serve as the bulk for defining topological invariants that accurately predict edge states and topological phase transitions. The basic principle is applicable to many other systems. By extending the study to disordered systems in which the asymmetric hoppings are randomly swapped, we show that two types of winding numbers can also be defined to account for the NHSE and topological edge states, respectively.
Data-driven modeling of interrelated dynamical systems
Authors: Elul Yonatan , Rozenberg Eyal , Boyarski Amit , Yaniv Yael , Schuster Assaf , Bronstein Alex M. 
Published: May 2024

AbstractNon-linear dynamical systems describe numerous real-world phenomena, ranging from the weather, to financial markets and disease progression. Individual systems may share substantial common information, for example patients’ anatomy. Lately, deep-learning has emerged as a leading method for data-driven modeling of non-linear dynamical systems. Yet, despite recent breakthroughs, prior works largely ignored the existence of shared information between different systems. However, such cases are quite common, for example, in medicine: we may wish to have a patient-specific model for some disease, but the data collected from a single patient is usually too small to train a deep-learning model. Hence, we must properly utilize data gathered from other patients. Here, we explicitly consider such cases by jointly modeling multiple systems. We show that the current single-system models consistently fail when trying to learn simultaneously from multiple systems. We suggest a framework for jointly approximating the Koopman operators of multiple systems, while intrinsically exploiting common information. We demonstrate how we can adapt to a new system using order-of-magnitude less new data and show the superiority of our model over competing methods, in terms of both forecasting ability and statistical fidelity, across chaotic, cardiac, and climate systems.
Extreme hydrometeorological events induce abrupt and widespread freshwater temperature changes across the Pacific Northwest of North America
Authors: Déry Stephen J. , Martins Eduardo G. , Owens Philip N. , Petticrew Ellen L. 
Published: May 2024

AbstractThe Pacific Northwest of North America experienced four extreme hydrometeorological events during 2021 including intense cold waves in mid-February and late December, the record-setting June heat dome, and catastrophic floods caused by two November atmospheric rivers. While the synoptic-scale patterns and terrestrial hydrological responses to these extreme events are well documented, scant information has been published on corresponding freshwater temperature responses. Here, we apply an observational database of hourly freshwater temperatures at 554 sites across the region to characterize their evolution during these four extreme hydrometeorological events. The two cold snaps and summer heat dome induced a general 1 °C decline and 2.7 °C increase, respectively, in water temperatures with subdued changes (+0.4 °C) during the mid-November floods. For 193 sites with long-term records, 478 daily maximum water temperatures were exceeded during the heat dome and 94 were surpassed during the flooding event, suggesting deleterious effects for water quality and aquatic species.
Aromatase inhibitors and fracture prevention – do 2017 guidelines work in real world?
Authors: Mirza Anem , Naing Zeyar Win , Khonsari Parisa , Khan Haseeb , Abbas Ali K. , Nisar Muhammad K. 
Published: May 2024

Abstract
Objectives
Aromatase inhibitor induced bone loss (AIBL) is a recognised adverse event with resultant increase in fracture risk. We aimed to determine the real-world impact of the 2017 consensus guidelines on AIBL and see if it is effective in fracture prevention.

Methods
Over a 7-year study period, 1001 women prescribed AI were split in two groups. First group were offered bone active treatment based on NOS 2008 guidelines whereas the second group followed the 2017 consensus guidelines.

Results
1001 women were included.
First group: 361 women had a baseline DEXA with 143 (40%) women who had a normal DEXA, 174 (48%) had osteopenia and 44 (12%) had osteoporosis. Of the women with osteopenia, 44 (25%) women were offered treatment, and 22 (13%) women had a fracture. Second group: 640 women had a baseline DEXA with 216 (33%) women with a normal result, 322(50%) had osteopenia and 107 (17%) had osteoporosis. Of the women with osteopenia, 127 (39%) women were offered treatment, and 8 (2.5%) women had a fracture.

Conclusions
Our study provides real world evidence of the success of 2017 consensus statement in lowering fracture risk. A significant reduction in fractures pre (13%) and post guidelines change (2.5%) was demonstrated (absolute risk reduction of 10.5%) which has implications for healthcare systems worldwide as we have demonstrated this approach can reduce morbidity.

Lay summary
Breast cancer is the most common cancer in women with over two million women diagnosed with it annually. Early diagnosis and treatment with hormonal therapies have helped reduce mortality. Aromatase inhibitors (AIs) are the main drugs in this class and have demonstrated improved survival. However, whilst conveying major benefits, AIs reduce oestrogen levels leading to significant bone loss and increasing fracture risk. Several protocols have been recommended to address this concern. We compared the two guidelines published by National Osteoporosis Society UK in 2008 and consensus statement recommended by seven breast cancer and bone health groups in 2017 to see which work better in preventing fractures in women prescribed AIs for breast cancer. Our study shows that the 2017 guidelines are better at preventing fractures in the real world. Hence, we suggest that these should be adopted by specialists treating breast cancer which can help women avoid fractures and improve long term health.
Fuzzy functional observer‐based sliding mode control for T‐S fuzzy cyber‐physical systems subject to disturbances and deception attacks
Authors: Zhu Baopeng , Xiong Wei , Li Shihua , Guo Lei , Chen Xisong 
Published: May 2024

AbstractThis paper is concerned with the fuzzy functional observer‐based sliding mode control for T‐S fuzzy cyber‐physical systems subject to disturbances and deception attacks. First, the deception attack is modeled by an exogenous system with state dependent nonlinearity. Second, a fuzzy learning functional observer with fuzzy logic systems learning unknown nonlinear function, whose parameters can be directly found, is designed to estimate the partly unavailable states, deception attacks and lumped disturbances simultaneously. Then, a fuzzy sliding mode control is designed to compensate the effect of deception attack and reject the disturbances. Furthermore, some sufficient conditions are given to guarantee that the closed‐loop fuzzy system is exponential convergent. Finally, a numerical example is given to illustrate the validity of the proposed method.
New resonant composite magnetic‐field probes with high detection sensitivity and electric‐field suppression ratio
Authors: Wang Lei , Wang Hongyue , Luo Chengyang , Chen Yiqiang 
Published: May 2024

AbstractThis work proposes two differential magnetic‐field resonant probes, consisting of a U‐shaped single‐loop resonant probe (S_probe) and a dual‐loops resonant probe (D_probe). These resonant probes are composed of a pair of LC resonant circuits, a pair of strip‐lines with two outputs, two rows of via fences, and two different sensing structures (single‐loop and dual‐loops). Among them, a pair of open stubs as capacitor and shorted stubs as inductor is used to form the LC resonant circuit, via fences are utilized to suppress unwanted electromagnetic wave modes, a U‐shaped single‐loop and a pair of differential dual‐loops is used to receive the electromagnetic signal. In order to prove the rationality of the design, simulation models of the S_probe and D_probe are optimized and manufactured based on four‐layers PCBs. In addition, a near‐field scanning system with a standard 50 Ω straight microstrip line as calibration kit is used to measure and calibrate these resonant probes. The tested results indicate that these designed magnetic‐field probes could operate at 1.575 GHz and have high detection sensitivity and electric‐field suppression.
Investigation into active‐gate‐driving performance and potential closed‐loop controller implementations for silicon carbide MOSFET modules
Authors: Şahin İlker , Parker Mason , Mathieson Ross , Finney Stephen , Judge Paul D. 
Published: May 2024

AbstractActive gate driving (AGD) is a promising concept for achieving high‐performance power transistor switching. This is particularly crucial for Silicon Carbide (SiC) MOSFETs since their inherently fast switching characteristics give rise to severe overshoots and oscillations which translate into increased levels of electromagnetic interference (EMI) emissions. In this paper, an AGD strategy using a single‐pulse applied during the switching transient is considered for a 1200 V 400 A SiC MOSFET module. The effect of single‐pulse timing, load current, and temperature on the switching performance is analyzed in detail. The radiated EMI reduction benefits are quantified by H‐field and E‐field probes. A conceptual closed‐loop AGD approach is presented and compared to open‐loop operation. For the transistor turn‐off case under full load current of 400 A, experimental results show that it is possible to reduce voltage overshoot by 43.3%, voltage and current oscillations by 69.7% and 52.2% respectively, and EMI by 76.6%, with a trade‐off in the switching energy by a relatively minor increase of 18.2%, compared to the conventional gate driving case. For the turn‐on case, current overshoot was reduced by 32.7%, EMI by 52%, voltage and current oscillations by 54.6% and 52.8%, respectively, with a penalty of 50.9% increase in the switching loss.