bluuebunny/crossref_metadata_2025_split huggingface dataset:
Anti-phage defence through inhibition of virion assembly
Authors: Patel Pramalkumar H. , Taylor Véronique L. , Zhang Chi , Getz Landon J. , Fitzpatrick Alexa D. , Davidson Alan R. , Maxwell Karen L. 
Published: February 2024

AbstractBacteria have evolved diverse antiviral defence mechanisms to protect themselves against phage infection. Phages integrated into bacterial chromosomes, known as prophages, also encode defences that protect the bacterial hosts in which they reside. Here, we identify a type of anti-phage defence that interferes with the virion assembly pathway of invading phages. The protein that mediates this defence, which we call Tab (for ‘Tail assembly blocker’), is constitutively expressed from a Pseudomonas aeruginosa prophage. Tab allows the invading phage replication cycle to proceed, but blocks assembly of the phage tail, thus preventing formation of infectious virions. While the infected cell dies through the activity of the replicating phage lysis proteins, there is no release of infectious phage progeny, and the bacterial community is thereby protected from a phage epidemic. Prophages expressing Tab are not inhibited during their own lytic cycle because they express a counter-defence protein that interferes with Tab function. Thus, our work reveals an anti-phage defence that operates by blocking virion assembly, thereby both preventing formation of phage progeny and allowing destruction of the infected cell due to expression of phage lysis genes.
TREX tetramer disruption alters RNA processing necessary for corticogenesis in THOC6 Intellectual Disability Syndrome
Authors: Werren Elizabeth A. , LaForce Geneva R. , Srivastava Anshika , Perillo Delia R. , Li Shaokun , Johnson Katherine , Baris Safa , Berger Brandon , Regan Samantha L. , Pfennig Christian D. , de Munnik Sonja , Pfundt Rolph , Hebbar Malavika , Jimenez-Heredia Raúl , Karakoc-Aydiner Elif , Ozen Ahmet , Dmytrus Jasmin , Krolo Ana , Corning Ken , Prijoles E. J. , Louie Raymond J. , Lebel Robert Roger , Le Thuy-Linh , Amiel Jeanne , Gordon Christopher T. , Boztug Kaan , Girisha Katta M. , Shukla Anju , Bielas Stephanie L. , Schaffer Ashleigh E. 
Published: February 2024

AbstractTHOC6 variants are the genetic basis of autosomal recessive THOC6 Intellectual Disability Syndrome (TIDS). THOC6 is critical for mammalian Transcription Export complex (TREX) tetramer formation, which is composed of four six-subunit THO monomers. The TREX tetramer facilitates mammalian RNA processing, in addition to the nuclear mRNA export functions of the TREX dimer conserved through yeast. Human and mouse TIDS model systems revealed novel THOC6-dependent, species-specific TREX tetramer functions. Germline biallelic Thoc6 loss-of-function (LOF) variants result in mouse embryonic lethality. Biallelic THOC6 LOF variants reduce the binding affinity of ALYREF to THOC5 without affecting the protein expression of TREX members, implicating impaired TREX tetramer formation. Defects in RNA nuclear export functions were not detected in biallelic THOC6 LOF human neural cells. Instead, mis-splicing was detected in human and mouse neural tissue, revealing novel THOC6-mediated TREX coordination of mRNA processing. We demonstrate that THOC6 is required for key signaling pathways known to regulate the transition from proliferative to neurogenic divisions during human corticogenesis. Together, these findings implicate altered RNA processing in the developmental biology of TIDS neuropathology.
Ethnicity data resource in population-wide health records: completeness, coverage and granularity of diversity
Authors: Pineda-Moncusí Marta , Allery Freya , Delmestri Antonella , Bolton Thomas , Nolan John , Thygesen Johan H. , Handy Alex , Banerjee Amitava , Denaxas Spiros , Tomlinson Christopher , Denniston Alastair K. , Sudlow Cathie , Akbari Ashley , Wood Angela , Collins Gary S. , Petersen Irene , Coates Laura C. , Khunti Kamlesh , Prieto-sAlhambra Daniel , Khalid Sara ,   on behalf of the CVD-COVID-UK/COVID-IMPACT Consortium
Published: February 2024

AbstractIntersectional social determinants including ethnicity are vital in health research. We curated a population-wide data resource of self-identified ethnicity data from over 60 million individuals in England primary care, linking it to hospital records. We assessed ethnicity data in terms of completeness, consistency, and granularity and found one in ten individuals do not have ethnicity information recorded in primary care. By linking to hospital records, ethnicity data were completed for 94% of individuals. By reconciling SNOMED-CT concepts and census-level categories into a consistent hierarchy, we organised more than 250 ethnicity sub-groups including and beyond “White”, “Black”, “Asian”, “Mixed” and “Other, and found them to be distributed in proportions similar to the general population. This large observational dataset presents an algorithmic hierarchy to represent self-identified ethnicity data collected across heterogeneous healthcare settings. Accurate and easily accessible ethnicity data can lead to a better understanding of population diversity, which is important to address disparities and influence policy recommendations that can translate into better, fairer health for all.
Exploring the effects of competition and predation on the success of biological invasion through mathematical modeling
Authors: Rivera-Estay Viviana , Córdova-Lepe Fernando , Moreno-Gómez Felipe N. , Benitez Hugo , Gutiérrez Rodrigo 
Published: February 2024

AbstractBiological invasions are a major cause of species extinction and biodiversity loss. Exotic predators are the type of introduced species that have the greatest negative impact, causing the extinction of hundreds of native species. Despite this, they continue to be intentionally introduced by humans. Understanding the causes that determine the success of these invasions is a challenge within the field of invasion biology. Mathematical models play a crucial role in understanding and predicting the behavior of exotic species in different ecosystems. This study examines the effect of predation and competition on the invasion success of an exotic generalist predator in a native predator-prey system. Considering that the exotic predator both consumes the native prey and competes with the native predator, it is necessary to study the interplay between predation and competition, as one of these interspecific interactions may either counteract or contribute to the impact of the other on the success of a biological invasion. Through a mathematical model, represented by a system of ordinary differential equations, it is possible to describe four different scenarios upon the arrival of the exotic predator in a native predator-prey system. The conditions for each of these scenarios are described analytically and numerically. The numerical simulations are performed considering the American mink (Mustela vison), an invasive generalist predator. The results highlight the importance of considering the interplay between interspecific interactions for understanding biological invasion success.
Effect of post-weld heat-treatment and solid-state thermomechanical treatment on the properties of the AA6082 MIG welded joints
Authors: El-Fahhar Hossam Hemdan , Gadallah Elshafey Ahmed , Habba Mohamed I. A. , Seleman Mohamed M. El-Sayed , Ahmed Mohamed M. Z. , Mohamed Abdelkarim Yousif , Fouad Ramy A. 
Published: February 2024

AbstractPost-weld heat treatment (PWHT) and solid-state thermomechanical treatment (TMT) via friction stir processing (FSP) have been shown to enhance the mechanical properties of aluminum alloys. The current work investigates the effects of PWHT and TMT on the microstructure and mechanical performance of AA6082-T6 welded butt joints welded using the MIG process. The 5 mm thick AA 6082-T6 plates were joined in butt configuration using MIG welding with ER 5356 filler wire, 120 A current, 0.3 mm/s weld speed, and argon shielding gas at 15 L/min flow rate. PWHT was performed on the MIG welds per the T6 temper procedure. TMT was implemented via FSP using a pinless tool rotating at 800 rpm and traversing speed at 200 mm/min with a 3° tilt angle. Microstructural analysis, hardness mapping, tensile testing, and fracture surface evaluation were utilized to characterize the as-welded, PWHT, and TMT samples. The results demonstrate that both PWHT and TMT significantly refine and homogenize the microstructures of the welded joints. However, the TMT samples displayed superior hardness and tensile strength compared to the as-welded and PWHT conditions. The TMT-processed welds achieved approximately 99% joint efficiency versus only 69% and 85% for the as-welded and PWHT samples. In summary, PWHT and especially TMT via FSP are effective at enhancing the mechanical properties of MIG welded AA6082-T6.
Determinants of low birth weight among newborns delivered at Mettu Karl comprehensive specialized hospital, southwest Ethiopia: a case–control study
Authors: Ejeta Chibsa Samuel , Adem Hussen Mustafa , Bayisa Kenbon , Tefera Kefeni Bilisumamulifna 
Published: February 2024

AbstractLow birth weight is a newborn delivered with birth weight of less than 2500 g regardless of gestational age is called. It is a significant issue affecting over 30 million infants worldwide. Thus, the study determine factors associated with low birth weight among newborns delivered at Mettu Karl Comprehensive Specialized Hospital, Southwest Ethiopia. A facility-based case–control study was conducted with 336 newborns (112 cases and 224 controls) from September 12 to December 23, 2022. The study population was newborns with birth weights of 2500 g to 4000 g as controls and newborns with birth weights < 2500 g were cases. Simple random sampling techniques were used to recruit study participants with a ratio of 1 to 3 cases to controls, respectively. Data was collected by interviews and a checklist. Data were entered and analysed using SPSS version 23. Binary and multivariate logistic regression analyses were computed to identify factors associated with low birth weight, a p-value less than 0.05 was used to declare the strength of statistical significance. A total of 327 newborns were contacted, yielding a 97% response rate. MUAC < 23 cm (AOR = 2.72, 95% CI 1.24 to 6.19), inadequate diet diversification (AOR = 4.19, 95% CI 2.04 to 8.60), lack of iron and folic acid supplementation (AOR = 2.94, 95% CI 1.25 to 6.88), history of hypertension (AOR = 2.55, 95% CI 1.09 to 6.00), and lack of nutritional counselling (AOR = 4.63, 95% CI 2.22 to 9.64) were determinants of low birth weight. Low birth weight is linked to residence, maternal MUAC, hypertension history, and ANC visit. Lifestyle modifications, early detection, management, and nutrition information can reduce risk.
Dynamic educational recommender system based on Improved LSTM neural network
Authors: Ahmadian Yazdi Hadis , Seyyed Mahdavi Seyyed Javad , Ahmadian Yazdi Hooman 
Published: February 2024

AbstractNowadays, virtual learning environments have become widespread to avoid time and space constraints and share high-quality learning resources. As a result of human–computer interaction, student behaviors are recorded instantly. This work aims to design an educational recommendation system according to the individual's interests in educational resources. This system is evaluated based on clicking or downloading the source with the help of the user so that the appropriate resources can be suggested to users. In online tutorials, in addition to the problem of choosing the right source, we face the challenge of being aware of diversity in users' preferences and tastes, especially their short-term interests in the near future, at the beginning of a session. We assume that the user's interests consist of two parts: (1) the user's long-term interests, which include the user's constant interests based on the history of the user's dynamic activities, and (2) the user's short-term interests, which indicate the user's current interests. Due to the use of Bilstm networks and their gradual learning feature, the proposed model supports learners' behavioral changes. An average accuracy of 0.9978 and a Loss of 0.0051 offer more appropriate recommendations than similar works.
An adaptive adjacency matrix-based graph convolutional recurrent network for air quality prediction
Authors: Chen Quanchao , Ding Ruyan , Mo Xinyue , Li Huan , Xie Linxuan , Yang Jiayu 
Published: February 2024

AbstractIn recent years, air pollution has become increasingly serious and poses a great threat to human health. Timely and accurate air quality prediction is crucial for air pollution early warning and control. Although data-driven air quality prediction methods are promising, there are still challenges in studying spatial–temporal correlations of air pollutants to design effective predictors. To address this issue, a novel model called adaptive adjacency matrix-based graph convolutional recurrent network (AAMGCRN) is proposed in this study. The model inputs Point of Interest (POI) data and meteorological data into a fully connected neural network to learn the weights of the adjacency matrix thereby constructing the self-ringing adjacency matrix and passes the pollutant data with this matrix as input to the Graph Convolutional Network (GCN) unit. Then, the GCN unit is embedded into LSTM units to learn spatio-temporal dependencies. Furthermore, temporal features are extracted using Long Short-Term Memory network (LSTM). Finally, the outputs of these two components are merged and air quality predictions are generated through a hidden layer. To evaluate the performance of the model, we conducted multi-step predictions for the hourly concentration of PM2.5, PM10 and O3 at Fangshan, Tiantan and Dongsi monitoring stations in Beijing. The experimental results show that our method achieves better predicted effects compared with other baseline models based on deep learning. In general, we designed a novel air quality prediction method and effectively addressed the shortcomings of existing studies in learning the spatio-temporal correlations of air pollutants. This method can provide more accurate air quality predictions and is expected to provide support for public health protection and government environmental decision-making.
Deep learning segmentation of fibrous cap in intravascular optical coherence tomography images
Authors: Lee Juhwan , Kim Justin N. , Dallan Luis A. P. , Zimin Vladislav N. , Hoori Ammar , Hassani Neda S. , Makhlouf Mohamed H. E. , Guagliumi Giulio , Bezerra Hiram G. , Wilson David L. 
Published: February 2024

AbstractThin-cap fibroatheroma (TCFA) is a prominent risk factor for plaque rupture. Intravascular optical coherence tomography (IVOCT) enables identification of fibrous cap (FC), measurement of FC thicknesses, and assessment of plaque vulnerability. We developed a fully-automated deep learning method for FC segmentation. This study included 32,531 images across 227 pullbacks from two registries (TRANSFORM-OCT and UHCMC). Images were semi-automatically labeled using our OCTOPUS with expert editing using established guidelines. We employed preprocessing including guidewire shadow detection, lumen segmentation, pixel-shifting, and Gaussian filtering on raw IVOCT (r,θ) images. Data were augmented in a natural way by changing θ in spiral acquisitions and by changing intensity and noise values. We used a modified SegResNet and comparison networks to segment FCs. We employed transfer learning from our existing much larger, fully-labeled calcification IVOCT dataset to reduce deep-learning training. Postprocessing with a morphological operation enhanced segmentation performance. Overall, our method consistently delivered better FC segmentation results (Dice: 0.837 ± 0.012) than other deep-learning methods. Transfer learning reduced training time by 84% and reduced the need for more training samples. Our method showed a high level of generalizability, evidenced by highly-consistent segmentations across five-fold cross-validation (sensitivity: 85.0 ± 0.3%, Dice: 0.846 ± 0.011) and the held-out test (sensitivity: 84.9%, Dice: 0.816) sets. In addition, we found excellent agreement of FC thickness with ground truth (2.95 ± 20.73 µm), giving clinically insignificant bias. There was excellent reproducibility in pre- and post-stenting pullbacks (average FC angle: 200.9 ± 128.0°/202.0 ± 121.1°). Our fully automated, deep-learning FC segmentation method demonstrated excellent performance, generalizability, and reproducibility on multi-center datasets. It will be useful for multiple research purposes and potentially for planning stent deployments that avoid placing a stent edge over an FC.
Machine learning using clinical data at baseline predicts the medium-term efficacy of ustekinumab in patients with ulcerative colitis
Authors: Morikubo Hiromu , Tojima Ryuta , Maeda Tsubasa , Matsuoka Katsuyoshi , Matsuura Minoru , Miyoshi Jun , Tamura Satoshi , Hisamatsu Tadakazu 
Published: February 2024

AbstractPredicting the therapeutic response to biologics before administration is a key clinical challenge in ulcerative colitis (UC). We previously reported a model for predicting the efficacy of vedolizumab (VDZ) for UC using a machine-learning approach. Ustekinumab (UST) is now available for treating UC, but no model for predicting its efficacy has been developed. When applied to patients with UC treated with UST, our VDZ prediction model showed positive predictive value (PPV) of 56.3% and negative predictive value (NPV) of 62.5%. Given this limited predictive ability, we aimed to develop a UST-specific prediction model with clinical features at baseline including background factors, clinical and endoscopic activity, and blood test results, as we did for the VDZ prediction model. The top 10 features (Alb, monocytes, height, MCV, TP, Lichtiger index, white blood cell count, MCHC, partial Mayo score, and CRP) associated with steroid-free clinical remission at 6 months after starting UST were selected using random forest. The predictive ability of a model using these predictors was evaluated by fivefold cross-validation. Validation of the prediction model with an external cohort showed PPV of 68.8% and NPV of 71.4%. Our study suggested the importance of establishing a drug-specific prediction model.
Sequencing of cerebrospinal fluid cell-free DNA facilitated early differential diagnosis of intramedullary spinal cord tumors
Authors: Chai Ruichao , An Songyuan , Lin Han , Pang Bo , Yan Hao , Liu Yun , Wu Yilin , Wang Long , Liu Xing , Chen Huiyuan , Yang Xueyu , Chang Qing , Jia Wenqing , Wang Yongzhi 
Published: February 2024

AbstractPre-surgery differential diagnosis is valuable for personalized treatment planning in intramedullary spinal cord tumors. This study assessed the performance of sequencing cell-free DNA (cfDNA) in cerebrospinal fluid (CSF) for differential diagnosis of these tumors. Prospectively enrolling 45 patients with intramedullary spinal cord lesions, including diffuse midline glioma (DMG), H3K27-altered (14/45), glioblastoma (1/45), H3-wildtype-astrocytoma (10/45), ependymoma (11/45), and other lesions (9/45), CSF samples were collected via lumbar puncture (41/45), intraoperative extraction (3/45), and Ommaya reservoir (1/45). Then, these samples underwent targeted sequencing along with paired tissue DNA. DMG, H3K27-altered patients exhibited a higher ctDNA positivity (85.7%, 12/14) compared to patients with H3-wildtype-astrocytoma (0/8, P = 0.0003), ependymoma (2/10, P = 0.003), and glioneuronal tumor (0/3, P = 0.009). The histological-grade-IV (P = 0.0027), Ki-67 index ≥10% (P = 0.014), and tumor reaching spinal cord surface (P = 0.012) are also associated with higher ctDNA positivity. Interestingly, for patients with TERT promoter mutant tumors, TERT mutation was detectable in the CSF cfDNA of one DMG case, but not other five cases with histological-grade-II tumors. Shared copy number variants were exclusively observed in DMG, H3K27-altered, and showed a strong correlation (Correlation = 0.95) between CSF and tissue. Finally, H3K27M mutations in CSF exhibited high diagnostic efficiency for DMG, H3K27-altered (Sensitivity = 85.7%, Specificity = 100.0%, AUC = 0.929). Notably, H3K27M was detectable in CSF from patients with recurrent tumors, making it easily applicable for postoperative monitoring. In conclusion, the molecular profile from ctDNA released into CSF of malignant tumors was more frequently detected compared to relatively benign ones. Sequencing of ctDNA in CSF exhibited high efficiency for the differential diagnosis of DMG, H3K27-altered.
Toward Generalizable Machine Learning Models in Speech, Language, and Hearing Sciences: Estimating Sample Size and Reducing Overfitting
Authors: Ghasemzadeh Hamzeh , Hillman Robert E. , Mehta Daryush D. 
Published: February 2024

Purpose:

Many studies using machine learning (ML) in speech, language, and hearing sciences rely upon cross-validations with single data splitting. This study's first purpose is to provide quantitative evidence that would incentivize researchers to instead use the more robust data splitting method of nested
k
-fold cross-validation. The second purpose is to present methods and MATLAB code to perform power analysis for ML-based analysis during the design of a study.

Method:
First, the significant impact of different cross-validations on ML outcomes was demonstrated using real-world clinical data. Then, Monte Carlo simulations were used to quantify the interactions among the employed cross-validation method, the discriminative power of features, the dimensionality of the feature space, the dimensionality of the model, and the sample size. Four different cross-validation methods (single holdout, 10-fold, train–validation–test, and nested 10-fold) were compared based on the statistical power and confidence of the resulting ML models. Distributions of the null and alternative hypotheses were used to determine the minimum required sample size for obtaining a statistically significant outcome (5% significance) with 80% power. Statistical confidence of the model was defined as the probability of correct features being selected for inclusion in the final model.

Results:

ML models generated based on the single holdout method had very low statistical power and confidence, leading to overestimation of classification accuracy. Conversely, the nested 10-fold cross-validation method resulted in the highest statistical confidence and power while also providing an unbiased estimate of accuracy. The required sample size using the single holdout method could be 50% higher than what would be needed if nested
k
-fold cross-validation were used. Statistical confidence in the model based on nested
k
-fold cross-validation was as much as four times higher than the confidence obtained with the single holdout–based model. A computational model, MATLAB code, and lookup tables are provided to assist researchers with estimating the minimum sample size needed during study design.

Conclusion:

The adoption of nested
k
-fold cross-validation is critical for unbiased and robust ML studies in the speech, language, and hearing sciences.

Supplemental Material:

https://doi.org/10.23641/asha.25237045