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An artificial intelligence model for predicting an appropriate mAs with target exposure indicator for chest digital radiography
Abstract In digital radiography, image quality is synergistically affected by anatomy-specific examinations, exposure factors, body parameters, detector types, and vendors/systems. However, estimating appropriate exposure factors before radiography with optimized image quality without overexposure or underexposure to patients is difficult. Thus, there is an unmet need to establish a model to predict appropriate mAs for optimizing image quality before radiography. This study aimed to establish a machine learning (ML) model for predicting an appropriate current–time product (mAs) using the target exposure indicator in chest digital radiography. An anthropomorphic chest phantom was used to establish a target exposure indicator which was used to define overexposure and underexposure in the human study. This study enrolled 1,000 (M/F = 915/85) subjects who underwent regular chest radiography. The chest thickness, height, weight, body mass index, mAs, and concomitant reached exposure (REX) were recorded. To construct the prediction model, the dataset was randomly separated into training (80%) and testing (20%) sets by matching their demographic characteristics. Five ML models were trained using the training set with 10-fold cross validation, and the model performance was evaluated using the testing set with correlation coefficients, root–mean–square error, and mean average error. The phantom study showed that the average REX was 355.6 which served as the target exposure indicator. In human study, the comparisons showed that the artificial neural network (ANN) model was the most suitable for predicting both REX and mAs values. The results demonstrated that, on average, the predicted mAs values were 10% lower and 8% higher than the values determined by AEC in the overexposure (REX > 355.6) and underexposure (REX < 355.6) groups, respectively. Moreover, the predicted mAs values were further reduced in all patients when lowering the target REX values. We concluded that the ML approach was feasible for building an artificial intelligence model for predicting appropriate mAs with target exposure indicator for chest digital radiography.
Correction: Pharmacokinetic evaluation of single-dose migalastat in non-Fabry disease subjects with ESRD receiving dialysis treatment, and use of modeling to select dose regimens in Fabry disease subjects with ESRD receiving dialysis treatment
A tardive dyskinesia drug target VMAT-2 participates in neuronal process elongation
Assessment of performance and challenges of small and micro enterprises: Fireweyni town, Ethiopia
Micro and small enterprises are crucial drivers of economic growth, job creation, and poverty reduction in developing countries. Recognizing this, Ethiopia has implemented measures to enhance the operation of micro and small enterprises. However, many micro and small enterprises in the country face significant challenges and exhibit deteriorating performance, with limited progression from one enterprise level to the next. This study aims to assess the performance and challenges of micro and small enterprises in Fireweyni town. Employing a mixed-methods approach, the study integrates both qualitative and quantitative research. Primary data were collected using a structured questionnaire. A probability sampling technique, combining convenience and stratified random sampling, was applied to a sample of 373 micro and small enterprises in Fireweyni town. The study is descriptive and cross-sectional, focusing on the performance of MSEs and the obstacles they encounter. Data collection included responses from 5483 micro and small enterprise operators, distributed across service (853), manufacturing (341), urban agriculture (1073), construction (155), and trade (3061) sectors. The data were analyzed using descriptive and inferential statistical methods, with the aid of the Statistical Package for Social Sciences (SPSS) version 20 and STATA version 16. Results of the study revealed that, every variable (factor) incorporated in this study was statistically significant, impacting the performance of SMEs and businesses within the study area. The Pearson correlation coefficient test results showed that all independent variables have a significant relationship with MSE business performance. The study’s conclusions indicate that financial control, marketing strategy, managerial expertise, government regulations, and business information services significantly affect the performance of small and micro businesses in Fireweyni town.
Enhanced performance of T2SLs LWIR avalanche photodiodes with a separate AlxGa1-xSb multiplication layer
Exploring the temporal correlations of factors affecting traffic safety on mountain freeways: Through new crash frequency modelling methods
The potential factors contributing to safety risks on mountainous freeways exhibit significant seasonal clustering and temporal correlations. However, these temporal characteristics have not been accurately captured by existing crash modeling methods, which severely compromise model fit and may lead to erroneous conclusions. This study makes three major contributions. Firstly, a multidimensional crash dataset involving design features, traffic conditions, pavement performance, and weather conditions was established based on eight quarterly datasets of mountain freeways in China. Secondly, two new crash modeling methods considering temporal correlations were proposed. The first model embedded an autoregressive structure and a time linear trend function within a Poisson model, while the second model incorporated an autoregressive structure and time-varying regression coefficients within a Poisson model. The superiority of the new models over seven existing time-correlated models was validated in terms of goodness-of-fit and prediction accuracy, and the significant associations between crash frequencies across different quarters were also confirmed. Moreover, this study quantitatively analyzed the causes of crash frequency on mountainous freeways in China, revealing several significant conclusions. For instance, special road sections such as interchanges, tunnels, and service areas exhibit higher crash risks. Increased traffic volumes, especially with a higher proportion of trucks, are associated with elevated crash risks. Enhancing pavement smoothness and skid resistance was found to effectively mitigate crashes. Moderate rainfall increases crash risks, whereas heavy rainfall alters travel plans and paradoxically reduces crash frequency. To the best of our knowledge, this study introduced the first temporal correlation modeling method specifically addressing the unique temporal characteristics of safety-influencing factors on China’s mountainous freeways, offering valuable insights for the development of effective safety countermeasures.
Tertiary butylhydroquinone regulates oxidative stress in spleen injury induced by gas explosion via the Nrf2/HO-1 signaling pathway
AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments
Continuous access to up-to-date food price data is crucial for monitoring food security and responding swiftly to emerging risks. However, in many food-insecure countries, price data is often delayed, lacks spatial detail, or is unavailable during crises when markets may become inaccessible, and rising prices can rapidly exacerbate hunger. Recent innovations, such as AI-driven data imputation and crowdsourcing, present new opportunities to generate continuous, localized price data. This paper evaluates the reliability of these approaches by comparing them to traditional enumerator-led data collection in northern Nigeria, a region affected by conflict, food insecurity, and data scarcity. The analysis examines crowdsourced prices for two staple food commodities, maize and rice, submitted daily by volunteers through a smartphone application over 36 months (2019–2021), and compares them with data collected concurrently by trained enumerators during the final eight months of 2021. Additionally, the crowdsourced dataset is compared to AI-imputed prices from the World Bank’s Real-Time Prices (RTP) database. Data from the alternative methods reflected similar price inflation trends during the COVID-19 pandemic. Pearson’s correlation coefficients indicate strong statistical agreement between crowdsourced and enumerator-collected prices (r = 0.94 for yellow and white maize, r = 0.96 for Indian rice, and r = 0.78 for Thailand rice). Furthermore, the crowdsourced data shows a high correlation with the AI-imputed prices (r = 0.99 for maize, and r = 0.94 for rice). The results from additional statistical tests of normality and paired means shows that the discrepancies between price datasets are consistent with measurement error rather than differences in actual price dynamics. Further tests of equivalence confirmed that enumerator and crowdsourced prices represent the same underlying market processes for specific commodity subtypes, and connotes that crowdsourced price data is a credible reference for validating AI-imputed estimates. The results support the use of AI imputation and crowdsourcing methods to improve price data collection and track market dynamics in near real time. These data innovations can be particularly valuable in areas that are underrepresented in national aggregate data due to limited monitoring capacity, and where high-frequency local data can aid targeted interventions.
Transcranial brain-wide functional ultrasound and ultrasound localization microscopy in mice using multi-array probes
Abstract Functional ultrasound imaging (fUS) and ultrasound localization microscopy (ULM) are advanced ultrasound imaging modalities for assessing both functional and anatomical characteristics of the brain. However, the application of these techniques at a whole-brain scale has been limited by technological challenges. While conventional linear acoustic probes provide a narrow 2D field of view and matrix probes lack sufficient sensitivity for 3D transcranial fUS, multi-array probes have been developed to combine high sensitivity to blood flow with fast 3D acquisitions. In this study, we present a novel approach for the combined implementation of transcranial whole-brain fUS and ULM in mice using a motorized multi-array probe. This technique provides high-resolution, non-invasive imaging of neurovascular dynamics across the entire brain. Our findings reveal a significant correlation between absolute cerebral blood volume (ΔCBV) increases and microbubble speed, indicating vessel-level dependency of the evoked response. However, the lack of correlation with relative CBV (rCBV) suggests that fUS cannot distinguish functional responses alterations across different arterial vascular compartments. This methodology holds promise for advancing our understanding of neurovascular coupling and could be applied in brain disease diagnostics and therapeutic monitoring.
Rapid micropropagation and chemical profiling of in vitro plantlets and agarwood of Gyrinops walla Gaertn. by gas-chromatography and mass-spectrometry
Gyrinops walla Garten., which is an endemic and endangered species of Sri Lanka, produces the world’s most expensive agarwood used in perfume industry. The high demand for agarwood has resulted in indiscriminate felling of trees, thus threatening the survival of the species. The present study aimed to develop an efficient in vitro rapid multiplication technique to conserve the existing trees from extinction, by ensuring the sustainable supply of planting materials for commercial cultivations and to investigate the possibility of producing fragrance compounds by in vitro plantlets without felling trees. Efficient micropropagation protocol was developed from axillary buds and shoot tip explants. Murashige and Skoog (MS) medium supplemented with 1.0 mg/L BAP was the best for the establishment of both shoot tips (80.0%) and axillary buds (86.0%). Regenerated buds were further multiplied (10.6 ± 0.93 shoot buds/regenerated shoot) and elongated (4.0 ± 0.26 cm) by transferring to MS medium supplemented with 1.0 mg/L BAP, 0.1 mg/L IBA and 40 g/L sucrose. Highest in vitro rooting percentage (66.7%) was recorded in ½ MS medium supplemented with 1.0 mg/L IAA and 40 g/L sucrose. However, none of the shoots rooted on MS media could be acclimatized. Significantly higher percentage of rooted shoots (93.3%) were produced on sand medium without auxin treatment compared to shoots cultured on MS medium supplemented with 1.0 mg/L IAA (66%) and successfully acclimatized with 83.6% survival rate in a medium consisted of sand, topsoil, and compost (1:1:1 ratio). TLC fingerprints of ethyl acetate extracts of in vitro grown plantlets and agarwood produced similar spots at the retention factors (Rf) of 0.60, 0.66, and 0.87 under 15% methanol: 85% chloroform solvent system. Chemicals present in in vitro plantlets were identified and compared with the agarwood of naturally grown G. walla by GC-MS. Both natural agarwood and in vitro grown shoot extracts contained 4-Hydroxypyridine 1-oxide (23.2%), 2-tetradecene (16.3%), 1-hexadecene (0.3%), E-15-heptadecenal (19.8%), 18-norabietane (0.6%) and eicosane (0.4%). Present study successfully developed a protocol for rapid multiplication of G. walla and indicates the possibility of using of in vitro plantlets to produce agarwood resinous compounds.
Author Correction: Comprehensive analysis of mitochondrial-related gene signature for prognosis, tumor immune microenvironment evaluation, and candidate drug development in colon cancer
A study on innovation resistance of artificial intelligence voice assistants based on privacy infringement and risk perception
As a vital tool for human-computer interaction, artificial intelligence (AI) voice assistants have become an integral part of individuals’ everyday routines. However, there are still a series of problems caused by privacy violations in current use. This research aims to explore users’ risk perceptions and innovation resistance arising from privacy concerns when utilizing AI voice assistants. Descriptive statistics and correlation analysis were conducted using SPSS21.0 software to examine each variable. The mediating and moderating effects were analyzed through specific models provided by PROCESS. The findings of this research indicate that perceptions of risk serve as a mediator in the relationship between privacy violations and resistance to innovation. This elucidates the indirect pathway through which privacy concerns impact opposition to new technologies. Furthermore, the study reveals that anthropomorphism and informativeness can mitigate the perceived risks associated with AI voice assistants, consequently reducing resistance to innovation. By focusing on user psychology, this study offers valuable insights for the development and enhancement of AI voice assistants, underscoring the importance of addressing user concerns regarding privacy and risk.
α-Lipoic acid alleviate myocardial infarction by suppressing age-independent macrophage senescence
Sex Differences in Peripheral Vascular Disease: A Scientific Statement From the American Heart Association
Sex differences in the risk factors, diagnosis, treatment, and outcomes of patients with cardiovascular disease have been well described; however, the bulk of the literature has focused on heart disease in women. Data on sex differences in peripheral vascular disease are ill defined, and there is a need to report and understand those sex-related differences to mitigate adverse outcomes related to those disparities. Although peripheral vascular disease is a highly diverse group of disorders affecting the arteries, veins, and lymphatics, this scientific statement focuses on disorders affecting the peripheral arteries to include the aorta and its branch vessels. The purpose of this scientific statement is to report the current status of sex-based differences and disparities in peripheral vascular disease and to provide research priorities to achieve health equity for women with peripheral vascular disease.
Trillin protects against doxorubicin-induced cardiotoxicity through regulating Nrf2/HO-1 signaling pathway
Doxorubicin (DOX) is widely employed in anticancer therapy, but its clinical application is constrained by its cardiotoxic effects. Trillin, a bioactive compound derived from Trillium tschonoskii Maxim., has been identified as a natural antioxidant possessing cardioprotective properties. This study aimed to ascertain whether trillin can protect against DOX-induced cardiotoxicity (DIC) through its inherent antioxidant capabilities. In vivo studies, C57BL/6 mice were administered DOX (5 mg/kg i.p.) via intraperitoneal injection once weekly for a total of five consecutive weeks and received trillin (25, 50 and 100 mg/kg i.g.) through intragastric administration once daily for six weeks. In vitro studies, H9c2 cardiomyocytes were utilized to verify the protective efficacy of trillin (0.5, 1 and 2 μM) against DIC. Trillin significantly mitigated DOX-induced myocardial damage, which encompassed improvements in left ventricular function, reductions in serum cardiac enzymes levels, and diminution of heart cell vacuolation. Moreover, trillin effectively attenuated DIC while preserving the anticancer efficacy of DOX. Trillin also alleviated oxidative injury by elevating levels of SOD and GSH and reducing MDA levels. Additionally, trillin restored the expression of Nrf2 and HO-1 in mouse hearts and H9c2 cardiomyocytes treated with DOX. Trillin safeguarded against DIC by inhibiting oxidative stress via upregulation of the Nrf2/HO-1 pathway. These findings furnish evidence suggesting trillin may serve as a therapeutic agent for the prevention of DIC.
Novel digital markers of sleep dynamics: causal inference approach revealing age and gender phenotypes in obstructive sleep apnea
Induced Cytokinesis Generates Highly Proliferative Mononuclear Cardiomyocytes at the Expense of Contractility
BACKGROUND: Cytokinesis is the last step in the eukaryotic cell cycle, which physically separates a mitotic cell into 2 daughter cells. A few days after birth in mouse cardiomyocytes, DNA synthesis occurs without cytokinesis, leading to the majority of cardiomyocytes becoming binucleated instead of generating 2 daughter cells with 1 nucleus each. This results in cell cycle arrest of cardiomyocytes, and the mouse heart is no longer able to regenerate. A longstanding unanswered question is whether binucleation of cardiomyocytes is a result of cytokinesis failure. METHODS: To address this, we generated several transgenic mouse models to determine whether forced induction of cardiomyocyte cytokinesis generates mononucleated cardiomyocytes and restores the endogenous regenerative properties of the myocardium. We focused on 2 complementary regulators of cytokinesis: Plk1 (polo-like kinase 1) and Ect2 (epithelial cell-transformation sequence 2). RESULTS: We found that cardiomyocyte-specific transgenic overexpression of constitutively active Plk1(T210D) promotes mitosis and cytokinesis in adult hearts, whereas overexpression of Ect2 alone promotes only cytokinesis. Cardiomyocyte-specific overexpression of both Plk1(T210D) and Ect2 concomitantly (double transgenic) prevents binucleation of cardiomyocytes postnatally and results in widespread cardiomyocyte mitosis, cardiac enlargement, contractile failure, and death before 2 weeks of age. In contrast, doxycycline-inducible cardiomyocyte-specific overexpression of both genes (inducible double transgenic) in the adult heart results in cardiomyocyte mitosis and transient contractile dysfunction. Importantly, this transient induction of cytokinesis in adult mice improves left ventricular systolic function after myocardial infarction. CONCLUSIONS: These results collectively demonstrate that cytokinesis failure mediates cardiomyocyte multinucleation and cell cycle exit of postnatal cardiomyocytes, but may be a protective mechanism to preserve the contractile function of the myocardium.
Analysing similarities between legal court documents using natural language processing approaches based on transformers
Recent advancements in Artificial Intelligence have yielded promising results in addressing complex challenges within Natural Language Processing (NLP), serving as a vital tool for expediting judicial proceedings in the legal domain. This study focuses on the detection of similarity among judicial documents within an inference group, employing eight NLP techniques grounded in transformer architecture, specifically applied to a case study of legal proceedings in the Brazilian judicial system. The transformer-based models utilised — BERT, GPT-2, RoBERTa, and LlaMA — were pre-trained on general-purpose corpora of Brazilian Portuguese and subsequently fine-tuned for the legal sector using a dataset of 210,000 legal cases. Vector representations of each legal document were generated based on their embeddings, facilitating the clustering of lawsuits and enabling an evaluation of each model’s performance through the cosine distance between group elements and their centroid. The results demonstrated that transformer-based models outperformed traditional NLP techniques, with the LlaMA model, specifically fine-tuned for the Brazilian legal domain, achieving the highest accuracy. This research presents a methodology employed in a real case involving substantial documentary content that can be adapted for various applications. It conducts a comparative analysis of existing techniques focused on a non-English language to quantitatively explain the results obtained with various NLP transformers-based models. This approach advances the current state of the art in NLP applications within the legal sector and contributes to the achievement of Sustainable Development Goals.
Home surveillance system based on LoRa backscattering
Abstract This work proposes a battery-free wireless burglar alarm system based on LoRa backscattering. The system consists of multiple wireless nodes that send an alert when its sensor is triggered. The primary goal is to significantly reduce the power consumption of these wireless nodes, eliminating the need for battery replacements and maintenance associated with commercial systems. The paper addresses several crucial aspects of the application, including the hardware design of the tag, which encompasses energy management up to the RF front-end. Additionally, this paper explores the wireless communication protocol by presenting a fully analog modulation approach to optimize power consumption and system responsiveness. This novel method leverages the intrinsic frequency detection function of commercial LoRa transceivers to determine the origin of backscattered LoRa packets, thereby enabling the integration of a large number of sensors within the same frequency channel, all using a single receiver. Finally, the paper provides a proof of concept validated through on-site measurements.