bluuebunny/crossref_metadata_2025_split huggingface dataset:
A deep learning-based radiomics approach to predict head and neck tumor regression for adaptive radiotherapy
Authors: Tanaka Shohei , Kadoya Noriyuki , Sugai Yuto , Umeda Mariko , Ishizawa Miyu , Katsuta Yoshiyuki , Ito Kengo , Takeda Ken , Jingu Keiichi 
Published: May 2022

AbstractEarly regression—the regression in tumor volume during the initial phase of radiotherapy (approximately 2 weeks after treatment initiation)—is a common occurrence during radiotherapy. This rapid radiation-induced tumor regression may alter target coordinates, necessitating adaptive radiotherapy (ART). We developed a deep learning-based radiomics (DLR) approach to predict early head and neck tumor regression and thereby facilitate ART. Primary gross tumor volume (GTVp) was monitored in 96 patients and nodal GTV (GTVn) in 79 patients during treatment. All patients underwent two computed tomography (CT) scans: one before the start of radiotherapy for initial planning and one during radiotherapy for boost planning. Patients were assigned to regression and nonregression groups according to their median tumor regression rate (ΔGTV/treatment day from initial to boost CT scan). We input a GTV image into the convolutional neural network model, which was pretrained using natural image datasets, via transfer learning. The deep features were extracted from the last fully connected layer. To clarify the prognostic power of the deep features, machine learning models were trained. The models then predicted the regression and nonregression of GTVp and GTVn and evaluated the predictive performance by 0.632 + bootstrap area under the curve (AUC). Predictive performance for GTVp regression was highest using the InceptionResNetv2 model (mean AUC = 0.75) and that for GTVn was highest using NASNetLarge (mean AUC = 0.73). Both models outperformed the handcrafted radiomics features (mean AUC = 0.63 for GTVp and 0.61 for GTVn) or clinical factors (0.64 and 0.67, respectively). DLR may facilitate ART for improved radiation side-effects and target coverage.
Flexible adjustment of anticipations in human outcome processing
Authors: Habiby Alaoui Selim , Adam-Darqué Alexandra , Schnider Armin 
Published: May 2022

AbstractTo sense whether thoughts refer to current reality or not, a capacity called orbitofrontal reality filtering, depends on an orbitofrontal signal when anticipated outcomes fail to occur. Here, we explored the flexibility and precision of outcome processing in a deterministic reversal learning task. Healthy subjects decided which one of two colored squares hid a target stimulus. Brain activity was measured with high-density electroencephalography. Stimuli resembling, but not identical with, the target stimuli were initially processed like different stimuli from 210 to 250 ms, irrespective of behavioral relevance. From 250 ms on, they were processed according to behavioral relevance: If they required a subsequent switch, they were processed like different stimuli; if they had been declared potential targets, they were treated like true targets. Stimuli requiring a behavioral switch induced strong theta activity in orbitofrontal, ventromedial, and medial temporal regions. The study indicates flexible adaptation of anticipations but precise processing of outcomes, mainly determined by behavioral relevance.
Detecting damaged buildings using real-time crowdsourced images and transfer learning
Authors: Chachra Gaurav , Kong Qingkai , Huang Jim , Korlakunta Srujay , Grannen Jennifer , Robson Alexander , Allen Richard M. 
Published: May 2022

AbstractAfter significant earthquakes, we can see images posted on social media platforms by individuals and media agencies owing to the mass usage of smartphones these days. These images can be utilized to provide information about the shaking damage in the earthquake region both to the public and research community, and potentially to guide rescue work. This paper presents an automated way to extract the damaged buildings images after earthquakes from social media platforms such as Twitter and thus identify the particular user posts containing such images. Using transfer learning and ~ 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and when ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey. Furthermore, to better understand how the model makes decisions, we also implemented the Grad-CAM method to visualize the important regions on the images that facilitate the decision.
Cryopreservation Does Not Affect the Clinical Pregnancy Rate of Blastocysts Derived from Vitrified Oocytes
Authors: Ramadan Hadi , Pakrashi Tarita , Thurman Andrea R. , Pomeroy Kimball O. , Celia Gerard 
Published: May 2022

AbstractVitrified, or “frozen”, donor eggs can either be fertilized and cultured for fresh transfer (group 1), or fertilized and cryopreserved for transfer in a “frozen embryo transfer” cycle (group 2). This study compared the pregnancy rates between the two groups. Frozen donor egg cycles (N = 1213) were analyzed at the World Egg Bank. The outcome studied was clinical pregnancy rate. Cycles included only single embryo transfers (ET) without preimplantation genetic testing (PGT). A total of 600 cycles met the inclusion criteria. Group 1 included 409 cycles and group 2 had 191 cycles. There was no statistical significance in clinical pregnancy rate between the two groups (38.63% vs 32.46%, p = 0.14). Mean embryo age was higher in group 2 (5.1 vs. 5.4 days, p < 0.01). The compounding effect of vitrification when applied to two distinct stages (oocyte and embryo), has not been studied. When comparing the two groups, we found no difference in pregnancy rate. However, there was a trend towards fewer pregnancies in group 2. A larger study should be done to determine the validity of this result (Ramadan et al. in Fertil Steril, 2020).
Lasiodiplodia theobromae as a causal pathogen of leaf blight, stem canker, and pod rot of Theobroma cacao in Malaysia
Authors: Huda-Shakirah Abd Rahim , Mohamed Nor Nik Mohd Izham , Zakaria Latiffah , Leong Yin-Hui , Mohd Masratul Hawa 
Published: May 2022

AbstractSymptoms of leaf blight, stem canker, and pod rot were observed onT. cacaoduring a series of samplings conducted in several states of Malaysia from September 2018 to March 2019. The identity of the pathogen that was responsible for the diseases was determined using morphological characteristics, DNA sequences, and phylogenetic analyses of multiple genes, namely, internal transcribed spacer (ITS), elongation translation factor 1-alpha (tef1-α), β-tubulin (tub2), and RNA polymerase subunit II (rpb2). A total of 57 isolates recovered from diseased leaves ofT. cacao(13 isolates), stems (20 isolates), and pods (24 isolates) showed morphological features that resembledLasiodiplodiasp. The identity of the isolates was further determined up to the species level by comparing DNA sequences and phylogenetic analyses of multiple genes. The phylogenetic analysis of the combined dataset of ITS,tef1-α,tub2, andrpb2elucidated that all of the isolates obtained wereLasiodiplodia theobromaeas supported by 97% bootstrap value. The results of pathogenicity tests revealedL. theobromaeas the causal pathogen of leaf blight, stem canker, and pod rot ofT. cacao.
A free customizable tool for easy integration of microfluidics and smartphones
Authors: Schaumburg Federico , Vidocevich Juan P. , Gerlero Gabriel S. , Pujato Nazarena , Macagno Joana , Kler Pablo A. , Berli Claudio L. A. 
Published: May 2022

AbstractThe integration of smartphones and microfluidics is nowadays the best possible route to achieve effective point-of-need testing (PONT), a concept increasingly demanded in the fields of human health, agriculture, food safety, and environmental monitoring. Nevertheless, efforts are still required to integrally seize all the advantages of smartphones, as well as to share the developments in easily adoptable formats. For this purpose, here we present the free platform appuente that was designed for the easy integration of microfluidic chips, smartphones, and the cloud. It includes a mobile app for end users, which provides chip identification and tracking, guidance and control, processing, smart-imaging, result reporting and cloud and Internet of Things (IoT) integration. The platform also includes a web app for PONT developers, to easily customize their mobile apps and manage the data of administered tests. Three application examples were used to validate appuente: a dummy grayscale detector that mimics quantitative colorimetric tests, a root elongation assay for pesticide toxicity assessment, and a lateral flow immunoassay for leptospirosis detection. The platform openly offers fast prototyping of smartphone apps to the wide community of lab-on-a-chip developers, and also serves as a friendly framework for new techniques, IoT integration and further capabilities. Exploiting these advantages will certainly help to enlarge the use of PONT with real-time connectivity in the near future.
Outcome prediction for acute kidney injury among hospitalized children via eXtreme Gradient Boosting algorithm
Authors: Deng Ying-Hao , Luo Xiao-Qin , Yan Ping , Zhang Ning-Ya , Liu Yu , Duan Shao-Bin 
Published: May 2022

AbstractAcute kidney injury (AKI) is common among hospitalized children and is associated with a poor prognosis. The study sought to develop machine learning-based models for predicting adverse outcomes among hospitalized AKI children. We performed a retrospective study of hospitalized AKI patients aged 1 month to 18 years in the Second Xiangya Hospital of Central South University in China from 2015 to 2020. The primary outcomes included major adverse kidney events within 30 days (MAKE30) (death, new renal replacement therapy, and persistent renal dysfunction) and 90-day adverse outcomes (chronic dialysis and death). The state-of-the-art machine learning algorithm, eXtreme Gradient Boosting (XGBoost), and the traditional logistic regression were used to establish prediction models for MAKE30 and 90-day adverse outcomes. The models’ performance was evaluated by split-set test. A total of 1394 pediatric AKI patients were included in the study. The incidence of MAKE30 and 90-day adverse outcomes was 24.1% and 8.1%, respectively. In the test set, the area under the receiver operating characteristic curve (AUC) of the XGBoost model was 0.810 (95% CI 0.763–0.857) for MAKE30 and 0.851 (95% CI 0.785–0.916) for 90-day adverse outcomes, The AUC of the logistic regression model was 0.786 (95% CI 0.731–0.841) for MAKE30 and 0.759 (95% CI 0.654–0.864) for 90-day adverse outcomes. A web-based risk calculator can facilitate the application of the XGBoost models in daily clinical practice. In conclusion, XGBoost showed good performance in predicting MAKE30 and 90-day adverse outcomes, which provided clinicians with useful tools for prognostic assessment in hospitalized AKI children.
Functional IKK/NF-κB signaling in pancreatic stellate cells is essential to prevent autoimmune pancreatitis
Authors: Chan Lap Kwan , Tsesmelis Miltiadis , Gerstenlauer Melanie , Leithäuser Frank , Kleger Alexander , Frick Lukas Daniel , Maier Harald Jacob , Wirth Thomas 
Published: May 2022

AbstractPancreatic stellate cells (PSCs) are resident cells in the exocrine pancreas which contribute to pancreatic fibrogenesis and inflammation. Studies on NF-κB in pancreatitis so far focused mainly on the parenchymal and myeloid compartments. Here we show a protective immunomodulatory function of NF-κB in PSCs. Conditional deletion of NEMO (IKKγ) in PSCs leads to spontaneous pancreatitis with elevated circulating IgM, IgG and antinuclear autoantibodies (ANA) within 18 weeks. When further challenged with caerulein, NEMOΔCol1a2 mice show an exacerbated autoimmune phenotype characterized by increased infiltration of eosinophils, B and T lymphocytes with reduced latency period. Transcriptomic profiling shows that NEMOΔCol1a2 mice display molecular signatures resembling autoimmune pancreatitis patients. Mechanistically, we show that PSCΔNEMO cells produce high levels of CCL24 ex vivo which contributes to eosinophil recruitment, as neutralization with a CCL24 antibody abolishes the transwell migration of eosinophils. Our findings uncover an unexpected immunomodulatory role specifically of NF-κB in PSCs during pancreatitis.
Electropolymerization without an electric power supply
Authors: Iwai Suguru , Suzuki Taichi , Sakagami Hiroki , Miyamoto Kazuhiro , Chen Zhenghao , Konishi Mariko , Villani Elena , Shida Naoki , Tomita Ikuyoshi , Inagi Shinsuke 
Published: May 2022

AbstractElectrifying synthesis is now a common slogan among synthetic chemists. In addition to the conventional two- or three-electrode systems that use batch-type cells, recent progress in organic electrochemical processes has been significant, including microflow electrochemical reactors, Li-ion battery-like technology, and bipolar electrochemistry. Herein we demonstrate an advanced electrosynthesis method without the application of electric power based on the concept of streaming potential-driven bipolar electrochemistry. As a proof-of-concept study, the electrochemical oxidative polymerization of aromatic monomers successfully yielded the corresponding polymer films on an electrode surface, which acted as an anode under the flow of electrolyte in a microchannel without an electric power supply.
Group mixing drives inequality in face-to-face gatherings
Authors: Oliveira Marcos , Karimi Fariba , Zens Maria , Schaible Johann , Génois Mathieu , Strohmaier Markus 
Published: May 2022

AbstractUncovering how inequality emerges from human interaction is imperative for just societies. Here we show that the way social groups interact in face-to-face situations can enable the emergence of disparities in the visibility of social groups. These disparities translate into members of specific social groups having fewer social ties than the average (i.e., degree inequality). We characterize group degree inequality in sensor-based data sets and present a mechanism that explains these disparities as the result of group mixing and group-size imbalance. We investigate how group sizes affect this inequality, thereby uncovering the critical size and mixing conditions in which a critical minority group emerges. If a minority group is larger than this critical size, it can be a well-connected, cohesive group; if it is smaller, minority cohesion widens inequality. Finally, we expose group under-representation in degree rankings due to mixing dynamics and propose a way to reduce such biases.
Climate warming amplified the 2020 record-breaking heatwave in the Antarctic Peninsula
Authors: González-Herrero Sergi , Barriopedro David , Trigo Ricardo M. , López-Bustins Joan Albert , Oliva Marc 
Published: May 2022

AbstractFebruary 2020 was anomalously warm in the Antarctic Peninsula region and registered one of the most intense heatwaves ever recorded in Western Antarctica. The event featured unprecedented regional mean temperature anomalies (+4.5 °C) over the Antarctic Peninsula between 6 and 11 February 2020 and the highest local temperature of the continental Antarctic region. Taking flow analogs of the event from past (1950–1984) and recent (1985–2019) periods of the ERA5 reanalysis, here we quantify the role of recent climate change in the magnitude of this 6-day regional heatwave. Results show that 2020-like heatwaves over the Antarctic Peninsula are now at least ~0.4 °C warmer than in the past period, which represents a ~25% increase in magnitude. Given the observed atmospheric circulation conditions, the probability of experiencing 6-day regional mean anomalies above ~2 °C has increased ten times since 1950–1984. The aggravated severity of the event can be largely ascribed to long-term summer warming of the Antarctic Peninsula rather than recent atmospheric circulation trends.
Immune parameters of HIV susceptibility in the female genital tract before and after penile-vaginal sex
Authors: Mohammadi Avid , Bagherichimeh Sareh , Choi Yoojin , Fazel Azadeh , Tevlin Elizabeth , Huibner Sanja , Good Sara V. , Tharao Wangari , Kaul Rupert 
Published: May 2022

Abstract
Background
In women, most HIV infections are acquired through penile-vaginal sex. Inflammation in the female genital tract (FGT) increases the risk of HIV acquisition and transmission, likely through recruitment of HIV target cells and disruption of epithelial barrier integrity. Although sex may have important immune and epithelial effects, the impact of receptive penile-vaginal sex on the immune correlates of HIV susceptibility in the female genital tract is not well described.

Methods
STI-free heterosexual couples were recruited to the Sex, Couples and Science (SECS) Study, with the serial collection of cervical secretions (CVS), endocervical cytobrushes, blood and semen before and up to 72 h after either condomless (n = 29) or condom-protected (n = 8) penile-vaginal sex. Immune cells were characterized by flow cytometry, and immune factors including cytokines and soluble E-cadherin (sE-cad; a marker of epithelial disruption) were quantified by multiplex immunoassay. Co-primary endpoints were defined as levels of IP-10 and IL-1α, cytokines previously associated with increased HIV susceptibility.

Results
Here we show that cervicovaginal levels of vaginal IP-10, sE-cad and several other cytokines increase rapidly after sex, regardless of condom use. The proportion of endocervical HIV target cells, including Th17 cells, activated T cells, and activated or mature dendritic cells (DCs) also increase, particularly after condomless sex. Although most of these immune changes resolve within 72 h, increases in activated cervical CD4 + T cells and Tcm persist beyond this time.

Conclusions
Penile-vaginal sex induces multiple genital immune changes that may enhance HIV susceptibility during the 72 h post-sex window that is critical for virus acquisition. This has important implications for the mucosal immunopathogenesis of HIV transmission.
Perceptual Learning of Altered Vowel Space Improves Identification of Vowels Produced by Individuals With Dysarthria Secondary to Amyotrophic Lateral Sclerosis
Authors: Olmstead Annie J. , Lee Jimin , Chen Janice 
Published: May 2022

Purpose:
This study examines the efficacy of perceptual training for improving typical listeners' identification of vowels produced by individuals with dysarthria. We examined whether training on a subset of vowels can generalize to (a) untrained vowels and (b) other speakers with similar overall intelligibility.

Method:
Sixty naive listeners completed a pretest/posttest perceptual learning task. In the pretraining test and posttraining test, participants identified nine American English monophthongs produced by two speakers with dysarthria secondary to amyotrophic lateral sclerosis (ALS). In the 20-min training task, a two-alternative forced choice (2AFC) task with feedback trained listeners on a subset of the vowels and speakers presented in the pretraining test.

Results:
Vowel identification accuracy improved overall as a function of training. However, patterns of generalization between speakers and vowel types were not symmetric. Specifically, listeners generalized training from front vowels to back vowels but not vice versa. Likewise, listeners generalized from one speaker to another but not in the opposite direction. Examination of confusion matrices for the pretraining and posttraining revealed complex patterns of vowel-specific improvement.

Conclusions:
This study demonstrates that listeners benefit from a very simple training paradigm targeting vowels. Additionally, error patterns revealed that vowels are both resistant to and responsive to perceptual learning. Implications for future research and clinical training paradigms are discussed.
Development and Evaluation of Pediatric Versions of the Vanderbilt Fatigue Scale for Children With Hearing Loss
Authors: Hornsby Benjamin W. Y. , Camarata Stephen , Cho Sun-Joo , Davis Hilary , McGarrigle Ronan , Bess Fred H. 
Published: May 2022

Purpose:Growing evidence suggests that fatigue associated with listening difficulties is particularly problematic for children with hearing loss (CHL). However, sensitive, reliable, and valid measures of listening-related fatigue do not exist. To address this gap, this article describes the development, psychometric evaluation, and preliminary validation of a suite of scales designed to assess listening-related fatigue in CHL: the pediatric versions of the Vanderbilt Fatigue Scale (VFS-Peds).Method:Test development employed best practices, including operationalizing the construct of listening-related fatigue from the perspective of target respondents (i.e., children, their parents, and teachers). Test items were developed based on input from these groups. Dimensionality was evaluated using exploratory factor analyses (EFAs). Item response theory (IRT) and differential item functioning (DIF) analyses were used to identify high-quality items, which were further evaluated and refined to create the final versions of the VFS-Peds.Results:The VFS-Peds is appropriate for use with children aged 6–17 years and consists of child self-report (VFS-C), parent proxy–report (VFS-P), and teacher proxy–report (VFS-T) scales. EFA of child self-report and teacher proxy data suggested that listening-related fatigue was unidimensional in nature. In contrast, parent data suggested a multidimensional construct, composed of mental (cognitive, social, and emotional) and physical domains. IRT analyses suggested that items were of good quality, with high information and good discriminability. DIF analyses revealed the scales provided a comparable measure of fatigue regardless of the child's gender, age, or hearing status. Test information was acceptable over a wide range of fatigue severities and all scales yielded acceptable reliability and validity.Conclusions:This article describes the development, psychometric evaluation, and validation of the VFS-Peds. Results suggest that the VFS-Peds provide a sensitive, reliable, and valid measure of listening-related fatigue in children that may be appropriate for clinical use. Such scales could be used to identify those children most affected by listening-related fatigue, and given their apparent sensitivity, the scales may also be useful for examining the effectiveness of potential interventions targeting listening-related fatigue in children.Supplemental Material:https://doi.org/10.23641/asha.19836154
Video‐based action recognition using spurious‐3D residual attention networks
Authors: Chen Bo , Tang Hongying , Zhang Zebin , Tong Guanjun , Li Baoqing 
Published: May 2022

AbstractRecently, 3D Convolutional Neural Networks (3D CNNs) have attracted extensive attention in extracting spatial and temporal features in videos for their efficient feature extraction ability. However, it also brings enormous model parameters by training very deep 3D CNNs. Here, a novel network named spurious‐3D Residual Attention Networks (S3D RANs) is proposed for video‐based action recognition, which has the powerful capacity to learn collaborative spatiotemporal features. In particular, by leveraging the merits from 2D Convolutional Neural Networks (2D CNNs) and 3D CNNs, 2D CNNs are applied rather than 3D CNNs on frames of the single view of volumetric videos data to learn temporal motion features directly. Furthermore, view and channel‐wise attention mechanism submodules are employed in the residual unit to learn the importance of each view for action recognition and guide the network to pay more attention to the more useful information for action recognition. Experimental results on UCF‐101, HMDB‐51 datasets demonstrate that our S3D RANs have higher accuracy and lower model complexity than existing works.