bluuebunny/crossref_metadata_2025_split huggingface dataset:
Question-answering system extracts information on injection drug use from clinical notes
Authors: Mahbub Maria , Goethert Ian , Danciu Ioana , Knight Kathryn , Srinivasan Sudarshan , Tamang Suzanne , Rozenberg-Ben-Dror Karine , Solares Hugo , Martins Susana , Trafton Jodie , Begoli Edmon , Peterson Gregory D. 
Published: April 2024

Abstract
Background
Injection drug use (IDU) can increase mortality and morbidity. Therefore, identifying IDU early and initiating harm reduction interventions can benefit individuals at risk. However, extracting IDU behaviors from patients’ electronic health records (EHR) is difficult because there is no other structured data available, such as International Classification of Disease (ICD) codes, and IDU is most often documented in unstructured free-text clinical notes. Although natural language processing can efficiently extract this information from unstructured data, there are no validated tools.

Methods
To address this gap in clinical information, we design a question-answering (QA) framework to extract information on IDU from clinical notes for use in clinical operations. Our framework involves two main steps: (1) generating a gold-standard QA dataset and (2) developing and testing the QA model. We use 2323 clinical notes of 1145 patients curated from the US Department of Veterans Affairs (VA) Corporate Data Warehouse to construct the gold-standard dataset for developing and evaluating the QA model. We also demonstrate the QA model’s ability to extract IDU-related information from temporally out-of-distribution data.

Results
Here, we show that for a strict match between gold-standard and predicted answers, the QA model achieves a 51.65% F1 score. For a relaxed match between the gold-standard and predicted answers, the QA model obtains a 78.03% F1 score, along with 85.38% Precision and 79.02% Recall scores. Moreover, the QA model demonstrates consistent performance when subjected to temporally out-of-distribution data.

Conclusions
Our study introduces a QA framework designed to extract IDU information from clinical notes, aiming to enhance the accurate and efficient detection of people who inject drugs, extract relevant information, and ultimately facilitate informed patient care.
Establishing ecological thresholds and targets for groundwater management
Authors: Rohde Melissa M. , Stella John C. , Singer Michael Bliss , Roberts Dar A. , Caylor Kelly K. , Albano Christine M. 
Published: April 2024

AbstractGroundwater is critical for many ecosystems, yet groundwater requirements for dependent ecosystems are rarely accounted for during water and conservation planning. Here we compile 38 years of Landsat-derived normalized difference vegetation index (NDVI) to evaluate groundwater-dependent vegetation responses to changes in depth to groundwater (DTG) across California. To maximize applicability, we standardized raw NDVI and DTG values using Z scores to identify groundwater thresholds, groundwater targets and map potential drought refugia across a diversity of biomes and local conditions. Groundwater thresholds were analysed for vegetation impacts where ZNDVI dropped below −1. ZDTG thresholds and targets were then evaluated with respect to groundwater-dependent vegetation in different condition classes and rooting depths. ZNDVI scores were applied statewide to identify potential drought refugia supported by groundwater. Our approach provides a simple and robust methodology for water and conservation practitioners to support ecosystem water needs so biodiversity and sustainable water-management goals can be achieved.
Characterizing functional DNA damage and response caused by the combination of CHK1 and WEE1 inhibitors in ovarian and breast cancer models
Authors: Stewart Adam , Song Jiin , Pickard Lisa , Muggiolu Giovanna , Sauvaigo Sylvie , Brandon Alexis De Haven , Raynaud Florence , Banerji Udai 
Published: April 2024

Abstract
Background
We proposed to quantify reduction of functional DNA damage response (DDR) mechanisms caused by the combination of CHK1 and WEE1 inhibitors.

Methods
Survival of cells and tumor growth in-vitro and in-vivo caused by the combination of the CHK1 inhibitor SRA737 and the WEE1 inhibitor adavosertib was studied in OVCAR3 and MDA-MB 436 cells. Functional DNA damage was quantified using in vitro cell free DNA assays.

Results
The combination of SRA737 and adavosertib caused significant reduction of survival of cells and DNA damage in-vitro and growth inhibition in-vivo. Studies using functional DDR assays found significant changes in the functional capacity of OVCAR3 but not MDA-MB 436 cells to repair DNA damage using multiple mechanisms including intra strand cross link repair, nucleotide excision repair, homologous recombination and non-homologous end joining. This study, for the first time provides a mechanistic insight into differences in the reduction in functional capacity of cells to repair DNA when exposed to CHK1 and WEE1 inhibitors.

Conclusion
The combination of the CHK1 inhibitor SRA737 and WEE1 inhibitor adavosertib causes growth inhibition in-vitro and in-vivo, but differential functional inhibition of DDR in the models studied.
Will the promise of translational neuropsychopharmacology research ever deliver? The lion’s roar; the kitten’s purr
Authors: McGinty Jacqueline F. , Arango Victoria , Brady Kathleen T. , Comer Sandra D. , Goldstein Rita Z. , Nestler Eric J. , Stoops William W. , Nader Michael A. 
Published: April 2024

AbstractThe gap between neuropsychopharmacology research claims (the lion’s roar) and effective treatments for neuropsychiatric disorders (the kitten’s purr) persists. However, a pattern of purrs over time may be as important as a loud roar. This perspective pulls together diverse preclinical and clinical voices of major figures in the neuropsychopharmacology research field to address how inter-disciplinary scientific approaches progress from thinking about the brain and its disorders to testing novel hypotheses to implementing treatments that may improve brain health in individuals with neuropsychiatric disorders.
Food Microbiology
Authors: Adams Martin R. , McClure Peter J. , Moss Maurice O. 
Published: April 2024

Looking for a comprehensive textbook that covers the interaction between micro-organisms and food? Spoilage, foodborne illness, and fermentation.
Food Microbiology has been the most popular textbook in this area since it was first published in 1995. Now in its fifth edition, the highly successful authors bring the book right up to date.
Maintaining its general structure and philosophy to encompass modern food microbiology, this new edition provides updated and revised individual chapters and uses new examples to illustrate incidents. It covers the three main aspects of the interaction between micro-organisms and food and the positive and negative features that result. Attention is paid to the illustrations included and there is a discussion on the factors affecting the presence of micro-organisms in foods and their capacity to survive and grow. Finally, recent developments in procedures used to assay and control the microbiological quality of food and protect public health are reported.
Thorough and accessible, this book is designed for students in the biological sciences, biotechnology, and food science, as well as a valuable resource for researchers, teachers, and practising food microbiologists.
Age Impacts Speech-in-Noise Recognition Differently for Nonnative and Native Listeners
Authors: Phillips Ian , Bieber Rebecca E. , Dirks Coral , Grant Ken W. , Brungart Douglas S. 
Published: April 2024

Purpose:
The purpose of this study was to explore potential differences in suprathreshold auditory function among native and nonnative speakers of English as a function of age.

Method:
Retrospective analyses were performed on three large data sets containing suprathreshold auditory tests completed by 5,572 participants who were self-identified native and nonnative speakers of English between the ages of 18–65 years, including a binaural tone detection test, a digit identification test, and a sentence recognition test.

Results:
The analyses show a significant interaction between increasing age and participant group on tests involving speech-based stimuli (digit strings, sentences) but not on the binaural tone detection test. For both speech tests, differences in speech recognition emerged between groups during early adulthood, and increasing age had a more negative impact on word recognition for nonnative compared to native participants. Age-related declines in performance were 2.9 times faster for digit strings and 3.3 times faster for sentences for nonnative participants compared to native participants.

Conclusions:
This set of analyses extends the existing literature by examining interactions between aging and self-identified native English speaker status in several auditory domains in a cohort of adults spanning young adulthood through middle age. The finding that older nonnative English speakers in this age cohort may have greater-than-expected deficits on speech-in-noise perception may have clinical implications on how these individuals should be diagnosed and treated for hearing difficulties.
Orthographic and Semantic Learning During Shared Reading: Investigating Relations to Early Word Reading
Authors: Heintzman Savannah M. , Deacon S. Hélène 
Published: April 2024

Purpose:
Shared reading provides preschool-age children with the opportunity to learn novel, low-frequency words. Abundant empirical evidence demonstrates that children can learn the meanings of such words during shared reading, referred to as “semantic learning.” However, less is known about whether children learn the spellings of words during shared reading, referred to as “orthographic learning,” and whether this learning is related to early word reading. The present study tested relations between individual differences in 4- to 6-year-old children's semantic and, critically, orthographic learning during shared reading and their early word reading skill.

Method:
In an adaptation of the self-teaching paradigm, children listened to a storybook about novel inventions referred to with nonword names. Children then completed orthographic and semantic choice tests, as well as standardized measures of early word reading and phonological awareness.

Results:
Individual differences in orthographic, but not semantic, learning during shared reading were related to early word reading, after controls for age and phonological awareness.

Conclusions:
This study provides a novel test of learning during shared reading, helping to specify the relation between orthographic and semantic learning and early word reading skill. These findings hold implications for theoretical perspectives on relations between learning during shared reading and early word reading, as well as implications for educational practice.

Supplemental Material:

https://doi.org/10.23641/asha.25492765
Causal inference for out‐of‐distribution recognition via sample balancing
Authors: Wang Yuqing , Li Xiangxian , Liu Yannan , Cao Xiao , Meng Xiangxu , Meng Lei 
Published: April 2024

AbstractImage classification algorithms are commonly based on the Independent and Identically Distribution (i.i.d.) assumption, but in practice, the Out‐Of‐Distribution (OOD) problem widely exists, that is, the contexts of images in the model predicting are usually unseen during training. In this case, existing models trained under the i.i.d. assumption are limiting generalisation. Causal inference is an important method to learn the causal associations which are invariant across different environments, thus improving the generalisation ability of the model. However, existing methods usually require partitioning of the environment to learn invariant features, which mostly have imbalance problems due to the lack of constraints. In this paper, we propose a balanced causal learning framework (BCL), starting from how to divide the dataset in a balanced way and the balance of training after the division, which automatically generates fine‐grained balanced data partitions in an unsupervised manner and balances the training difficulty of different classes, thereby enhancing the generalisation ability of models in different environments. Experiments on the OOD datasets NICO and NICO++ demonstrate that BCL achieves stable predictions on OOD data, and we also find that models using BCL focus more accurately on the foreground of images compared with the existing causal inference method, which effectively improves the generalisation ability.
Self‐paced learning long short‐term memory based on intelligent optimization for robust wind power prediction
Authors: Yang Shun , Deng Xiaofei , Song Dongran 
Published: April 2024

AbstractGiven the unpredictable and intermittent nature of wind energy, precise forecasting of wind power is crucial for ensuring the safe and stable operation of power systems. To reduce the influence of noise data on the robustness of wind power prediction, a wind power prediction method is proposed that leverages an enhanced multi‐objective sand cat swarm algorithm (MO‐SCSO) and a self‐paced long short‐term memory network (spLSTM). First, the actual wind power data is processed into time series as input and output. Then, the progressive advantage of self‐paced learning is used to effectively solve the instability caused by noisy data during long short‐term memory network (LSTM) training. Following this, the improved MO‐SCSO is employed to iteratively optimize the hyperparameters of spLSTM. Ultimately, a combined MO‐SCSO‐spLSTM model is constructed for wind power prediction. This model is validated with the data of onshore wind farms in Austria and offshore wind farms in Denmark. The experimental results show that compared with the traditional LSTM prediction method, the proposed method has better prediction accuracy and robustness. Specifically, in the onshore and offshore wind power prediction experiments, the proposed method reduces the minimum MAE by 5.44% and 4.96%, respectively, and reduces the MAE range by 4.45% and 17.21%, respectively, which could be conducive to the safe and stable operation of power system.
Acoustic echo cancellation based on two‐stage BLSTM
Authors: Niu Zhiwei , Ou Shifeng , Song Peng , Gao Ying 
Published: April 2024

AbstractAcoustic echo cancellation (AEC) methods aim to suppress the acoustic coupling for hands‐free speech communication. Traditional AEC works by identifying the acoustic impulse response using adaptive algorithms. With recent research advances, deep learning has become an attractive choice for AEC. This paper introduces a two‐stage bidirectional long short term memory (TS‐BLSTM) framework, incorporating multi‐head self‐attention mechanisms after each BLSTM block. This is aimed at better capturing contextual information and further enhancing ability of the model to handle complex acoustic scenarios. The BLSTM blocks are utilized to aggregate magnitude spectrum information, modelling both time and frequency dependencies. Additionally, dilation convolution is introduced to broaden the range of information in each convolution output. The magnitude decoder estimates a mask for the input, resulting in the generation of an estimated magnitude spectrum for near‐end speech. Experimental results indicate that the proposed method achieves promising outcomes.
3D segmentation denoising technology of millimeter wave human body security imaging
Authors: An Deyue , Zhang Huakun , Tu Hao 
Published: April 2024

AbstractTo meet the demand of noise reduction in millimeter‐wave human body security imaging, this paper proposes a new method for 3D segmentation denoising in millimeter‐wave images. The test results indicate that with the incorporation of 3D segmentation denoising technology, the noise in the background area of millimeter wave images has decreased by approximately 20–40 dB, significantly improving image quality. The detection rate has increased from 90% to 95%, while the false positive rate has decreased from 13% to 5%. This has important practical significance for real‐world applications.
Reordered static layered schedule for 5G low‐density parity‐check codes
Authors: Zhou Yangcan , Wang Zhongfeng 
Published: April 2024

AbstractThe error performance of the 5G low‐density parity‐check codes is significantly impacted by the update order of layers. At present, the row degree (RD) and the number of punctured bits (PBs) are widely used to decide the update order of layers. Here, the authors propose a metric to assess the soft message recovery speed of coded bits and then suggest updating layers with the lowest RD first rather than those with the least PBs first. Besides, a new criterion is introduced to further determine the update order of layers with the same number of PBs and the same RD. The resulting reordered layered schedule yields better error performance and faster convergence speed than the prior arts.
Mixed reality guided root canal therapy
Authors: Li Fangjie , Gao Qingying , Wang Nengyu , Greene Nicholas , Song Tianyu , Dianat Omid , Azimi Ehsan 
Published: April 2024

AbstractRoot canal therapy (RCT) is a widely performed procedure in dentistry, with over 25 million individuals undergoing it annually. This procedure is carried out to address inflammation or infection within the root canal system of affected teeth. However, accurately aligning CT scan information with the patient's tooth has posed challenges, leading to errors in tool positioning and potential negative outcomes. To overcome these challenges, a mixed reality application is developed using an optical see‐through head‐mounted display (OST‐HMD). The application incorporates visual cues, an augmented mirror, and dynamically updated multi‐view CT slices to address depth perception issues and achieve accurate tooth localization, comprehensive canal exploration, and prevention of perforation during RCT. Through the preliminary experimental assessment, significant improvements in the accuracy of the procedure are observed. Specifically, with the system the accuracy in position was improved from 1.4 to 0.4 mm (more than a 70% gain) using an Optical Tracker (NDI) and from 2.8 to 2.4 mm using an HMD, thereby achieving submillimeter accuracy with NDI. 6 participants were enrolled in the user study. The result of the study suggests that the average displacement on the crown plane of 1.27 ± 0.83 cm, an average depth error of 0.90 ± 0.72 cm and an average angular deviation of 1.83 ± 0.83°. Our error analysis further highlights the impact of HMD spatial localization and head motion on the registration and calibration process. Through seamless integration of CT image information with the patient's tooth, our mixed reality application assists dentists in achieving precise tool placement. This advancement in technology has the potential to elevate the quality of root canal procedures, ensuring better accuracy and enhancing overall treatment outcomes.
Combining magneto-hydrostatic constraints with Stokes profile inversions
Authors: Borrero J. M. , Pastor Yabar A. , Ruiz Cobo B. 
Published: April 2024

Context. Inferences of the magnetic field in the solar atmosphere by means of spectropolarimetric inversions (i.e., Stokes inversion codes) yield magnetic fields that are non-solenoidal (∇ ⋅ B ≠ 0). Because of this, results obtained by such methods are sometimes put into question.
Aims. We aim to develop and implement a new technique that, in conjunction with Stokes inversion codes, can retrieve magnetic fields that are simultaneously consistent with observed polarization signals and with the null divergence condition.
Methods. The method used in this work strictly imposes ∇ ⋅ B = 0 by determining the vertical component of the magnetic field (Bz) from the horizontal ones (Bx, By). We implement this technique, which we refer to as solenoidal inversion, into the FIRTEZ Stokes inversion code and apply it to spectropolarimetric observations of a sunspot observed with the Hinode/SP instrument.
Results. We show that the solenoidal inversion retrieves a vertical component of the magnetic field that is consistent in 80% of the analyzed three-dimensional (x, y, z) domain, with the vertical component of the magnetic field inferred from the non-solenoidal inversion. We demonstrate that the solenoidal inversion is capable of a better overall fitting to the observed Stokes vector than the non-solenoidal inversion. In fact, the solenoidal magnetic field fits Stokes V worse, but this is compensated by a better fit to Stokes I. We find a direct correlation between the worsening in the fit to the circular polarization profiles by the solenoidal inversion and the deviations in the inferred Bz with respect to the non-solenoidal inversion. Finally, we also show that the spatial distribution of the electric currents given by ∇ × B does not change significantly after imposing the null divergence condition.
Conclusions. In spite of being physically preferable, solenoidal magnetic fields are topologically very similar in 80% of the analyzed three-dimensional domain to the non-solenoidal fields obtained from spectropolarimetric inversions. These results support the idea that common Stokes inversion techniques fail to reproduce ∇ ⋅ B = 0 mainly as a consequence of the uncertainties in the determination of the individual components of the magnetic field. In the remaining 20% of the analyzed domain, where the Bz inferred by the solenoidal and non-solenoidal inversions disagree, it remains to be proven that the solenoidal inversion is to be preferred because even though the overall fit to the Stokes parameters improves, the fit to Stokes V worsens. It is in these regions where the application of the Stokes inversion constrained by the null divergence condition can yield new insights about the topology of the magnetic field in the solar photosphere.