bluuebunny/crossref_metadata_2025_split huggingface dataset:
Interannual climate variability improves niche estimates for ectothermic but not endothermic species
Authors: Karger Dirk Nikolaus , Saladin Bianca , Wüest Rafael O. , Graham Catherine H. , Zurell Damaris , Mo Lidong , Zimmermann Niklaus E. 
Published: August 2023

AbstractClimate is an important limiting factor of species’ niches and it is therefore regularly included in ecological applications such as species distribution models (SDMs). Climate predictors are often used in the form of long-term mean values, yet many species experience wide climatic variation over their lifespan and within their geographical range which is unlikely captured by long-term means. Further, depending on their physiology, distinct groups of species cope with climate variability differently. Ectothermic species, which are directly dependent on the thermal environment are expected to show a different response to temporal or spatial variability in temperature than endothermic groups that can decouple their internal temperature from that of their surroundings. Here, we explore the degree to which spatial variability and long-term temporal variability in temperature and precipitation change niche estimates for ectothermic (730 amphibian, 1276 reptile), and endothermic (1961 mammal) species globally. We use three different species distribution modelling (SDM) algorithms to quantify the effect of spatial and temporal climate variability, based on global range maps of all species and climate data from 1979 to 2013. All SDMs were cross-validated and accessed for their performance using the Area under the Curve (AUC) and the True Skill Statistic (TSS). The mean performance of SDMs using only climatic means as predictors was TSS = 0.71 and AUC = 0.90. The inclusion of spatial variability offers a significant gain in SDM performance (mean TSS = 0.74, mean AUC = 0.92), as does the inclusion of temporal variability (mean TSS = 0.80, mean AUC = 0.94). Including both spatial and temporal variability in SDMs shows the highest scores in AUC and TSS. Accounting for temporal rather than spatial variability in climate improved the SDM prediction especially in ectotherm groups such as amphibians and reptiles, while for endothermic mammals no such improvement was observed. These results indicate that including long term climate interannual climate variability into niche estimations matters most for ectothermic species that cannot decouple their physiology from the surrounding environment as endothermic species can.
Effects of N-acetylcysteine on CG8005 gene-mediated proliferation and apoptosis of Drosophila S2 embryonic cells
Authors: Chen Wanyin , Yin Yifei , Zhang Zheng 
Published: August 2023

AbstractTo investigate the effect of the antioxidant N-acetylcysteine (NAC) on the proliferation and apoptosis in CG8005 gene-interfering Drosophila S2 embryonic cells by scavenging intracellular reactive oxygen species (ROS). The interfering efficiency of CG8005 gene in Drosophila S2 embryonic cells was verified by real-time quantitative PCR (qRT-PCR). Different concentrations of NAC and phosphate buffered saline (PBS) were used to affect the Drosophila S2 embryonic cells. The growth state of Drosophila S2 embryonic cells was observed by light microscope. Two probes dihydroethidium (DHE) and 2,7-dichlorodihydrofluorescein-acetoacetate (DCFH-DA) were used to observe the ROS production in each group after immunofluorescence staining. TUNEL staining and flow cytometry were used to investigate the apoptosis level of Drosophila S2 embryos, and CCK-8 (Cell Counting Kit-8) was used to detect the cell viability of Drosophila S2 embryos. The knockdown efficiency of siCG8005-2 fragment was high and stable, which was verified by interference efficiency (P < 0.05). There was no significant change in the growth of Drosophila S2 embryonic cells after the treatment of NAC as compared to PBS group. Moreover, knockdowning CG8005 gene resulted in an increase in ROS and apoptosis in Drosophila S2 embryonic cells (P < 0.05) and a decrease in proliferation activity (P < 0.05). In addition, the pretreatment of antioxidant NAC could inhibit ROS production in Drosophila S2 embryonic cells (P < 0.05), reduce cell apoptosis (P < 0.05), and improve cell survival (P < 0.05). The CG8005 gene in Drosophila S2 embryonic cells could regulate the proliferation and apoptosis of S2 embryonic cells by disrupting the redox homeostasis, and antioxidant NAC could inhibit cell apoptosis and promotes cell proliferation by scavenging ROS in Drosophila S2 embryonic cells, which is expected to provide novel insights for the pathogenesis of male infertility and spermatogenesis.
Equivalent circuit of a silicon–lithium p–i–n nuclear radiation detector
Authors: Saymbetov Ahmet , Muminov Ramizulla , Jing Zhang , Nurgaliyev Madiyar , Japashov Nursultan , Toshmurodov Yorkin , Kuttybay Nurzhigit , Kapparova Ainur , Zholamanov Batyrbek , Orynbassar Sayat , Koshkarbay Nursultan 
Published: August 2023

AbstractNuclear radiation detectors are indispensable for research in the field of nuclear radiation, X-ray spectroscopy and other areas. Interest in silicon p–i–n detectors of nuclear radiation is increasing today due to the possibility of their operation under normal conditions. In this paper, an equivalent circuit of a silicon–lithium p–i–n nuclear radiation detector is proposed. The proposed circuit is obtained using the classical Shockley equation for silicon semiconductors and the telegraph equations. The parameters of the equivalent circuit were determined using the multiple regression method. As a result of simulation of the model in the MATLAB Simulink graphical development environment, the amplitude-frequency and phase-frequency characteristics of the proposed model were obtained. Using the Monte Carlo method, the alpha-decay of the uranium isotope$${}_{92}{}^{233}\mathrm{U}$$92233U, thorium isotope$${}_{90}{}^{227}\mathrm{Th}$$90227Thand americium isotope$${}_{95}{}^{241}\mathrm{Am}$$95241Amthe alpha-decay spectrum was obtained. Obtained alpha-decay spectra coincides with the experimental data, presented in previous works of other authors.
A machine learning model for distinguishing Kawasaki disease from sepsis
Authors: Li Chi , Liu Yu-chen , Zhang De-ran , Han Yan-xun , Chen Bang-jie , Long Yun , Wu Cheng 
Published: August 2023

AbstractKD is an acute systemic vasculitis that most commonly affects children under 5 years old. Sepsis is a systemic inflammatory response syndrome caused by infection. The main clinical manifestations of both are fever, and laboratory tests include elevated WBC count, C-reactive protein, and procalcitonin. However, the two treatments are very different. Therefore, it is necessary to establish a dynamic nomogram based on clinical data to help clinicians make timely diagnoses and decision-making. In this study, we analyzed 299 KD patients and 309 sepsis patients. We collected patients' age, sex, height, weight, BMI, and 33 biological parameters of a routine blood test. After dividing the patients into a training set and validation set, the least absolute shrinkage and selection operator method, support vector machine and receiver operating characteristic curve were used to select significant factors and construct the nomogram. The performance of the nomogram was evaluated by discrimination and calibration. The decision curve analysis was used to assess the clinical usefulness of the nomogram. This nomogram shows that height, WBC, monocyte, eosinophil, lymphocyte to monocyte count ratio (LMR), PA, GGT and platelet are independent predictors of the KD diagnostic model. The c-index of the nomogram in the training set and validation is 0.926 and 0.878, which describes good discrimination. The nomogram is well calibrated. The decision curve analysis showed that the nomogram has better clinical application value and decision-making assistance ability. The nomogram has good performance of distinguishing KD from sepsis and is helpful for clinical pediatricians to make early clinical decisions.
Direct inference and control of genetic population structure from RNA sequencing data
Authors: Fachrul Muhamad , Karkey Abhilasha , Shakya Mila , Judd Louise M. , Harshegyi Taylor , Sim Kar Seng , Tonks Susan , Dongol Sabina , Shrestha Rajendra , Salim Agus ,   STRATAA study group, Adhikari Anup , Banda Happy Chimphako , Blohmke Christoph , Darton Thomas C. , Farooq Yama , Ghimire Maheshwar , Hill Jennifer , Hoang Nhu Tran , Jere Tikhala Makhaza , Kamzati Moses , Kao Yu-Han , Masesa Clemens , Mbewe Maurice , Msuku Harrison , Munthali Patrick , Nga Tran Vu Thieu , Nkhata Rose , Saad Neil J. , Van Tan Trinh , Thindwa Deus , Khanam Farhana , Meiring James , Clemens John D. , Dougan Gordon , Pitzer Virginia E. , Qadri Firdausi , Heyderman Robert S. , Gordon Melita A. , Voysey Merryn , Baker Stephen , Pollard Andrew J. , Khor Chiea Chuen , Dolecek Christiane , Basnyat Buddha , Dunstan Sarah J. , Holt Kathryn E. , Inouye Michael 
Published: August 2023

AbstractRNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.
C-reactive protein kinetics as a predictive marker for long-term outcome of immune checkpoint inhibitors in oesophagogastric cancer
Authors: Nose Yohei , Saito Takuro , Kurokawa Yukinori , Takahashi Tsuyoshi , Yamamoto Kazuyoshi , Momose Kota , Yamashita Kotaro , Tanaka Koji , Makino Tomoki , Eguchi Hidetoshi , Doki Yuichiro , Wada Hisashi 
Published: August 2023

Abstract
Background
The treatment efficacy of immune checkpoint inhibitors (ICIs) is limited, and biomarkers that identify responders are urgently needed. We investigated whether C-reactive protein (CRP) kinetics are associated with the treatment efficacy of ICIs and prognosis in oesophagogastric cancers.

Methods
We analysed 76 gastric cancer patients treated with nivolumab monotherapy. Patients were classified as CRP-spike, CRP-flat or CRP-increase according to CRP kinetics within 6 weeks after nivolumab initiation, and the treatment response and prognosis were compared. We further validated this classification in 71 oesophageal cancer patients with nivolumab monotherapy.

Results
In the gastric cancer cohort, the CRP-spike, CRP-flat, and CRP-increase subgroups included 9, 37 and 30 patients, respectively. The CRP-spike subgroup had higher disease control rates than the CRP-increase subgroup (P = 0.0068) and had significantly better progression-free survival (PFS) (vs. CRP-flat: P = 0.045, CRP-increase: P = 0.0001). Multivariate analysis for PFS identified CRP-spike (HR = 0.38, P = 0.029) as an independent favourable prognostic factor. In the oesophageal cancer cohort, the CRP-spike, CRP-flat, and CRP-increase subgroups included 13, 27 and 31 patients, respectively, and multivariate analysis for PFS also identified CRP-spike (HR = 0.28, P = 0.0044) as an independent favourable prognostic factor.

Conclusions
CRP kinetics may be useful in predicting the long-term outcome of nivolumab treatment in oesophagogastric cancers.
The role of virtual consultations in cancer genetics: challenges and opportunities introduced by the COVID-19 pandemic
Authors: Redman Melody G. , Aguda Vernie , Dore Rhys , Lim Jen O. , Speight Beverley , McVeigh Terri P. 
Published: August 2023

AbstractThe COVID-19 pandemic changed the delivery of healthcare within the United Kingdom. A virtual model of care, utilising telephone and video consultations, was rapidly imposed upon cancer genetics teams. This large-scale change in service delivery has led to new opportunities that can be harnessed to improve patient care. There is a clear potential to mitigate geographical barriers, meet increasing patient expectations of implementing virtual consultations, reduce hospital carbon footprints, and decrease hospital costs while increasing efficiency. However, there are also significant challenges introduced by this model of care. Virtual healthcare consultations introduce another new level of digital exclusion for patients and clinicians. There are also potential challenges for maintaining patient confidentiality, and limited utility in circumstances where a physical exam may be warranted. For clinicians, there may be impacts on empathetic responses delivered and challenges in workflow and workload. Virtual consultations are likely to continue being a feature of cancer genetics services. A flexible approach is needed to allow for virtual and traditional models of care to work together and best meet patients’ needs. Cancer genetics services should harness the opportunities provided by virtual processes to improve patient care, whilst collaborating with patient groups and other stakeholders to carefully examine and address the challenges that virtual consultations introduce.
Involvement of DPP3 in modulating oncological features and oxidative stress response in esophageal squamous cell carcinoma
Authors: Arora Mohit , Kumari Sarita , Kadian Lokesh , Anupa Geethadevi , Singh Jay , Kumar Anurag , Verma Deepika , Pramanik Raja , Kumar Sunil , Yadav Rajni , Chopra Anita , Chauhan Shyam S. 
Published: August 2023

Abstract
Resistance to therapy in esophageal squamous cell carcinoma (ESCC) is a critical clinical problem and identification of novel therapeutic targets is highly warranted. Dipeptidyl peptidase III (DPP3) is a zinc-dependent aminopeptidase and functions in the terminal stages of the protein turnover. Several studies have reported overexpression and oncogenic functions of DPP3 in numerous malignancies. The present study aimed to determine the expression pattern and functional role of DPP3 in ESCC. DPP3 expression was assessed in normal and tumor tissues using quantitative real-time (qRT)-PCR and corroborated with ESCC gene expression datasets from Gene Expression Omnibus (GEO) and The cancer genome atlas (TCGA). DPP3 stable knockdown was performed in ESCC cells by shRNA and its effect on cell proliferation, migration, cell cycle, apoptosis, and activation of nuclear factor erythroid 2-related factor 2 (NRF2) pathway was assessed. The results suggested that DPP3 is overexpressed in ESCC and its knockdown leads to reduced proliferation, increased apoptosis, and inhibited migration of ESCC cells. Additionally, DPP3 knockdown leads to down-regulation of the NRF2 pathway proteins, such as NRF2, G6PD, and NQO1 along with increased sensitivity toward oxidative stress-induced cell death and chemotherapy. Conclusively, these results demonstrate critical role of DPP3 in ESCC and DPP3/NRF2 axis may serve as an attractive therapeutic target against chemoresistance in this malignancy.
Abnormal event detection model using an improved ResNet101 in context aware surveillance system
Authors: Kalshetty Rakesh , Parveen Asma 
Published: August 2023

AbstractSurveillance system plays a significant role for achieving security monitoring in the place of crowd areas. Offline monitoring of these crowd activity is quite challenging because it requires huge number of human resources for attaining efficient tracking. For shortcoming these issue automated and intelligent based system must be developed for efficiently monitor crowd and detect abnormal activity. However the existing methods faces issues like irrelevant features, high cost and process complexity. In this current research context aware surveillance‐system utilising hybrid ResNet101‐ANN is developed for effective abnormal activity detection. For this proposed approach video acquired from surveillance camera is considered as input. Then, acquired video is segmented into multiple frames. After that pre‐processing techniques such as denoising using mean filter, motion deblurring, contrast enhancement using Histogram Equalisation and canny edge detection is applied in this segmented frames. Further, the pre‐processed frame is fetched into hybrid ResNet101‐ANN classifier for abnormal event classification. Here, ResNet101 is used for extracting the features from the frames and Artificial neural network which replaces the fully connected layer of ResNet101 us used to detect the abnormal activity. If once abnormal‐events detected the context aware services generate alert to the user for preventing abnormal‐activities. Accuracy, precision, recall, and error values reached for the proposed‐model on simulation were 0.98, 0.98, 0.98 and 0.017 respectively. Using this proposed model effective crowd monitoring and abnormal activity detection can be achieved.
A security model for smart grid SCADA systems using stochastic neural network
Authors: Rabie Osama Bassam J. , Selvarajan Shitharth , Alghazzawi Daniyal , Kumar Abhishek , Hasan Syed , Asghar Muhammad Zubair 
Published: August 2023

AbstractDetection of cyber‐threats in the smart grid Supervisory Control and Data Acquisition (SCADA) is still remains one of the complex and essential processes need to be highly concentrated in present times. Typically, the SCADA is more prone to the security issues due to their environmental problems and vulnerabilities. Therefore, the proposed work intends to design a new detection approach by integrating the optimization and classification models for smart grid SCADA security. In this framework, the min‐max normalization is performed at first for noise removal and attributes arrangement. Here, the correlation estimation mechanism is mainly deployed to reduce the dimensionality of features by choosing the relevant features used for attack prediction. Moreover, the optimal features are selected by using the optimal solution provided by the Holistic Harris Hawks Optimization (H3O). Finally, the Perceptron Stochastic Neural Network (PSNN) is utilized to categorize the normal and attacking data flow in the network with minimal processing time and complexity. By using the combination of proposed H3O‐PSNN technique, the detection accuracy is improved up to 99% for all datasets used in this study, and also other measures such as precision to 99.2%, recall to 99%, f1‐score to 99.2% increased, when compared to the standard techniques.
Self‐trigger and on‐demand drug delivery system based on TiO2 nanotube arrays and its drug release behaviour
Authors: Zhang Tao , Liu Nannan , Xie Chunling , Xiao Xiufeng 
Published: August 2023

AbstractThe foundation of drug delivery systems based on titanium dioxide nanotube arrays has become an important means to increase the drug release performance of bone implantation materials. However, most of the conventional platforms have some disadvantages, such as large sudden release, uncontrollable, unintelligent, or not on‐demand drug release process. Herein, the authors develop a unique self‐triggering drug release system (SDDS), utilizing the advantages of 1‐Tetradecanol (TD), such as decent biocompatibility, a phase transition temperature of 37.8°C, and the inexistence of complex chemical reaction process. The establishment of the platform can make a large amount of the anti‐inflammatory drug (ibuprofen, IBU) released when the body or the affected area is inflamed, that is, when the temperature rises. Conversely, only a small amount or no drug is released when there is no inflammation, thereby achieving self‐trigger release and on‐demand release. The experimental results show that the system combines good self‐trigger release properties, release sensitivity, drug release cycle, and low selectivity to the loaded drugs.
The nature of the solar wind electron temperature and electron heat flux
Authors: Hubert Daniel , Salem Chadi S. , Pulupa Marc 
Published: August 2023

Aims. We aim to analyze the solutions of the solar wind electron energy equation in a spherical expansion with a spiral interplanetary magnetic field (IMF), a radial power law of the electron heat flux with a constant index α, and a constant or a smooth increase of the solar wind speed.
Methods. Generic analytical electron temperature profiles for constant co-latitude of the radial vector r and different power law indices of the electron heat flux are established. We concentrate on the solution of the energy equation for an expansion in the heliospheric equatorial plane. We define a critical electron heat flux that is a fraction of the electron thermal energy convected at the solar wind speed and plays a crucial role in the electron energy equation solution.
Results. When the electron heat flux density is equal to the critical heat flux, the electron temperature is driven by the dissipation of the electron heat flux and the effect of the IMF. This corresponds to a heat dissipation dominated (HDD) expansion of the electrons. When the electron heat flux is not equal to the critical electron heat flux, three effects drive the electron temperature evolution: an adiabatic cooling, the dissipation of the electron heat flux and the spiral IMF effect. These contributions are quantitatively evaluated along the radial expansion. For a same electron heat flux and solar wind velocity, we show an important effect, that the solar wind electron temperature with a spiral IMF is higher than with a radial IMF up to some large radial distances, and that this difference increases with an increasing power law index α up to −2. Based on the phenomenological energy equation, we show that the Spitzer and Härm law is approximately verified in a spiral IMF for moderate radial distances from the Sun lower than 2 AU, with an electron heat flux power law index a little lower than −2.40 and an electron temperature with a power law a little higher than −0.40. A complete study requires the solution of the electron fluid equation for different solar wind speed profiles. The study of data collected on the Ulysses mission, along a portion of a southward high-latitude orbit, needs a specific analysis because a large variation of the co-latitude is observed along that orbit leg. From this study, we conclude that the dissipation of the electron heat flux between 1.52 and 2.3 AU cannot sustain the measured total electron temperature in this distance range; we show that the core-strahl electron population has a temperature driven by the heat flux dissipation between 1.52 and 2.3 AU, and that this core-strahl temperature profile has the property of an HDD expansion.
Conclusions. The results, in Parts 1 and 2, suggest we should study the energetics of the solar wind core-strahl electron population as a whole and revisit the Spitzer and Härm law corresponding to this population while taking into account the spiral IMF.