Miracle-dz/UniFAR-FTUs huggingface dataset:
Neurophysiological effects of frequency, length, phonological neighborhood density, and iconicity on sign recognition

Current theories on lexical recognition are mostly based on studies from spoken languages or their written forms. Much less is known about the process of lexical recognition in sign languages. This study aims to examine the neural correlates of sign recognition by investigating the effects of lexical frequency, length, phonological neighborhood density, and iconicity during Chinese Sign Language comprehension. Twenty-two deaf signers viewed a set of sign videos that varied in the 4 lexical properties and decided if they referred to animals, while event-related potential responses were recorded. Data were analyzed through linear mixed-effects models with the lexical variables treated as continuous measures. The results showed that frequency modulated ERP amplitude as early as around 200 ms and in the late N400 time frame. Sign length invoked effects throughout the process, starting from 200 ms and pertaining to the last epoch. Neighborhood density effects were also observed early around 200 ms and later on the N400 and late positive complex (LPC). Iconicity produced robust effects on the N400 and LPC amplitude. Lexical frequency, length, and neighborhood density influence the neural dynamics of sign recognition in a similar way as to spoken words. Iconicity can confer a processing advantage due to closer form-meaning mappings. The results indicate that lexical recognition engages some mechanisms that are universal across the signed and spoken modality, but it can also be regulated by modality-specific properties such as the prevalent iconicity in sign languages.
Household Wealth in the National Accounts of Europe, the United States and Japan

This working paper has been presented and discussed at the October 2002 OECD National Accounts Experts Meeting. The objective of the paper is two fold: (1) present comparable results for household financial and non financial assets and liabilities for Europe, the USA and Japan, (2) analyse the change in the composition of household gross and net wealth of these countries and zones between 1995 and 2000. Data is based on official financial accounts sources. However the paper proposes (and estimate the corresponding data) an original classification, better adapted to analysis: pension funds assets are broken down between defined-benefit and defined contribution, life insurance between unit-linked and guaranteed-rate, mutual-funds between equity, bond and mixed. The paper discusses the reliability of the data, including non listed shares and non financial assets. The paper then discusses global and detailed compared trends of household wealth between countries . Ce document de travail a ete presente et discute durant la reunion d’octobre 2002 des experts coptables nationaux de l’OCDE. L’objectif du papier est double : (1) construire et presenter des resultats comparables pour les actifs et passifs des menages financiers et non financiers pour l’Europe, les Etats-Unis et le Japon, (2) analyser les modifications de la composition de la richesse brute et nette des menages de ces pays et zones entre 1995 et 2000. Les donnees sont issues des comptes financiers officiels. Cependant le papier propose (et donne les resultats) une nomenclature originale, mieux adaptee a l’analyse: les actifs dans les fonds de pension sont distingues suivant qu’ils sont a prestations definies ou a cotisation definies, l’assurance-vie est decomposee en « unit-linked » ou « a taux garanti », les OPCVM (ou fonds d’investissement) sont decomposees en actions, obligations ou mixtes. Le document analyse la fiabilite des donnees, en particulier concernant les actions non .
The ecohydrological role of soil texture in a water‐limited ecosystem

Soil texture is a key variable in the coupled relationship between climate, soil, and vegetation. This coupling and its dependence on soil texture are studied in this paper using analytical descriptions for soil moisture dynamics and the corresponding vegetation water stress. Results confirm the importance of soil texture in partitioning rainfall into the mean values of the water balance loss components, namely, evapotranspiration, leakage, and runoff, and in determining vegetation water stress for the vegetation and climatic regimes of the La Copita Research Area in Texas. Two separate mechanisms by which this sensitivity to soil texture can impact the ecological structure at La Copita are illustrated. First, dynamics similar to the inverse texture effect, whereby the optimal soil texture for a given vegetation type changes with rainfall amount, are demonstrated for the two most common species at this site. Second, it is shown that soil texture plays a major role in the modulation of the impact that interannual rainfall fluctuations have on the fitness and coexistence of trees and grasses.
LncRNADisease v3.0: an updated database of long non-coding RNA-associated diseases

Abstract Systematic integration of lncRNA-disease associations is of great importance for further understanding their underlying molecular mechanisms and exploring lncRNA-based biomarkers and therapeutics. The database of long non-coding RNA-associated diseases (LncRNADisease) is designed for the above purpose. Here, an updated version (LncRNADisease v3.0) has curated comprehensive lncRNA (including circRNA) and disease associations from the burgeoning literatures. LncRNADisease v3.0 exhibits an over 2-fold increase in experimentally supported associations, with a total of 25440 entries, compared to the last version. Besides, each lncRNA-disease pair is assigned a confidence score based on experimental evidence. Moreover, all associations between lncRNAs/circRNAs and diseases are classified into general associations and causal associations, representing whether lncRNAs or circRNAs can directly lead to the development or progression of corresponding diseases, with 15721 and 9719 entries, respectively. In a case study, we used the data of LncRNADisease v3.0 to calculate the phenotypic similarity between human and mouse lncRNAs. This database will continue to serve as a valuable resource for potential clinical applications related to lncRNAs and circRNAs. LncRNADisease v3.0 is freely available at http://www.rnanut. net/lncrnadisease.
Cell Block-Based Two-Dimensional and Immunocytochemical Analyses Could Reduce Atypical/Indeterminate Case Frequency in Breast Fine-Needle Aspiration Cytology: A Retrospective Analysis

Introduction: In fine-needle aspiration of the breast (FNAB), the “atypical” category encompasses both benign and malignant lesions, particularly papillary proliferative lesions, as per the latest WHO classification. We aimed to reduce atypical cases and improve diagnostic accuracy by investigating the utility of cell block (CB) analysis. Methods: FNAB CB samples (2018–2022) were reviewed using smear only or CBs. CB-based diagnosis was performed with 2D morphological analysis and immunocytochemistry using ER, CK5/6, p63, SMA, and CD56. Samples were reclassified as “benign,” “atypical,” “suspicious of malignancy,” “malignant,” or “insufficient/inadequate. ” Atypical cases were reexamined. Diagnoses were validated histologically. Results: On examining the FNAB samples (n = 149; 32 atypical), 2D CB sectioning achieved a clearer definition of myoepithelial cells and fibrovascular cores than Papanicolaou staining. Immunocytochemistry was evaluated for 36 cases: estrogen receptor (ER)− and CK5/6+ tumors were reclassified as benign; ER+ and CK5/6− tumors as malignant; p63− tumors as invasive; papillary malignant tumors with a smooth muscle actin (SMA)+ fibrovascular core and p63− myoepithelial cells as encapsulated papillary carcinoma; and CD56+ carcinomas as neuroendocrine carcinoma. Diagnostic rates were as follows: benign (44% FNAB, 51% CB), atypical (21% FNAB, 3% CB), suspicious of malignancy and malignant (28% FNAB, 40% CB), and insufficient/inadequate (7% FNAB, 6% CB). CB achieved >85% sensitivity, specificity, and positive and negative predictive values. Conclusion: CBs represent 3D FNA cell morphology using 2D sections, enabling adaption of pathology criteria to the cytological material. Immunocytochemical staining of CBs can predict low nuclear grade papillary tumors and reduce atypical case frequency, improving diagnostic accuracy.
Atlantic and Pacific sea surface temperatures and corn yields in the southeastern USA: lagged relationships and forecast model development

Sea surface temperatures (SSTs) can provide valuable predictive information for regional climate and agricultural yields in many parts of the world. This study was conducted to identify relationships between Atlantic and Pacific SSTs and corn yields in Alabama, Florida, and Georgia and to use these relationships to develop forecasts that can be used at lead times prior to spring planting. The relationships between seasonal SSTs and detrended county corn yields were analysed using singular value decomposition (SVD) analysis and confirmed using principal component analysis (PCA). Using a Monte Carlo approach, field‐significant results were found with SSTs in the July–September (JAS−1) and October–December (OND−1) seasons in the previous year and with the January–March (JFM) season of the current. A significant portion of the variability of SSTs with corn yield residuals was connected with anomalous heating and cooling associated with the El Niño Southern Oscillation (ENSO). Based on the results found by SVD analysis and confirmed by PCA, indices of spatially averaged SSTs in regions of the North Pacific and Atlantic Oceans were derived. Using these indices along with the Niño3. 4 index to represent tropical Pacific SSTs, cross‐validated multiple linear regression models were developed to predict corn yield residuals using index values in the JAS−1 and OND−1 seasons. Using the cross‐validated models 91.5 and 98.4% of forecasted county corn yield residuals showed predictive skill (based on tercile hit scores) with seasonal index values in the JAS−1 and OND−1 seasons, respectively. The results of the models indicate that the indices of SSTs show significant predictability with corn yield residuals at lead times up to 4–7 months prior to spring planting and are a significant improvement over the use of an index of ENSO alone. Copyright © 2010 Royal Meteorological Society
Towards using pedotransfer functions for estimating infiltration parameters

ABSTRACT Water infiltration into soils is an important component of hydrological processes. Direct measurement of infiltration is time consuming, expensive and often involves large spatial and temporal variability. The objective of this study was to develop and verify parametric pedotransfer functions (PTFs) to predict infiltration parameters. Consequently, 119 double-ring infiltration data were collected. The parameters of Philip, Kostiakov, Kostiakov-Lewis and Horton models were obtained, using the sum of squares error optimization method. Some parametric PTFs were then derived to predict the parameters of the infiltration models, using stepwise regression analysis. The results indicated a reasonable estimation of infiltration parameters by the derived PTFs. These results were more accurate when the land use of the studied area was considered. Overall results of this study suggest infiltration-based PTFs could be established as a reasonable indirect method for estimating infiltration parameters. Editor M.C. Acreman; Associate editor N. Verhoest
Stochastic Precipitation Generation Based on a Multivariate Autoregression Model

Abstract The problem of stochastic precipitation generation has long been of interest. A good generator should produce time series with statistical properties to match those of the real precipitation. Here, a multivariate autoregression model designed to capture the covariance and lag-1 cross-covariance structure of the precipitation measurements is presented. A truncated and power-transformed normal distribution is used to simultaneously model both occurrences and amounts of daily precipitation. The methodology is illustrated using daily rain gauge datasets for three areas in the continental United States.
Adjunctive posterior wall isolation for the treatment of persistent and longstanding persistent atrial fibrillation (CORNERSTONE AF) trial: Design and rationale

Abstract Background A left atrial posterior wall isolation (LAPWI) is one of the atrial fibrillation (AF) ablation strategies. Hypothesis We hypothesized that an additional empirical LAPWI would increase the freedom from recurrent atrial arrhythmias as compared to standard AF ablation in persistent AF patients. Methods The CORNERSTONE AF study is a prospective, randomized, multicenter study investigating patients with AF persisting for >7 days and <3 years undergoing first‐time AF ablation. They will be randomized to pulmonary vein isolation (PVI) or PVI + LAPWI in a 1:1 manner. Although PVI can be performed with either radiofrequency catheters or cryoballoons, only radiofrequency catheters will be permitted to achieve LAPWIs. Additional focal ablation targeting non‐pulmonary vein triggers will be allowed. A total of 516 patients will be enrolled in 17 centers between August 2022 and February 2024 based on the calculation with 80% power, considering the assumption that 65% and 75% of the PVI and PVI + LAPWI group patients will be free from atrial arrhythmia recurrence 18‐months postprocedure (10% of dropout). The primary endpoint is freedom from documented atrial arrhythmias 18 months postsingle procedures. Clinical follow‐up will include 7‐day ambulatory electrocardiograms and routine outpatient consultations by electrophysiologists at 1, 3, 6, 9, 12, and 18 months postprocedure. Results As of August 2023, a total of 331 patients (68 ± 9 years, 270 men, 43 longstanding persistent AF) have been enrolled. Conclusions The CORNERSTONE AF study is a prospective, randomized, multicenter trial designed to evaluate the efficacy and safety of an adjunctive empirical LAPWI following standard AF ablation in persistent AF patients.
Bioavailability and Toxicity Models of Copper to Freshwater Life: The State of Regulatory Science

Efforts to incorporate bioavailability adjustments into regulatory water quality criteria in the United States have included four major procedures: hardness‐based single‐linear regression equations, water‐effect ratios (WERs), biotic ligand models (BLMs), and multiple‐linear regression models (MLRs) that use dissolved organic carbon, hardness, and pH. The performance of each with copper (Cu) is evaluated, emphasizing the relative performance of hardness‐based versus MLR‐based criteria equations. The WER approach was shown to be inherently highly biased. The hardness‐based model is in widest use, and the MLR approach is the US Environmental Protection Agency's (USEPA's) present recommended approach for developing aquatic life criteria for metals. The performance of criteria versions was evaluated with numerous toxicity datasets that were independent of those used to develop the MLR models, including olfactory and behavioral toxicity, and field and ecosystem studies. Within the range of water conditions used to develop the Cu MLR criteria equations, the MLR performed well in terms of predicting toxicity and protecting sensitive species and ecosystems. In soft waters, the MLR outperformed both the BLM and hardness models. In atypical waters with pH <5.5 or >9, neither the MLR nor BLM predictions were reliable, suggesting that site‐specific testing would be needed to determine reliable Cu criteria for such settings. The hardness‐based criteria performed poorly with all toxicity datasets, showing no or weak ability to predict observed toxicity. In natural waters, MLR and BLM criteria versions were strongly correlated. In contrast, the hardness‐criteria version was often out of phase with the MLR and, depending on waterbody and season, could be either strongly overprotective or underprotective. The MLR‐based USEPA‐style chronic criterion appears to be more generally protective of ecosystems than other models. Environ Toxicol Chem 2023;42:2529–2563. © 2023 The Authors. Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.
A Simple Statistical Procedure for Partitioning Soil Test Correlation Data Into Two Classes1

Most soil test laboratories divide soil test results into two or more classes for the purpose of making fertilizer recommendations. This is usually done for the practical reason that it reduces the number of different recommendations necessary. However, the basis for defining the different classes (e.g., Very Low, Low, Medium, etc.) is often subjective or arbitrary. This paper explains a simple, yet statistically sound, method for setting the class limits. The procedure 'is to split the data into two groups, using successive tentative critical levels to ascertain that particular critical level which will maximize overall predictive ability (R), with the means of the two groups (classes) as the predictor values. The paper presents an actual example, using data believed typical of this kind of problem. Several continuous correlation models were also fitted to the same data. 1 Paper no. 3372 of the Journal Series of the North Carolina State Univ. Exp. Sta. , Raleigh, N.C. This study was supported by the Agency for International Development, Department of State, Washington, D.C. Received Jan. 18, 1971. Approved Mar. 18, 1971. 2 Research Associate, International Soil Fertility Evaluation & Improvement Program, Dept, of Soil Science, and Professor, Dept, of Statistics, North Carolina State Univ., Raleigh, N.C. None gave as high an R as a single Low-High split defined by the suggested procedure. Similar results have been obtained with a majority of 200 sets of soil test correlation data, indicating that the new procedure may be widely applicable. Additional
Pedogenesis of Volcanic Ash Soils in Andean Ecuador

Weathering and development of volcanic ash soils show similar patterns in different regions of the world; however, the specific environmental conditions at a given location result in a unique combination of factors and processes governing soil formation. This research was conducted to study pedogenesis on volcanic slopes in the inter-Andean valley of northern Ecuador. Twelve pedons representing different pedogenic environments were sampled at elevations between 2410 and 4050 m above sea level (asl). In pedons above 3200 m asl, allophane and Al-humus complexes dominate the colloidal fraction, the topsoils are high in organic matter, and the soils classify as Andisols with melanic epipedons. In pedons below 2700 m asl, halloysite is the predominant colloidal constituent, the topsoils contain <1% organic C, and the soils are Inceptisols and Entisols. The pedons at intermediate elevations mark a transition zone, in which allophane and halloysite coexist and the soils generally classify as Andisols with umbric epipedons. This general altitudinal pattern was found to be altered in unstable landscape positions, where andic soil properties have been removed along with the erosion of topsoil material. Virtually the same altitudinal weathering sequence was observed in the 3000-yr-old soils and in buried paleosols, which are considered to be older than 40 000 years. Thus, different time of pedogenesis has not caused marked differences in the composition of the colloidal fraction. Climate is considered the overriding factor responsible for the observed altitudinal differences in soil development by affecting leaching regime and organic matter decomposition.
Unsupervised deep learning registration model for multimodal brain images

Abstract Multimodal image registration is a key for many clinical image‐guided interventions. However, it is a challenging task because of complicated and unknown relationships between different modalities. Currently, deep supervised learning is the state‐of‐theart method at which the registration is conducted in end‐to‐end manner and one‐shot. Therefore, a huge ground‐truth data is required to improve the results of deep neural networks for registration. Moreover, supervised methods may yield models that bias towards annotated structures. Here, to deal with above challenges, an alternative approach is using unsupervised learning models. In this study, we have designed a novel deep unsupervised Convolutional Neural Network (CNN)‐based model based on computer tomography/magnetic resonance (CT/MR) co‐registration of brain images in an affine manner. For this purpose, we created a dataset consisting of 1100 pairs of CT/MR slices from the brain of 110 neuropsychic patients with/without tumor. At the next step, 12 landmarks were selected by a well‐experienced radiologist and annotated on each slice resulting in the computation of series of metrics evaluation, target registration error (TRE), Dice similarity, Hausdorff, and Jaccard coefficients. The proposed method could register the multimodal images with TRE 9.89, Dice similarity 0.79, Hausdorff 7.15, and Jaccard 0.75 that are appreciable for clinical applications. Moreover, the approach registered the images in an acceptable time 203 ms and can be appreciable for clinical usage due to the short registration time and high accuracy. Here, the results illustrated that our proposed method achieved competitive performance against other related approaches from both reasonable computation time and the metrics evaluation.
Fast likelihood computation of epidemic type aftershock‐sequence model

The Epidemic Type Aftershock-Sequences (ETAS) model is a point process representing the temporal activity of earthquakes in a certain geophysical region. A method for fast and accurate computation of the likelihood is suggested for the ETAS model. Double sums of variables in the logarithm of the likelihood function with respect to the earthquake occurrence data is reduce to a single sum by using the recursive relation of the integrands in the Gamma transformation of the modified Omori functions. Then the integral is computed in unusually high accuracy by using the Double Exponential transformation followed by an application of the trapezoidal rule with an equal mesh size. The computing run time is greatly improved to be proportional to the number of recorded earthquakes N compared to the order of N² for the naive computation method. The present numerical procedures put the parameter estimation of the ETAS model to practical use in large number of occurrence records.