bluuebunny/crossref_metadata_2025_split huggingface dataset:
Recent Progress in Accessing Multi-functionalized Caged Hydrocarbons: En Route to Highly Functionalized Saturated (Bio)isosteres of Benzene Rings
Authors: Nagasawa Shota , Iwabuchi Yoshiharu 
Published: July 2024

AbstractRecently, many saturated bioisosteres of the benzene ring have been developed, and their applications in drug development have been evaluated. Most of these bioisosteres are caged hydrocarbons, which have rigid skeletons and three-dimensional spaces. Recent efforts to synthesize these caged hydrocarbons have enabled access to multi-functionalized congeners that are expected to be (bio)isosteres of multi-functionalized benzenes. This short review summarizes recently reported methods for obtaining multi-functionalized (typically more than disubstituted) caged hydrocarbons.1	Introduction2	Proposed Structures of Caged Hydrocarbons as Saturated (Bio)isosteres of the Benzene Ring: A Brief Summary3	Access to Multi-functionalized Caged Hydrocarbons: De Novo Synthetic Approaches3.1	Bicyclo[1.1.1]pentanes (BCPs)3.2	Bicyclo[2.1.1]hexanes (BCHs)3.3	Bicyclo[3.1.1]heptanes (BCHeps)3.4	Others4	Access to Multi-functionalized Caged Hydrocarbons: C–H Functionalization5	Conclusion
Sciatic Nerve Entrapment from Cerclage Wiring in Intramedullary Nail Fixation
Authors: Hussein Mohsin , Shah Ankit Bipin , Shah Bipin Ramanlal , Iyengar Karthikeyan Parthasarathy , Botchu Rajesh 
Published: July 2024

AbstractCerclage wiring may be used to optimize the stability of intramedullary nail or plate fixations in comminuted proximal femoral fractures, periprosthetic fractures, and other selected cases. In this article, we presented a novel case of iatrogenic sciatic nerve entrapment from cerclage wiring used to supplement intramedullary nail fixation. We also illustrate and highlight the role of ultrasound in assessing the sciatic nerve to make a timely diagnosis.
Discretionary decisions in capital requirements under Solvency II
Authors: Grochola Nicolaus , Schlütter Sebastian 
Published: July 2024

AbstractEuropean insurers are allowed to make discretionary decisions in the calculation of Solvency II capital requirements. These choices include the design of risk models (ranging from a standard formula to a full internal model) and the use of long-term guarantees measures. This article examines the situation of insurers that utilize the discretionary scope regarding capital requirements for market risks. In a first step of our analysis, we assess the risk profiles of 49 stock insurers using daily market data. In a second step, we exploit hand-collected Solvency II data for the years 2016 to 2020. We find that long-term guarantees measures substantially influence the reported solvency ratios. The measures are chosen particularly by less solvent insurers and those with high interest rate and sovereign credit risk sensitivities. Internal models are used more frequently by large insurers and especially for market risks for which they have already found adequate immunization strategies.
Plant growth promoting rhizobacteria mitigate drought and salinity stresses, and improve the physiological and agronomic performances in crops: A systematic review
Authors: Kibret Mulugeta , Devkota Krishana , Bakrim Widad Ben , Ezzariai Amine , Terefe Habtamu , Karouach Fadoua , Sobeh Mansour , Hafidi Mohamed , Kouisni Lamfeddal 
Published: July 2024

Abstract
Drought and salinity are environmental factors that negatively impact crop productivity in arid and semi-arid climates. Plant growth promoting rhizobia (PGPR) can help mitigate the harmful effects of drought and salinity on plant growth and productivity. This systematic review aims to summarize studies on PGPR and their mechanisms for reducing the impacts of drought and salinity, as well as to explore the effects of PGPR on different crops to improve agronomic indicators focusing on recent findings. Search was performed on PubMed, Web of Sciences and Google Scholar for papers published from 2013 to 2022. A total of 174 papers were analyzed in this review. The results showed that the PGPRs were found to increase the activity of antioxidant enzymes and stimulate plant growth under adverse conditions. The PGPRs also reduced electrolyte leakage and malondialdehyde contents, while increasing exopolysaccharide, chlorophyll, and relative water contents, for mitigating both drought and salinity stresses. PGPR strains are effective in increasing root and shoot length, root and shoot dry weight, leaf area, and leaf content. Most of the studies regarding the applications of PGPRs to enhance abiotic stress tolerance in crops were conducted under laboratory and/or greenhouse conditions, with Asia having the highest number of publications on cereals. This review shows the potential for PGPR to manage soil salinity and drought across diverse crops and ecologies. Studies on the roles of PGPRs for crop production at the field level to achieve large-scale adoption with agronomic and economic improvements are recommended.
Nanosecond pulsed gliding arc plasma for ammonia synthesis: better insight from discharge mode and vibrational temperature
Authors: Xu Xiaofang , Sun Meng , Song Qinlong , Liu Guangyi , Zhang Haibao 
Published: July 2024

Abstract
Low-temperature plasma technology is a promising technological route to achieve green and efficient ammonia synthesis at ambient temperature and pressure. In this work, a Laval nozzle type gliding arc plasma reactor was designed for the direct synthesis of ammonia from N2 and H2 discharges ignited by a high voltage nanosecond pulsed power supply to investigate the effect of different electrode gaps, pulse voltages, and V
N2:V
H2 on ammonia synthesis. The nanosecond pulsed plasma discharges were characterized through oscilloscope and optical emission spectroscopy (OES). The maximum rate of NH3 synthesis was 538.12 μmol·h−1 at 1.5 mm electrode gap, 16 kV peak pulse voltage, 6 kHz pulse repetition frequency, 100 ns pulse width, 100 ns pulse rising edge, 100 ns pulse falling edge, and 200 mL·min−1 total gas flow rate with V
N2:V
H2 = 1:1. It was demonstrated that the discharge mode of the nanosecond pulsed gliding arc plasma can transit from a unipolar state to a bipolar state determined by the duty cycle accompanied with higher discharge power and vibrational temperature. Bipolar discharge mode is beneficial to improve the efficiency of plasma ammonia synthesis because of it can strengthen the plasma discharge and increase the vibrational temperature. The ammonia synthesis rate and N2 conversion rate increased with the increase of the discharge power and vibrational temperature.
Dynamical analysis of a discrete Aihara neuron under a locally active memristor as electromagnetic radiation and its DSP implementation
Authors: Cao Hongli , Cao Yinghong , Xu Xianying , Mou Jun 
Published: July 2024

Abstract
The study of neuron model under electromagnetic radiation is significant for learning brain principles and treating mental diseases. In this paper, a novel discrete locally active memristor(DLAM) model is designed and its characteristics are investigated thoroughly. Then, the DLAM is used to imitate electromagnetic radiation to stimulate Aihara neuron, called EMR-Aihara neuron model. The equilibrium point of this discrete model is analyzed. Dynamical characteristics are studied by means of phase diagram, iteration sequence, bifurcation diagram, Lyapunov Exponent spectrum(LEs), Kolmogorow entropy(KE) and Spectral Entropy(SE) complexity. With these analysis methods, rich dynamical behaviors and neuron firing patterns are discovered from the EMR-Aihara neuron map, including hyperchaos, chaos and period. In addition, complex multistability and state transition phenomena concerning various attractors and neuron firing modes are observed. This EMR-Aihara neuron map is implemented in digital circuit by DSP platform as well, confirming the physical availability of the model. The EMR-Aihara neuron model can simulate biological neuron under electromagnetic radiation and apply to image encryption.
Shifting climate and the associated impacts on regional biodiversity: a present and future outlook from the Indian subcontinent
Authors: Sachan Disha , Kumar Pankaj 
Published: July 2024

AbstractAnthropogenic climate change accelerates the decline of global biodiversity and disrupts ecosystem functioning, forcing terrestrial and aquatic species to change their ranges, phenology, physiology, and morphology. In our study, we have employed univariate and a newly-defined vector-algebra-derived multivariate estimate of the velocity of climate change (VoCC) derived from near-surface temperature and total precipitation to present the historical (1980–2005) and projected (2020–2097) shifts in the climate space over the Indian subcontinent. The multivariate VoCC was further used to derive climatic divergence (stress) and residence time of eight representative protected areas (PAs). VoCC is a versatile metric that approximates the ‘required’ migration speeds for the species. Our results from observations (CRU, ERA5) and model simulations (CMIP5, Regional Earth System Model) show that regions with relatively flatter terrain, such as Deserts, Semi-Arid, Deccan Peninsula and Gangetic Plains, displayed the highest historical velocities in the range of 2–15 km yr−1, which are also projected to increase in the future period to range of 4–20 km yr−1. The estimates of multivariate velocities were generally higher than the univariate velocities, leading to a better representation of shifts in real climate space. The high-resolution regional earth system model, ROM, performed better than the global circulations models in producing realistic VoCCs. The climatic stress (diverging vectors closer to 180 degrees) was higher for the Trans-Himalayas, Himalayas, Gangetic Plains, and parts of the Deccan Peninsula, and it is projected to increase in the near and mid future. The PAs with the shortest residence times were found to be Sundarbans (63 years) and Ranthambore (32 years), illustrating a severe challenge for conservationists under changing climate. Our results present the importance of employing multivariate velocities to simulate more realistic estimates of shifting climate and added benefits of measures of climatic divergence and stress on biodiversity.
Antibacterial efficacy of decorated carbon nanotubes by nano silver against pseudomonas aeruginosa
Authors: Khalil Alaa M , Gharieb Menna Ali , Abdelaty Shokry M , El-Khatib Ahmed M 
Published: July 2024

Abstract
Carbon nanotubes (CNTs) are considered as a promising nanomaterial for a variety of applications. It has desirable physicochemical characteristics of high surface area, superior mechanical and thermal strength, and electrochemical activity. In this study, CNTs decorated with silver nanoparticles (AgNPs) were manufactured by an arc discharge technique. As a result, etching produces stable AgNPs-CNTs aqueous suspensions of pure silver and carbon electrodes in ethanol vapor condensed in water. UV- visible photometer, x-ray Diffraction, transmission electron microscope, and energy dispersive x-ray characterized the synthesized AgNPs-CNTs. The synthesized AgNPs-CNTs crystals showed CNTs formation of an average 9.5 nm, with intermediate length position of about 75.8 nm decorated by homogeneous spherical AgNPs of average size 15.2 nm. The antibacterial efficiency of AgNPs-CNTs against Pseudomonas aeruginosa was established at a series of concentrations (45 g ml−1: 0.0879 g ml−1) while values for the MIC and MBC were determined. The MIC and MBC levels were found to be (>5.625 μg ml−1) and (>11.25 μg/ml) respectively. The bacteria cytotoxicity was evaluated through LDH and protein leakage levels. Treated samples with 3.2 μg ml−1 AgNPs-CNTs revealed significant injuries in the cell membrane by two times greater in LDH and protein leakage levels than samples treated by 5.0 μg ml−1 AgNPs. Results in this work substantiate the synergistic effect of combining AgNPs with CNTs to enhanced antibacterial properties and performance compared to AgNPs alone. The efficiency of using synthesized AgNPs-CNTs showed high antibacterial potential against Pseudomonas aeruginosa in a concentration-dependent manner. The arc discharge method can be adapted to incorporate different materials or change the synthesis conditions, allowing for the production of AgNPs-CNTs with tailored properties for specific applications.
A causal machine-learning framework for studying policy impact on air pollution: a case study in COVID-19 lockdowns
Authors: Heffernan Claire , Koehler Kirsten , Zamora Misti Levy , Buehler Colby , Gentner Drew R , Peng Roger D , Datta Abhirup 
Published: July 2024

Abstract
When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies or the opening or closing of an industrial facility, careful statistical analysis is needed to separate causal changes from other confounding factors. Using COVID-19 lockdowns as a case study, we present a comprehensive framework for estimating and validating causal changes from such perturbations. We propose using flexible machine learning–based comparative interrupted time series (CITS) models for estimating such a causal effect. We outline the assumptions required to identify causal effects, showing that many common methods rely on strong assumptions that are relaxed by machine learning models. For empirical validation, we also propose a simple diagnostic criterion, guarding against false effects in baseline years when there was no intervention. The framework is applied to study the impact of COVID-19 lockdowns on atmospheric nitrogen dioxide (NO2) levels in the eastern United States. The machine learning approaches guard against false effects better than common methods and suggest decreases in NO2 levels in 4 US cities (Boston, Massachusetts; New York, New York; Baltimore, Maryland; and Washington, DC) during the pandemic lockdowns. The study showcases the importance of our validation framework in selecting a suitable method and the utility of a machine learning–based CITS model for studying causal changes in air pollution time series.
This article is part of a Special Collection on Environmental Epidemiology.
Prevalence and correlates of diagnosed and probable polycystic ovary syndrome (PCOS) in a cohort of parous women
Authors: Perng Wei , Fitz Victoria W , Salmon Kyle , Hivert Marie-France , Kazemi Maryam , Rifas-Shiman Sheryl L , Shifren Jan , Oken Emily , Chavarro Jorge E 
Published: July 2024

Abstract
Correlates of diagnosed and probable polycystic ovary syndrome (PCOS) among parous women were assessed in this study. A total of 557 women were recruited from multi-specialty clinics in eastern Massachusetts. The women were categorized as being diagnosed with PCOS based on medical records and self-reported clinician-diagnoses. A category of “probable PCOS” was created for women without a diagnosis but with ≥ 2 of the following: ovulatory dysfunction (cycle length &lt; 21 or ≥ 35 days), hyperandrogenism (free testosterone concentration &gt; 75th percentile), or elevated anti-Müllerian hormone (AMH) concentration (&gt; 75th percentile). The remaining participants were placed in the “no PCOS” category, and characteristics were compared across groups. Of the total cohort, 9.7% had diagnosed and 9.2% had probable PCOS. The frequency of irregular cycles was similar for diagnosed and probable PCOS. Free testosterone and AMH levels were higher in women with probable than with diagnosed PCOS. Frequency of irregular cycles and both hormones were higher for the 2 PCOS groups vs the no PCOS group. Obesity prevalence for diagnosed PCOS was twice that of probable PCOS (43.9% vs 19.6%), yet the 2 groups had similar HbA1c and adiponectin values. Women with probable PCOS are leaner but have comparable glycemic traits to those with a formal diagnosis, highlighting the importance of assessing biochemical profiles among women with irregular cycles, even in the absence of overweight/obesity.
How does viewing angle affect the perceived accuracy of Batesian mimicry in hoverflies?
Authors: Baker Lucy , Taylor Chris , Gilbert Francis , Reader Tom 
Published: July 2024

Abstract
Despite Batesian mimicry often eliciting predator avoidance, many Batesian mimics, such as some species of hoverfly (Syrphidae), are considered to have an “imperfect” resemblance to their model. One possible explanation for the persistence of apparently imperfect mimicry is that human perceptions of mimicry are different from those of natural predators. Natural predators of hoverflies have different visual and cognitive systems from humans, and they may encounter mimics in a different way. For example, whilst humans often encounter hoverflies at rest on vegetation, or in photographs or textbooks, where they are typically viewed from above, natural predators may approach hoverflies from the side or below. To test how viewing angle affects the perception of mimicry, images of mimetic hoverflies and their models (wasps and bees) were shown from different angles in an online survey. Participants were asked to distinguish between the images of models and mimics. The results show that the viewing angle does affect perceived mimicry in some species, although it does not provide a complete explanation for the persistence of imperfect mimicry in nature. The effect is also highly species-specific. This suggests that to understand better how selection has shaped mimetic accuracy in hoverflies and other taxa, further study is required of the viewing angles that predators utilize most commonly in nature.
A method for determining the layout of a yaw system based on the Delphi method and the analytic hierarchy process
Authors: Lin ShuTing , Lu Lanxing , Zhang Peng , Li Hua 
Published: July 2024

Abstract
Wind power technology has been widely used due to its characteristics of environmental protection, sustainability and low cost. The yaw system plays a vital role in improving the energy capture efficiency of a wind turbine. However, the method of layout determination is lacking in the yaw system. To solve this problem, a method that combines the Delphi method and the analytic hierarchy process was proposed in this study. Twelve evaluation indexes, including transmission efficiency, ratio range, operating temperature range and others, were identified by screening 18 technical indicators using the Delphi method. Subsequently, the evaluation system of the yaw system was established. Then, six configuration schemes were selected. Experts’ scores of schemes were collected according to the evaluation system and the score matrix of evaluation indexes was obtained. The hierarchical model of the evaluation indexes of the yaw system was established and the comprehensive weight was obtained by using the analytic hierarchy process. After calculating the comprehensive evaluation score, the comprehensive evaluation result was obtained. The 2Z-X(A) negative mechanism, which achieved the highest score of 0.9227, is the optimal scheme. A new method and specific process are provided for designers. The research gap in the scheme selection method for yaw systems is filled.
An artificial intelligence grading system of apical periodontitis in cone-beam computed tomography data
Authors: Zhao Tianyin , Wu Huili , Leng Diya , Yao Enhui , Gu Shuyun , Yao Minhui , Zhang Qinyu , Wang Tong , Wu Daming , Xie Lizhe 
Published: July 2024

Abstract

Objectives
In order to assist junior doctors in better diagnosing apical periodontitis (AP), an artificial intelligence AP grading system was developed based on deep learning (DL) and its reliability and accuracy were evaluated.

Methods
One hundred and twenty cone-beam computed tomography (CBCT) images were selected to construct a classification dataset with four categories, which were divided by CBCT periapical index (CBCTPAI), including normal periapical tissue, CBCTPAI 1-2, CBCTPAI 3-5, and young permanent teeth. Three classic algorithms (ResNet50/101/152) as well as one self-invented algorithm (PAINet) were compared with each other. PAINet were also compared with two recent Transformer-based models and three attention models. Their performance was evaluated by accuracy, precision, recall, balanced F score (F1-score), and the area under the macro-average receiver operating curve (AUC). Reliability was evaluated by Cohen’s kappa to compare the consistency of model predicted labels with expert opinions.

Results
PAINet performed best among the four algorithms. The accuracy, precision, recall, F1-score, and AUC on the test set were 0.9333, 0.9415, 0.9333, 0.9336, and 0.9972, respectively. Cohen’s kappa was 0.911, which represented almost perfect consistency.

Conclusions
PAINet can accurately distinguish between normal periapical tissues, CBCTPAI 1-2, CBCTPAI 3-5, and young permanent teeth. Its results were highly consistent with expert opinions. It can help junior doctors diagnose and score AP, reducing the burden. It can also be promoted in areas where experts are lacking to provide professional diagnostic opinions.
Preoperative risk evaluation and optimization for patients with liver disease
Authors: Bhalla Sameer , Mcquillen Brendan , Cay Edward , Reau Nancy 
Published: July 2024

Abstract
The prevalence of liver disease is rising and more patients with liver disease are considered for surgery each year. Liver disease poses many potential complications to surgery; therefore, assessing perioperative risk and optimizing a patient’s liver health is necessary to decrease perioperative risk. Multiple scoring tools exist to help quantify perioperative risk and can be used in combination to best educate patients prior to surgery. In this review, we go over the various scoring tools and provide a guide for clinicians to best assess and optimize perioperative risk based on the etiology of liver disease.
Proteomic Stratification of Prognosis and Treatment Options for Small Cell Lung Cancer
Authors: Huo Zitian , Duan Yaqi , Zhan Dongdong , Xu Xizhen , Zheng Nairen , Cai Jing , Sun Ruifang , Wang Jianping , Cheng Fang , Gao Zhan , Xu Caixia , Liu Wanlin , Dong Yuting , Ma Sailong , Zhang Qian , Zheng Yiyun , Lou Liping , Kuang Dong , Chu Qian , Qin Jun , Wang Guoping , Wang Yi 
Published: July 2024

Abstract
Small cell lung cancer (SCLC) is a highly malignant and heterogeneous cancer with limited therapeutic options and prognosis prediction models. Here, we analyzed formalin-fixed, paraffin-embedded (FFPE) samples of surgical resections by proteomic profiling, and stratified SCLC into three proteomic subtypes (S-I, S-II, and S-III) with distinct clinical outcomes and chemotherapy responses. The proteomic subtyping was an independent prognostic factor and performed better than current tumor–node–metastasis or Veterans Administration Lung Study Group staging methods. The subtyping results could be further validated using FFPE biopsy samples from an independent cohort, extending the analysis to both surgical and biopsy samples. The signatures of the S-II subtype in particular suggested potential benefits from immunotherapy. Differentially overexpressed proteins in S-III, the worst prognostic subtype, allowed us to nominate potential therapeutic targets, indicating that patient selection may bring new hope for previously failed clinical trials. Finally, analysis of an independent cohort of SCLC patients who had received immunotherapy validated the prediction that the S-II patients had better progression-free survival and overall survival after first-line immunotherapy. Collectively, our study provides the rationale for future clinical investigations to validate the current findings for more accurate prognosis prediction and precise treatments.