bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating attributes of a collaborative model of service delivery for hereditary cancer risk assessment
Authors: Cohen Stephanie A. , Nixon Dawn M. 
Published: May 2023

AbstractThe purpose of this nonrandomized study was to compare several attributes of hereditary cancer risk assessment using a collaborative model of service delivery. Arm 1 included patients seen in‐person by a board‐certified genetic counselor (CGC), Arm 2 included high‐complexity triaged patients from distant sites who received telegenetics with a CGC, and Arm 3 included low‐complexity triaged patients from distant sites who had in‐person risk assessment with a locally placed genetic counselor extender (GCE). A total of 152 patients consented and 98 had complete data available for analysis (35 in Arm 1, 33 in Arm 2, and 30 in Arm 3). The three groups were comparable in age, ethnicity, education, employment, and cancer status. There was no significant difference in median wait time or distance traveled to receive care across all three arms. However, if patients in Arms 2 and 3 had to access the CGC in‐person, they would have had to travel significantly further (p < 0.0001). The time spent in a session was significantly longer in Arm 3 with a GCE than with a CGC in‐person or by telegenetics (p < 0.01). There was no difference in the number of essential elements covered in the appointment, change in cancer worry, or appointment satisfaction across all three arms, although the sample size was small. Employing a collaborative model of service delivery with GCEs and telegenetics is feasible, satisfactory to patients and reduces the distance patients travel to access hereditary cancer genetic services.
Relationship Between Microstructural Alterations and Cognitive Decline After Whole‐Brain Radiation Therapy for Brain Metastases: An Exploratory Whole‐Brain MR Analysis Based on Neurite Orientation Dispersion and Density Imaging
Authors: Zhou Weiwen , Xie Xuyun , Hu Jiamiao , Wang Mengjia , Hu Xiao , Shi Liming , Zhou Chen , Sun Xiaonan 
Published: May 2023

BackgroundCognitive impairment frequently occurs in patients with brain metastases (BM) after whole‐brain radiotherapy (WBRT). It is crucial to explore the underlying mechanisms of cognitive impairment in BM patients receiving WBRT.PurposeTo detect brain microstructural alterations in patients after WBRT by neurite orientation dispersion and density imaging (NODDI), and evaluate the performance of microstructural alterations in predicting cognitive impairment.Study TypeProspective.PopulationTwenty‐six patients (seven female; mean age, 60.9 years).Field Strength/Sequence3‐T, multi‐shell diffusion‐weighted single‐shot echo‐planar sequence. Three‐dimensional magnetization‐prepared rapid acquisition with gradient echo sequence.AssessmentMini‐mental state examination (MMSE) evaluations were conducted prior to, following, 1 and 3 months after WBRT. The diffusion data were collected twice, 1 week before and 1 week after WBRT. NODDI analysis was conducted to assess microstructural alterations in whole brain (orientation dispersion index, neurite density index, volume fraction of isotropic water molecules). Reliable change indices (RCI) of MMSE were used to measure cognitive decline. The performance of support vector machine models based on NODDI parameters and clinical features (prednisone usage, tumor volume, etc.) in predicting MMSE‐RCI was evaluated.Statistical TestsPaired t‐test to assess alterations of NODDI measures and MMSE during follow‐up. Statistical significance level of P‐value <0.05.ResultsSignificantly decreased MMSE score was found at 3 months after WBRT. After WBRT, corpus callosum, medial prefrontal cortex, limbic lobe, occipital lobe, parietal lobe, putamen, globus pallidus lentiform, and thalamus demonstrated damage in NODDI parameters. The predicted MMSE‐RCI based on NODDI features was significantly associated with the measured MMSE‐RCI at 1 month (R = 0.573; P = 0.003) and 3 months (R = 0.687; P < 0.0001) after WBRT.Data ConclusionMicrostructural alterations in several brain regions including the middle prefrontal and limbic cortexes were observed in patients with BM following WBRT, which may contribute to subsequent cognitive decline.Evidence Level2Technical EfficacyStage 2
Progressive structural damage in sleep‐related hypermotor epilepsy
Authors: Wan Xinyue , Wang Weina , Wu Xintong , Tan Qiaoyue , Su Xiaorui , Zhang Simin , Yang Xibiao , Li Shuang , Shao Hanbing , Yue Qiang , Gong Qiyong 
Published: May 2023

AbstractThis study aimed to explore the alterations in gray matter volume (GMV) based on high‐resolution structural data and the temporal precedence of structural alterations in patients with sleep‐related hypermotor epilepsy (SHE). After preprocessing of T1 structural images, the voxel‐based morphometry and source‐based morphometry (SBM) methods were applied in 60 SHE patients and 56 healthy controls to analyze the gray matter volumetric alterations. Furthermore, a causal network of structural covariance (CaSCN) was constructed using Granger causality analysis based on structural data of illness duration ordering to assess the causal impact of structural changes in abnormal gray matter regions. The GMVs of SHE patients were widely reduced, mainly in the bilateral cerebellums, fusiform gyri, the right angular gyrus, the right postcentral gyrus, and the left parahippocampal gyrus. In addition to those regions, the results of the SBM analysis also found decreased GMV in the bilateral frontal lobes, precuneus, and supramarginal gyri. The analysis of CaSCN showed that along with disease progression, the cerebellum was the prominent node that tended to affect other brain regions in SHE patients, while the frontal lobe was the transition node and the supramarginal gyrus was the prominent node that may be easily affected by other brain regions. Our study found widely affected regions of decreased GMVs in SHE patients; these regions underlie the morphological basis of epileptic networks, and there is a temporal precedence relationship between them.
Validity of MENARI plus (self‐pulse assessment and clinical scoring) mobile apps for detecting atrial fibrillation in high‐risk population
Authors: Fadlan Muhamad R. , Rizal Ardian , Satrijo Budi , Astiawati Tri , Rohman Mohammad S. , Baskoro Shalahuddin S. 
Published: May 2023

AbstractBackgroundEven before it is clinically diagnosed, atrial fibrillation (AF) can cause a stroke. This study validates self‐pulse assessment and clinical scoring (MENARI Plus) based on android apps.ObjectiveThe aim of this study was to examine the validity of AF screening using MENARI Plus compared with an ECG recording.MethodsWe collected a total of 1385 subjects from high‐risk population according to CHA2DS2‐VASc score ≥2, attending 8 primary care centers (PCCs) in Malang between July 2021 and December 2021. Every participant underwent self‐pulse assessment, and then was evaluated for MENARI Plus Score on android Apps. These cases had been classified as low or high probability for AF (cut‐off score 7). After that, electrocardiography examinations were performed and classified with AF and Sinus Rhythm group.ResultsIn this study, the mean age of these patients was 61.5 ± 6.9 years old. We found that 156/1385 (11%) patients had AF. There were 68/156 (43.5%) new cases of AF. The sensitivity for self‐pulse palpation was 73.1% (95% CI: 68%–76%) and specificity was 68.3% (95% CI: 65%–72%). MENARI Plus had an area under the receiver operating curve (AUC) of 0.86 (95% CI: 0.82–0.89) with sensitivity per measurement occasion was (84%, 95% CI: 82%–88%) and specificity was (87.9%, 95% CI: 82%–90%).ConclusionIn this study, we found that MENARI Plus has high sensitivity and specificity for AF. It is therefore useful for ruling out AF. It may also be a useful screen that can be applied opportunistically for previously undetected AFs.
Dominant modes of precipitation over Africa, and their associated atmospheric circulations from observations
Authors: Lim Kam Sian Kenny T. C. , Dosio Alessandro , Ayugi Brian O. , Hagan Daniel F. T. , Kebacho Laban L. , Ongoma Victor 
Published: May 2023

AbstractThis study uses the empirical orthogonal function (EOF) analysis to identify and describe continental‐scale seasonal (MAM, JJA, SON and DJF) precipitation modes over Africa based on observation (Global Precipitation Climatology Centre; GPCC) and reanalysis (ERA5) data from 1982 to 2014. Using composite analysis, we attempt to identify the atmospheric circulations (wind and relative humidity) associated with each precipitation mode. Precipitation from ERA5 has a good agreement with that of GPCC, as observed in the good spatial congruence in the EOF analysis and precipitation composites. The EOF results for each season show that the loading patterns of Mode 1 (variance >20% for all seasons) match the long‐term mean precipitation distribution. Atmospheric conditions across the continent are primarily driven by the four main high‐pressure systems (Azores, St. Helena, Arabian and Mascarene High) that influence moisture distribution and subsequently modulate the seasonal rain belt distribution. Modes 2 (variance >11% for all seasons) and 3 (variance >10% for all seasons) are deviations of the leading mode associated with smaller‐scale atmospheric systems. However, large‐scale factors still dominate the overall precipitation pattern by modulating the seasonal energy transfer between the hemispheres. The results provide a comprehensive understanding of the continental‐wide African seasonal precipitation modes and the associated atmospheric circulation from observations. The findings can be used as a reference for future work using model data for historical and projection studies.
Estimation of forest above‐ground biomass in Guangxi, China, by integrating forest age and stack learning
Authors: Ju Ting , Liu Bo , Yue Yuemin , Du Hu , Li Qian , Wang Xu , Wang Kelin 
Published: May 2023

AbstractWith the implementation of large‐scale ecological restoration projects, Southwest China has become one of the fastest forest growth areas in the world in terms of vegetation cover and above‐ground biomass (AGB). It is expected to be a potential area for achieving the carbon neutrality target in China. Accurate estimation of forest AGB is becoming an increasingly urgent necessity for carbon neutrality and forest management. However, due to the complex geological background, there is significant uncertainty in estimating forest AGB in the southwestern region, which generally results in underestimating carbon sinks from forest restoration. To address the issue, we propose a method by incorporating forest age information and stack learning technique to estimate forest AGB. Based on remote sensing, forest inventory and in situ forest biomass data, three fundamental methods (Multiple regression, Random forest and Support vector machine) are employed and compared to build the AGB estimation model with vegetation indices, texture feature factors and forest age. Optimal basic models are further enhanced by integration learning to improve the estimation performance and then applied to the study area Guangxi to obtain regional AGB information of different forest types. The results show that: (1) forest age plays a vital role in reducing the uncertainty of AGB estimation. By incorporating forest age information, R2 of AGB estimation is improved by 0.07–0.27 and RMSE is decreased by 16.35%–47.47% for different forest types; (2) with R2 value >0.78, random forest model outperforms support vector machine and multiple linear regression models. Compared with the single optimal model, integration model by stack learning further enhances R2 of estimation by 0.02–0.03 and decreases RMSE by 5.20%–14.89%. (3) The total forest AGB in Guangxi is 988.17 Tg and the average forest AGB level is 73.30 t/ha. Natural broadleaf forest has the highest AGB level (86.75 t/ha), followed by natural coniferous forest (81.19 t/ha), planted coniferous forest (63.23 t/ha) and planted eucalyptus forest (49.71 t/ha). AGB level in karst areas is lower than that in non‐karst areas due to soil and water constraints. The majority of plantation forests in Guangxi is in the early and middle stages of forest succession, with the rapid growth of forest AGB, and has hence significant potential as carbon sinks. This study indicates that stack learning and incorporation of forest age could significantly decrease the uncertainty of forest AGB estimation. Our study helps to provide more accurate AGB information for karst ecological project management and regional carbon neutrality assessment.
Photoresponsive Diarylethene‐Containing Polymers: Recent Advances and Future Challenges
Authors: Jiang Jiawei , Chen Qing , Xu Muhuan , Chen Jian , Wu Si 
Published: May 2023

AbstractPhotoresponsive polymers have attracted increasing interest owing to their potential applications in anticounterfeiting, information encryption, adhesives, etc. Among them, diarylethene (DAE)‐containing polymers are one of the most promising photoresponsive polymers and have unique thermal stability and fatigue resistance compared to azobenzene‐ and spiropyran‐containing polymers. Herein, the design of DAE‐containing polymers based on different types of structures, including main chain polymers, side‐chain polymers, and crosslinked polymers, is introduced. The mechanism and applications of DAE‐containing polymers in anti‐counterfeiting, information encryption, light‐controllable adhesives, and photoinduced healable materials are reviewed. In addition, the remaining challenges of DAE‐containing polymers are also discussed.
Respondent characteristics associated with adherence in a general population ecological momentary assessment study
Authors: Murray Aja , Yang Yi , Zhu Xinxin , Speyer Lydia , Brown Ruth , Eisner Manuel , Ribeaud Denis 
Published: May 2023

AbstractObjectivesEcological momentary assessment (EMA) has seen an explosion in popularity in recent years; however, an improved understanding of how to minimise (selective) non‐adherence is needed.MethodsWe examined a range of respondent characteristics predictors of adherence (defined as the number of EMA surveys completed) in the D2M EMA study. Participants were a sample of n = 255 individuals drawn from the longitudinal z‐proso cohort who completed up to 4 EMA surveys per day for a period of 2 weeks.ResultsIn unadjusted analyses, lower moral shame, lower self‐control, lower levels of self‐injury, and higher levels of aggression, tobacco use, psychopathy, and delinquency were associated with lower adherence. In fully adjusted analyses with predictors selected using lasso, only alcohol use was related to adherence: beer and alcopops to higher adherence and spirits to lower adherence.ConclusionsThese findings provide potential insights into some of the psychological mechanisms that may underlie adherence in EMA. They also point to respondent characteristics for which additional or tailored efforts may be needed to promote adherence.
Assessing within‐subject rates of change of placental MRI diffusion metrics in normal pregnancy
Authors: Cromb Daniel , Slator Paddy J. , De La Fuente Miguel , Price Anthony N. , Rutherford Mary , Egloff Alexia , Counsell Serena J. , Hutter Jana 
Published: May 2023

PurposeStudying placental development informs when development is abnormal. Most placental MRI studies are cross‐sectional and do not study the extent of individual variability throughout pregnancy. We aimed to explore how diffusion MRI measures of placental function and microstructure vary in individual healthy pregnancies throughout gestation.MethodsSeventy‐nine pregnant, low‐risk participants (17 scanned twice and 62 scanned once) were included. T2‐weighted anatomical imaging and a combined multi‐echo spin‐echo diffusion‐weighted sequence were acquired at 3 T. Combined diffusion–relaxometry models were performed using both a ‐ADC and a bicompartmental ‐intravoxel‐incoherent‐motion () model fit.ResultsThere was a significant decline in placental and ADC (both P < 0.01) over gestation. These declines are consistent in individuals for (covariance = −0.47), but not ADC (covariance = −1.04). The model identified a consistent decline in individuals over gestation in from both the perfusing and diffusing placental compartments, but not in ADC values from either. The placental perfusing compartment fraction increased over gestation (P = 0.0017), but this increase was not consistent in individuals (covariance = 2.57).ConclusionWhole placental and ADC values decrease over gestation, although only values showed consistent trends within subjects. There was minimal individual variation in rates of change of values from perfusing and diffusing placental compartments, whereas trends in ADC values from these compartments were less consistent. These findings probably relate to the increased complexity of the bicompartmental model, and differences in how different placental regions evolve at a microstructural level. These placental MRI metrics from low‐risk pregnancies provide a useful benchmark for clinical cohorts.
Quantification of spatially localized MRS by a novel deep learning approach without spectral fitting
Authors: Zhang Yan , Shen Jun 
Published: May 2023

PurposeTo propose a novel end‐to‐end deep learning model to quantify absolute metabolite concentrations from in vivo J‐point resolved spectroscopy (JPRESS) without using spectral fitting.MethodsA novel encoder‐decoder‐style neural network was created, which was trained to predict metabolite concentrations and individual component signals concurrently from 3T JPRESS data in the time domain. The training data set contained 100 000 samples created by spin‐density simulations using experimentally used RF pulses. Concentrations, phase, frequencies, linewidths, and T2 relaxation times in the training data set were varied over a large range with uniform distributions. Random synthesized noise and extraneous signals were added to the data set. Two thousand validation samples were created similarly to the training data set but with mean concentrations close to in vivo values. An in vivo test was conducted with 20 samples acquired from the human brain.ResultsBoth validation and in vivo test results showed that the proposed model successfully predicted metabolite concentrations as well as individual metabolite signals without involving spectral fitting, while extraneous peaks or unregistered signals were filtered out. Compared with the short‐TE spectral fitting by LCModel, the proposed method had the advantage that the undesired correlations between the estimated concentrations and noise levels and between metabolites were eliminated or substantially reduced.ConclusionThe proposed method provides a working deep learning model that directly maps in vivo JPRESS data to metabolite concentrations. Because spectral fitting is not used, the trained model does not depend on the assumptions associated with parameter tuning when applied to in vivo data.
Fabrication and characterization of hexagonal boron nitride/polyester composites to study the effect of filler loading and surface modification for microelectronic applications
Authors: Kushwaha Akash Deep , Patel Brijesh , Khan Irshad Ahmad , Agrawal Alok 
Published: May 2023

AbstractThis work investigates a composite prepared with polyester and hexagonal boron nitride (hBN) in terms of the properties required in the material for microelectronics applications. The influence of filler loading and the surface treatment of hBN with a silane coupling agent (γ‐aminopropyl triethoxy silane) is studied. It has been observed that the silane‐modified hBN improves the compatibility and adhesion with the matrix as verified by SEM images. The experimental investigation reveals that the addition of hBN in polyester unwillingly rises the density, voids content, and water absorption rate. Though, the increment is limited with surface‐modified hBN. The material's mechanical properties improve with filler loading over a particular range of filler loading. The material's thermal conductivity and glass transition temperature increase with hBN content, whereas the coefficient of thermal expansion reduces. Polyester when filled with 40 wt% of the silane‐hBN, the thermal conductivity improves around six times that of unfilled polyester. The dielectric properties are marginally augmented with filler loading whereas, the increase in frequency results in the reduction of dielectric properties. It can be observed from the overall experimental analysis that the developed material can be used in different microelectronic applications.Highlights
A new class of composite is developed with polyester and raw/modified hBN.
Composite with modified hBN shows improved physical/mechanical properties.
Thermal properties of polyester improved appreciably with treated hBN.
The dielectric properties of composites remain well within the range.
The fabricated composites are recommended for microelectronics applications.
Manufacture and rheological behavior of all recycled PET/PP microfibrillar blends
Authors: Kharghanian Mojtaba , Perchicot Romain , Irusta Silvia , Argon Cristina Yus , Leonardi Frederic , Dagreou Sylvie 
Published: May 2023

AbstractFor several years, new plastic bottles made from opaque polyethylene terephtalate (PET) have been on the market. When their waste is mixed to those of recycled clear PET, the obtained material cannot be recycled anymore due to a loss of properties. Moreover, because of an important grade variety, the large number of additives used and poor homogeneity, the processing and recycling of opaque PET appears as a challenge. The way chosen to revalue this recycled opaque PET (r‐OPET) is its microfibrillation in a recycled polypropylene (rPP) matrix. The effects of the concentration of r‐OPET were studied by rheology, supported by scanning electron microscope images and related to the morphology of the mixture. It was demonstrated that (20/80)w r‐OPET/rPP blend was the best ratio to favor microfibrillation, as it offers the appropriate amount of solid filler to obtain a sea‐island morphology.Thereafter, the influence of the temperature of microfibrillation is presented. The thermomechanical curve of the (20/80)w r‐OPET/rPP blend suggests that microfibrillation should be more effective around the Tg of r‐OPET. The optimum temperature was found, where an improvement of rheological and mechanical properties is observed. A Cross model with yield stress is proposed to describe the rheological behavior of this material.
The Clinical Interview for Borderline Personality Disorder for Adolescents (CI‐BOR‐A): Development, acceptability and expert panel evaluation
Authors: Carreiras Diogo , Cunha Marina , Sharp Carla , Castilho Paula 
Published: May 2023

AbstractBorderline personality disorder (BPD) is a severe mental disorder with marked impulsivity, instability, emotional dysregulation and self‐harm. These features tend to develop over time and can be identified in adolescence. Early diagnosis is the first step to prevent the development of these features to a personality disorder. The purpose of this study was to develop the Clinical Interview for BPD for Adolescents (CI‐BOR‐A), a new instrument based on a sound clinical interview for BPD in youth (CI‐BPD). We tested its acceptability with 43 adolescents and its content validity with the quantitative and qualitative evaluation of 23 experts in mental health. The CI‐BOR‐A is a hybrid semi‐structured interview that considers both categorical and dimensional approaches of personality disorders of DSM‐5‐TR, including 16 items, decision tables for diagnosis, and an appendix to explore self‐harm history further. Adolescents accepted the interview, and none refused to complete the assessment. The expert panel considered the interview relevant, clear, accurate and complete. Important feedback was provided in terms of structure and content to improve the CI‐BOR‐A quality. In general, the CI‐BOR‐A is a rigorous interview to assess BPD in adolescents and adds an important contribution to early detection in clinical and community settings.