-
Optics Express Jun 2024With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission...
With the increasing capacity and complexity of optical fiber communication systems, both academic and industrial requirements for the essential tasks of transmission systems simulation, digital signal processing (DSP) algorithms verification, system performance evaluation, and quality of transmission (QoT) optimization are becoming significantly important. However, due to the intricate and nonlinear nature of optical fiber communication systems, these tasks are generally implemented in a divide-and-conquer manner, which necessitates a profound level of expertise and proficiency in software programming from researchers or engineers. To lower this threshold and facilitate professional research easy-to-start, a GPT-based versatile research assistant named OptiComm-GPT is proposed for optical fiber communication systems, which flexibly and automatically performs system simulation, DSP algorithms verification, performance evaluation, and QoT optimization with only natural language. To enhance OptiComm-GPT's abilities for complex tasks in optical fiber communications and improve the accuracy of generated results, a domain information base containing rich domain knowledge, tools, and data as well as the comprehensive prompt engineering with well-crafted prompt elements, techniques, and examples is established and performs under a LangChain-based framework. The performance of OptiComm-GPT is evaluated in multiple simulation, verification, evaluation, and optimization tasks, and the generated results show that OptiComm-GPT can effectively comprehend the user's intent, accurately extract system parameters from the user's request, and intelligently invoke domain resources to solve these complex tasks simultaneously. Moreover, the statistical results, typical errors, and running time of OptiComm-GPT are also investigated to illustrate its practical reliability, potential limitations, and further improvements.
PubMed: 38859450
DOI: 10.1364/OE.522026 -
Nutrients Oct 2023The level of association between 25-hydroxyvitamin D (25[OH]D) levels and students' academic performance has not yet been established. The current study aimed to...
The level of association between 25-hydroxyvitamin D (25[OH]D) levels and students' academic performance has not yet been established. The current study aimed to investigate the association between serum 25(OH)D levels and academic performance among schoolchildren in Sudan. A cross-sectional study was conducted among schoolchildren during the 2021/2022 academic year from four randomly selected schools in Almatamah, River Nile State, northern Sudan. Sociodemographic data were collected using a questionnaire. Anthropometric measurements were performed in accordance with standard procedures. Academic performance was obtained from school records. Serum 25(OH)D levels were measured, and regression (multiple linear regression and multivariate logistic) analyses were performed. A total of 241 participants were enrolled in this study, of whom 129 (53.5%) were female. The mean standard deviation (SD) of the participants' ages was 15 ± 1.6 years. In multiple linear regression tests, being female, age, employment, and serum 25(OH)D level were positively associated with academic performance. The average overall academic score was 33.74%. Of the 241 participants, 95 (39.4%) and 149 (61.6%) had good and poor academic performances, respectively. In multivariable logistic regressions, age and 25(OH)D level were inversely associated with poor academic performance and vitamin D deficiency was associated with poor performance. The current study revealed a positive association between 25(OH)D levels and adolescents' academic performance. Effective interventional programs are needed to maintain sufficient vitamin D levels during childhood and adolescence and, as a consequence, to improve academic performance.
Topics: Humans; Female; Adolescent; Child; Adult; Male; Cross-Sectional Studies; Vitamin D; Calcifediol; Vitamin D Deficiency; Academic Performance
PubMed: 37960205
DOI: 10.3390/nu15214552 -
Scientific Reports Feb 2024The inverse effects of creatine supplementation and sleep deprivation on high energy phosphates, neural creatine, and cognitive performances suggest that creatine is a...
The inverse effects of creatine supplementation and sleep deprivation on high energy phosphates, neural creatine, and cognitive performances suggest that creatine is a suitable candidate for reducing the negative effects of sleep deprivation. With this, the main obstacle is the limited exogenous uptake by the central nervous system (CNS), making creatine only effective over a long-term diet of weeks. Thus far, only repeated dosing of creatine over weeks has been studied, yielding detectable changes in CNS levels. Based on the hypothesis that a high extracellular creatine availability and increased intracellular energy consumption will temporarily increase the central creatine uptake, subjects were orally administered a high single dose of creatinemonohydrate (0.35 g/kg) while performing cognitive tests during sleep deprivation. Two consecutive P-MRS scans, H-MRS, and cognitive tests were performed each at evening baseline, 3, 5.5, and 7.5 h after single dose creatine (0.35 g/kg) or placebo during sub-total 21 h sleep deprivation (SD). Our results show that creatine induces changes in PCr/Pi, ATP, tCr/tNAA, prevents a drop in pH level, and improves cognitive performance and processing speed. These outcomes suggest that a high single dose of creatine can partially reverse metabolic alterations and fatigue-related cognitive deterioration.
Topics: Humans; Creatine; Sleep Deprivation; Central Nervous System; Cognition; Phosphates
PubMed: 38418482
DOI: 10.1038/s41598-024-54249-9 -
PloS One 2023The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for...
The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep learning methods for the analysis of whole-slide images (WSIs) have shown excellent performance on these tasks, and have the potential to substantially reduce the workload of pathologists. However, WSIs present a number of unique challenges for analysis, requiring special consideration of image annotations, slide and image artefacts, and evaluation of WSI-trained model performance. Here we introduce SliDL, a Python library for performing pre- and post-processing of WSIs. SliDL makes WSI data handling easy, allowing users to perform essential processing tasks in a few simple lines of code, bridging the gap between standard image analysis and WSI analysis. We introduce each of the main functionalities within SliDL: from annotation and tile extraction to tissue detection and model evaluation. We also provide 'code snippets' to guide the user in running SliDL. SliDL has been designed to interact with PyTorch, one of the most widely used deep learning libraries, allowing seamless integration into deep learning workflows. By providing a framework in which deep learning methods for WSI analysis can be developed and applied, SliDL aims to increase the accessibility of an important application of deep learning.
Topics: Deep Learning; Image Interpretation, Computer-Assisted; Coloring Agents; Image Processing, Computer-Assisted
PubMed: 37549131
DOI: 10.1371/journal.pone.0289499 -
BMJ Open Aug 2023Childhood cataract is a chronic condition that may interfere with the child's learning capacities. We aimed to investigate whether childhood cataract influences academic...
OBJECTIVES
Childhood cataract is a chronic condition that may interfere with the child's learning capacities. We aimed to investigate whether childhood cataract influences academic development by comparing school performance in reading and mathematics in children with cataract to a matched control group.
DESIGN
Nationwide registry-based cohort study.
SETTINGS
Two surgical centres that perform all treatments for childhood cataract in Denmark.
PARTICIPANTS
Children born between 2000 and 2009 diagnosed with cataract before 10 years of age (n=275) and an age-matched and sex-matched control group (n=2473).
MAIN OUTCOME MEASURES
School performance was assessed as test scores in national tests performed at regular intervals from grade 2 to grade 8 in reading and mathematics. Analyses were corrected for birth origin, child somatic and mental disorder and parental socioeconomic status and mental disorders.
RESULTS
Of 275 children, 85 (30.9%) were operated for bilateral cataract, 79 (28.7%) unilateral cataract and 111 (40,4%) were not operated. We found that children with cataract have lower participation rate in the tests (62.5%) compared with the control cohort (77.2%) (p value=0.0001). After adjusting the pooled analyses for birth origin, somatic and mental disease in the child and parental socioeconomic status and mental disorders, we found that the children with cataract scored significantly lower in mathematics compared with those without cataract (mean difference=-4.78, 95% CI: -8.18 to -1.38, p value=0.006), whereas no difference was found regarding scores in reading (p=0.576). The lower score in mathematics was driven by children who had been operated for bilateral cataract (p-value=0.004).
CONCLUSION
Children with cataract without somatic or neurodevelopmental comorbidities or psychosocial adversities seem to do well in school, whereas children operated for bilateral cataract have higher frequencies of difficulties in mathematical tasks.
Topics: Humans; Child; Cohort Studies; Academic Performance; Cataract; Schools; Comorbidity
PubMed: 37532485
DOI: 10.1136/bmjopen-2023-072984 -
Nature Communications Mar 2024It is likely that individuals are turning to Large Language Models (LLMs) to seek health advice, much like searching for diagnoses on Google. We evaluate clinical...
It is likely that individuals are turning to Large Language Models (LLMs) to seek health advice, much like searching for diagnoses on Google. We evaluate clinical accuracy of GPT-3·5 and GPT-4 for suggesting initial diagnosis, examination steps and treatment of 110 medical cases across diverse clinical disciplines. Moreover, two model configurations of the Llama 2 open source LLMs are assessed in a sub-study. For benchmarking the diagnostic task, we conduct a naïve Google search for comparison. Overall, GPT-4 performed best with superior performances over GPT-3·5 considering diagnosis and examination and superior performance over Google for diagnosis. Except for treatment, better performance on frequent vs rare diseases is evident for all three approaches. The sub-study indicates slightly lower performances for Llama models. In conclusion, the commercial LLMs show growing potential for medical question answering in two successive major releases. However, some weaknesses underscore the need for robust and regulated AI models in health care. Open source LLMs can be a viable option to address specific needs regarding data privacy and transparency of training.
Topics: Humans; Animals; Camelids, New World; Decision Support Systems, Clinical; Search Engine; Benchmarking; Health Facilities
PubMed: 38448475
DOI: 10.1038/s41467-024-46411-8 -
International Journal of Molecular... Feb 2024Immunoassays (IAs) with fluorescence-based detection are already well-established commercialized biosensing methods, such as enzyme-linked immunosorbent assay (ELISA)... (Review)
Review
Immunoassays (IAs) with fluorescence-based detection are already well-established commercialized biosensing methods, such as enzyme-linked immunosorbent assay (ELISA) and lateral flow immunoassay (LFIA). Immunoassays with surface-enhanced Raman spectroscopy (SERS) detection have received significant attention from the research community for at least two decades, but so far they still lack a wide clinical commercial application. This review, unlike any other review that we have seen, performs a three-dimensional performance comparison of SERS IAs vs. fluorescence IAs. First, we compared the limit of detection (LOD) as a key performance parameter for 30 fluorescence and 30 SERS-based immunoassays reported in the literature. We also compared the clinical performances of a smaller number of available reports for SERS vs. fluorescence immunoassays (FIAs). We found that the median and geometric average LODs are about 1.5-2 orders of magnitude lower for SERS-based immunoassays in comparison to fluorescence-based immunoassays. For instance, the median LOD for SERS IA is 4.3 × 10 M, whereas for FIA, it is 1.5 × 10 M. However, there is no significant difference in average relative standard deviation (RSD)-both are about 5-6%. The analysis of sensitivity, selectivity, and accuracy reported for a limited number of the published clinical studies with SERS IA and FIA demonstrates an advantage of SERS IA over FIA, at least in terms of the median value for all three of those parameters. We discussed common and specific challenges to the performances of both SERS IA and FIA, while proposing some solutions to mitigate those challenges for both techniques. These challenges include non-specific protein binding, non-specific interactions in the immunoassays, sometimes insufficient reproducibility, relatively long assay times, photobleaching, etc. Overall, this review may be useful for a large number of researchers who would like to use immunoassays, but particularly for those who would like to make improvements and move forward in both SERS-based IAs and fluorescence-based IAs.
Topics: Reproducibility of Results; Spectrum Analysis, Raman; Immunoassay; Coloring Agents; Enzyme-Linked Immunosorbent Assay; Gold; Metal Nanoparticles
PubMed: 38396756
DOI: 10.3390/ijms25042080 -
MethodsX Dec 2023Quantum field theory (QFTh) simulators simulate physical systems using quantum circuits that process quantum information (qubits) via single field (SF) and/or quantum... (Review)
Review
Quantum field theory (QFTh) simulators simulate physical systems using quantum circuits that process quantum information (qubits) via single field (SF) and/or quantum double field (QDF) transformation. This review presents models that classify states against pairwise particle states , given their state transition (ST) probability . A quantum AI (QAI) program, weighs and compares the field's distance between entangled states as qubits from their scalar field of radius . These states distribute across with expected probability and measurement outcome . A quantum-classical hybrid model of processors via QAI, classifies and predicts states by decoding qubits into classical bits. For example, a QDF as a quantum field computation model (QFCM) in IBM-QE, performs the doubling of for a strong state prediction outcome. QFCMs are compared to achieve a universal QFCM (UQFCM). This model is novel in making strong event predictions by simulating systems using QAI. Its expected measurement fidelity is in classifying states to select 7 optimal QFCMs to predict 's on QFTh observables. This includes QFCMs' commonality of against QFCMs limitations in predicting system events. Common measurement results of QFCMs include their expected success probability over STs occurring in the system. Consistent results with high 's, are averaged over STs as yielding performed by an SF or QDF of certain QFCMs. A combination of QFCMs with this fidelity level predicts error rates (uncertainties) in measurements, by which a is weighed as a QAI output to a QFCM user. The user then decides which QFCMs perform a more efficient system simulation as a reliable solution. A UQFCM is useful in predicting system states by preserving and recovering information for intelligent decision support systems in applied, physical, legal and decision sciences, including industry 4.0 systems.
PubMed: 37767157
DOI: 10.1016/j.mex.2023.102366 -
BioRxiv : the Preprint Server For... Feb 2024Coordinated multi-joint limb and digit movements - "manual dexterity" - underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying...
Coordinated multi-joint limb and digit movements - "manual dexterity" - underlie both specialized skills (e.g., playing the piano) and more mundane tasks (e.g., tying shoelaces). Impairments in dexterous skill cause significant disability, as occurs with motor cortical injury, Parkinson's Disease, and a range of other pathologies. Clinical observations, as well as basic investigations, suggest that cortico-striatal circuits play a critical role in learning and performing dexterous skills. Furthermore, dopaminergic signaling in these regions is implicated in synaptic plasticity and motor learning. Nonetheless, the role of striatal dopamine signaling in skilled motor learning remains poorly understood. Here, we use fiber photometry paired with a genetically encoded dopamine sensor to investigate striatal dopamine release as mice learn and perform a skilled reaching task. Dopamine rapidly increases during a skilled reach and peaks near pellet consumption. In dorsolateral striatum, dopamine dynamics are faster than in dorsomedial and ventral striatum. Across training, as reaching performance improves, dopamine signaling shifts from pellet consumption to cues that predict pellet availability, particularly in medial and ventral areas of striatum. Furthermore, performance prediction errors are present across the striatum, with reduced dopamine release after an unsuccessful reach. These findings show that dopamine dynamics during skilled motor behaviors change with learning and are differentially regulated across striatal subregions.
PubMed: 38370850
DOI: 10.1101/2024.02.06.579240 -
Journal of Reconstructive Microsurgery Nov 2023Preparation of the recipient vessels is a crucial step in autologous breast reconstruction, with limited opportunity for resident training intraoperatively. The...
BACKGROUND
Preparation of the recipient vessels is a crucial step in autologous breast reconstruction, with limited opportunity for resident training intraoperatively. The Blue-Blood-infused porcine chest wall-a cadaveric pig thorax embedded in a mannequin shell, connected to a saline perfusion system-is a novel, cost-effective ($55) simulator of internal mammary artery (IMA) dissection and anastomosis intended to improve resident's comfort, safety, and expertise with all steps of this procedure. The purpose of this study was to assess the effect of the use of this chest wall model on resident's confidence in performing dissection and anastomosis of the IMA, as well as obtain resident's and faculty's perspectives on model realism and utility.
METHODS
Plastic surgery residents and microsurgery faculty at the University of Wisconsin were invited to participate. One expert microsurgeon led individual training sessions and performed as the microsurgical assistant. Participants anonymously completed surveys prior to and immediately following their training session to assess their change in confidence performing the procedure, as well as their perception of model realism and utility as a formal microsurgical training tool on a five-point scale.
RESULTS
Every participant saw improvement in confidence after their training session in a minimum of one of seven key procedural steps identified. Of participants who had experience with this procedure in humans, the majority rated model anatomy and performance of key procedural steps as "very" or "extremely" realistic as compared with humans. 100% of participants believed practice with this model would improve residents' ability to perform this operation in the operating room and 100% of participants would recommend this model be incorporated into the microsurgical training curriculum.
CONCLUSION
The Blue-Blood porcine chest wall simulator increases trainee confidence in performing key steps of IMA dissection and anastomosis and is perceived as valuable to residents and faculty alike.
Topics: Humans; Swine; Animals; Internship and Residency; Clinical Competence; Education, Medical, Graduate; Simulation Training; Thorax
PubMed: 36931312
DOI: 10.1055/a-2057-0766