bluuebunny/crossref_metadata_2025_split huggingface dataset:
Non-invasive VOCs detection to monitor the gut microbiota metabolism in-vitro
Authors: Dell’Olio Andrea , Rubert Josep , Capozzi Vittorio , Tonezzer Matteo , Betta Emanuela , Fogliano Vincenzo , Biasioli Franco 
Published: July 2024

AbstractThis work implemented a non-invasive volatile organic compounds (VOCs) monitoring approach to study how food components are metabolised by the gut microbiota in-vitro. The fermentability of a model food matrix rich in dietary fibre (oat bran), and a pure prebiotic (inulin), added to a minimal gut medium was compared by looking at global changes in the volatilome. The substrates were incubated with a stabilised human faecal inoculum over a 24-h period, and VOCs were monitored without interfering with biological processes. The fermentation was performed in nitrogen-filled vials, with controlled temperature, and tracked by automated headspace-solid-phase microextraction coupled with gas chromatography–mass spectrometry. To understand the molecular patterns over time, we applied a multivariate longitudinal statistical framework: repeated measurements—ANOVA simultaneous component analysis. The methodology was able to discriminate the studied groups by looking at VOCs temporal profiles. The volatilome showed a time-dependency that was more distinct after 12 h. Short to medium-chain fatty acids showed increased peak intensities, mainly for oat bran and for inulin, but with different kinetics. At the same time, alcohols, aldehydes, and esters showed distinct trends with discriminatory power. The proposed approach can be applied to study the intertwined pathways of gut microbiota food components interaction in-vitro.
Apolipoprotein E polymorphisms and female fertility in a transgenic mouse model of Alzheimer’s disease
Authors: Medegan Fagla Bani , York Jason , Christensen Amy , Dela Rosa Cielo , Balu Deebika , Pike Christian J. , Tai Leon M. , Buhimschi Irina A. 
Published: July 2024

AbstractApolipoprotein E (APOE) is a major cholesterol carrier responsible for lipid transport and injury repair in the brain. The human APOE gene (h-APOE) has 3 naturally occurring alleles: ε3, the common allele; ε4, which increases Alzheimer’s disease (AD) risk up to 15-fold; and ε2, the rare allele which protects against AD. Although APOE4 has negative effects on neurocognition in old age, its persistence in the population suggests a survival advantage. We investigated the relationship between APOE genotypes and fertility in EFAD mice, a transgenic mouse model expressing h-APOE. We show that APOE4 transgenic mice had the highest level of reproductive performance, followed by APOE3 and APOE2. Intriguingly, APOE3 pregnancies had more fetal resorptions and reduced fetal weights relative to APOE4 pregnancies. In conclusion, APOE genotypes impact fertility and pregnancy outcomes in female mice, in concordance with findings in human populations. These mouse models may help elucidate how h-APOE4 promotes reproductive fitness at the cost of AD in later life.
Disease burden of stroke and its subtypes attributable to low dietary fiber in China, 1990–2019
Authors: Jin Shuai , Xie Lang , Wang Junwen , Xia Kaide , Zhang Haiwang 
Published: July 2024

AbstractThis study aimed to assess the current status and changing trends of the disease burden of stroke and its subtypes due to low dietary fiber intake in China from 1990 to 2019. In cases of stroke and its subtypes attributable to low dietary fiber, deaths, disability-adjusted life-years (DALYs), age-standardized mortality rates (ASMR), age-standardized DALYs rates (ASDR), and percentage change were used to assess disease burden. Data were obtained from the 2019 global burden of disease study. Trends were assessed using Joinpoint regression and age-period-cohort analysis. Between 1990 and 2019, there was a declining trend in stroke and its subtypes, ASDR and ASMR, as well as the corresponding number of deaths and DALYs, due to low dietary fiber intake in China. Subarachnoid hemorrhage (SH) showed the greatest decrease, followed by intracerebral hemorrhage (IH) and ischemic stroke (IS). Local drift curves showed a U-shaped distribution of stroke, IS, and IH DALYs across the whole group and sex-based groups. For mortality, the overall and male trends were similar to those for DALYs, whereas female stroke, IH, and IS showed an upward trend. The DALYs for stroke and IH showed a clear bimodal distribution, IS showed an increasing risk with age. For mortality, the SH subtype showed a decreasing trend, whereas other subtypes showed an increasing risk with age. Both the period and cohort rates of stroke DALYs and motality due to low dietary fiber have declined. Males had a higher risk of DALYs and mortality associated with low fiber levels. The burden of stroke and its subtypes associated with a low-fiber diet in China has been declining over the past 30 years, with different patterns of change for different stroke subtypes and a higher burden for males, highlighting the differential impact of fiber intake on stroke and its subtypes.
A comprehensive investigation of morphological features responsible for cerebral aneurysm rupture using machine learning
Authors: Zakeri Mostafa , Atef Amirhossein , Aziznia Mohammad , Jafari Azadeh 
Published: July 2024

AbstractCerebral aneurysms are a silent yet prevalent condition that affects a significant global population. Their development can be attributed to various factors, presentations, and treatment approaches. The importance of selecting the appropriate treatment becomes evident upon diagnosis, as the severity of the disease guides the course of action. Cerebral aneurysms are particularly vulnerable in the circle of Willis and pose a significant concern due to the potential for rupture, which can lead to irreversible consequences, including fatality. The primary objective of this study is to predict the rupture status of cerebral aneurysms. To achieve this, we leverage a comprehensive dataset that incorporates clinical and morphological data extracted from 3D real geometries of previous patients. The aim of this research is to provide valuable insights that can help make informed decisions during the treatment process and potentially save the lives of future patients. Diagnosing and predicting aneurysm rupture based solely on brain scans is a significant challenge with limited reliability, even for experienced physicians. However, by employing statistical methods and machine learning techniques, we can assist physicians in making more confident predictions regarding rupture likelihood and selecting appropriate treatment strategies. To achieve this, we used 5 classification machine learning algorithms and trained them on a substantial database comprising 708 cerebral aneurysms. The dataset comprised 3 clinical features and 35 morphological parameters, including 8 novel morphological features introduced for the first time in this study. Our models demonstrated exceptional performance in predicting cerebral aneurysm rupture, with accuracy ranging from 0.76 to 0.82 and precision score from 0.79 to 0.83 for the test dataset. As the data are sensitive and the condition is critical, recall is prioritized as the more crucial parameter over accuracy and precision, and our models achieved outstanding recall score ranging from 0.85 to 0.92. Overall, the best model was Support Vector Machin with an accuracy and precision of 0.82, recall of 0.92 for the testing dataset and the area under curve of 0.84. The ellipticity index, size ratio, and shape irregularity are pivotal features in predicting aneurysm rupture, respectively, contributing significantly to our understanding of this complex condition. Among the multitude of parameters under investigation, these are particularly important. In this study, the ideal roundness parameter was introduced as a novel consideration and ranked fifth among all 38 parameters. Neck circumference and outlet numbers from the new parameters were also deemed significant contributors.
Predictive value of inflammatory prognostic index for contrast-induced nephropathy in patients undergoing coronary angiography and/or percutaneous coronary intervention
Authors: Jiang Yan , Luo Baolin , Chen Yaqin , Peng Yanchun , Lu Wen , Chen Liangwan , Lin Yanjuan 
Published: July 2024

AbstractThe purpose of this study was to investigate the relationship between Inflammatory Prognostic Index (IPI) levels and Contrast-Induced Nephropathy (CIN) risk and postoperative clinical outcomes in patients undergoing coronary angiography (CAG) and/or percutaneous coronary intervention (PCI). A total of 3,340 consecutive patients who underwent CAG and/or PCI between May 2017 and December 2022 were enrolled in this study. Based on their baseline IPI levels, patients were categorized into four groups. Clinical characteristics and postoperative outcomes were compared among these groups. In-hospital outcomes focused on CIN risk, repeated revascularization, major bleeding, and major adverse cardiovascular events (MACEs), while the long-term outcome examined the all-cause readmission rate. Quartile analysis found a significant link between IPI levels and CIN risk, notably in the highest quartile (P < 0.001). Even after adjusting for baseline factors, this association remained significant, with an adjusted Odds Ratio (aOR) of 2.33 (95%CI 1.50–3.64; P = 0.001). Notably, baseline IPI level emerged as an independent predictor of severe arrhythmia, with aOR of 0.50 (95%CI 0.35–0.69; P < 0.001), particularly driven by the highest quartile. Furthermore, a significant correlation between IPI and acute myocardial infarction was observed (P < 0.001), which remained significant post-adjustment. For patients undergoing CAG and/or PCI, baseline IPI levels can independently predict clinical prognosis. As a comprehensive inflammation indicator, IPI effectively identifies high-risk patients post-procedure. This study underscores IPI's potential to assist medical professionals in making more precise clinical decisions, ultimately reducing mortality and readmission rates linked to cardiovascular disease (CVD).
An efficient, reliable and valid assessment for affective states during online learning
Authors: Siu Oi-ling , Lui Kelvin F. H. , Huang Yi , Ng Ting Kin , Yeung Wai Lan Victoria 
Published: July 2024

AbstractThe current study aims to develop an efficient, reliable and valid assessment, the affective states for online learning scale (ASOLS), for measuring learners’ affective states during online learning using a sample of 173 young learners. The assessment consists of 15 items which assess five affective states, including concentration, motivation, perseverance, engagement, and self-initiative. To improve efficiency, five items (one for each affective state) are randomly selected and presented every 30 min during online learning. In addition, 14 among the participants were further invited to perform on-site online learning, and their affective states were validated by observations conducted by two psychologists. The ASOLS was found to be reliable and valid, with high internal consistency reliabilities and good construct, convergent and criterion validity. Confirmatory factor analyses showed that the hypothesized five-factor structure demonstrated a satisfactory fit to the data. Moreover, engagement was found to be positively associated with learning performance. Our findings suggest that the ASOLS provides a useful tool for teachers to identify students in upper primary and junior secondary schools with deficits in affective states and offer appropriate remedy or support. It can also be used to evaluate the effectiveness of interventions aimed at enhancing students’ affective states during online learning.
Assessing the Language Abilities of Preterm-Born Children: An Examination of Standardized Testing and Language Sample Analysis
Authors: Coughlan Sarah , Quigley Jean , Nixon Elizabeth 
Published: July 2024

Purpose:
To understand how best to assess the language abilities of preterm-born children, this study: (a) compared preterm- and term-born children's language skills using standardized testing and language sample analysis (LSA), (b) investigated how executive function skills and the language sampling context respectively affect standardized test and LSA scores, and (c) examined the pattern of associations between standardized test and LSA scores among preterm-/term-born groups.

Method:
Twenty-five term-born and 23 preterm-born 2-year-old singletons were administered the language scales of the Bayley Scales of Infant and Toddler Development–Third Edition (receptive communication, expressive communication, language composite scores). Parent–child free-play recordings were used to quantify the (para)linguistic features of the children's speech. Executive function was measured via parent report.

Results:
The preterm-born group obtained significantly lower scores than the term-born group on all Bayley language measures (though differences were not consistently observed when using cutoff scores). Few preterm–term differences in LSA measures were found. The preterm–term differences in Bayley scores were not explained by between-group differences in executive function. Some preterm–term differences in LSA scores were moderated by the language sampling context. The preterm- and term-born groups exhibited different patterns of Bayley–LSA correlations.

Conclusions:
Preterm language difficulties were more apparent on standardized test than LSA scores. Nonetheless, the Bayley–LSA correlations indicate that poor test performance (linked with preterm birth) is associated with functional communication difficulties. The discussion outlines the complementary utility of standardized tests and LSA while acknowledging the limited utility of cutoff scores and the confounding influence of the language sampling context.

Supplemental Material:

https://doi.org/10.23641/asha.26142661
Links Among Attention-Deficit/Hyperactivity Disorder Symptoms and Psycholinguistic Abilities Are Different for Children With and Without Developmental Language Disorder
Authors: Redmond Sean M. , Ash Andrea C. , Li Haojia , Zhang Yue 
Published: July 2024

Purpose:
Both developmental language disorder (DLD) and attention-deficit/hyperactivity disorder (ADHD) represent relatively common and chronic neurodevelopmental conditions associated with increased risk for poor academic and interpersonal outcomes. Reports of common co-occurrence suggest these neurodevelopmental disruptions might also be linked. Most of the data available on the issue have been based on case–control studies vulnerable to ascertainment and other biases.

Method:
Seventy-eight children, representing four neurodevelopmental profiles (DLD, ADHD, co-occurring ADHD + DLD, and neurotypical development), were administered a battery of psycholinguistic tests. Parents provided standardized ratings of the severity of their children's inattention, hyperactivity/impulsivity, and executive function symptoms. Examiners were blinded to children's clinical status. Group differences, correlations, and best subset regression analyses were used to examine potential impacts of children's ADHD symptoms on their psycholinguistic abilities.

Results:
For children with DLD, significant links between their ADHD symptoms and psycholinguistic abilities were limited to the contributions of elevated hyperactivity/impulsivity symptoms to lower pragmatic abilities. For children without DLD, inattention symptoms contributed to lower levels of performance in pragmatic, sentence recall, receptive vocabulary, and narrative abilities.

Discussion:
Links among children's ADHD symptoms and their psycholinguistic abilities were different for children with and without DLD. Implications for the provision of clinical services are discussed.
Retracted: The Relationship Between Oral and Written Language in Narrative Production by Arabic-Speaking Children: Fundamental Skills and Influences
Authors: Kawar Khaloob 
Published: July 2024

Notice of Retraction:

https://doi.org/10.1044/2024_Nov2024ASHA

Purpose:
This study aims to investigate the relationship between oral and written language skills in narrative production among Arabic-speaking children, focusing on cognitive and linguistic abilities. It examines the differences in narrative parameters between oral and written narratives and explores the associations between these parameters and cognitive and linguistic skills.

Method:
The research involved 237 sixth-grade Arabic-speaking students from low–socioeconomic status schools in Israel. Each participant was instructed to orally tell a narrative and to write another narrative based on two sets of six sequential pictures. Various narrative features were analyzed, including word count for length, type–token ratio (TTR) for lexical diversity, mean length of utterance (MLU) for morphosyntax, and number of episodes for macrostructure. Cognitive linguistic measures, including Raven's Progressive Matrices, reading comprehension (RC), and morphological awareness (MA) were also assessed.

Results:
The study found significant differences between oral and written narratives regarding lexical diversity and macrostructure. Participants exhibited significantly higher TTR in written narratives compared to oral narratives, whereas the number of episodes was significantly higher in oral narratives than in written ones. However, no significant differences were observed in narrative length or MLU. Moreover, the study identified significant predictors for various aspects of written narratives, particularly MA and RC, which significantly predicted TTR, MLU, and macrostructure. Additionally, the inclusion of word count in oral narratives significantly enhanced the explained variance for narrative length and macrostructure in written language.

Conclusions:
The results highlight the importance of the oral–written interface in both micro- and macrostructure representations in both oral and written modalities. They suggest that cognitive and linguistic skills, such as MA and RC, play a crucial role in narrative production. The findings have implications for educational practices and literacy outcomes in the Arab world, enhancing the understanding of the challenges and strategies involved in written language production among Arabic-speaking children.
Existence of optimal virtual element weights for mmWave FMCW MIMO radar based heart rate estimation
Authors: Gilles Yowel Massala Mboyi , Han Jung‐Hoon 
Published: July 2024

AbstractMeasuring heart rate is a critical component of assessing cardiac health and detecting potential heart diseases at an early stage. Various sensors, including electrocardiogram and photoplethysmograph, are commonly employed for this purpose. However, these conventional methods necessitate direct contact with the patient's skin which may be impractical or uncomfortable in situations involving patients with skin diseases or burn injuries. To address these limitations, a concerted effort has been to develop non‐contact methods leveraging frequency‐modulated continuous‐wave multiple‐input multiple‐output radar systems. Recent studies have illustrated the sensitivity of the phase component of received signals to micro‐motion, presenting a promising avenue for effective heart rate estimation. However, existing literature predominantly focuses on single‐antenna setups, overlooking the potential benefits offered by multiple‐input multiple‐output systems, which provide diverse channels with varying precision levels. This correspondence introduces a novel phase extraction model grounded in the argument of the analytic signal derived from both in‐phase and quadrature channels. Furthermore, leveraging the normal equation, we establish the feasibility of optimizing weights assigned to individual virtual antennas to achieve a robust approximation of ground truth data.
An equivalent and simplified approach for acoustic noise prediction in a PM synchronous motor based on the semi‐analytical‐FEM model
Authors: Saki Armin , Kiyoumarsi Arash , Ariaei Alireza 
Published: July 2024

AbstractThe vibration and acoustic noise of a permanent‐magnet synchronous motor are analysed and evaluated. Acoustic noise sound levels, their prominent frequencies, and the noisiest motor speed range are predicted. According to many resources, the radial vibration of the stator system due to the magnetic attraction between rotor permanent magnets and stator iron teeth is the main reason for the acoustic noise propagation of the PMSMs. The dominant orders of the spatial circumferential harmonics of the radial magnetic forces (RMFs) have been investigated and compared. The complete and complicated mechanical components, materials, and harmonics of the magnetic forces of the motor and their interaction are simplified and equalised to a vibration model, which consists of the interaction between the stator system and the dominant harmonic orders of the RMFs. Based on this approach, the simplest and most adequate semi‐analytical‐FEM model for noise prediction in PMSMs is proposed. This model, in addition to simplification, clarifies the role of the main mechanical and electromagnetic origins of noise generation. The acoustic noise prediction, using the proposed method has been compared with the experimental results. There is a proper agreement between the analytical, the FEM, and measurement results.
Psitrum: An open source simulator for universal quantum computers
Authors: Alghadeer Mohammed , Aldawsari Eid , Selvarajan Raja , Alutaibi Khaled , Kais Sabre , Alharbi Fahhad H. 
Published: July 2024

AbstractQuantum computing is a radical new paradigm for a technology that is capable to revolutionise information processing. Simulators of universal quantum computer are important for understanding the basic principles and operations of the current noisy intermediate‐scale quantum processors, and for building in future fault‐tolerant quantum computers. As next‐generation quantum technologies continue to advance, it is crucial to address the impact on education and training in quantum physics. The emergence of new industries driven by progress in quantum computing and simulation will create a demand for a specialised quantum workforce. In response to these challenges, the authors present Psitrum, an open‐source simulator for universal quantum computers. Psitrum serves as a powerful educational and research tool, enabling a diverse range of stakeholders to understand the fundamental principles and operations of quantum systems. By offering a comprehensive platform for emulating and debugging quantum algorithms through quantum circuits, Psitrum aids in the exploration and analysis of various quantum applications using both MATLAB and MATLAB application programming interface to use the software on other platforms. Psitrum software and source codes are fully available at GitHub.