bluuebunny/crossref_metadata_2025_split huggingface dataset:
Brain connectivity analysis based classification of obstructive sleep apnea using electroencephalogram signals
Authors: Rajeswari J. , Jagannath M. 
Published: March 2024

AbstractObstructive sleep apnea (OSA) is a disorder which blocks the upper airway during sleep. The severity of OSA will lead heart attack, stroke and end of life. This proposed study explored the classification of OSA and healthy subjects using brain connectivity analysis from electroencephalogram (EEG) signals. Institute of System and Robotics—University of Coimbra (ISRUC) database were used for acquiring 50 EEG signals using 4 channels and noise removal has been accomplished by 50 Hz notch filter. The Institute of System and Robotics—University of Coimbra (ISRUC) database contained 50 EEG signals, with four channels, and a 50 Hz notch filter was applied to remove noise. Wavelet packet decomposition method was performing the segregation of EEG signals into five bands; Gamma (γ), beta (β), alpha (α), theta (θ) and delta (δ). A total of 4 electrode positions were used for the brain connectivity analysis for each EEG band. Pearson correlation method was effectively used for measuring the correlation between healthy and OSA subjects. The nodes and edges were highlighted the connection between brain and subjects. The highest correlation was achieved in delta band of OSA subjects which starts from 0.7331 to 0.9172 respectively. For healthy subjects, the positive correlation achieved was 0.6995. The delta band has been correlated well with brain when compared other bands. It has been noted that the positive correlation well associated with brain in OSA subjects, which classifies OSA from healthy subjects.
New pan-ALK inhibitor-resistant EML4::ALK mutations detected by liquid biopsy in lung cancer patients
Authors: Villa Matteo , Malighetti Federica , Sala Elisa , Sharma Geeta G. , Arosio Giulia , Gemelli Maria , Manfroni Chiara , Fontana Diletta , Cordani Nicoletta , Meneveri Raffaella , Zambon Alfonso , Piazza Rocco , Pagni Fabio , Cortinovis Diego , Mologni Luca 
Published: March 2024

AbstractALK and ROS1 fusions are effectively targeted by tyrosine kinase inhibitors (TKIs), however patients inevitably relapse after an initial response, often due to kinase domain mutations. We investigated circulating DNA from TKI-relapsed NSCLC patients by deep-sequencing. New EML4::ALK substitutions, L1198R, C1237Y and L1196P, were identified in the plasma of NSCLC ALK patients and characterized in a Ba/F3 cell model. Variants C1237Y and L1196P demonstrated pan-inhibitor resistance across 5 clinical and 2 investigational TKIs.
Advance in additive manufacturing of 2D materials at the atomic and close-to-atomic scale
Authors: Chen Yixin , Fang Fengzhou , Zhang Nan 
Published: March 2024

AbstractAtomic and close-to-atomic scale manufacturing (ACSM) has emerged as promising technologies in the manufacturing paradigm. Among various materials, 2D materials have garnered significant attention for ACSM due to their atomic-scale characteristics and physical properties. While chemical vapor deposition (CVD) can be employed to produce high-quality 2D materials, achieving patterning often relies on photolithography techniques, which limit scalability and introduce impurities. To address these challenges, this article serves as a review by focusing on exploring atomic-scale additive manufacturing methods for 2D materials. Several potential techniques are reviewed, including site-selective CVD, area-selective atomic layer deposition, electrodeposition, laser-assisted synthesis, print methods, and atomic layer-aligned stacking. The applications of atomic-scale additive manufacturing in various fields, such as electronics, biosensing, and nanoelectromechanical systems, are discussed. Finally, the future prospects of atomic-scale additive manufacturing for 2D materials based on existing research are delved into.
Building trust in deep learning-based immune response predictors with interpretable explanations
Authors: Borole Piyush , Rajan Ajitha 
Published: March 2024

AbstractThe ability to predict whether a peptide will get presented on Major Histocompatibility Complex (MHC) class I molecules has profound implications in designing vaccines. Numerous deep learning-based predictors for peptide presentation on MHC class I molecules exist with high levels of accuracy. However, these MHC class I predictors are treated as black-box functions, providing little insight into their decision making. To build turst in these predictors, it is crucial to understand the rationale behind their decisions with human-interpretable explanations. We present MHCXAI, eXplainable AI (XAI) techniques to help interpret the outputs from MHC class I predictors in terms of input peptide features. In our experiments, we explain the outputs of four state-of-the-art MHC class I predictors over a large dataset of peptides and MHC alleles. Additionally, we evaluate the reliability of the explanations by comparing against ground truth and checking their robustness. MHCXAI seeks to increase understanding of deep learning-based predictors in the immune response domain and build trust with validated explanations.
Dynamic contrast optical coherence tomography (DyC-OCT) for label-free live cell imaging
Authors: Ren Chao , Hao Senyue , Wang Fei , Matt Abigail , Amaral Marcello Magri , Yang Daniel , Wang Leyao , Zhou Chao 
Published: March 2024

AbstractDynamic contrast optical coherence tomography (DyC-OCT), an emerging imaging method, utilizes fluctuation patterns in OCT signals to enhance contrast, thereby enabling non-invasive label-free volumetric live cell imaging. In this mini review, we explain the core concepts behind DyC-OCT image formation and its system configurations, serving as practical guidance for future DyC-OCT users. Subsequently, we explore its applications in delivering high-quality, contrast-enhanced images of cellular morphology, as well as in monitoring changes in cellular activity/viability assay experiments.
Environmental DNA identifies coastal plant community shift 1,000 years ago in Torrens Island, South Australia
Authors: Foster Nicole R. , Jones Alice R. , Serrano Oscar , Lafratta Anna , Lavery Paul S. , van Dijk Kor-jent , Biffin Ed , Gillanders Bronwyn M. , Young Jennifer , Masque Pere , Gadd Patricia S. , Jacobsen Geraldine E. , Zawadzki Atun , Greene Andria , Waycott Michelle 
Published: March 2024

AbstractAnthropogenic activities are causing detrimental changes to coastal plants– namely seagrass, mangrove, and tidal marshes. Looking beyond recent times to past vegetation dynamics is critical to assess the response and resilience of an environment to change. Here, we develop a high-resolution multi-proxy approach, providing a new evidence base to decipher long-term change in coastal plant communities. Combining targeted environmental DNA analysis with chemical analysis of soils, we reconstructed 4,000 years of change at a temperate wetland on Torrens Island South Australia and identified an ecosystem shift that occurred ~ 1000 years ago. What was once a subtidal seagrass system shifted to an intertidal mangrove environment that persists at this site today. We demonstrate that high-resolution historical changes in coastal vegetation can be attained using these proxies. This approach could be applied to other ecosystems to improve the way we protect, conserve, and restore vegetated ecosystems.
Mental health outcomes, literacy and service provision in low- and middle-income settings: a systematic review of the Democratic Republic of the Congo
Authors: Ngamaba Kayonda Hubert , Lombo Laddy Sedzo , Makopa Israël Kenda , Webber Martin , Liuta Jack M. , Madinga Joule Ntwan , Mampunza Samuel Ma Miezi , Heap Cheyann 
Published: March 2024

AbstractIn the Democratic Republic of the Congo (DRC), the prevalence of mental health issues could be greater than in other low-income and middle-income countries because of major risk factors related to armed conflicts and poverty. Given that mental health is an essential component of health, it is surprising that no systematic evaluation of mental health in the DRC has yet been undertaken. This study aims to undertake the first systematic review of mental health literacy and service provision in the DRC, to bridge this gap and inform those who need to develop an evidence base. This could support policymakers in tackling the issues related to limited mental health systems and service provision in DRC. Following Cochrane and PRISMA guidelines, a systematic (Web of Science, Medline, Public Health, PsycINFO, and Google Scholar) search was conducted (January 2000 and August 2023). Combinations of key blocks of terms were used in the search such as DRC, war zone, mental health, post-traumatic stress disorder (PTSD), anxiety, depression, sexual violence, war trauma, resilience, mental health systems and service provision. We followed additional sources from reference lists of included studies. Screening was completed in two stages: title and abstract search, and full-text screening for relevance and quality. Overall, 50 studies were included in the review; the majority of studies (n = 31) were conducted in the Eastern region of the DRC, a region devastated by war and sexual violence. Different instruments were used to measure participants’ mental health such as the Hopkins Symptoms Checklist (HSCL-25), The Harvard Trauma Questionnaire, Patient Health Questionnaire (PHQ-9); General Anxiety Disorder (GAD-7), and Positive and Negative Symptoms Scale (PANSS). Our study found that wartime sexual violence and extreme poverty are highly traumatic, and cause multiple, long-term mental health difficulties. We found that depression, anxiety, and PTSD were the most common problems in the DRC. Psychosocial interventions such as group therapy, family support, and socio-economic support were effective in reducing anxiety, depression, and PTSD symptoms. This systematic review calls attention to the need to support sexual violence survivors and many other Congolese people affected by traumatic events. This review also highlights the need for validating culturally appropriate measures, and the need for well-designed controlled intervention studies in low-income settings such as the DRC. Better public mental health systems and service provision could help to improve community cohesion, human resilience, and mental wellbeing. There is also an urgent need to address wider social issues such as poverty, stigma, and gender inequality in the DRC.
Opportunistic plant observations reveal spatial and temporal gradients in phenology
Authors: Rzanny Michael , Mäder Patrick , Wittich Hans Christian , Boho David , Wäldchen Jana 
Published: March 2024

AbstractOpportunistic plant records provide a rapidly growing source of spatiotemporal plant observation data. Here, we used such data to explore the question whether they can be used to detect changes in species phenologies. Examining 19 herbaceous and one woody plant species in two consecutive years across Europe, we observed significant shifts in their flowering phenology, being more pronounced for spring-flowering species (6-17 days) compared to summer-flowering species (1-6 days). Moreover, we show that these data are suitable to model large-scale relationships such as “Hopkins’ bioclimatic law” which quantifies the phenological delay with increasing elevation, latitude, and longitude. Here, we observe spatial shifts, ranging from –5 to 50 days per 1000 m elevation to latitudinal shifts ranging from –1 to 4 days per degree northwards, and longitudinal shifts ranging from –1 to 1 day per degree eastwards, depending on the species. Our findings show that the increasing volume of purely opportunistic plant observation data already provides reliable phenological information, and therewith can be used to support global, high-resolution phenology monitoring in the face of ongoing climate change.
Structural basis for RAD18 regulation by MAGEA4 and its implications for RING ubiquitin ligase binding by MAGE family proteins
Authors: Griffith-Jones Simonne , Álvarez Lucía , Mukhopadhyay Urbi , Gharbi Sarah , Rettel Mandy , Adams Michael , Hennig Janosch , Bhogaraju Sagar 
Published: March 2024

AbstractMAGEA4 is a cancer-testis antigen primarily expressed in the testes but aberrantly overexpressed in several cancers. MAGEA4 interacts with the RING ubiquitin ligase RAD18 and activates trans-lesion DNA synthesis (TLS), potentially favouring tumour evolution. Here, we employed NMR and AlphaFold2 (AF) to elucidate the interaction mode between RAD18 and MAGEA4, and reveal that the RAD6-binding domain (R6BD) of RAD18 occupies a groove in the C-terminal winged-helix subdomain of MAGEA4. We found that MAGEA4 partially displaces RAD6 from the RAD18 R6BD and inhibits degradative RAD18 autoubiquitination, which could be countered by a competing peptide of the RAD18 R6BD. AlphaFold2 and cross-linking mass spectrometry (XL-MS) also revealed an evolutionary invariant intramolecular interaction between the catalytic RING and the DNA-binding SAP domains of RAD18, which is essential for PCNA mono-ubiquitination. Using interaction proteomics, we found that another Type-I MAGE, MAGE-C2, interacts with the RING ubiquitin ligase TRIM28 in a manner similar to the MAGEA4/RAD18 complex, suggesting that the MAGEA4 peptide-binding groove also serves as a ligase-binding cleft in other type-I MAGEs. Our data provide new insights into the mechanism and regulation of RAD18-mediated PCNA mono-ubiquitination.
Association between brachial-ankle pulse wave velocity and the risk of new-onset atrial fibrillation: A report from Kailuan prospective cohort study
Authors: Song Wenhua , Song Zongshuang , Zhang Nan , Zhang Meijuan , Tse Gary , Chou Oscar Hou In , Li Guangping , Yan Gan-Xin , Lip Gregory Y. H. , Wu Shouling , Liu Tong 
Published: March 2024

AbstractOne marker of arterial stiffness (AS) is the brachial-ankle pulse wave velocity (baPWV). We aim to investigate the predictive value of baPWV with regard to new-onset atrial fibrillation (AF). All participants without AF from 2010 to 2020 in the Kailuan cohort were included. The primary endpoint was new-onset AF. Participants were categorized into three study groups based on baPWV, with a normal baPWV group as a reference. The predictive value of baPWV was analyzed as a continuous variable. Multivariable Cox proportional hazard regression models were used to investigate the association. A total of 49,872 subjects (mean age: 47.57 years old, 74.2% male) were included with a mean follow-up of 6.17 (3.95–8.46) years. The risk of AF increased as the baseline baPWV increased, whereby the adjusted hazard ratio (aHR) of the borderline AS group and the elevated AS group were 1.82 (95% confidence interval [CI]: 1.18–2.80) and 2.08 (95% CI: 1.31–3.30), respectively. When considered as a continuous variable, each 361 cm/s increase in baseline baPWV, increased the risk of AF by 21.7% (aHR: 1.22; 95% CI: 1.08–1.37). In the subgroup analysis of non-hypertensive patients, the risks of AF were significantly higher in the borderline AS group (aHR: 3.16, 95% CI: 1.74–5.74) and elevated AS group (aHR: 2.26, 95% CI: 1.02–5.05). For patients with elevated BMI, the risk of AF in the elevated AS group was significantly higher (aHR: 1.69, 95% CI: 1.00–2.83). Baseline baPWV was associated with new-onset AF after adjustments. (Trial registration site and registration number are, respectively, http://www.chictr.org.cn/index.aspx and ChiCTR-TNRC-11001489).
An Exploration of Associations Between Self-Care Practices and Clinical Self-Efficacy in Speech-Language Pathology Graduate Students
Authors: Findley Brooke R. 
Published: March 2024

Purpose:
Previous studies have suggested that many speech-language pathology graduate students experience significant levels of stress throughout their programs. If not effectively mitigated, these stressors could negatively impact students' academic and clinical development. Self-care practices are one potential option that could help address the stressors students encounter throughout their graduate programs. The purpose of this research was to explore potential associations between self-care practices and clinical self-efficacy in speech-language pathology graduate students.

Method:
This topic was explored using a nonexperimental research design that included descriptive and correlational analyses. A survey that included demographic questions, the Mindful Self-Care Scale–Standard, and a modified version of the Clinical Self-Efficacy Appraisal Inventory was completed by 68 students who were enrolled in a speech-language pathology graduate program.

Results:
Small positive and significant correlational associations were observed between Mindful Awareness and total Mindful Self-Care Scale–Standard scores and all measured aspects of clinical self-efficacy. The associations between Supportive Relationships and Supportive Structure subscale scores and some aspects of clinical self-efficacy also reached significance. No significant associations were observed between Mindful Relaxation, Physical Care, or Self-Compassion and Purpose subscale scores and any measured domain of clinical self-efficacy.

Conclusion:
While limitations exist due to the exploratory nature of this study, the results provide an initial examination of associations between self-care practices and clinical self-efficacy.
Two‐step attribute reduction for AIoT networks
Authors: Ren Chao , Lyu Gaoxin , Wang Xianmei , Huang Yao , Li Wei , Sun Lei 
Published: March 2024

AbstractThe evolution of Artificial Intelligence of Things (AIoT) pushes connectivity from human‐to‐things and things‐to‐things, to AI‐to‐things, has resulted in more complex physical networks and logical associations. This has driven the demand for Internet of Things (IoT) devices with powerful edge data processing capabilities, leading to exponential growth in device quantity and data generation. However, conventional data preprocessing methods, such as data compression and encoding, often require edge devices to allocate computational resources for decoding. Additionally, some lossy compression methods, like JPEG, may result in the loss of important information, which has negative impact on the AI training. To address these challenges, this paper proposes a two‐step attribute reduction approach, targeting devices and dimensions, to reduce the massive amount of data in the AIoT network while avoiding unnecessary utilization of edge device resources for decoding. The device‐oriented and dimension‐oriented attribute reductions identify important devices and dimensions, respectively, to mitigate the multimodal interference caused by the large‐scale devices in the AIoT network and the curse of dimensionality associated with high‐dimensional AIoT data. Numerical results and analysis show that this approach effectively eliminates redundant devices and numerous dimensions in the AIoT network while maintaining the basic data correlation.
Constructing prediction intervals for circuit board fault detection: A neural network approach using VI Curve
Authors: Pan Qingguo , Zhao Yan , Zhao Zheng , Lin Peng 
Published: March 2024

AbstractThe accuracy and reliability of circuit board fault detection are significantly influenced by the uncertainty inherent in the VI curve. Here, an ensemble neural network is proposed, which combines the neural network‐based prediction interval and ensemble approaches, to improve the accuracy of fault detection using the VI curve. First, a loss function with multiple objectives is formulated by integrating curve fitting and prediction interval. The aim is to achieve the curve fitting between current and voltage while simultaneously determining the optimal upper and lower bounds of the prediction interval. Second, an ensemble approach is employed to reduce model uncertainty and derive the ultimate current predictions and intervals. These predictions and intervals are then used in a comparative approach to automatically detect faults in circuit boards. Finally, the effectiveness of the proposed algorithm in improving the accuracy of fault detection is verified on experimental circuit boards.
Electronic structure, oscillator strength, and rovibrationally resolved photodissociation of 24MgH
Authors: Deng Zhihao , Li Na , Kun Yang Yu , Gong Maomao , Yan Bing , Wu Yong , Guo Wang Jian , Cheng Yongjun , Bin Zhang Song 
Published: March 2024

Aims. A series of high-precision calculations for the electronic structure of MgH have been reported in the past two decades; however, most of them d not include the core-valence correlation and still exhibit distinct differences. Furthermore, the latest high-precision results have not been applied to the studies of photodissociation dynamics. The primary motivations of this paper are to calculate a more precise electronic structure of MgH consering a core-valence correlation and to prove the photodissociation cross-sections.
Methods. The electronic structure of MgH is investigated by multi-reference configuration interaction calculations with Davson correction (MRCI+Q). We performed two different sets of calculations to investigate the core-valence correlation and, as a result, obtained accurate potential energy curves (PECs) and transition dipole moments (TDMs). An extrapolation procedure was also employed to eliminate the error of basis set. Then, the photodissociation cross-sections were calculated using high-precision PECs and TDMs.
Results. The PECs and TDMs of the five lowest doublet electronic states, X2Σ+, B′2Σ+, E2Σ+, A2Π, and C2Π, are obtained from calculations including core-valence correlation, termed as CV-MRCI, while PECs of the ten lowest doublet states and three quartet states are also obtained from NCV-MRCI calculations without core-valence correlation. The spectroscopic constants and band oscillator strengths are also proved with high precision levels. The equilibrium Re and vertical excitation energy Te are only 0.1% different from the measurements. Based on the CV-MRCI results, the rovibrationally resolved photodissociation cross-sections for transitions from X2Σ+ to the other four states, as well as the total local thermodynamic equilibrium cross-sections for temperatures up to 10000 K, are calculated.