bluuebunny/crossref_metadata_2025_split huggingface dataset:
Short-read and long-read full-length transcriptome of mouse neural stem cells across neurodevelopmental stages
Authors: Ding Chaoqiong , Yan Xiang , Xu Mengying , Zhou Ran , Zhao Yuancun , Zhang Dan , Huang Zongyao , Pan Zhenzhong , Xiao Peng , Li Huifang , Chen Lu , Wang Yuan 
Published: March 2022

AbstractDuring brain development, neural stem cells (NSCs) undergo multiple fate-switches to generate various neuronal subtypes and glial cells, exhibiting distinct transcriptomic profiles at different stages. However, full-length transcriptomic datasets of NSCs across different neurodevelopmental stages under similar experimental settings are lacking, which is essential for uncovering stage-specific transcriptional and post-transcriptional mechanisms underlying the fate commitment of NSCs. Here, we report the full-length transcriptome of mouse NSCs at five different stages during embryonic and postnatal development. We used fluorescent-activated cell sorting (FACS) to isolate CD133+Blbp+ NSCs from C57BL/6 transgenic mice that express enhanced green fluorescent protein (EGFP) under the control of a Blbp promoter. By integrating short- and long-read full-length RNA-seq, we created a transcriptomic dataset of gene and isoform expression profiles in NSCs at embryonic days 15.5, 17.5, and postnatal days 1.5, 8, and 60. This dataset provides a detailed characterization of full-length transcripts in NSCs at distinct developmental stages, which could be used as a resource for the neuroscience community to study NSC fate determination, neural development, and disease.
Vectorized rooftop area data for 90 cities in China
Authors: Zhang Zhixin , Qian Zhen , Zhong Teng , Chen Min , Zhang Kai , Yang Yue , Zhu Rui , Zhang Fan , Zhang Haoran , Zhou Fangzhuo , Yu Jianing , Zhang Bingyue , Lü Guonian , Yan Jinyue 
Published: March 2022

AbstractReliable information on building rooftops is crucial for utilizing limited urban space effectively. In recent decades, the demand for accurate and up-to-date data on the areas of rooftops on a large-scale is increasing. However, obtaining these data is challenging due to the limited capability of conventional computer vision methods and the high cost of 3D modeling involving aerial photogrammetry. In this study, a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery. This framework is used to generate vectorized data for rooftops in 90 cities in China. The data was validated on test samples of 180 km2across different regions with spatial resolution, overall accuracy, and F1 score of 1 m, 97.95%, and 83.11%, respectively. In addition, the generated rooftop area conforms to the urban morphological characteristics and reflects urbanization level. These results demonstrate that the generated dataset can be used for data support and decision-making that can facilitate sustainable urban development effectively.
A dataset of 175k stable and metastable materials calculated with the PBEsol and SCAN functionals
Authors: Schmidt Jonathan , Wang Hai-Chen , Cerqueira Tiago F. T. , Botti Silvana , Marques Miguel A. L. 
Published: March 2022

AbstractIn the past decade we have witnessed the appearance of large databases of calculated material properties. These are most often obtained with the Perdew-Burke-Ernzerhof (PBE) functional of density-functional theory, a well established and reliable technique that is by now the standard in materials science. However, there have been recent theoretical developments that allow for increased accuracy in the calculations. Here, we present a dataset of calculations for 175k crystalline materials obtained with two functionals: geometry optimizations are performed with PBE for solids (PBEsol) that yields consistently better geometries than the PBE functional, and energies are obtained from PBEsol and from SCAN single-point calculations at the PBEsol geometry. Our results provide an accurate overview of the landscape of stable (and nearly stable) materials, and as such can be used for reliable predictions of novel compounds. They can also be used for training machine learning models, or even for the comparison and benchmark of PBE, PBEsol, and SCAN.
Free convection flow of second grade dusty fluid between two parallel plates using Fick’s and Fourier’s laws: a fractional model
Authors: Khan Zahid , ul Haq Sami , Ali Farhad , Andualem Mulugeta 
Published: March 2022

AbstractThe paper aims to investigate the channel flow of second grade visco-elastic fluid generated due to an oscillating wall. The effect of heat and mass transfer has been taken into account. The phenomenon has been modelled in terms of PDEs. The constitutive equations are fractionalized by using the definition of the Caputo fractional operator with Fick’s and Fourier’s Laws. The system of fractional PDEs is non-dimensionalized by using appropriate dimensionless variables. The closed-form solutions of thermal and concentration boundary layers are obtained by using the Laplace and finite Fourier-Sine transforms, while the momentum equation is solved by a numerical approach by Zakian using $$\textit{PYTHON}$$
PYTHON
. Furthermore, the parametric influence of various embedded physical parameters on momentum, temperature, and concentration distributions is depicted through various graphs. It is observed that the fractional approach is more convenient and realistic as compared to the classical approach. It is worth noting that the increasing values of $$M$$
M
, $$Sc$$

Sc

and $$Re$$

Re

retard the boundary layer profile. For instance, this behaviour of $$M$$
M
is significant where boundary control is necessary. That is, in the case of resonance, the physical solution may be obtained by adding the effect of MHD. The Reynolds number is useful in characterising the transport properties of a fluid or a particle travelling through a fluid. The Reynolds number is one of the main controlling parameters in all viscous flow. It determines whether the fluid flow is laminar or turbulent. The evolution of the rate of heat, mass transfer, and skin friction on the left plate with various physical parameters are presented in tables. These quantities are of high interest for engineers. Keeping in mind the effect of various parameters on these engineering quantities, they make their feasibility reports.
Detection of COVID-19 using multimodal data from a wearable device: results from the first TemPredict Study
Authors: Mason Ashley E. , Hecht Frederick M. , Davis Shakti K. , Natale Joseph L. , Hartogensis Wendy , Damaso Natalie , Claypool Kajal T. , Dilchert Stephan , Dasgupta Subhasis , Purawat Shweta , Viswanath Varun K. , Klein Amit , Chowdhary Anoushka , Fisher Sarah M. , Anglo Claudine , Puldon Karena Y. , Veasna Danou , Prather Jenifer G. , Pandya Leena S. , Fox Lindsey M. , Busch Michael , Giordano Casey , Mercado Brittany K. , Song Jining , Jaimes Rafael , Baum Brian S. , Telfer Brian A. , Philipson Casandra W. , Collins Paula P. , Rao Adam A. , Wang Edward J. , Bandi Rachel H. , Choe Bianca J. , Epel Elissa S. , Epstein Stephen K. , Krasnoff Joanne B. , Lee Marco B. , Lee Shi-Wen , Lopez Gina M. , Mehta Arpan , Melville Laura D. , Moon Tiffany S. , Mujica-Parodi Lilianne R. , Noel Kimberly M. , Orosco Michael A. , Rideout Jesse M. , Robishaw Janet D. , Rodriguez Robert M. , Shah Kaushal H. , Siegal Jonathan H. , Gupta Amarnath , Altintas Ilkay , Smarr Benjamin L. 
Published: March 2022

AbstractEarly detection of diseases such as COVID-19 could be a critical tool in reducing disease transmission by helping individuals recognize when they should self-isolate, seek testing, and obtain early medical intervention. Consumer wearable devices that continuously measure physiological metrics hold promise as tools for early illness detection. We gathered daily questionnaire data and physiological data using a consumer wearable (Oura Ring) from 63,153 participants, of whom 704 self-reported possible COVID-19 disease. We selected 73 of these 704 participants with reliable confirmation of COVID-19 by PCR testing and high-quality physiological data for algorithm training to identify onset of COVID-19 using machine learning classification. The algorithm identified COVID-19 an average of 2.75 days before participants sought diagnostic testing with a sensitivity of 82% and specificity of 63%. The receiving operating characteristic (ROC) area under the curve (AUC) was 0.819 (95% CI [0.809, 0.830]). Including continuous temperature yielded an AUC 4.9% higher than without this feature. For further validation, we obtained SARS CoV-2 antibody in a subset of participants and identified 10 additional participants who self-reported COVID-19 disease with antibody confirmation. The algorithm had an overall ROC AUC of 0.819 (95% CI [0.809, 0.830]), with a sensitivity of 90% and specificity of 80% in these additional participants. Finally, we observed substantial variation in accuracy based on age and biological sex. Findings highlight the importance of including temperature assessment, using continuous physiological features for alignment, and including diverse populations in algorithm development to optimize accuracy in COVID-19 detection from wearables.
Identifying core habitats and corridors of a near threatened carnivore, striped hyaena (Hyaena hyaena) in southwestern Iran
Authors: Almasieh Kamran , Mohammadi Alireza , Alvandi Rahim 
Published: March 2022

AbstractConservation of large carnivores requires preservation of extensive core habitats and linkages among them. The goal of this study was to identify core habitats and corridors by predicting habitat suitability (an ensemble approach), and calculating resistant kernel and factorial least-cost path modeling for a relatively unknown carnivore, the striped hyaena in Khuzestan area in southwestern Iran. We used the procedure of spatial randomization test to evaluate the coincidence of striped hyaena road crossing with the predicted corridors. The results revealed that elevation, distance to conservation areas, categorical climate and grasslands density were the most influential variables for predicting the occurrence of the striped hyaena in the study area. In the estimated dispersal distance of 70 km, four core habitats were identified. The largest core habitat was located in the northeast of the study area with the highest connectivity contribution. Only about 12% and 1.5% of core habitats and corridors were protected by conservation areas, respectively. Predicted corridors, crossed by roads represented a high risk for striped hyaenas. Adaptive management plan throughout the landscape (conservation of core habitats and corridors, and reducing species mortality on the roads) must be considered by wildlife managers in Iran.
Investigation of the relationship between ocular sarcoidosis and dry eye
Authors: Aoki Takanori , Yokoi Norihiko , Nagata Kenji , Deguchi Hideto , Sekiyama Yuki , Sotozono Chie 
Published: March 2022

AbstractA relationship between ocular sarcoidosis (OcSar) and dry eye (DE) has previously been reported. Here we investigated the tear function in OcSar, and the other uveitis, Vogt–Koyanagi–Harada disease (VKH), to elucidate the association between OcSar and DE. This study involved 27 eyes of 27 female OcSar patients [mean age: 67.3 ± 13.5 (SD) years], 18 eyes of 18 female VKH patients (53.1 ± 14.1 years), and 17 eyes of 17 female healthy control subjects (59.7 ± 9.6 years). In all examined eyes, tear meniscus height (TMH, mm), fluorescein breakup time (FBUT, s), scoring of keratoconjunctival epithelial damage (ED), and the Schirmer 1 test (ST1, mm/5 min) were analyzed. In the OcSar patients, VKH patients, and control subjects, the respective TMH, FBUT, ED, and ST1 values (mean ± SD) were 0.26 ± 0.10, 0.24 ± 0.09, and 0.24 ± 0.08, 4.3 ± 2.5, 7.3 ± 2.9, and 6.3 ± 3.1, 1.1 ± 1.1, 0.7 ± 0.8, and 0.1 ± 0.3, and 12.9 ± 10.5, 21.5 ± 12.1, and 24.3 ± 10.5. Statistically, the ST1 values were significantly lower in the OcSar patients than in the VKH patients (P = 0.004) and control subjects (P = 0.001). ST1 findings revealed that the neural reflex arc and lacrimal gland system, which attenuate the vicious cycle between the tear film and ocular surface epithelium in DE, are significantly impaired in OcSar cases, thus indicating a possible association between OcSar and DE.
Identifying causal relationships of cancer treatment and long-term health effects among 5-year survivors of childhood cancer in Southern Sweden
Authors: Holst Anders , Ekman Jan , Petersson-Ahrholt Magnus , Relander Thomas , Wiebe Thomas , Linge Helena M. 
Published: March 2022

Abstract
Background
Survivors of childhood cancer can develop adverse health events later in life. Infrequent occurrences and scarcity of structured information result in analytical and statistical challenges. Alternative statistical approaches are required to investigate the basis of late effects in smaller data sets.

Methods
Here we describe sex-specific health care use, mortality and causal associations between primary diagnosis, treatment and outcomes in a small cohort (n = 2315) of 5-year survivors of childhood cancer (n = 2129) in southern Sweden and a control group (n = 11,882; age-, sex- and region-matched from the general population). We developed a constraint-based method for causal inference based on Bayesian estimation of distributions, and used it to investigate health care use and causal associations between diagnoses, treatments and outcomes. Mortality was analyzed by the Kaplan–Meier method.

Results
Our results confirm a significantly higher health care usage and premature mortality among childhood cancer survivors as compared to controls. The developed method for causal inference identifies 98 significant associations (p < 0.0001) where most are well known (n = 73; 74.5%). Hitherto undescribed associations are identified (n = 5; 5.1%). These were between use of alkylating agents and eye conditions, topoisomerase inhibitors and viral infections; pituitary surgery and intestinal infections; and cervical cancer and endometritis. We discuss study-related biases (n = 20; 20.4%) and limitations.

Conclusions
The findings contribute to a broader understanding of the consequences of cancer treatment. The study shows relevance for small data sets and causal inference, and presents the method as a complement to traditional statistical approaches.
Exciplex host coupled with a micro-cavity enabling high efficiency OLEDs with narrow emission profile
Authors: Wen Ya , Wang Ya-Kun , Zhou Jun-Gui , Li Jiao-Yang , He Wei , Zhang Yi-Jie , Zhu Xiao-Zhao , Liao Liang-Sheng , Fung Man-Keung 
Published: March 2022

A combination strategy that unites an exciplex host with the micro-cavity effect is developed to resolve the color purity issue. The proposed strategy enables OLEDs to exhibit 2-fold narrower FWHM (26 nm vs. 71 nm) and a CE of 180.2 cd A−1.
Determining the role of SGLT2 inhibition with Empagliflozin in the development of diabetic retinopathy
Authors: Matthews Jennifer , Herat Lakshini , Rooney Jennifer , Rakoczy Elizabeth , Schlaich Markus , Matthews Vance B. 
Published: March 2022

Abstract
Diabetes mellitus is a chronic metabolic disease that occurs when the pancreas is not producing enough insulin or when the insulin that it does produce is not able to be used effectively in the body. This results in hyperglycemia and if the blood sugars are not controlled, then it can lead to serious damage of various body systems, especially the nerves and the blood vessels. Uncontrolled diabetes is a major cause of kidney failure, heart attacks, stroke and amputation. One of the most devastating complications for patients is diabetic retinopathy (DR) which represents the leading cause of preventable vision loss in people between 20 and 65 years of age. Sodium glucose transporter 2 (SGLT2) inhibitors have been shown to reduce the risk for cardiovascular and renal events, however literature highlighting their potential role to prevent DR is limited. We therefore used a relevant mouse model (Akimba) to explore the effects of the SGLT2 inhibitor, Empagliflozin (EMPA), on the development of diabetic retinal changes. Here we show that when given in the early stages of type 1 diabetes (T1D), EMPA reduced the weight loss usually associated with T1D, decreased diabetes-associated polydipsia, lowered fasting blood glucose levels, decreased kidney-to-body weight ratios and, most importantly in the current context, substantially reduced retinal abnormalities associated with DR. We show that EMPA reduces vascular leakage indicated by lower albumin staining in the vitreous humor and diminishes expression of the pathogenic factor VEGF in the retina. Additionally, EMPA significantly alters the retinal genetic signature. Our findings suggest that SGLT2 inhibition may be a useful therapeutic approach to prevent the development of DR and its severity if given early in the disease process.
Intelligibility of Noise-Adapted and Clear Speech in Energetic and Informational Maskers for Native and Nonnative Listeners
Authors: Meemann Kirsten , Smiljanić Rajka 
Published: March 2022

Purpose:
This study explored clear speech (CS) and noise-adapted speech (NAS) intelligibility benefits for native and nonnative English listeners. It also examined how the two speaking style adaptations interact with maskers that vary from purely energetic to largely informational at different signal-to-noise ratios (SNRs).

Method:
Materials consisted of 40 sentences produced by 10 young adult talkers in a conversational and a clear speaking style under two conditions: (a) in quiet and (b) in response to speech-shaped noise (SSN) played over headphones (NAS). Young adult native (Experiment 1) and nonnative (Experiment 2) English listeners heard target sentences presented in two-talker (2T) babble, six-talker (6T) babble, or SSN and at an “easier” and a “harder” SNR.

Results:
When talkers produced CS and NAS, word recognition accuracy was significantly improved for both listener groups. The largest intelligibility benefit was obtained for the CS produced in response to noise (CS+NAS). Overall accuracy was highest in 2T babble. Accuracy was higher in SSN than in 6T babble for nonnative listeners at both levels of listening difficulty but only at a more difficult SNR for native listeners. Listeners benefited from CS and NAS most in the presence of SSN and least in 2T babble. When SNRs were the same for the two listener groups, native listeners outperformed nonnative listeners in almost all listening conditions, but nonnative listeners benefited more from CS and NAS in 6T babble than native listeners did.

Conclusions:
Combined speaking style enhancements, CS+NAS, provided the largest intelligibility increases for native and nonnative listeners in all listening conditions. The results add to the body of evidence supporting speech-oriented, behavioral therapy techniques for maximizing speech intelligibility in everyday listening situations.
Modified three stage Kalman filtering for stochastic non‐linear systems with randomly occurring faults and intermittent measurements
Authors: Nejati Zahra , Abedi Mostafa , Faraji Alireza 
Published: March 2022

AbstractA robust three stage Kalman filtering problem is investigated in this article for non‐linear systems with stochastic non‐linearities, random faults and intermittent missing measurements. Therefore, a more general system model is governed in which unknown stochastic non‐linearities are considered in both the system state and measurement equations. The fault terms are included by stochastic coefficients. Also, the sensor measurements occur in a random behaviour that encounters stochastic missing of data for each sensor. Both faults and unknown inputs are covered in the proposed filter. Moreover, the robust features of the developed estimator resolve the problems arising from the need for accurate models of faults and unknown inputs. Furthermore, any predetermined knowledge about the statistical characteristics of the above factors is not required. The augmented filters are mostly used to provide the fault diagnosis features, however, in the current work, this filter is decoupled to reduce the computational volume attributed to this approach. It is proved that the estimation error is ultimately bounded despite different stochastic terms and inaccurate information about the fault and unknown inputs. Finally, an illustrative example is proposed to demonstrate the effectiveness of the developed method.
Dynamic control for LNG carrier with output constraints
Authors: Liang Xiaoling , Zhang Yuxiang , Ge Shuzhi Sam , How Bernard Voon Ee , Li Dongyu 
Published: March 2022

AbstractThis paper addresses constrained robust control for dynamic positioning of the Liquefied Natural Gas (LNG) Carrier in side‐by‐side offloading operations subject to input time delay. During the LNG offloading operation, the relative motions of the multibody system are connected by the LNG offloading arm. To ensure the operation safety, robust control with asymmetric Barrier Lyapunov Function is proposed considering predefined output constraints under the unknown dynamics and external disturbances. In offshore engineering, due to the mechanical effects, the thrusters of the LNG carrier in the presence of time‐delay should be investigated to guide the design practice. The predictor‐based method is employed to deal with the time delay induced by the control inputs. It is proven that the proposed scheme can guarantee semi‐global uniform ultimate boundedness. The performance and feasibility of the proposed control are further verified by simulation studies.
A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China
Authors: Zhao Xinyue , Wang Jianxiao , Zhang Tiance , Cui Da , Li Gengyin , Zhou Ming 
Published: March 2022

AbstractWith the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short‐term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province‐level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM.
Optimal frequency regulation in an uncertain islanded microgrid: A modified direct search algorithm
Authors: Abolpour Roozbeh , Javanmardi Hamidreza , Dehghani Maryam , Hesamzadeh Mohammad Reza 
Published: March 2022

AbstractThis paper presents a methodology for frequency regulation in a microgrid involving renewable energy sources (RES) using a dynamic controller, which is an output feedback controller (OFC). The parameters of OFC are tuned by searching the design space of the controller. Since the RES model is not exactly known, the uncertain model is derived and the OFC is considered for it. The goal of controller tuning is to find appropriate parameters of the controller such that the norm of frequency deviations, even in presence of uncertainties in the RES parameters is minimized. An algorithm based on searching the controller design space is suggested to find the suitable controller gains. The algorithm assumes the controller parameters lie in a convex space and searches the space systematically such that an appropriate solution is found. The method is proved mathematically and two theorems are mentioned, accordingly. Finally, a simulated model of a RES is utilized for algorithm evaluation and the results demonstrate the algorithm capability in optimal frequency regulation.