bluuebunny/crossref_metadata_2025_split huggingface dataset:
Survival, growth, and functional traits of tropical wet forest tree seedlings across an experimental soil moisture gradient in Puerto Rico
Authors: Matlaga David , Lammerant Roel , Hogan J. Aaron , Uriarte María , Rodriguez‐Valle Celimar , Zimmerman Jess K. , Muscarella Robert 
Published: March 2024

AbstractDroughts are predicted to become more frequent and intense in many tropical regions, which may cause shifts in plant community composition. Especially in diverse tropical communities, understanding how traits mediate demographic responses to drought can help provide insight into the effects of climate change on these ecosystems. To understand tropical tree responses to reduced soil moisture, we grew seedlings of eight species across an experimental soil moisture gradient at the Luquillo Experimental Forest, Puerto Rico. We quantified survival and growth over an 8‐month period and characterized demographic responses in terms of tolerance to low soil moisture—defined as survival and growth rates under low soil moisture conditions—and sensitivity to variation in soil moisture—defined as more pronounced changes in demographic rates across the observed range of soil moisture. We then compared demographic responses with interspecific variation in a suite of 11 (root, stem, and leaf) functional traits, measured on individuals that survived the experiment. Lower soil moisture was associated with reduced survival and growth but traits mediated species‐specific responses. Species with relatively conservative traits (e.g., high leaf mass per area), had higher survival at low soil moisture whereas species with more extensive root systems were more sensitive to soil moisture, in that they exhibited more pronounced changes in growth across the experimental soil moisture gradient. Our results suggest that increasing drought will favor species with more conservative traits that confer greater survival in low soil moisture conditions.
Unclear relationships between mean survival rate and its environmental variance in vertebrates
Authors: Pärt Tomas , Jeppsson Tobias , Paquet Matthieu , Arlt Debora , Laugen Ane T. , Low Matthew , Knape Jonas , Qvarnström Anna , Forslund Pär 
Published: March 2024

AbstractCurrent environmental changes may increase temporal variability of life history traits of species thus affecting their long‐term population growth rate and extinction risk. If there is a general relationship between environmental variances (EVs) and mean annual survival rates of species, that relationship could be used as a guideline for analyses of population growth and extinction risk for populations, where data on EVs are missing. For this purpose, we present a comprehensive compilation of 252 EV estimates from 89 species belonging to five vertebrate taxa (birds, mammals, reptiles, amphibians and fish) covering mean annual survival rates from 0.01 to 0.98. Since variances of survival rates are constrained by their means, particularly for low and high mean survival rates, we assessed whether any observed relationship persisted after applying two types of commonly used variance stabilizing transformations: relativized EVs (observed/mathematical maximum) and logit‐scaled EVs. With raw EVs at the arithmetic scale, mean–variance relationships of annual survival rates were hump‐shaped with small EVs at low and high mean survival rates and higher (and widely variable) EVs at intermediate mean survival rates. When mean annual survival rates were related to relativized EVs the hump‐shaped pattern was less distinct than for raw EVs. When transforming EVs to logit scale the relationship between mean annual survival rates and EVs largely disappeared. The within‐species juvenile‐adult slopes were mainly positive at low (<0.5) and negative at high (>0.5) mean survival rates for raw and relativized variances while these patterns disappeared when EVs were logit transformed. Uncertainties in how to interpret the results of relativized and logit‐scaled EVs, and the observed high variation in EV's for similar mean annual survival rates illustrates that extrapolations of observed EVs and tests of life history drivers of survival–EV relationships need to also acknowledge the large variation in these parameters.
Co‐Catalyzed Peroxidation of Cyclic β‐Dicarbonyls
Authors: Bityukov Oleg V. , Serdyuchenko Pavel Yu. , Vil' Vera A. , Nikishin Gennady I. , He Liang‐Nian , Terent'ev Alexander O. 
Published: March 2024

AbstractA Co‐catalyzed peroxidation of cyclic β‐dicarbonyls (cyclic 1,3‐diketones, 4‐hydroxy‐2(5H)‐furanones and Meldrum's acids) with TBHP has been disclosed. A series of the alkylperoxy derivatives of 4‐hydroxy‐2(5H)‐furanones and Meldrum's acids were synthesized in moderate to good yields (13–86 %). The functionalization of 4‐hydroxy‐2(5H)‐furanones by the tBuOO group was performed with high selectivity in the presence of the cocktail of reactive oxidizing species, including metal and radical intermediates. The key species in the peroxidation process are probably the tert‐butylperoxy radical or its Co(III) complex, which are generated from the Co(II)/TBHP system. Cycle cleavage, which would be expected for cyclic β‐dicarbonyls based on previous reports, was observed to a large extent only for cyclic 1,3‐diketones.
Surface characterization of barium(II)‐selective potentiometric sensor based on a newly synthesized thiosemicarbazone derivative molecule
Authors: Altunoluk Onur Cem , Özbek Oguz , Kalay Erbay , Tokalı Feyzi Sinan , Aslan Osman Nuri 
Published: March 2024

AbstractIn this study, we synthesized a new thiosemicarbazone derivative molecule (5) that exhibits sensor properties. The characterization of the synthesized molecule was performed using spectroscopic methods such as 1H−, 13C–NMR, FT–IR and Q–TOF. PVC‐membrane ion‐selective sensors were prepared in which the synthesized molecule was used as an ionophore. Surface images of the prepared sensors were examined by scanning electron microscope (SEM) technique. The prepared sensors exhibited selectivity towards barium(II) ions. The developed barium(II)‐selective sensor had a low detection limit of 5.54×10−7 M in the concentration range of 1.0×10−1–1.0×10−6 M. The proposed barium(II)‐selective sensor had fast response time, good repeatability and stability, and can be manufactured with low cost. The sensor can detect barium(II) ions in various water samples with very high recoveries.
Ion exchanger‐based electrodes for determination of highly lipophilic physiologically active amines: Peculiarities of functioning and ways for improving analytical characteristics
Authors: Salih Faisal A. , Novakovskii Andrei D. , Zakhvitsevich Veronika A. , Egorov Vladimir V. 
Published: March 2024

AbstractNew experimental data are presented demonstrating a much stronger pH dependence of the potential of electrodes with plasticized ion exchange membranes sensitive to cations of highly lipophilic physiologically active amines than would be expected from protolytic equilibrium in solution. Using an example of a vinpocetine‐sensitive electrode, the effectiveness of the dynamic diffusion model of the boundary potential for describing the effect of pH on the form of the electrode function is demonstrated. A simple equation for estimating the effect of distribution of the molecular form of amine between the solution and the membrane phases on the lower detection limit was obtained in the framework of a stationary diffusion model. It has also been shown that the selectivity of electrodes to cations of tertiary physiologically active amines relative to inorganic ions drastically depends on the basicity of the plasticizer (up to 6 orders of magnitude), and among the commercially available ones, ortho‐nitrophenyl octyl ether is the best. Besides, this plasticizer provides the lowest detection limit (1.7×10−7 M) and the best potential reproducibility (0.06 mV) for vinpocetine‐sensitive electrode.
Forecasting elections from partial information using a Bayesian model for a multinomial sequence of data
Authors: Deb Soudeep , Roy Rishideep , Das Shubhabrata 
Published: March 2024

AbstractPredicting the winner of an election is of importance to multiple stakeholders. To formulate the problem, we consider an independent sequence of categorical data with a finite number of possible outcomes in each. The data is assumed to be observed in batches, each of which is based on a large number of such trials and can be modeled via multinomial distributions. We postulate that the multinomial probabilities of the categories vary randomly depending on batches. The challenge is to predict accurately on cumulative data based on data up to a few batches as early as possible. On the theoretical front, we first derive sufficient conditions of asymptotic normality of the estimates of the multinomial cell probabilities and present corresponding suitable transformations. Then, in a Bayesian framework, we consider hierarchical priors using multivariate normal and inverse Wishart distributions and establish the posterior convergence. The desired inference is arrived at using these results and ensuing Gibbs sampling. The methodology is demonstrated with election data from two different settings—one from India and the other from the United States. Additional insights of the effectiveness of the proposed methodology are attained through a simulation study.
How well do neural signatures of resting‐state EEG detect consciousness? A large‐scale clinical study
Authors: Ma Xiulin , Qi Yu , Xu Chuan , Weng Yijie , Yu Jie , Sun Xuyun , Yu Yamei , Wu Yuehao , Gao Jian , Li Jingqi , Shu Yousheng , Duan Shumin , Luo Benyan , Pan Gang 
Published: March 2024

AbstractThe assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large‐scale clinical resting‐state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG‐based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph‐based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index‐related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.
Optimising brain age estimation through transfer learning: A suite of pre‐trained foundation models for improved performance and generalisability in a clinical setting
Authors: Wood David A. , Townend Matthew , Guilhem Emily , Kafiabadi Sina , Hammam Ahmed , Wei Yiran , Al Busaidi Ayisha , Mazumder Asif , Sasieni Peter , Barker Gareth J. , Ourselin Sebastien , Cole James H. , Booth Thomas C. 
Published: March 2024

AbstractEstimated age from brain MRI data has emerged as a promising biomarker of neurological health. However, the absence of large, diverse, and clinically representative training datasets, along with the complexity of managing heterogeneous MRI data, presents significant barriers to the development of accurate and generalisable models appropriate for clinical use. Here, we present a deep learning framework trained on routine clinical data (N up to 18,890, age range 18–96 years). We trained five separate models for accurate brain age prediction (all with mean absolute error ≤4.0 years, R2 ≥ .86) across five different MRI sequences (T2‐weighted, T2‐FLAIR, T1‐weighted, diffusion‐weighted, and gradient‐recalled echo T2*‐weighted). Our trained models offer dual functionality. First, they have the potential to be directly employed on clinical data. Second, they can be used as foundation models for further refinement to accommodate a range of other MRI sequences (and therefore a range of clinical scenarios which employ such sequences). This adaptation process, enabled by transfer learning, proved effective in our study across a range of MRI sequences and scan orientations, including those which differed considerably from the original training datasets. Crucially, our findings suggest that this approach remains viable even with limited data availability (as low as N = 25 for fine‐tuning), thus broadening the application of brain age estimation to more diverse clinical contexts and patient populations. By making these models publicly available, we aim to provide the scientific community with a versatile toolkit, promoting further research in brain age prediction and related areas.
Comparison of approaches to control for intracranial volume in research on the association of brain volumes with cognitive outcomes
Authors: Wang Jingxuan , Hill‐Jarrett Tanisha , Buto Peter , Pederson Annie , Sims Kendra D. , Zimmerman Scott C. , DeVost Michelle A. , Ferguson Erin , Lacar Benjamin , Yang Yulin , Choi Minhyuk , Caunca Michelle R. , La Joie Renaud , Chen Ruijia , Glymour M. Maria , Ackley Sarah F. 
Published: March 2024

AbstractMost neuroimaging studies linking regional brain volumes with cognition correct for total intracranial volume (ICV), but methods used for this correction differ across studies. It is unknown whether different ICV correction methods yield consistent results. Using a brain‐wide association approach in the MRI substudy of UK Biobank (N = 41,964; mean age = 64.5 years), we used regression models to estimate the associations of 58 regional brain volumetric measures with eight cognitive outcomes, comparing no correction and four ICV correction approaches. Approaches evaluated included: no correction; dividing regional volumes by ICV (proportional approach); including ICV as a covariate in the regression (adjustment approach); and regressing the regional volumes against ICV in different normative samples and using calculated residuals to determine associations (residual approach). We used Spearman‐rank correlations and two consistency measures to quantify the extent to which associations were inconsistent across ICV correction approaches for each possible brain region and cognitive outcome pair across 2320 regression models. When the association between brain volume and cognitive performance was close to null, all approaches produced similar estimates close to the null. When associations between a regional volume and cognitive test were not null, the adjustment and residual approaches typically produced similar estimates, but these estimates were inconsistent with results from the crude and proportional approaches. For example, when using the crude approach, an increase of 0.114 (95% confidence interval [CI]: 0.103–0.125) in fluid intelligence was associated with each unit increase in hippocampal volume. However, when using the adjustment approach, the increase was 0.055 (95% CI: 0.043–0.068), while the proportional approach showed a decrease of −0.025 (95% CI: −0.035 to −0.014). Different commonly used methods to correct for ICV yielded inconsistent results. The proportional method diverges notably from other methods and results were sometimes biologically implausible. A simple regression adjustment for ICV produced biologically plausible associations.
The neuroanatomy of visual extinction following right hemisphere brain damage: Insights from multivariate and Bayesian lesion analyses in acute stroke
Authors: Sperber Christoph , Wiesen Daniel , Karnath Hans‐Otto , de Haan Bianca 
Published: March 2024

AbstractMulti‐target attention, that is, the ability to attend and respond to multiple visual targets presented simultaneously on the horizontal meridian across both visual fields, is essential for everyday real‐world behaviour. Given the close link between the neuropsychological deficit of extinction and attentional limits in healthy subjects, investigating the anatomy that underlies extinction is uniquely capable of providing important insights concerning the anatomy critical for normal multi‐target attention. Previous studies into the brain areas critical for multi‐target attention and its failure in extinction patients have, however, produced heterogeneous results. In the current study, we used multivariate and Bayesian lesion analysis approaches to investigate the anatomical substrate of visual extinction in a large sample of 108 acute right hemisphere stroke patients. The use of acute stroke patient data and multivariate/Bayesian lesion analysis approaches allowed us to address limitations associated with previous studies and so obtain a more complete picture of the functional network associated with visual extinction. Our results demonstrate that the right temporo‐parietal junction (TPJ) is critically associated with visual extinction. The Bayesian lesion analysis additionally implicated the right intraparietal sulcus (IPS), in line with the results of studies in neurologically healthy participants that highlighted the IPS as the area critical for multi‐target attention. Our findings resolve the seemingly conflicting previous findings, and emphasise the urgent need for further research to clarify the precise cognitive role of the right TPJ in multi‐target attention and its failure in extinction patients.
Prior‐guided individualized thalamic parcellation based on local diffusion characteristics
Authors: Gao Chaohong , Wu Xia , Wang Yaping , Li Gang , Ma Liang , Wang Changshuo , Xie Sangma , Chu Congying , Madsen Kristoffer Hougaard , Hou Zhongyu , Fan Lingzhong 
Published: March 2024

AbstractComprising numerous subnuclei, the thalamus intricately interconnects the cortex and subcortex, orchestrating various facets of brain functions. Extracting personalized parcellation patterns for these subnuclei is crucial, as different thalamic nuclei play varying roles in cognition and serve as therapeutic targets for neuromodulation. However, accurately delineating the thalamic nuclei boundary at the individual level is challenging due to intersubject variability. In this study, we proposed a prior‐guided parcellation (PG‐par) method to achieve robust individualized thalamic parcellation based on a central‐boundary prior. We first constructed probabilistic atlas of thalamic nuclei using high‐quality diffusion MRI datasets based on the local diffusion characteristics. Subsequently, high‐probability voxels in the probabilistic atlas were utilized as prior guidance to train unique multiple classification models for each subject based on a multilayer perceptron. Finally, we employed the trained model to predict the parcellation labels for thalamic voxels and construct individualized thalamic parcellation. Through a test–retest assessment, the proposed prior‐guided individualized thalamic parcellation exhibited excellent reproducibility and the capacity to detect individual variability. Compared with group atlas registration and individual clustering parcellation, the proposed PG‐par demonstrated superior parcellation performance under different scanning protocols and clinic settings. Furthermore, the prior‐guided individualized parcellation exhibited better correspondence with the histological staining atlas. The proposed prior‐guided individualized thalamic parcellation method contributes to the personalized modeling of brain parcellation.
Weekly assessment of volumetric and dosimetric changes during volumetric modulated arc therapy of locally advanced head and neck carcinoma: Implications for adaptive radiation therapy—A prospective study
Authors: Sreejeev Aathira T. , Joseph Deepa , Krishnan Ajay S. , Pasricha Rajesh , Gupta Sweety , Ahuja Rachit , Sharma Nidhi , Sikdar Debanjan , Raut Sagar , Sasi Abhijith , Gupta Manoj 
Published: March 2024

AbstractBackgroundChemoradiation in head and neck carcinoma (HNC) shows significant anatomical resulting in erroneous dose deposition in the target or the organ at risk (OAR). Adaptive radiotherapy (ART) can overcome this. Timing of significant target and OAR changes with dosimetric impact; thus, most suitable time and frequency of ART is unclear.MethodsThis dosimetric study used prospective weekly non‐contrast CT scans in 12 HNC patients (78 scans). OARs and TVs were manually contoured after registration with simulation scan. Dose overlay done on each scan without reoptimization. Dosimetric and volumetric variations assessed.ResultsCommonest site was oropharynx. Gross Tumor Volume (GTV) reduced from 47.5 ± 19.2 to 17.8 ± 10.7 cc. Nodal GTV reduced from 15.7 ± 18.8 to 4.7 ± 7.1 cc. Parotid showed mean volume loss of 35%. T stage moderately correlated with GTV regression.ConclusionMaximum GTV changes occurred after 3 weeks. Best time to do single fixed interval ART would be by the end of 3 weeks.
Effect of seasonal freeze–thaw process on spatial and temporal distribution of soil water and its infiltration to recharge groundwater
Authors: Cheng Zhongle , Wang Fugang , Sun Jingwei , Ding Lujiao , Wang Yaohui , Wang He 
Published: March 2024

AbstractClarifying the distribution and dynamics of soil moisture during the freeze–thaw process is crucial for surface ecology and is an objective requirement to investigate the mechanism of changes during the groundwater recharge process in a freeze–thaw zone. Based on the monitoring data of soil moisture and temperature in the Changbai Mountain area, the freeze–thaw process is classified into four periods. This study investigates the hydrothermal migration processes during different periods. The simultaneous heat and water model is used to simulate and analyse the infiltration of soil moisture into groundwater under five precipitation insurance rates. The results are as follows: (1) The smaller the soil depth, the stronger is the correlation between soil temperature and air temperature during the freeze–thaw process. (2) The redistribution of soil moisture before and after freeze–thaw is significantly affected by the soil texture, and soil permeability affects the recharge of soil moisture from the upper region to the lower region during the thawing period. (3) Groundwater receives vertical infiltration recharge mainly during non‐freezing and is supplied by freezing and snowmelt recharge during the stable thawing period. The percentage of soil water infiltration during the stable thawing period in the total annual infiltration increases gradually with the precipitation insurance rate.