Browse Articles
Discover research articles across all indexed journals
Design and optimal tuning of fractional order PID controller for paper machine headbox using jellyfish search optimizer algorithm
Impedance mapping with high-density microelectrode array chips reveals dynamic heterogeneity of in vitro epithelial barriers
Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments
Associations between surrogate insulin resistance indexes and osteoarthritis: NHANES 2003–2016
AbstractInsulin resistance (IR) and abdominal obesity are key in osteoarthritis (OA) development. The triglyceride glucose (TyG) index, along with indicators such as the visceral adiposity index (VAI), and lipid accumulation product (LAP), are increasingly used to measure IR. This study aims to explore the associations between surrogate IR indexes and OA, assessing their diagnostic efficacy within American populations. This study included 14,715 adults from the National Health and Nutrition Examination Survey 2003–2016. Logistic regression models and restricted cubic spline were used to explore the relationship between surrogate IR indexes and OA. Receiver operating characteristic curves were constructed to assess the diagnostic efficacy of these indices, with the area under the curve (AUC) as the metric. TyG, glucose triglyceride-waist circumference (TyG-WC), glucose triglyceride-body mass index (TyG-BMI), glucose triglyceride-waist height ratio (TyG-WHtR), VAI and LAP were significantly and positively associated with the prevalence of OA (all p < 0.01). After adjusting for various potential confounders, TyG-WC, TyG-BMI, TyG-WHtR and LAP remained significantly correlated with the prevalence of OA. Furthermore, restricted cubic spline revealed a nonlinear association between TyG-BMI, TyG-WHtR and LAP (all P-non-linear < 0.05). Receiver operating characteristic curves indicated that TyG-WHtR (AUC 0.633) demonstrated more robust diagnostic efficacy. Additionally, the sensitivity analysis produced results consistent with the primary findings. TyG and its combination with obesity indicators and LAP, are positively associated with the prevalence of OA, with TyG-WHtR showing the highest diagnostic efficacy.
Comparative measurement of short-term fluoride release and inhibition of caries around restoration by ion releasing restorative materials: an in vitro study
AbstractThe main objective of the current study is to compare short-term fluoride release of three ion releasing restorative materials and assess their inhibitory effect on secondary caries. Materials used in this study included, Self-adhesive hybrid composite (group A), Ion releasing flowable composite liner (group B), and alkasite restorative material (group C). Twenty-two discs were fabricated from each material for short-term fluoride release test, conducted on days 1, 7, and 14. For assessing secondary caries inhibition, sixty-six sound molar teeth were used and standardized class V cavities were prepared. Teeth were divided into three groups according to each material, followed by 800 cycles of thermocycling. Subsequently, teeth were immersed in a solution containing cariogenic bacteria for 30 days. After that, teeth were sectioned bucco-lingually and analyzed using a polarized light microscope to measure inhibition area, outer lesion depth, and extension. Data was statistically analyzed using different tests. The study results revealed a statistically significant differences in fluoride release existed among materials. Self-adhesive hybrid composite exhibited the highest fluoride release. Lesion extension and depth were statistically significantly greater next to Ion-releasing flowable composite liner. The inhibition areas next to the Self-adhesive hybrid composite were statistically significantly larger than the other two materials. In conclusion, all tested ion-releasing restorative materials displayed fluoride release and the potential to inhibit secondary caries formation. Self-adhesive hybrid composite demonstrated the highest fluoride-releasing potential and the greatest ability to inhibit secondary caries. Conversely, Ion-releasing flowable composite liner exhibited the least fluoride release with minimal secondary caries inhibition. Increasing fluoride release correlated with larger inhibition areas and reduced outer lesion depth and extension.
Optimizing design and stability of open pit slopes in Tolay coal mine, Ethiopia
Dust deposition characteristics on photovoltaic arrays investigated through wind tunnel experiments
Machine learning in the prediction of human wellbeing
AbstractSubjective wellbeing data are increasingly used across the social sciences. Yet, despite the widespread use of such data, the predictive power of approaches commonly used to model wellbeing is only limited. In response, we here use tree-based Machine Learning (ML) algorithms to provide a better understanding of respondents’ self-reported wellbeing. We analyse representative samples of more than one million respondents from Germany, the UK, and the United States, using data from 2010 to 2018. We make three contributions. First, we show that ML algorithms can indeed yield better predictive performance than standard approaches, and establish an upper bound on the predictability of wellbeing scores with survey data. Second, we use ML to identify the key drivers of evaluative wellbeing. We show that the variables emphasised in the earlier intuition- and theory-based literature also appear in ML analyses. Third, we illustrate how ML can be used to make a judgement about functional forms, including the existence of satiation points in the effects of income and the U-shaped relationship between age and wellbeing.
Exploring the utility of unretouched lithic flakes as markers of cultural change
AbstractLithic artefacts provide the principal means to study cultural change in the deep human past. Tools and cores have been the focus of much prior research based on their perceived information content and cultural relevance. Unretouched flakes rarely attract comparable attention in archaeological studies, despite being the most abundant assemblage elements and featuring prominently in ethnographic and experimental work. Here, we examine the potential of flake morphology for tracing cultural change utilising 4,512 flakes, each characterised by 16 standard mixed-scale attributes, from a well-documented cultural sequence at the Middle Stone Age site of Sibhudu, South Africa. We quantified multivariate similarities among flakes using FLEXDIST, a highly versatile method capable of handling mixed, correlated, incomplete, and high-dimensional data. Our findings reveal a significant gradual change in flake morphology that aligns with the documented cultural succession at Sibhudu. Furthermore, our analysis provides new insights into the patterning of variability throughout the studied sequence. The demonstrated potential of flakes to track cultural change opens up additional avenues for comparative research due to their ubiquity, the availability of commonly recorded attributes, and especially in the absence of cores or tools. FLEXDIST, with its versatile applicability to complex lithic datasets, holds particular promise in this regard.
Patient satisfaction and feasibility with a novel drug-injectable urethral catheter set for hyaluronic acid administration: a multicenter randomized trial
Boosting any learning algorithm with Statistically Enhanced Learning
An optimized LSTM-based deep learning model for anomaly network intrusion detection
Abstract The increasing prevalence of network connections is driving a continuous surge in the requirement for network security and safeguarding against cyberattacks. This has triggered the need to develop and implement intrusion detection systems (IDS), one of the key components of network perimeter aimed at thwarting and alleviating the issues presented by network invaders. Over time, intrusion detection systems have been instrumental in identifying network breaches and deviations. Several researchers have recommended the implementation of machine learning approaches in IDSs to counteract the menace posed by network intruders. Nevertheless, most previously recommended IDSs exhibit a notable false alarm rate. To mitigate this challenge, exploring deep learning methodologies emerges as a viable solution, leveraging their demonstrated efficacy across various domains. Hence, this article proposes an optimized Long Short-Term Memory (LSTM) for identifying anomalies in network traffic. The presented model uses three optimization methods, i.e., Particle Swarm Optimization (PSO), JAYA, and Salp Swarm Algorithm (SSA), to optimize the hyperparameters of LSTM. In this study, NSL KDD, CICIDS, and BoT-IoT datasets are taken into consideration. To evaluate the efficacy of the proposed model, several indicators of performance like Accuracy, Precision, Recall, F-score, True Positive Rate (TPR), False Positive Rate (FPR), and Receiver Operating Characteristic curve (ROC) have been chosen. A comparative analysis of PSO-LSTMIDS, JAYA-LSTMIDS, and SSA-LSTMIDS is conducted. The simulation results demonstrate that SSA-LSTMIDS surpasses all the models examined in this study across all three datasets.
High Ano1 expression as key driver of resistance to radiation and cisplatin in HPV-negative head and neck squamous cell carcinoma
Abstract Human papilloma virus-negative head and neck squamous cell carcinoma (HNSCC) frequently harbors 11q13 amplifications. Among the oncogenes at this locus, CCND1 and ANO1 are linked to poor prognosis; however, their individual roles in treatment resistance remain unclear. The impact of Cyclin D1 and Ano1 overexpression on survival was analyzed using the TCGA HNSCC dataset and a Charité cohort treated with cisplatin (CDDP)-based radiochemotherapy. High Ano1 expression was primarily associated with poor overall survival in both datasets. The effects of CCND1 and ANO1 knockdown (KD) on radio- and drug sensitivity, along with changes in global protein expression, cell viability, growth, and DNA repair, were studied in an 11q13-amplified HNSCC cell line model of primary cisplatin resistance. Unique pathway alterations– VEGF in CCND1 KD and the Rho GTPase cycle in ANO1 KD– were observed, along with shared changes like DNA damage and cell cycle dysregulation. Silencing CCND1 or ANO1 increased CDDP sensitivity, while only ANO1 silencing increased radiosensitivity. Copanlisib and afatinib were identified as promising candidates for combination therapy of 11q13-amplified HNSCC tumors. We demonstrated a predominant role for Ano1 in treatment resistance in Cyclin D1 high Ano1 high HNSCC tumors and identified novel potential treatment combinations for this high-risk patient group.
Association between five novel anthropometric indices and erectile dysfunction in US adults from NHANES database
AMPD1 and MTHFR genes are not associated with calcium levels in rheumatoid arthritis patients with methotrexate therapy in Indonesia
AbstractRheumatoid Arthritis (RA) is a chronic and progressive autoimmune disease that affects synovial tissues has greater risk of developing secondary osteoporosis (OP). In particular, polymorphisms in Adenosine Monophosphate Deaminase 1 (AMPD1) and Methylenetetrahydrofolate Reductase (MTHFR) affect the outcome of methotrexate (MTX) treatment in patients with RA. Therefore, this study aimed to determine the association of AMPD1 rs17602729, MTHFR C677T, and MTHFR A1298C polymorphisms with MTX activity in RA patients. A retrospective design was adopted to collect data from medical records and blood samples of 99 patients experiencing outpatient care at a referral hospital in Bandung. The inclusion criteria were patients diagnosed with RA, aged 18–59 years, and receiving MTX therapy for ≥ 6 months. DNA was isolated and then amplified using Polymerase Chain Reaction (PCR), and genotyping was performed with Sanger sequencing. The kinetic photometric method was used to measure the levels of calcium in the samples. The results showed that there is no significant association between the MTHFR C677T genotype variant or allele with calcium levels, as indicated by p-values of 0.177 and 0.174, respectively. The association between the MTHFR A1298C genotype variant or alleles with calcium levels was also not significant (p = 0.206 and p = 0.090, respectively). However, most patients had normal calcium levels (76 patients; 77.6%) with the MTHFR C677T genotype variant CC and the MTHFR A1298C genotype variant AA (84 patients; 84.9%). AMPD1 rs17602729 in all patients had a CC genotype with normal calcium levels. The results suggested that there was no significant association between the genetic variation of AMPD1 rs17602729, MTHFR C677T, and MTHFR A1298C with serum calcium levels in patients with RA receiving MTX therapy.