bluuebunny/crossref_metadata_2025_split huggingface dataset:
VINNA for neonates: Orientation independence through latent
augmentations
Authors: Henschel Leonie , Kügler David , Zöllei Lilla , Reuter Martin 
Published: May 2024

Abstract
A robust, fast, and accurate segmentation of neonatal brain images is highly desired to better understand and detect changes during development and disease, specifically considering the rise in imaging studies for this cohort. Yet, the limited availability of ground truth datasets, lack of standardized acquisition protocols, and wide variations of head positioning in the scanner pose challenges for method development. A few automated image analysis pipelines exist for newborn brain Magnetic Resonance Image (MRI) segmentation, but they often rely on time-consuming non-linear spatial registration procedures and require resampling to a common resolution, subject to loss of information due to interpolation and down-sampling. Without registration and image resampling, variations with respect to head positions and voxel resolutions have to be addressed differently. In deep learning, external augmentations such as rotation, translation, and scaling are traditionally used to artificially expand the representation of spatial variability, which subsequently increases both the training dataset size and robustness. However, these transformations in the image space still require resampling, reducing accuracy specifically in the context of label interpolation. We recently introduced the concept of resolution-independence with the Voxel-size Independent Neural Network framework, VINN. Here, we extend this concept by additionally shifting all rigid-transforms into the network architecture with a four degree of freedom (4-DOF) transform module, enabling resolution-aware internal augmentations (VINNA) for deep learning. In this work, we show that VINNA (i) significantly outperforms state-of-the-art external augmentation approaches, (ii) effectively addresses the head variations present specifically in newborn datasets, and (iii) retains high segmentation accuracy across a range of resolutions (0.5–1.0 mm). Furthermore, the 4-DOF transform module together with internal augmentations is a powerful, general approach to implement spatial augmentation without requiring image or label interpolation. The specific network application to newborns will be made publicly available as VINNA4neonates.
Idiosyncratic patterns of interhemispheric connectivity in the face and scene networks of the human brain
Authors: Quinn Bartholomew P.A. , Watson David M. , Noad Kira , Andrews Timothy J. 
Published: May 2024

Abstract
Neuroimaging studies have revealed a network of regions in both hemispheres of the human brain that respond selectively to faces. Neural models of face processing have typically focused on functional connectivity between regions in the same hemisphere (intrahemispheric), with a particular bias toward the right hemisphere. Here, we explored the role of interhemispheric connectivity using fMRI. We used three datasets to compare functional connectivity, as shown by correlations between the time-courses of neural activity of face regions during different natural viewing paradigms. We found higher correlations of neural activity between corresponding interhemispheric regions (e.g., rFFA–lFFA) than between intrahemispheric regions (e.g., rFFA–rOFA), indicating a bias towards higher interhemispheric than intrahemispheric connectivity. A similar interhemispheric bias was evident in scene-selective regions. In contrast, we did not find an interhemispheric bias in early visual regions (V1–V3), where intrahemispheric connectivity between corresponding regions was generally higher than interhemispheric connectivity. Next, we asked whether the higher interhemispheric connectivity in the face and scene networks between corresponding regions was consistent across participants. We found that the interhemispheric bias was significantly attenuated when we compared the time-course of response across participants. This shows that interhemispheric bias in connectivity between corresponding regions in the face and scene networks is specific to the individual. These findings raise the possibility that idiosyncratic variation in interhemispheric connectivity may explain individual differences in perception.
Neural Networks as Cognitive Models of the Processing of Syntactic Constraints
Authors: Arehalli Suhas , Linzen Tal 
Published: May 2024

Abstract
Languages are governed by syntactic constraints—structural rules that determine which sentences are grammatical in the language. In English, one such constraint is subject-verb agreement, which dictates that the number of a verb must match the number of its corresponding subject: “the dogs run”, but “the dog runs”. While this constraint appears to be simple, in practice speakers make agreement errors, particularly when a noun phrase near the verb differs in number from the subject (for example, a speaker might produce the ungrammatical sentence “the key to the cabinets are rusty”). This phenomenon, referred to as agreement attraction, is sensitive to a wide range of properties of the sentence; no single existing model is able to generate predictions for the wide variety of materials studied in the human experimental literature. We explore the viability of neural network language models—broad-coverage systems trained to predict the next word in a corpus—as a framework for addressing this limitation. We analyze the agreement errors made by Long Short-Term Memory (LSTM) networks and compare them to those of humans. The models successfully simulate certain results, such as the so-called number asymmetry and the difference between attraction strength in grammatical and ungrammatical sentences, but failed to simulate others, such as the effect of syntactic distance or notional (conceptual) number. We further evaluate networks trained with explicit syntactic supervision, and find that this form of supervision does not always lead to more human-like syntactic behavior. Finally, we show that the corpus used to train a network significantly affects the pattern of agreement errors produced by the network, and discuss the strengths and limitations of neural networks as a tool for understanding human syntactic processing.
Twenty years of the Open Access movement: A retrospective study on the relationship between publishing strategies and scientific capital of Brazilian researchers in biological science
Authors: Sarzi Deise Schröder , Leta Jacqueline 
Published: May 2024

Abstract
In this study, we aim to investigate the publishing strategies adopted by the Brazilian scientific community and how they are related to the researchers’ scientific capital. The “research productivity” grant (PQ grant) is taken as an indicator of scientific capital: The greater the PQ grant a researcher receives, the greater is their scientific capital. Personal data from 6,993 researchers linked to at least one Brazilian graduate program in biological sciences were obtained through the Sucupira Platform, data on articles published from 2000 to 2019 were retrieved from the Lattes Platform, and the Directory of Open Access Journals was consulted to classify articles as Open Access (OA). Our main findings indicate that subscription-based journals are the most prevalent publishing strategy, but the proportion of OA publications is increasing over time, mainly with article processing charges (APC). We also observed that the lower the level of PQ grant, the higher the share of articles in OA journals. Finally, we observed a growing trend in the percentage of researchers with high and mid-high adherence to OA from all levels of PQ grant, but mainly with APC. Mapping the dynamics of publishing strategies can play an important step towards driving policies oriented to the promotion of OA.
Latent variable modeling of scientific impact: Estimation of the Q model parameters with structural equation models
Authors: Forthmann Boris , Nestler Steffen 
Published: May 2024

Abstract
Statistical modeling of scientific productivity and impact provides insights into bibliometric measures used also to quantify differences between individual scholars. The Q model decomposes the log-transformed impact of a published paper into a researcher capacity parameter and a random luck parameter. These two parameters are then modeled together with the log-transformed number of published papers (i.e., an indicator of productivity) by means of a trivariate normal distribution. In this work we propose a formulation of the Q model that can be estimated as a structural equation model. The Q model as a structural equation model allows us to quantify the reliability of researchers’ Q parameter estimates. It can be extended to incorporate person covariates and multivariate extensions of the Q model could also be estimated. We empirically illustrate our approach to estimate the Q model and also provide openly available code for R and Mplus.
Attrition and the Gender Patenting Gap
Authors: Aneja Abhay , Reshef Oren , Subramani Gauri 
Published: May 2024

Abstract
Women are underrepresented in patenting. In this study, we consider differential responsiveness to rejection as a contributor to the gender gap in invention. Leveraging the prosecution histories of almost one million U.S. patent applications and the quasi-random assignment of applications to examiners, we show that women are 3.6-6.9 percentage points less likely to continue in the application process following an early-stage rejection. Conditional on applying for a patent, male-female disparities in the propensity to abandon applications account for more than half of the overall gender gap in issued patents. We provide suggestive evidence that institutional support can help reduce the attrition gap.
Permanent and Transitory Responses to Capital Gains Taxes: Evidence from a Lifetime Exemption in Canada
Authors: Lavecchia Adam M. , Tazhitdinova Alisa 
Published: May 2024

Abstract
Using panel data on a 20% random sample of Canadian taxpayers, we study behavioral responses to the cancellation of a lifetime capital gains exemption that resulted in increased capital gains taxation for some individuals. We show that the exemption did not change the number of taxpayers reporting positive capital gains, and thus unlikely resulted in increased participation in capital markets. Furthermore, our results suggest that the cancellation increased the long-run capital gains realizations of tax filers with more unused exemption room but had a small, statistically insignificant impact on the capital gains realizations of those with little unused exemption room.
Destruction, Policy, and the Evolving Consequences of Washington, DC's 1968 Civil Disturbance
Authors: Brooks Leah , Rose Jonathan , Veuger Stan 
Published: May 2024

Abstract
We study the aftermath of the 1968 Washington, DC civil disturbance to illuminate the mechanisms that drive urban redevelopment in the presence of low demand and racial tension. Using a within-block identification strategy, we show that destruction caused lots to remain vacant for the next thirty years and only recently converge in terms of structure value. The city acted to preclude for-profit land owners from leaving land vacant until demand conditions improved by purchasing nearly half of all properties in damaged neighborhoods. Despite this and other steps, the city had limited success in speeding up redevelopment.
Critical Values Robust to P-hacking
Authors: McCloskey Adam , Michaillat Pascal 
Published: May 2024

Abstract
P-hacking is prevalent in reality but absent from classical hypothesis-testing theory. We therefore build a model of hypothesis testing that accounts for p-hacking. From the model, we derive critical values such that, if they are used to determine significance, and if p-hacking adjusts to the new significance standards, spurious significant results do not occur more often than intended. Because of p-hacking, such robust critical values are larger than classical critical values. In the model calibrated to medical science, the robust critical value is the classical critical value for the same test statistic but with one fifth of the significance level.
Aid Fragmentation and Corruption
Authors: Child Travers B. , Wright Austin L. , Xiao Yun 
Published: May 2024

Abstract
Aid fragmentation—the simultaneous operation of multiple development agencies in one setting—has long raised concerns about coordination challenges and opportunities for corruption. Leveraging unique data on project delivery in Afghanistan, we present the first microlevel empirical analysis of aid fragmentation. We find that aid delivered by a single donor can significantly reduce corruption. Projects delivered under conditions of aid fragmentation, by contrast, can facilitate corruption. We find evidence for a theoretical mechanism linking infrastructure and physical goods with waste and leakage. Our results clarify the policy losses tied to fragmentation, yielding insights for combating misappropriation of aid.
Simultaneous Selection and Adaptation of Source Data via Four-Level Optimization
Authors: Xie Pengtao , Zhao Xingchen , He Xuehai 
Published: May 2024

Abstract
In many NLP applications, to mitigate data deficiency in a target task, source data is collected to help with target model training. Existing transfer learning methods either select a subset of source examples that are close to the target domain or try to adapt all source examples into the target domain, then use selected or adapted source examples to train the target model. These methods either incur significant information loss or bear the risk that after adaptation, source examples which are originally already in the target domain may be outside the target domain. To address the limitations of these methods, we propose a four-level optimization based framework which simultaneously selects and adapts source data. Our method can automatically identify in-domain and out-of-domain source examples and apply example-specific processing methods: selection for in-domain examples and adaptation for out-of-domain examples. Experiments on various datasets demonstrate the effectiveness of our proposed method.
Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies
Authors: Pan Liangming , Saxon Michael , Xu Wenda , Nathani Deepak , Wang Xinyi , Wang William Yang 
Published: May 2024

Abstract
While large language models (LLMs) have shown remarkable effectiveness in various NLP tasks, they are still prone to issues such as hallucination, unfaithful reasoning, and toxicity. A promising approach to rectify these flaws is correcting LLMs with feedback, where the LLM itself is prompted or guided with feedback to fix problems in its own output. Techniques leveraging automated feedback—either produced by the LLM itself (self-correction) or some external system—are of particular interest as they make LLM-based solutions more practical and deployable with minimal human intervention. This paper provides an exhaustive review of the recent advances in correcting LLMs with automated feedback, categorizing them into training-time, generation-time, and post-hoc approaches. We also identify potential challenges and future directions in this emerging field.
CloudRoots-Amazon22: Integrating Clouds with Photosynthesis by Crossing Scales
Authors: Vilà-Guerau de Arellano J. , Hartogensis O. K. , de Boer H. , Moonen R. , González-Armas R. , Janssens M. , Adnew G. A. , Bonell-Fontás D. J. , Botía S. , Jones S. P. , van Asperen H. , Komiya S. , de Feiter V. S. , Rikkers D. , de Haas S. , Machado L. A. T. , Dias-Junior C. Q. , Giovanelli-Haytzmann G. , Valenti W. I. D. , Figueiredo R. C. , Farias C. S. , Hall D. H. , Mendonça A. C. S. , da Silva F. A. G. , Marton da Silva J. L. , Souza R. , Martins G. , Miller J. N. , Mol W. B. , Heusinkveld B. , van Heerwaarden C. C. , D’Oliveira F. A. F. , Rodrigues Ferreira R. , Gotuzzo R. Acosta , Pugliese G. , Williams J. , Ringsdorf A. , Edtbauer A. , Quesada C. A. , Takeshi Tanaka Portela B. , Gomes Alves E. , Pöhlker C. , Trumbore S. , Lelieveld J. , Röckmann T. 
Published: May 2024

Abstract
How are rain forest photosynthesis and turbulent fluxes influenced by clouds? To what extent are clouds affected by local processes driven by rain forest energy, water, and carbon fluxes? These interrelated questions were the main drivers of the intensive field experiment CloudRoots-Amazon22 which took place at the Amazon Tall Tower Observatory (ATTO)/Campina supersites in the Amazon rain forest during the dry season, in August 2022. CloudRoots-Amazon22 collected observational data to derive cause–effect relationships between processes occurring at the leaf level up to canopy scales in relation to the diurnal evolution of the clear-to-cloudy transition. First, we studied the impact of cloud and canopy radiation perturbations on the subdiurnal variability of stomatal conductance. Stoma opening is larger in the morning, modulated by the cloud optical thickness. Second, we combined 1-Hz frequency measurements of the stable isotopologues of carbon dioxide and water vapor with measurements of turbulence to determine carbon dioxide and water vapor sources and sinks within the canopy. Using scintillometer observations, we inferred 1-min sensible heat flux that responded within minutes to the cloud passages. Third, collocated profiles of state variables and greenhouse gases enabled us to determine the role of clouds in vertical transport. We then inferred, using canopy and upper-atmospheric observations and a parameterization, the cloud cover and cloud mass flux to establish causality between canopy and cloud processes. This shows the need for a comprehensive observational set to improve weather and climate model representations. Our findings contribute to advance our knowledge of the coupling between cloudy boundary layers and primary carbon productivity of the Amazon rain forest.
Identification of Chronic Pancreatitis Associated microRNAs and Genes for the Diagnosis of Pancreatic Cancer
Authors: Gong Jing , Zhang Qinghua , Peng Qimin , Shi Dongling 
Published: May 2024

Objective The timely identification of both malignant and nonmalignant pancreatic lesions has the potential to significantly enhance prognosis and implement risk management strategies across various levels. microRNAs (miRs) and their corresponding targets play a crucial role in the development of pancreatic lesions and can serve as valuable diagnostic and therapeutic targets. The objective of our study was to investigate potential diagnostic markers that can effectively differentiate between malignant and nonmalignant pancreatic lesions. Methods Gene Expression Omnibus (GEO) database with GSE24279 dataset was utilized to screen differentially expressed miRNAs (DEMs). We utilized the TargetScanHuman database to predict the target genes associated with hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p. Furthermore, a cohort comprising healthy individuals (n = 52), chronic pancreatitis (CP; n = 34), and pancreatic adenocarcinoma (PAAD; n = 53) patients was recruited to ascertain the levels of plasma markers. Results We identified 3 miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and 2 proteins (PCDH1 and AMN) as potential diagnostic markers for distinguishing between CP and PAAD. The area under the curve (AUC) values for all markers exceeded .800. Notably, a combination of plasma PCDH1 and AMN demonstrated excellent diagnostic performance (AUC = .921; 95% CI: .866-.977; sensitivity = .792; specificity = .941) in discriminating between CP and PAAD. In addition, the model of hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p yielded an AUC of .928, sensitivity of .830, and specificity of .912, respectively. Conclusion Plasma levels of miRNAs (hsa-miR-150-3p, hsa-miR-150-5p, and hsa-miR-214-3p) and their corresponding targets (PCDH1 and AMN) hold promise as potential biomarkers for predicting PAAD in patients with CP.
Cytomegalovirus infection and disease in systemic lupus erythematosus patients at a high-complexity hospital in southwestern Colombia
Authors: Contreras-Valero Juan Fernando , Ruíz-Ordóñez Ingrid , Pinilla-Monsalve Gabriel D. , Bautista-Vargas Mario , Ocampo-Piraquive Vanessa , Aguirre-Valencia David 
Published: May 2024

Cytomegalovirus (CMV) infection and disease is a condition usually described in immunocompromised patients, but among them, those with connective tissue diseases are poorly represented. Here we present the clinical, laboratory characteristics, management and outcomes of systemic lupus erythematosus (SLE) patients who presented with a CMV infection/disease to a high complexity hospital in southwestern Colombia between 2011 and 2020. 16 SLE patients were found to have a CMV infection. SLE was predominantly characterized by renal involvement (10 patients; 62.50%), and 14 patients (87.5%) were receiving steroids previous to the CMV infection. The entire sample required hospital admission, mainly related to acute kidney injury, and nine patients were admitted to the intensive care unit (ICU). Gastrointestinal organ damage was the most common CMV disease manifestation. All patients received ganciclovir, five of them (31.25%) suffered from septic shock, and seven (43.75%) died. Age ≥38 years and the presence of septic shock at admission were correlated to the mortality outcome. To our knowledge, this is the first publication evaluating SLE patients with CMV infection/disease in a Colombian population.
Relationship of serum and dietary vitamin D with high cardiometabolic risk in Mexican systemic lupus erythematosus patients: A cross-sectional study
Authors: Ruiz-Ballesteros Adolfo I , Betancourt-Núñez Alejandra , Meza-Meza Mónica R , Rivera-Escoto Melissa , Mora-García Paulina E , Pesqueda-Cendejas Karen , Vizmanos Barbara , Parra-Rojas Isela , Campos-López Bertha , Montoya-Buelna Margarita , Cerpa-Cruz Sergio , De la Cruz-Mosso Ulises 
Published: May 2024

Objetive: Serum and dietary vitamin D could influence clinical disease activity and cardiometabolic outcomes in systemic lupus erythematosus (SLE). This study aimed to assess the relationship of serum and dietary vitamin D with cardiometabolic risk in Mexican SLE patients and healthy subjects (HS). Methods: 224 SLE patients and 201 HS were included in this cross-sectional study. Serum calcidiol was measured using a competitive enzyme-linked immunosorbent assay (ELISA). Vitamin D dietary intake was assessed by collecting three 24h food records. Dietary patterns (DPs) were identified using principal component analysis (PCA). Cardiometabolic status was analyzed through biochemical measurements and cardiometabolic indexes. Results: Calcidiol deficiency (<20 ng/mL) was associated with 1.66-fold higher risk of excess weight by body mass index (BMI) (≥25 kg/m2) (p = .02), 2.25-fold higher risk to low high-density lipoprotein-cholesterol (HDL-C) (<40 mg/dL) (p < .001), and 1.74-fold higher risk to high triglycerides (TG) ≥150 mg/dL (p = .02). Inadequate vitamin D dietary intake was associated with 1.92-fold higher risk of presenting non-healthy waist circumference (WC) (>80 cm) (p < .01), 2.05-fold higher risk of android waist to hip ratio (WHR ≥85) (p < .01), and 1.72-fold higher risk to excess weight (p = .02). Non-adherence to a DP rich in vitamin D food sources was associated with higher WC, WHR, triglycerides, and lower high-density lipoprotein-cholesterol (HDL-C); furthermore, in HS, non-adherence to the DP rich in vitamin D food sources provided 2.11-fold higher risk to calcidiol deficiency. In Cconclusion: A pattern of Calcidiol deficiency, inadequate vitamin D dietary intake, and non-adherence to a DP rich in vitamin D food sources was related to high cardiometabolic risk in SLE patients and HS.