Search Results (78)

Research on MICP technology for ground improvement began in the early 2000s, and since then, it has been considered as innovative research. The field of applications is showing signs of expanding from sandy soil stabilization to remediation. However, the research has not always progressed, because it is extremely difficult to evaluate the ability (viability rate) related to microorganisms and how to handle them quantitatively. In fact, this problem hinders the consensus of research results in terms of quantitative evaluation of microorganisms and the cross-comparison (evaluation) and use of MICP technology research. The crucial disadvantage of using bacteria is that their properties are not constant due to changes over time and in the surrounding environment. Therefore, for engineering purposes, we used the carbonate formation rate (CPR), instead of urease activity, as a function of the microbial mass (OD) with viable bacteria. Thus, the standard OD−CPR relationship was defined experimentally, and the estimation method of viability was established. The required amount of microorganisms for testing was given by OD*, and the relationship “OD = Rcv OD*” was defined to convert from OD* to OD. Rcv was defined as the viable bacterial rate. It was found that the Ca²⁺/OD ratio controls the inhibition behavior in MICP. At a Ca²⁺/OD ratio of >8.46 M, then inhibition occurs, while at Ca²⁺/OD = 8.46 M, CPR = 8.46 OD and the CPR is proportional to the viable OD, Rcv, and OD*. We show that it is possible to perform an experiment using OD* with aged bacteria, obtain Rcv from the standard OD−CPR and OD*−CPR relationships, convert OD* to OD and to perform a unified evaluation without actually determining the viability rate. Full article

Background: The aim of this study was the determination of reference values for the common laboratory parameters in early pregnancy using a direct method and to assess their clinically significant difference, which was compared to the reference intervals for non-pregnant women with respect to the reference change value (RCV). Methods: This study was conducted from September 2022 to December 2023 at the Clinical Department of Laboratory Diagnostics, Clinical Hospital Centre RIJEKA, Croatia. The inclusion criteria were as follows: age ≥ 18 years, singleton pregnancy, normal ultrasound examination, and prenatal screening. The exclusion criteria were as follows: recent illness, pregnancy-related complications, medically assisted reproduction, and medication use. The reference intervals were established using the non-parametric percentile method according to the CLSI EP28-A3c recommendation. The reference values were compared to those of non-pregnant women and judged against RCV values based on biological variation. Additionally, we tested the influence of food consumption and oral supplements. Results: The data of 299 participants were included in the study. Laboratory tests whose changes are clinically relevant lower in early pregnancy are as follows: hemoglobin, MCV, hematocrit, MCH, urea, creatinine, albumin, alkaline phosphatase, lactate dehydrogenase, sodium, and magnesium. The clinically relevant higher values are as follows: RDW, total leukocyte count, neutrophil granulocytes, monocytes, CRP, total cholesterol, triglycerides, and amylase. UIBC has a wider reference range. The consumption of food and supplements has no clinically significant influence in relation to the RCV. Conclusions: Establishing reference intervals in pregnancy remains a challenge due to the metabolic changes during pregnancy, as well as its clinical significance. Full article

The development of chirality descriptors for quantitative chirality structure–activity relationship (QCSAR) modeling has always attracted attention, owing to the importance of chiral molecules in pharmaceutical, agriculture, food, and fragrance industries, and environmental toxicology. The utility of a multidimensional space of novel relative chirality indices (RCIs) in the QCSAR modeling of twenty CCR2 antagonists is reported upon in this paper. The numerical characterization of chirality by the RCI approach gives a large pool of chirality descriptors with different degrees of mutual correlation (the correlation coefficient among the computed descriptors varied from 0.02 to 0.99). In the present study, the final data set contains 198 chirality descriptors for each of the twenty CCR2 antagonist molecules, providing a multidimensional space for modeling. The data reduction using principal component analysis resulted in the extraction of eight principal components (PCs). The linear regression using the principal component scores (PCSs) resulted in a three-predictor prediction model with good statistics: R² = 0.823; Adj R² = 0.790. The regression models were rebuilt using the chirality descriptors (RCIs) that are most correlated with each of the scores (PCSs) of the three principal components. The R² value for the regression models with three RCIs as the predictors is 0.742 and the five-fold cross validation, R_cv², is 0.839. The new chirality descriptors, namely, the RCIs calculated using a different weighting scheme, provide a multidimensional space of chirality descriptors for a set of chiral molecules, and such a multidimensional chirality space is a powerful tool to build quantitative chiral structure–activity relationship (QCSAR) models. Full article

Background and Objectives: Although reversible cerebral vasoconstriction syndrome (RCVS) is a rare disease, the condition may occur with COVID-19 infection. We aimed to investigate the clinical characteristics of RCVS through a systematic review of case reports and case series that reported on COVID-19-related RCVS. Methods: A literature search was performed in PubMed (MEDLINE), SCOPUS, and Web of Science. The search was confined to articles published between 17 November 2019 and 14 August 2024. The search terms were (“COVID-19” OR “SARS CoV-2”) AND (“RCVS” OR “Reversible cerebral vasoconstriction syndrome”). The search protocol was registered in PROSPERO (CRD42024491818). A total of twenty-four cases were included, nine case series consisting of nineteen cases and five cases from our hospital. Clinical characteristics were investigated, including risk factors, symptoms, brain and angiographic findings, treatment options, and prognoses. Results: The average age was 37.1 years, and females comprised 70.8% of the cohort. COVID-19 vaccination was administered in five cases. Vasoconstriction was most frequently noted in middle cerebral arteries (90.9%). Among the included patients, 12 (50.0%) experienced strokes as a complication of RCVS, and the mortality rate was 9.1%. Follow-up imaging findings were available for 14 of the 24 cases (58.3%). Among these, vasoconstriction was fully improved in 64.3%, partially improved in 28.6%, and aggravated in 7.1%. Conclusions: While the recovery rate of vasoconstriction on brain angiographic findings was not uncommon, our systematic review indicates a potential for a relatively poor neurological prognosis in COVID-19-related RCVS. Full article

Machine learning (ML) models are extensively used in spatial predictive modeling, including landslide susceptibility prediction. The performance statistics of these models are vital for assessing their reliability, which is typically obtained using the random cross-validation (R-CV) method. However, R-CV has a major drawback, i.e., it ignores the spatial autocorrelation (SAC) inherent in spatial datasets when partitioning the training and testing sets. We assessed the impact of SAC at three crucial phases of ML modeling: hyperparameter tuning, performance evaluation, and learning curve analysis. As an alternative to R-CV, we used spatial cross-validation (S-CV). This method considers SAC when partitioning the training and testing subsets. This experiment was conducted on regional landslide susceptibility prediction using different ML models: logistic regression (LR), k-nearest neighbor (KNN), linear discriminant analysis (LDA), artificial neural networks (ANN), support vector machine (SVM), random forest (RF), and C5.0. The experimental results showed that R-CV often produces optimistic performance estimates, e.g., 6–18% higher than those obtained using the S-CV. R-CV also occasionally fails to reveal the true importance of the hyperparameters of models such as SVM and ANN. Additionally, R-CV falsely portrays a considerable improvement in model performance as the number of variables increases. However, this was not the case when the models were evaluated using S-CV. The impact of SAC was more noticeable in complex models such as SVM, RF, and C5.0 (except for ANN) than in simple models such as LDA and LR (except for KNN). Overall, we recommend S-CV over R-CV for a reliable assessment of ML model performance in large-scale LSM. Full article

Multi-label text classification (MLTC) aims to assign the most appropriate label or labels to each input text. Previous studies have focused on mining textual information, ignoring the interdependence of labels and texts, thus leading to the loss of information about labels. In addition, previous studies have tended to focus on the single granularity of information in documents, ignoring the degree of inclination towards labels in different sentences in multi-labeled texts. In order to solve the above problems, this paper proposes a Label-Sentence Bi-Attention Fusion Network (LSBAFN) with multi-level feature extraction for mining multi-granularity information and label information in documents. Specifically, document-level and sentence-level word embeddings are first obtained. Then, the textual relevance of the labels to these two levels is utilized to construct sentence-level textual representations. Next, a multi-level feature extraction mechanism is utilized to acquire a sentence-level textual representation that incorporates contextual information and a document-level textual representation that reflects label features. Subsequently, the label-sentence bi-attention fusion mechanism is used to learn the feature relationships in the two text representations and fuse them. Label attention identifies text features related to labels from the document-level text representation, while sentence attention focuses on the tendency of sentences towards labels. Finally, the effective portion of the fused features is extracted for classification by a multi-layer perceptron. The experimental findings indicate that the LSBAFN can improve the effectiveness of the MLTC task. Compared with the baseline models, the LSBAFN obtains a significant improvement of 0.6% and 7.81% in Micro-F1 and Macro-F1 on the Article Topic dataset and improvements of 1.03% and 0.47% in P@k and 1.02% and 0.38% in nDCG@k on the Software Category dataset and RCV1 dataset. Full article

Authenticating poppy seed oil is essential to ensure product quality and prevent economic and health-related fraud. This study developed a non-targeted approach using FT-IR spectroscopy and pattern recognition analysis to verify the authenticity of poppy seed oil. Thirty-nine poppy seed oil samples were sourced from online stores and local markets in Turkiye. Gas chromatography–Flame Ionization Detector (GC-FID) analysis revealed adulteration in 23% of the samples, characterized by unusual fatty acid composition. Spectra of the oil samples were captured with a portable 5-reflection FT-IR sensor. Soft Independent Model of Class Analogies (SIMCA) was used to create class algorithms, successfully detecting all instances of adulteration. Partial least square regression (PLSR) models were then developed to predict the predominant fatty acid composition, achieving strong external validation performance (R_CV = 0.96–0.99). The models exhibited low standard errors of prediction (SEP = 0.03–1.40%) and high predictive reliability (RPD = 2.9–6.1; RER = 8.4–13.1). This rapid, non-destructive method offers a reliable solution for authenticating poppy seed oil and predicting its fatty acid composition, presenting valuable applications for producers and regulatory authorities. This approach aids in regulatory compliance, protection of public health, and strengthening of consumer confidence by ensuring the authenticity of the product. Full article

Background/Objectives: As the newcomer population in Canada continues to grow, we aimed to collaborate with newcomer families arriving in an urban center in Alberta, Canada to identify strategies to overcome identified barriers newcomers face in obtaining routine childhood vaccines (RCVs). Methods: We recruited newcomers living in Calgary, Alberta to participate in a workshop utilizing the Nominal Group Technique (NGT) to develop solutions addressing barriers to obtaining RCVs. Ranking exercises helped identify the top-proposed interventions based on perceived impact and feasibility for implementation. Based on the identified need for translated vaccine resources, infographics on school-based vaccines were developed. The infographics were pilot-tested in a first-language focus group before the final product was translated into 10 different languages. Results: Consensus from 15 NGT workshop participants identified five key solutions to facilitate obtaining routine childhood immunizations: (1) Increasing access to reliable vaccine information; (2) Ensuring vaccine information and healthcare services are available in different languages; (3) Increasing vaccine appointment availability and optimizing the booking system for ease of navigation; (4) Increasing the role of family doctors in vaccine counseling and administration; (5) Streamlining vaccine record tracking. We developed infographics on the vaccines children in Alberta can receive through school-based vaccine programs and these were pilot-tested with 16 participants in a first-language (Arabic) focus group. Conclusions: The collaborative and iterative process of solution development with newcomers provided a platform for knowledge translation through the development of educational resources on school-based vaccines, addressing the information barrier that newcomers identified when accessing RCVs. Full article

Objectives: To investigate the acceptance and factors influencing acceptance of rubella-containing vaccine (RCV) among women of reproductive age in Guangdong, Henan, Hubei, Liaoning, Shanxi, Sichuan and Zhejiang provinces of China. Methods: Using a stratified sampling method, we selected one urban and one rural community health services center in each of two cities in seven provinces. From these centers, we recruited women 15–49 years of age to complete a questionnaire on their willingness to receive RCV and factors influencing willingness. The survey instrument was adapted from the behavioral and social drivers (BeSD) of vaccination survey tool developed by the World Health Organization. Results: Among 1286 participants, 981 (76.3%) were willing to receive RCV. Awareness of rubella ranged from 12.4% to 70.6%. Willingness to receive RCV differed significantly by region, occupation, vaccination history, and awareness. All latent variables of the structural equation model (SEM) were positively correlated with willingness, and all standardized paths were statistically significant (p < 0.001). Thinking and Feeling had direct positive effects on Social Processes (β = 0.789) and Practical Issues (β = 0.542), thereby indirectly affecting motivation. Conclusions: Women of reproductive age had high willingness to receive the rubella vaccination, but their general awareness of rubella was relatively low. It is necessary to strengthen the health education of women of reproductive age regarding rubella to improve RCV coverage. Full article

Reversible cerebral vasoconstriction syndrome (RCVS) is a rare but significant cause of intracranial arteriopathy and stroke in young adults. The syndrome encompasses a spectrum of disorders radiologically characterized by reversible narrowing and dilation of intracranial arteries, often triggered by vasoactive drugs or the postpartum period. The hallmark clinical feature of RCVS is thunderclap headache with or without other neurological signs. Though endothelial dysfunction and sympathetic hyperactivation are hypothesized to be key mechanisms, the exact pathogenesis of RCVS is still unclear. RCVS’s diagnosis could be challenging, since vasospasm proceeds centripetally, initially involving distal small pial and cortical arteries, and angiographic studies, especially brain magnetic resonance angiography (MRA) and computed tomography angiography (CTA), may miss it in the early phase of the disease, while early signs such as vascular hyperintensities may be visible on T2/FLAIR sequences before vasospasm onset. Catheter angiography is the gold standard and it could be used to assess vasospasm reversibility post-intra-arterial vasodilator administration. Treatment is mainly symptomatic, and nimodipine is the most commonly administered therapy, given orally or intra-arterially in severe cases. Since many aspects of RCVS remain partially known, further research is needed to better understand the complex pathophysiology of this unique clinical condition and to optimize specific management strategies. Full article

Multi-label text classification (MLTC) aims to assign appropriate labels to each document from a given set. Prior research has acknowledged the significance of label information, but its utilization remains insufficient. Existing approaches often focus on either label correlation or label textual semantics, without fully leveraging the information contained within labels. In this paper, we propose a multi-perspective contrastive model (MPCM) with an attention mechanism to integrate labels and documents, utilizing contrastive methods to enhance label information from both textual semantic and correlation perspectives. Additionally, we introduce techniques for contrastive global representation learning and positive label representation alignment to improve the model’s perception of accurate labels. The experimental results demonstrate that our algorithm achieves superior performance compared to existing methods when evaluated on the AAPD and RCV1-V2 datasets. Full article

The COVID-19 pandemic, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), began in 2019. One of the strategies for pandemic control was mass vaccination. In Brazil, the recombinant COVID-19 vaccine (RCV) was produced on a large scale and offered at no charge to the population. The specifications for quality control analyses of RCV included identity and infectivity determination. To validate the results, a reference material (RM) must be analyzed in parallel with the sample vaccine. This research aimed to establish the RM for use in the identity and infectivity assay for RCV. The candidate RM was analyzed using homogeneity and stability studies. The RM was considered homogeneous for identity (cycle threshold (Ct) ≤ 25.19) and infectivity (average $\stackrel{-}{\mathrm{x}}$ was 9.25 log₁₀ infectious units/mL). The RM was considered adequately stable for identity during the total period in all studies, being stable at −70, 5, and 22.5 °C for 380, 313, and 14 days, respectively (Ct ≤ 21.81). For infectivity, the RM was stable at −70, 5, and 22.5 °C for 380, 97, and three days, respectively. Since the property identity and infectivity values of the RM were established, the new RM could be used in quality control analysis. Full article

Introduction: The WHO Measles and Rubella Strategic Framework 2021–2030 within the Immunization Agenda 2030 includes both measles and rubella elimination goals and provides guidance to countries for planning and implementing the measles and rubella elimination strategies. Namibia has been implementing measles elimination strategies since 1997. Methods: We reviewed and described the implementation of measles and rubella elimination strategies and the programmatic and epidemiological situation in Namibia during 2000–2023. Namibia introduced a rubella-containing vaccine (RCV) in 2016 as a combined measles–rubella (MR) vaccine using a MR catch-up campaign, targeting a wide age range based on detailed analysis and triangulation of multiple key data sources including MR vaccination coverage, MR case-based surveillance, detailed measles outbreak investigations, and serosurveys. Results: In 2020, estimated MCV1 coverage in Namibia reached 90% and has been sustained at 91% in 2021 and 2022. MCV2 was introduced in 2016, and the estimated MCV2 coverage has steadily increased to 79% in 2022. Following the MCV2 introduction and the implementation of the wide age range MR catch-up campaign in 2016, annual measles and rubella incidence decreased substantially. During 2017–2023, the period following the implementation of the catch-up MR vaccination SIA in 2016, average annual measles incidence per million population in Namibia decreased by 97% from the average during 2010–2016. Similarly, the average annual rubella incidence decreased by 95% from 2010–2016 to 2017–2023. Discussion: Successful implementation of the 2016 wide age range campaign and maintaining high routine immunization coverage likely led to the significant reduction in measles and rubella incidence in Namibia. To sustain the reduction in measles and rubella incidence and attain the elimination targets, Namibia needs to attain and maintain high routine immunization coverage with both doses of the MR vaccine and implement timely and high-quality periodic MR follow-up SIAs. High-quality elimination-standard measles and rubella surveillance will help guide strategies and serve as the basis for the eventual verification of measles and rubella elimination in Namibia according to the WHO-recommended framework. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly being used in a variety of applications, including entertainment, surveillance, and delivery. However, the real-time Motion Estimation (ME) of UAVs is challenging due to the high speed and unpredictable movements of these vehicles. This paper presents a novel algorithm for optimizing ME for Remotely Controlled Vehicles (RCVs), with a particular focus on UAVs. The proposed algorithm, called Motion Dynamics Input Search (MDIS), incorporates information from vehicle motion dynamics estimation to enhance the accuracy and efficiency of ME. The MDIS algorithm addresses the challenges associated with real-time ME in RCVs by leveraging user input to guide the search for the most similar blocks in the previous video frame. Through extensive experimentation and evaluation, this study demonstrates the effectiveness of the proposed algorithm in improving ME performance for RCVs. The findings highlight the potential impact of user interaction and motion dynamics estimation in shaping the future of ME algorithms for RCVs and similar applications. Full article

Spontaneous convexity subarachnoid hemorrhage (cSAH) is a vascular disease different from aneurysmal SAH in neuroimaging pattern, causes, and prognosis. Several causes might be considered in individual patients, with a limited value of the patient’s age for discriminating among these causes. Cerebral amyloid angiopathy (CAA) is the most prevalent cause in people > 60 years, but reversible cerebral vasoconstriction syndrome (RCVS) has to be considered in young people. CAA gained attention in the last years, but the most known manifestation of cSAH in this context is constituted by transient focal neurological episodes (TFNEs). CAA might have an inflammatory side (CAA-related inflammation), whose diagnosis is relevant due to the efficacy of immunosuppression in resolving essudation. Other causes are hemodynamic stenosis or occlusion in extracranial and intracranial arteries, infective endocarditis (with or without intracranial infectious aneurysms), primary central nervous system angiitis, cerebral venous thrombosis, and rarer diseases. The diagnostic work-up is fundamental for an etiological diagnosis and includes neuroimaging techniques, nuclear medicine techniques, and lumbar puncture. The correct diagnosis is the first step for choosing the most effective and appropriate treatment. Full article