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The increasing volume of air traffic has placed significant stress on the current Air Traffic Management (ATM) systems, especially concerning the use of Very High Frequency (VHF) communication bands. As air traffic continues to grow, the limitations of the existing spectrum and infrastructure necessitate significant upgrades to ensure safety, efficiency, and capacity. The modernization of air traffic management systems has led to the development and introduction of the L-band Digital Aeronautical Communication System (LDACS), a new communication protocol. LDACS is designed to operate alongside existing L-band systems, ensuring compatibility with legacy users. The coexistence of LDACS with legacy systems poses significant interference challenges, as any disruption in data retrieval can critically impact flight safety. This paper proposes four potential interference mitigation techniques that LDACS can employ to detect and reduce the primary source of interference: Distance Measuring Equipment (DME). The authors introduce a prototype LDACS receiver that uses Rank-Ordered Absolute Differences (ROAD) statistics for effective interference sensing and employs GAE-enhanced pulse peak processors to mitigate Distance Measuring Equipment (DME) interference. Unlike the current GAE-enhanced pulse peak processors, the proposed methods use ROAD value-based detection for identifying DME interference. The performance of the proposed methods - ROAD GAE enhanced Pulse Peak Attenuator (RGPPA), ROAD GAE enhanced Pulse Peak Limiter (RGPPL), ROAD joint GAE enhanced Pulse Peak Attenuator (RJGPPA), and ROAD joint GAE enhanced Pulse Peak Limiter (RJGPPL) is analyzed across different threshold ROAD values to determine their efficacy in various signal conditions. Moreover, the performance of the proposed methods is compared to existing methods such as conventional pulse blanking and GAE-enhanced nonlinear devices, which use the amplitude of the received signal for the detection of interference. Furthermore, the proposed method’s performance is compared to another method, ROAD PB, which uses ROAD statistics to detect DME interference and pulse blanking for DME mitigation. The comparative results show that the proposed methods outperformed conventional pulse blanking and ROADPB. Besides, these methods outperformed existing GAE-enhanced methods for their optimum threshold ROAD value. [ABSTRACT FROM AUTHOR]

This research delves into the evolving dynamics of antibiogram trends, the diversity of antibiotic resistance genes and antibiotic efficacy against Vibrio cholerae strains that triggered the cholera outbreak 2022 in Odisha, India. The study will provide valuable insights managing antimicrobial resistance during cholera outbreaks. Eighty V. cholerae strains isolated during the outbreak were analysed for genotypic variations in associated drug resistance genes using PCR assays. Antibiogram profiles and MIC gradient analysis were performed according CLSI guidelines to assess antibiotic effectiveness. Substitution of amino acid position in the QRDR Region was examined to understand the development of Fluoroquinolone resistance. Elevated resistances in V. cholerae strains were observed against doxycycline, azithromycin, ciprofloxacin, and chloramphenicol. The average MARI registered 0.63 value, exceeding the threshold value 0.2. PCR assays revealed higher prevalence of antibiotic resistance genes, and MIC values observed have surpassed the previously registered values during any cholera outbreaks in India. Novel mutations in the parC gene, specifically Tyr-88→Cys and Ser-85→Leu implicated Fluoroquinolone resistance in V. cholerae. This study urges moving beyond on antibiotic reliance to control cholera, emphasizing alternative strategies like OCV, rehydration therapy, probiotics and Water, Sanitation and Hygiene (WASH) interventions as effective tools to combat cholera outbreaks and mitigate antibiotic resistance. [ABSTRACT FROM AUTHOR]

To achieve high-performance trajectory tracking for a manipulator, this study proposes a novel sliding mode control strategy incorporating a nonlinear disturbance observer. The observer is designed to estimate unknown models in real-time, enabling feedforward compensation for various uncertainties such as modeling errors, joint friction, and external torque disturbances. The control law is formulated by integrating the Backstepping method, Lyapunov theory, and global fast terminal sliding mode theory, ensuring global convergence to zero within finite time and enhancing system robustness. To address the inherent chattering issue in sliding mode control, a hybrid reaching law is developed by combining the exponential and power reaching laws. Additionally, the improved-fal (Imp-fal) function replaces the sign function in the switching control law, improving system response speed, preventing overshoot, and optimizing gain beyond the threshold value. Through simulation and comparative experiments conducted using MATLAB/Simulink, the controller model exhibited a 16.4% average reduction in the mean square value of tracking errors compared to existing control strategies, with improvements observed in various performance indicators. When applied to a self-developed three-degree-of-freedom manipulator experimental platform, the controller demonstrated a roughly 55% increase in tracking accuracy and a decrease in response time by approximately 45% compared to existing strategies. The experimental results validate the effectiveness, superiority, and practicality of the control strategy, providing a feasible solution for high-performance trajectory tracking in robotic arm systems. [ABSTRACT FROM AUTHOR]

The association between early glycated hemoglobin (HbA1c) and the risk of neonatal respiratory distress syndrome (NRDS) with preterm premature rupture of membranes (PPROM) population remains largely unknown. The impact of diabetes mellitus (DM) on NRDS is also controversial. HbA1c was assessed in early and late pregnancy. Multivariate logistic regression and restricted cubic spline (RCS) analyses were performed to evaluate the association between the HbA1c and the incidence of NRDS in non-DM group. Propensity score matching (PSM) was performed to balance baseline characteristics between DM group and non-DM group. Among 536 patients with the mean age was 30.7 ± 5.1 years, 117 (21.8%) had DM. The RCS revealed that a linear relationship was found between HbA1c and the incidence of NRDS in non-DM group, with a threshold of approximately 5% (31 mmol/mol). The effect size and CI below and above the threshold value were 1.70 (1.24–2.31) and 1.26 (0.81–1.96), respectively. The ΔHbA1c (late HbA1c minus early HbA1c) was independently associated with incidence of NRDS in DM group. Before and after PSM, no significant association was not observed between DM and the incidence of NRDS. Our findings indicated that higher early HbA1c levels were associated with a risk of NRDS in women without diabetes. Women with diabetes who experience an increase in HbA1c level during pregnancy may be more likely to give birth to infants affected by NRDS. [ABSTRACT FROM AUTHOR]

Recently, understanding the complex relationship between the mechanical properties and material characteristics of desulfurized rubber asphalt has received significant research attention. In this study, the rheological properties of rubber asphalt prepared by three kinds of desulfurized rubber powder are analyzed, and the mechanical response of rubber asphalt mixture is predicted using various mathematical models. Firstly, the rheological properties of desulfurized rubber asphalt are assessed through multiple stress creep recovery test, linear amplitude scanning test, and low-temperature creep test. The results indicate that the high-temperature performance index of desulfurized rubber asphalt is highly sensitive to the degree of desulfurization. As the degree of desulfurization of the rubber powder increases, the fatigue damage rate of asphalt gradually decreases, leading to an increase in the fatigue life. Notably, the difference between the m-critical temperature and stiffness-critical temperature (ΔTc) for only ML40MA does not exceed the threshold value, indicating that it has the best low-temperature cracking resistance. Additionally, the swelling capacity of desulfurized rubber powder in asphalt is better than that of ordinary rubber powder. Finally, the comprehensive performance of desulfurized rubber asphalt is characterized based on the gray correlation method. The gray correlation coefficient of ML60MA is 0.78, which is higher than that of ML40MA and ML80MA, indicating its best overall performance. [ABSTRACT FROM AUTHOR]

The primary objective of this study was to identify the factors associated with the development of depressive symptoms in elderly breast cancer (BC) patients and to construct a nomogram model for predicting these symptoms. We recruited 409 patients undergoing BC treatment in the breast departments of two tertiary-level hospitals in Jiangsu Province from November 2023 to April 2024 as our study cohort. Participants were categorized into depressed and non-depressed groups based on their clinical outcomes. Univariate and multivariate logistic regression analyses were employed to identify independent risk factors for depression among BC patients. Multivariate analysis revealed that monthly income, pain score, family support score, and physical activity score significantly influenced the onset of depression in older BC patients (P < 0.05).The risk prediction model, constructed using these identified factors, demonstrated excellent discriminatory power, as evidenced by an area under the ROC curve (AUC) of 0.824. The maximum Youden index was 0.627, with a sensitivity of 90.60%, specificity of 72.10%, and a diagnostic threshold value of 1.501. The results of the Hosmer-Lemeshow goodness-of-fit test (χ² = 3.181, P = 0.923) indicated that the model fit the data well. The calibration curve for the model closely followed the ideal curve, suggesting a strong fit and high predictive accuracy. Our nomogram model exhibited superior predictive performance, enabling healthcare professionals to identify high-risk patients early and implement preventative measures to mitigate the development of depressive symptoms. This study is a cross-sectional study that lacks longitudinal data and has a small sample size. Future research could involve larger samples, multicenter studies, and prospective designs to build better clinical predictive models. [ABSTRACT FROM AUTHOR]

To investigate the distribution characteristics of selenium (Se) in mountainous soil-crop systems and examine the threshold value of Se-rich soil, 275 soil samples and 153 associated crop samples (rice, maize, tea, nuts, konjac, mushrooms, buckwheat, and coffee) were collected in Ximeng County, a typical mountainous area in southwest China. The total Se, available Se, organic matter, pH, sampling point elevation, and crop Se content were analyzed to examine the distribution characteristics of soil Se and the ability of primary crops to enrich Se in Ximeng County. Random forest and multiple regression models were established to identify the factors influencing the available soil Se and the crop Se enrichment coefficient. Finally, the Se-rich soil threshold was examined based on the total Se, available Se, and Se content in primary crops (rice, maize, and tea). The results showed soil Se resource abundance in the study region, with high Se soil accounting for 64.72% of the entire area. The soil Se content displayed significant spatial autocorrelation. The average Se enrichment coefficient of the main cultivated crops included mushrooms > nuts > rice > coffee > tea > maize > buckwheat > konjac. The total Se content in the soil had the highest impact on the available Se content in the soil and the Se enrichment coefficient of crops. A Se-rich soil threshold of 0.3 mg·kg−1 was used for rice and maize, while that of tea was 0.4 mg·kg−1. This result provided a theoretical basis for developing and utilizing Se resources in mountainous soil in southwestern China and dividing the Se-rich soil threshold. [ABSTRACT FROM AUTHOR]

In forensic commingled contexts, when the disarticulation occurs uniquely at the atlantoaxial joint, the correct match of atlas and axis may lead to the desirable assembly of the entire body. Notwithstanding the importance of this joint in such scenarios, no study has so far explored three-dimensional (3D) methodologies to match these two adjoining bones. In the present study, we investigated the potential of re-associating atlas and axis through 3D–3D superimposition by testing their articular surfaces congruency in terms of point-to-point distance (Root Mean Square, RMS). We analysed vertebrae either from the same individual (match) and from different individuals (mismatch). The RMS distance values were assessed for both groups (matches and mismatches) and a threshold value was determined to discriminate matches with a sensitivity of 100%. The atlas and the corresponding axis from 41 documented skeletons (18 males and 23 females), in addition to unpaired elements (the atlas or the axis) from 5 individuals, were superimposed, resulting in 41 matches and 1851 mismatches (joining and non-joining elements). No sex-related significant differences were found in matches and mismatches (p = 0.270 and p = 0.210, respectively), allowing to pool together the two sexes in each group. RMS values ranged between 0.41 to 0.77 mm for matches and between 0.37 and 2.18 mm for mismatches. Significant differences were found comparing the two groups (p < 0.001) and the highest RMS of matches (0.77 mm) was used as the discriminative value that provided a sensitivity of 100% and a specificity of 41%. In conclusion, the 3D–3D superimposition of the atlanto-axial articular facets cannot be considered as a re-association method per se, but rather as a screening one. However, further research on the validation of the 3D approach and on its application to other joints might provide clues to the complex topic of the reassociation of crucial adjoining bones. [ABSTRACT FROM AUTHOR]

Transportation of magnetized particles (MPs) against gravity is possible by applying a magnetic field to the particles. This transport phenomenon of MPs in microdroplets can be quantitatively assessed by determining the contribution of individual forces acting on the MPs. We studied the selective transportation of MPs in microdroplets. MPs in microdroplets were transported in the opposite direction to gravity when we applied an external magnetic field larger than a threshold value. We modulated the intensity of the external magnetic field and selectively manipulated the MPs. As a result, MPs were separated into different microdroplets based on their magnetic properties. Our quantitative investigation of transport dynamics shows that the threshold magnetic field depends only on the magnetic susceptibility and the density of MPs. This is a universal criterion for the selective transport of magnetized targets such as magnetized cells in microdroplets. [ABSTRACT FROM AUTHOR]

Skin Cancer is caused due to the mutational differences in epidermis hormones and patch appearances. Many studies are focused on the design and development of effective approaches in diagnosis and categorization of skin cancer. The decisions are made on independent training dataset under limited editions and scenarios. In this research, the kaggle based datasets are optimized and categorized into a labeled data array towards indexing using Federated learning (FL). The technique is developed on grey wolf optimization algorithm to assure the dataset attribute dependencies are extracted and dimensional mapping is processed. The threshold value validation of the dimensional mapping datasets is effectively optimized and trained under the neural networking framework further expanded via federated learning standards. The technique has demonstrated 95.82% accuracy under GWO technique and 94.9% on inter-combination of Trained Neural Networking (TNN) framework and Recessive Learning (RL) in accuracy. [ABSTRACT FROM AUTHOR]

Extracting knowledge from hybrid data, comprising both categorical and numerical data, poses significant challenges due to the inherent difficulty in preserving information and practical meanings during the conversion process. To address this challenge, hybrid data processing methods, combining complementary rough sets, have emerged as a promising approach for handling uncertainty. However, selecting an appropriate model and effectively utilizing it in data mining requires a thorough qualitative and quantitative comparison of existing hybrid data processing models. This research aims to contribute to the analysis of hybrid data processing models based on neighborhood rough sets by investigating the inherent relationships among these models. We propose a generic neighborhood rough set-based hybrid model specifically designed for processing hybrid data, thereby enhancing the efficacy of the data mining process without resorting to discretization and avoiding information loss or practical meaning degradation in datasets. The proposed scheme dynamically adapts the threshold value for the neighborhood approximation space according to the characteristics of the given datasets, ensuring optimal performance without sacrificing accuracy. To evaluate the effectiveness of the proposed scheme, we develop a testbed tailored for Parkinson's patients, a domain where hybrid data processing is particularly relevant. The experimental results demonstrate that the proposed scheme consistently outperforms existing schemes in adaptively handling both numerical and categorical data, achieving an impressive accuracy of 95% on the Parkinson's dataset. Overall, this research contributes to advancing hybrid data processing techniques by providing a robust and adaptive solution that addresses the challenges associated with handling hybrid data, particularly in the context of Parkinson's disease analysis. [ABSTRACT FROM AUTHOR]

There is a mounting demand for nonlinear optical materials with superior optical limiting performance which has a noticeable impact on protecting the delicate optical components from laser-induced damage. Transition metal molybdates have garnered attention in the nonlinear optics field due to their outstanding optical and luminescent properties, which give rise to widespread applications in next-generation optoelectronics devices. The structural confirmation of the as prepared silver molybdate nanoparticles were made by XRD and Raman spectroscopy analysis. The linear optical properties and the band gap of the synthesized material were studied using UV–Visible and photoluminescence spectroscopy. SEM analysis revealed the pebble like morphology of the silver molybdate nanostructures. The nonlinear responses of the samples were studied using open aperture z-scan approach with Nd:YAG pulsed laser (532 nm, 9 ns, 10 Hz). The sample exhibits reverse saturable absorption pattern attributed to the two photon absorption (2PA) mechanism. The obtained OL threshold value is in the order of 1012 which is suitable for fabricating optical limiters in nano second pulsed laser regime. [ABSTRACT FROM AUTHOR]

This is a comprehensive retrospective study to characterize female dogs with canine mammary tumors (CMTs) using a dataset retrieved from the archives of the Division of Animal Pathology, Institute of Veterinary Medicine in Warsaw, and to identify prognostic factors. Clinical and histopathological data of 1447 dogs with CMTs were included. Malignant tumours were found in 83.3% (n = 1206), benign tumours in 11.7% (n = 169), and non-neoplastic lesions in 5.0% (n = 72) of dogs. Dogs most often had grade II carcinomas (38.2%, 215/562) of a single histological subtype (88.5%, 1281/1447), mostly simple carcinoma (35.3%, 510/1447). Dogs with a median age of 10 years significantly often had larger (≥ 3 cm) and malignant CMTs, whereas intact females had smaller tumours (median size 2.0 cm). However, the threshold value for the age of the dog in the differentiation of malignant and non-neoplastic/benign masses could not be determined. Most females were hormonally active (76.4%, 372/487). Hormonally active dogs significantly more often had multiple tumours. Multiple tumours were significantly smaller (median 2.5 cm) than single ones. Among pedigree dogs, small-breed dogs were mostly recorded (43%, 428/1006). Twelve breeds had an increased risk of CMTs, regardless of tumour behaviour, compared with the theoretical distribution of pedigree dogs in Poland. Four breeds were often affected only by malignant and other four breeds only by non-neoplastic/benign CMT. Large-breed dogs were significantly younger and affected by larger CMT (median 4 cm) compared with small- and medium-breed dogs. Ninety dogs with a malignant CMT and complete records were included in the full analysis of CMT-specific survival (CMT-SS) with a median follow-up time of 20.0 months. We showed that the timing of ovariohysterectomy in relation to mastectomy was significantly associated with grade, CMT-SS, and CMT-related death. We indicated the low diagnostic accuracy of palpation of regional lymph nodes (RLN) in the prediction of their metastatic involvement. By multivariable analysis, dogs with neoplastic emboli, tumour ulceration, and simple or complex carcinoma had a significantly higher risk of local recurrence. Tumour size > 3 cm was as a strong independent predictor of lung metastases. Compared with dogs with an easily separated localized tumour, dogs with a multiple/diffuse malignant CMT pattern had a fivefold higher risk of death. The risk of death was significantly higher in the presence of neoplastic emboli (~ fivefold) and tumour ulceration (~ fourfold). Furthermore, the presence of neoplastic emboli and large tumour size were independent predictors of CMT-related death. [ABSTRACT FROM AUTHOR]

Notwithstanding the various uses of rubber, the fracture mechanism of filler-reinforced rubber remains unclear. This study used four-dimensional computed tomography (4D-CT) involving monochromatic synchrotron X-rays to examine the cavitation within silica-reinforced rubber quantitatively and systematically. The results suggested a threshold value of silica content for the cavitation morphology. Macroscopic fractures, such as those developed by void formation, occurred below the threshold value of silica content. Above this threshold, the density of rubber decreased but macroscopic voids rarely occurred. The lower-density rubber area in the high-silica-content rubber was reversible at the effective pixel size for 4D-CT. These results suggest that the growth of the damage points to macrosized voids could be stopped by the formation of a network of rigid polymer layers. This study allows the elucidation of the reinforcing mechanism and the cavitation morphology of filler-reinforced rubber. [ABSTRACT FROM AUTHOR]

With the continuous development of information technology and the running speed of computers, the development of informatization has led to the generation of increasingly more medical data. Solving unmet needs such as employing the constantly developing artificial intelligence technology to medical data and providing support for the medical industry is a hot research topic. Cytomegalovirus (CMV) is a kind of virus that exists widely in nature with strict species specificity, and the infection rate among Chinese adults is more than 95%. Therefore, the detection of CMV is of great importance since the vast majority of infected patients are in a state of invisible infection after the infection, except for a few patients with clinical symptoms. In this study, we present a new method to detect CMV infection status by analyzing high-throughput sequencing results of T cell receptor beta chains (TCRβ). Based on the high-throughput sequencing data of 640 subjects from cohort 1, Fisher's exact test was performed to evaluate the relationship between TCRβ sequences and CMV status. Furthermore, the number of subjects with these correlated sequences to different degrees in cohort 1 and cohort 2 were measured to build binary classifier models to identify whether the subject was CMV positive or negative. We select four binary classification algorithms: logistic regression (LR), support vector machine (SVM), random forest (RF), and linear discriminant analysis (LDA) for side-by-side comparison. According to the performance of different algorithms corresponding to different thresholds, four optimal binary classification algorithm models are obtained. The logistic regression algorithm performs best when Fisher's exact test threshold is 10−5, and the sensitivity and specificity are 87.5% and 96.88%, respectively. The RF algorithm performs better at the threshold of 10−5, with a sensitivity of 87.5% and a specificity of 90.63%. The SVM algorithm also achieves high accuracy at the threshold value of 10−5, with a sensitivity of 85.42% and specificity of 96.88%. The LDA algorithm achieves high accuracy with 95.83% sensitivity and 90.63% specificity when the threshold value is 10−4. This is probably because the two-dimensional distribution of CMV data samples is linearly separable, and linear division models such as LDA are more effective, while the division effect of nonlinear separable algorithms such as random forest is relatively inaccurate. This new finding may be a potential diagnostic method for CMV and may even be applicable to other viruses, such as the infectious history detection of the new coronavirus. [ABSTRACT FROM AUTHOR]

Anthrophony is an important determinant of habitat quality in the Anthropocene. Acoustic adaptation of birds at lower levels of anthrophony is known. However, threshold anthrophony, beyond which biophony starts decreasing, is less explored. Here, we present empirical results of the relationship between anthrophony and biophony in four terrestrial soundscapes. The constancy of the predicted threshold vector normalised anthropogenic power spectral density (~ 0.40 Watts/Hz) at all the study sites is intriguing. We propose the threshold value of anthropogenic power spectral density as an indicator of the avian acoustic tolerance level in the study sites. The findings pave the way to determine permissible sound levels within protected landscapes and directly contribute to conservation planning. [ABSTRACT FROM AUTHOR]

Non-alcoholic fatty liver disease (NAFLD) is one of the most common chronic liver diseases, whose severe form is associated with oxidative stress. Vitamin E as an antioxidant has a protective potential in NAFLD. Whether dietary intake of vitamin E, supplementary vitamin E use, and total vitamin E have a preventive effect on NAFLD requires investigation. A cross-sectional study used data from the National Health and Nutrition Examination Survey (2017–2020) was conducted. Vitamin E intake, including dietary vitamin E, supplementary vitamin E use, and total vitamin E, was obtained from the average of two 24-h dietary recall interviews. The extent of hepatic steatosis was measured by liver ultrasound transient elastography and presented as controlled attenuated parameter (CAP) scores. Participants were diagnosed with NAFLD based on CAP threshold values of 288 dB/m and 263 dB/m. The statistical software R and survey-weighted statistical models were used to examine the association between vitamin E intake and hepatic steatosis and NAFLD. Overall, 6122 participants were included for NAFLD analysis. After adjusting for age, gender, race, poverty level index, alcohol consumption, smoking status, vigorous recreational activity, body mass index, abdominal circumference, hyperlipidemia, hypertension, diabetes, and supplementary vitamin E use, dietary vitamin E was inversely associated with NAFLD. The corresponding odds ratios (OR) and 95% confidence intervals (CI) of NAFLD for dietary vitamin E intake as continuous and the highest quartile were 0.9592 (0.9340–0.9851, P = 0.0039) and 0.5983 (0.4136–0.8654, P = 0.0091) (Ptrend = 0.0056). Supplementary vitamin E was significantly inversely associated with NAFLD (fully adjusted model: OR = 0.6565 95% CI 0.4569–0.9432, P = 0.0249). A marginal improvement in total vitamin E for NAFLD was identified. The ORs (95% CIs, P) for the total vitamin E intake as continuous and the highest quartile in the fully adjusted model were 0.9669 (0.9471–0.9871, P = 0.0029) and 0.6743 (0.4515–1.0071, P = 0.0538). Sensitivity analysis indicated these findings were robust. The protective effects of vitamin E significantly differed in the stratum of hyperlipidemia (Pinteraction < 0.05). However, no statistically significant results were identified when the threshold value was set as 263 dB/m. Vitamin E intake, encompassing both dietary and supplemental forms, as well as total vitamin E intake, demonstrated a protective association with NAFLD. Augmenting dietary intake of vitamin E proves advantageous in the prevention of NAFLD, particularly among individuals devoid of hyperlipidemia. [ABSTRACT FROM AUTHOR]

The melting process of two-dimensional (2D) Yukawa crystals for dusty plasma medium induced by external perturbations has been explored using molecular dynamics simulations. A 2D monolayer of particles interacting via Yukawa pair potential is formed in the presence of an external confinement potential. The confinement potential is a combined effect of the gravitational force and an externally applied electric force, which mimics the sheath electric field in dusty plasma experiments. The response of the 2D crystalline layer to an external perturbation is investigated. It is shown that transverse surface waves are generated below a particular threshold value of initial perturbation, but the crystalline order remains. However, above a threshold value of initial disturbance, the crystalline order structure of the 2D layer breaks, and it melts. The melting process is shown to be a first-order phase transition. We have demonstrated that the nonlinear amplitude modulation of initial disturbance through the parametric decay instability is responsible for the melting. Our proposed mechanism of first-order phase transition in the context of 2D dusty plasma crystal is distinctly different from the existing theoretical models. This research can provide a deeper understanding of the experimental observations in the context of plasma crystal. [ABSTRACT FROM AUTHOR]

In this research, we delve into the dynamics of an infected predator–prey system in the presence of fear and refuge, presenting a novel inclusion of treatment for infected individuals in this type of model. Through our analytical efforts, we establish a significant reproduction number that holds a pivotal role in determining disease extinction or persistence within the system. A noteworthy threshold value for this reproduction number delineates a boundary below which the infected population cannot endure in the system. It's important to note that a range of reproduction numbers leads to both disease-free and endemic scenarios, yet the stability of these situations is contingent upon the initial population sizes. Furthermore, our investigation extends to the exploration of various types of bifurcation-namely, Backward, Saddle-node, and Hopf bifurcations. These findings unravel the intricate and diverse dynamics of the system. Of particular significance is the derivation of an optimal control policy for treatment, augmenting the practical utility of our work. The robustness of our analytical findings is fortified through meticulous verification via numerical simulations. These simulations not only bolster the credibility of our analytical results but also enhance their accessibility. Our study unveils that fear, refuge, and treatment possess individual capabilities to eradicate the disease from the system. Notably, increasing levels of fear and refuge exert a passive influence on the elimination of the infected population, whereas treatment wields an active influence-a crucial insight that bolsters the foundation of our model. Furthermore, our investigation uncovers a spectrum of system dynamics including bistability, one-period, two-period, and multi-period/chaotic behavior. These discoveries contribute to a profound enrichment of the system's dynamic landscape. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate perivascular reflectivity in patients with branched retinal vascular obstruction (BRVO) using en-face optical coherence tomography (OCT). The study retrospectively analyzed 45 patients with recurrent BRVO, 30 with indolent BRVO, and 45 age- and sex-matched controls. Using a 3.0 × 3.0-mm deep capillary plexus slab on macular scans, OCT angiography (OCTA) and structural en-face OCT scans were divided into four quadrants. Obstructive quadrants of OCTA scans were binarized using a threshold value of mean + 2 standard deviation. The selected area of high signal strength (HSS) was applied to the structural en-face OCT scans, and the corrected mean perivascular reflectivity was calculated as the mean reflectivity on the HSS area/overall en-face OCT mean reflectivity. The same procedure was performed in the quadrants of the matched controls. Regression analysis was conducted on several factors possibly associated with corrected perivascular reflectivity. The perivascular reflectivity in the obstructive BRVO quadrant was significantly higher than in the indolent BRVO and control quadrants (P = 0.009, P = 0.003). Both univariate and multivariate regression analyses showed a significant correlation between the average number of intravitreal injections (anti-vascular endothelial growth factor or dexamethasone implant) per year and refractive errors and image binarization threshold and perivascular reflectivity (P = 0.011, 0.013, < 0.001/univariate; 0.007, 0.041, 0.005/multivariate, respectively). En-face OCT scans of the deep capillary plexus slab revealed higher perivascular reflectivity in recurrent BRVO eyes than in indolent BRVO and control eyes. The results also indicate a remarkable correlation between perivascular reflectivity and the average number of intravitreal injections, suggesting a link to recurrence rates. [ABSTRACT FROM AUTHOR]

Breast cancer is considered one of the significant health challenges and ranks among the most prevalent and dangerous cancer types affecting women globally. Early breast cancer detection and diagnosis are crucial for effective treatment and personalized therapy. Early detection and diagnosis can help patients and physicians discover new treatment options, provide a more suitable quality of life, and ensure increased survival rates. Breast cancer detection using gene expression involves many complexities, such as the issue of dimensionality and the complicatedness of the gene expression data. This paper proposes a bio-inspired CNN model for breast cancer detection using gene expression data downloaded from the cancer genome atlas (TCGA). The data contains 1208 clinical samples of 19,948 genes with 113 normal and 1095 cancerous samples. In the proposed model, Array-Array Intensity Correlation (AAIC) is used at the pre-processing stage for outlier removal, followed by a normalization process to avoid biases in the expression measures. Filtration is used for gene reduction using a threshold value of 0.25. Thereafter the pre-processed gene expression dataset was converted into images which were later converted to grayscale to meet the requirements of the model. The model also uses a hybrid model of CNN architecture with a metaheuristic algorithm, namely the Ebola Optimization Search Algorithm (EOSA), to enhance the detection of breast cancer. The traditional CNN and five hybrid algorithms were compared with the classification result of the proposed model. The competing hybrid algorithms include the Whale Optimization Algorithm (WOA-CNN), the Genetic Algorithm (GA-CNN), the Satin Bowerbird Optimization (SBO-CNN), the Life Choice-Based Optimization (LCBO-CNN), and the Multi-Verse Optimizer (MVO-CNN). The results show that the proposed model determined the classes with high-performance measurements with an accuracy of 98.3%, a precision of 99%, a recall of 99%, an f1-score of 99%, a kappa of 90.3%, a specificity of 92.8%, and a sensitivity of 98.9% for the cancerous class. The results suggest that the proposed method has the potential to be a reliable and precise approach to breast cancer detection, which is crucial for early diagnosis and personalized therapy. [ABSTRACT FROM AUTHOR]

While numerous studies report shifts in vegetation phenology, in this regard eddy covariance (EC) data, despite its continuous high-frequency observations, still requires further exploration. Furthermore, there is no general consensus on optimal methodologies for data smoothing and extracting phenological transition dates (PTDs). Here, we revisit existing methodologies and present new prospects to investigate phenological changes in gross primary productivity (GPP) from EC measurements. First, we present a smoothing technique of GPP time series through the derivative of its smoothed annual cumulative sum. Second, we calculate PTDs and their trends from a commonly used threshold method that identifies days with a fixed percentage of the annual maximum GPP. A systematic analysis is performed for various thresholds ranging from 0.1 to 0.7. Lastly, we examine the relation of PTDs trends to trends in GPP across the years on a weekly basis. Results from 47 EC sites with long time series (> 10 years) show that advancing trends in start of season (SOS) are strongest at lower thresholds but for the end of season (EOS) at higher thresholds. Moreover, the trends are variable at different thresholds for individual vegetation types and individual sites, outlining reasonable concerns on using a single threshold value. Relationship of trends in PTDs and weekly GPP reveal association of advanced SOS and delayed EOS to increase in immediate primary productivity, but not to the trends in overall seasonal productivity. Drawing on these analyses, we emphasise on abstaining from subjective choices and investigating relationship of PTDs trend to finer temporal trends of GPP. Our study examines existing methodological challenges and presents approaches that optimize the use of EC data in identifying vegetation phenological changes and their relation to carbon uptake. [ABSTRACT FROM AUTHOR]

The segmentation of acute stroke lesions plays a vital role in healthcare by assisting doctors in making prompt and well-informed treatment choices. Although Magnetic Resonance Imaging (MRI) is a time-intensive procedure, it produces high-fidelity images widely regarded as the most reliable diagnostic tool available. Employing deep learning techniques for automated stroke lesion segmentation can offer valuable insights into the precise location and extent of affected tissue, enabling medical professionals to effectively evaluate treatment risks and make informed assessments. In this research, a deep learning approach is introduced for segmenting acute and sub-acute stroke lesions from MRI images. To enhance feature learning through brain hemisphere symmetry, pre-processing techniques are applied to the data. To tackle the class imbalance challenge, we employed a strategy of using small patches with balanced sampling during training, along with a dynamically weighted loss function that incorporates f1-score and IOU-score (Intersection over Union). Furthermore, the 3D U-Net architecture is used to generate predictions for complete patches, employing a high degree of overlap between patches to minimize the requirement for subsequent post-processing steps. The 3D U-Net model, utilizing ResnetV2 as the pre-trained encoder for IOU-score and Seresnext101 for f1-score, stands as the leading state-of-the-art (SOTA) model for segmentation tasks. However, recent research has introduced a novel model that surpasses these metrics and demonstrates superior performance compared to other backbone architectures. The f1-score and IOU-score were computed for various backbones, with Seresnext101 achieving the highest f1-score and ResnetV2 performing the highest IOU-score. These calculations were conducted using a threshold value of 0.5. This research proposes a valuable model based on transfer learning for the classification of brain diseases in MRI scans. The achieved f1-score using the recommended classifiers demonstrates the effectiveness of the approach employed in this study. The findings indicate that Seresnext101 attains the highest f1-score of 0.94226, while ResnetV2 achieves the best IOU-score of 0.88342, making it the preferred architecture for segmentation methods. Furthermore, the study presents experimental results of the 3D U-Net model applied to brain stroke lesion segmentation, suggesting prospects for researchers interested in segmenting brain strokes and enhancing 3D U-Net models. [ABSTRACT FROM AUTHOR]

The intensity of eddy diffusivity and the spatial average of water velocity at the depths of the water column in oceans and lakes play a fundamental role in phytoplankton production and phytoplankton and zooplankton biomass, and community composition. The critical depth and intensity of turbulent mixing within the water column profoundly affect phytoplankton biomass, which depends on the sinking characteristic of planktonic algal species. We propose an Nutrient-Phytoplankton-Zooplankton (NPZ) model in 3D space with light and nutrient-limited growth in a micro-scale ecological study. To incorporate micro-scale observation of phytoplankton intermittency in bloom mechanism in stationary as well as oceanic turbulent flows, a moment closure method has been applied in this study. Experimental observations imply that an increase in turbulence is sometimes ecologically advantageous for non-motile planktonic algae. How do we ensure whether there will be a bloom cycle or whether there can be any bloom at all when the existing phytoplankton group is buoyant, heavier, motile, or non-motile? To address these questions, we have explored the effects of critical depth, the intensity of eddy diffusivity, spatial average of water velocity, on the concentration as well as horizontal and vertical distribution of phytoplankton and zooplankton biomass using a mathematical model and moment closure technique. We quantify a critical threshold value of eddy diffusivity and the spatial average of water velocity and observe the corresponding changes in the phytoplankton bloom dynamics. Our results highlight the importance of eddy diffusivity and the spatial average of water velocity on seasonal bloom dynamics and also mimic different real-life bloom scenarios in Mikawa Bay (Japan), Tokyo Bay (Japan), Arakawa River (Japan), the Baltic Sea, the North Atlantic Ocean, Gulf Alaska, the North Arabian Sea, the Cantabrian Sea, Lake Nieuwe Meer (Netherlands) and several shallower lakes. [ABSTRACT FROM AUTHOR]

We study the rheological behaviour of bidisperse suspensions in three dimensions under a non-uniform shear flow, made by the superimposition of a linear shear and a sinusoidal disturbance. Our results show that (i) only a streamwise disturbance in the shear-plane alters the suspension dynamics by substantially reducing the relative viscosity, (ii) with the amplitude of the disturbance determining a threshold value for the effect to kick-in and its wavenumber controlling the amount of reduction and which of the two phases is affected. We show that, (iii) the rheological changes are caused by the effective separation of the two phases, with the large or small particles layering in separate regions. We provide a physical explanation of the phase separation process and of the conditions necessary to trigger it. We test the results in the whole flow curve, and we show that the mechanism remains substantially unaltered, with the only difference being the nature of the interactions between particles modified by the phase separation. [ABSTRACT FROM AUTHOR]

A comprehensive view of disease epidemics demands a deep understanding of the complex interplay between human behaviour and infectious diseases. Here, we propose a flexible modelling framework that brings conclusions about the influence of human mobility and disease transmission on early epidemic growth, with applicability in outbreak preparedness. We use random matrix theory to compute an epidemic threshold, equivalent to the basic reproduction number R 0 , for a SIR metapopulation model. The model includes both systematic and random features of human mobility. Variations in disease transmission rates, mobility modes (i.e. commuting and migration), and connectivity strengths determine the threshold value and whether or not a disease may potentially establish in the population, as well as the local incidence distribution. [ABSTRACT FROM AUTHOR]

Nebulization of mRNA therapeutics can be used to directly target the respiratory tract. A promising prospect is that mucosal administration of lipid nanoparticle (LNP)-based mRNA vaccines may lead to a more efficient protection against respiratory viruses. However, the nebulization process can rupture the LNP vehicles and degrade the mRNA molecules inside. Here we present a novel nebulization method able to preserve substantially the integrity of vaccines, as tested with two SARS-CoV-2 mRNA vaccines. We compare the new method with well-known nebulization methods used for medical respiratory applications. We find that a lower energy level in generating LNP droplets using the new nebulization method helps safeguard the integrity of the LNP and vaccine. By comparing nebulization techniques with different energy dissipation levels we find that LNPs and mRNAs can be kept largely intact if the energy dissipation remains below a threshold value, for LNP integrity 5–10 J/g and for mRNA integrity 10–20 J/g for both vaccines. [ABSTRACT FROM AUTHOR]

Insects play a critical role in providing numerous ecosystem services. However, insect diversity and biomass have been declining dramatically, with artificial light being suggested as a contributing factor. Despite the importance of understanding the dose–effect responses of insects to light emissions, these responses have been rarely studied. We examined the dose–effect responses of the greater wax moth (Galleria mellonella L.) to different light intensities (14 treatments and a dark control) by observing their behavioural responses in a light-tight box equipped with a LED light source (4070 K) and infrared cameras. Our findings reveal dose–effect responses to light, as the frequency of walking on the light source increased with higher light intensity. Additionally, moths exhibited jumps in front of the light source and jump frequency increased with light intensity. No direct flight-to-light behaviour or activity suppression in response to light was observed. Based on our analysis of the dose–effect responses, we identified a threshold value of 60 cd/m2 for attraction (walking on the light source) and the frequency of jumps. The experimental design in this study offers a valuable tool for investigating dose–effect relationships and behavioural responses of various species to different light levels or specific light sources. [ABSTRACT FROM AUTHOR]

To detect lanes at night, each detecting image is the fusion of the multiple images in a video sequence. The valid lane line detection region is identified on region merging. Then, the image preprocessing algorithm based on the Fragi algorithm and Hessian matrix is applied to enhance lanes; to extract the lane line center feature points, the image segmentation algorithm based on Fractional differential is proposed; and according to the possible lane line positions, the algorithm detects the centerline points in four directions. Subsequently, the candidate points are determined, and the recursive Hough transformation is applied to obtain the possible lane lines. Finally, to obtain the final lane lines, we assume that one lane line should have an angle between 25 and 65 degrees, while the other should have an angle between 115 and 155 degrees, if the detected line is not in the regions, the Hough line detection will be continued by increasing the threshold value until the two lane lines are got. By testing more than 500 images and comparing deep learning methods and image segmentation algorithms, the lane detection accuracy by the new algorithm is up to 70%. [ABSTRACT FROM AUTHOR]

Cereal cyst nematode (CCN) is a major threat to cereal crop production globally including wheat (Triticum aestivum L.). In the present study, single-locus and multi-locus models of Genome-Wide Association Study (GWAS) were used to find marker trait associations (MTAs) against CCN (Heterodera avenae) in wheat. In total, 180 wheat accessions (100 spring and 80 winter types) were screened against H. avenae in two independent years (2018/2019 "Environment 1" and 2019/2020 "Environment 2") under controlled conditions. A set of 12,908 SNP markers were used to perform the GWAS. Altogether, 11 significant MTAs, with threshold value of −log10 (p-values) ≥ 3.0, were detected using 180 wheat accessions under combined environment (CE). A novel MTA (wsnp_Ex_c53387_56641291) was detected under all environments (E1, E2 and CE) and considered to be stable MTA. Among the identified 11 MTAs, eight were novel and three were co-localized with previously known genes/QTLs/MTAs. In total, 13 putative candidate genes showing differential expression in roots, and known to be involved in plant defense mechanisms were reported. These MTAs could help us to identify resistance alleles from new sources, which could be used to identify wheat varieties with enhanced CCN resistance. [ABSTRACT FROM AUTHOR]

It has been known that altruistic punishments solve the free rider problem in public goods games. Considering spatial structure and considering pure strategies significant advances have been made in understanding the evolution of altruistic punishments. However, these models have not considered key behavior regularities observed in experimental and field settings, where the individuals behave like conditional cooperators who are more willing to donate and are also more willing to punish free riders. Considering these behavioral regularities, without imposing a spatial structure on the population, I propose an evolutionary agent-based model in which agents behave like conditional cooperators, each agent's donation conditional on the difference between the number of donations in the past and the threshold value and the propensity value of the agent. Altruistic punishment depends on the difference between the threshold value of the focal agent and the randomly matched another agent. The simulations show that, for certain inflicted costs of punishments, generous altruistic punishments evolve and stabilize cooperation. The results show that, unlike previous models, it is not necessary to punish all free riders equally; it is necessary to do so in the case of the selfish free riders but not in the case of negative reciprocators. [ABSTRACT FROM AUTHOR]

Data acquired by a dense seismic network deployed in the Cerdanya basin (Eastern Pyrenees) is used to track the temporal and spatial evolution of meteorological events such as rainfall episodes or thunderstorms. Comparing seismic and meteorological data, we show that for frequencies above 40 Hz, the dominant source of seismic noise is rainfall and hence the amplitude of the seismic data can be used as a proxy of rainfall. The interstation distance of 1.5 km provides an unprecedented spatial resolution of the evolution of rainfall episodes along the basin. Two specific episodes, one dominated by stratiform rain and the second one dominated by convective rain, are analyzed in detail, using high resolution disdrometer data from a meteorological site near one of the seismic instruments. Seismic amplitude variations follow a similar evolution to radar reflectivity values, but in some stratiform precipitation cases, it differs from the radar-derived precipitation estimates in this region of abrupt topography, where radar may suffer antenna beam blockage. Hence, we demonstrate the added value of seismic data to complement other sources of information such as rain-gauge or weather radar observations to describe the evolution of ground-level rainfall fields at high spatial and temporal resolution. The seismic power and the rainfall intensity have an exponential relationship and the periods with larger seismic power are coincident. The time intervals with rain drops diameters exceeding 3.5 mm do not result in increased seismic amplitudes, suggesting that there is a threshold value from which seismic data are no longer proportional to the size of the drops. Thunderstorms can be identified by the recording of the sonic waves generated by thunders, with. Single thunders detected to distances of a few tens of kilometers. As the propagation of these acoustic waves is expected to be strongly affected by parameters as air humidity, temperature variations or wind, the seismic data could provide an excellent tool to investigate atmospheric properties variations during thunderstorms. [ABSTRACT FROM AUTHOR]

Neural network models have been used to analyze thyroid ultrasound (US) images and stratify malignancy risk of the thyroid nodules. We investigated the optimal neural network condition for thyroid US image analysis. We compared scratch and transfer learning models, performed stress tests in 10% increments, and compared the performance of three threshold values. All validation results indicated superiority of the transfer learning model over the scratch model. Stress test indicated that training the algorithm using 3902 images (70%) resulted in a performance which was similar to the full dataset (5575). Threshold 0.3 yielded high sensitivity (1% false negative) and low specificity (72% false positive), while 0.7 gave low sensitivity (22% false negative) and high specificity (23% false positive). Here we showed that transfer learning was more effective than scratch learning in terms of area under curve, sensitivity, specificity and negative/positive predictive value, that about 3900 images were minimally required to demonstrate an acceptable performance, and that algorithm performance can be customized according to the population characteristics by adjusting threshold value. [ABSTRACT FROM AUTHOR]

The diagnosis of metabolic syndrome (MetS) focuses on the assessment of risk factors such as insulin resistance, dyslipidemia, central adiposity and elevated blood pressure. Evidence suggests that markers of systemic inflammation may also be included in the definition of MetS and play some role in its pathogenesis. The study was designed to evaluate low-grade inflammation status in older adults with MetS in relation to increased body fat tissue and an attempt was made to evaluate new predictors for MetS through the analysis of the ROC Curve. Ninety-six middle-aged (69.2 ± 4.9) individuals from University of Third Age (women n = 75 and men n = 21) were allocated to two groups: without metabolic syndrome (n = 37) and with metabolic syndrome (n = 59) according to International Diabetes Federation criteria in agreement with American Heart Association/National Heart, Lung and Blood Institute 2009. Participants' current health status was assessed using medical records from a routine follow-up visit to a primary care physician. Statistical analysis was performed using R studio software. Depending on the normal distribution, ANOVA or the Kruskal–Wallis test was used. The optimal threshold value for clinical stratification (cut-off value) was obtained by calculating the Youden index. The AUC was observed to be the highest for a new anthropometric index i.e. lipid accumulation product (0.820). Low-grade inflammation dominated in MetS group (BMI 28.0 ± 4.4 kg/m2, WHR 0.9 ± 0.1, FM 24.7 ± 7.9 kg) where significantly higher values of TNF-α (p = 0.027) and HGMB-1 protein (p = 0.011) were recorded.The optimal threshold values for immunological indices assessed as new predictors of the metabolic syndrome were: 93.4 for TNF-α, 88.2 for HGMB-1 protein and 1992.75 for ghrelin. High AUC values for these indices additionally confirmed their high diagnostic usefulness in MetS. [ABSTRACT FROM AUTHOR]

Anemia is a major global disease burden factor linked to an adverse impact on overall prognosis and negatively affects the quality of life. There are some suggested findings for anemia on non-contrast chest CT, like relatively dense interventricular septum (septal sign) or fairly dense aortic wall (aortic ring sign). The measured attenuation value is a reproducible physical density measurement, readily obtainable from a standard CT examination. There is no reliable cut-off for blood attenuation to suggest anemia on the non-contrast chest CT. In the current study, we evaluated subjective and objective criteria' diagnostic accuracy for diagnosing anemia on unenhanced thoracic CT. This study is approved by Mashhad University of Medical Sciences. The patients admitted in the internal medicine ward of our hospital from June 2019 to March 2020 for whom a non-contrast chest CT was acquired for any non-traumatic medical indication, were enrolled in this retrospective study. For the subjective assessment, the radiologists were asked to record the presence or absence of the "aortic ring sign" and "interventricular septum sign". For the objective evaluations, blood density was measured at various anatomic locations. A total of 325 patients were included in this study. There was a significant correlation between blood attenuation in all measured segments and Hb level (0.78 (R2: 0.61), p = 0.000). Findings revealed that considering the aortic arch threshold value as 20 HU is the best diagnostic performance for detecting severe anemia. Subjective analysis revealed that the aortic ring sign was more sensitive (82.5%) than the interventricular septum sign (32%) in detecting anemia, whereas the latter character was more specific (87% and 99.2%, respectively). The results suggest that it is possible to detect anemia from an unenhanced chest CT scan. Both objective and subjective criteria show promising sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Electroporation (EP), the increase of cell membrane permeability due to the application of electric pulses, is a universal phenomenon with a broad range of applications. In medicine, some of the foremost EP-based tumor treatments are electrochemotherapy (ECT), irreversible electroporation, and gene electrotransfer (GET). The electroporation phenomenon is explained as the formation of cell membrane pores when a transmembrane cell voltage reaches a threshold value. Predicting the outcome of an EP-based tumor treatment consists of finding the electric field distribution with an electric threshold value covering the tumor (electroporated tissue). Threshold and electroporated tissue are also a function of the number of pulses, constituting a complex phenomenon requiring mathematical modeling. We present OpenEP, an open-source specific purpose simulator for EP-based tumor treatments, modeling among other variables, threshold, and electroporated tissue variations in time. Distributed under a free/libre user license, OpenEP allows the customization of tissue type; electrode geometry and material; pulse type, intensity, length, and frequency. OpenEP facilitates the prediction of an optimal EP-based protocol, such as ECT or GET, defined as the critical pulse dosage yielding maximum electroporated tissue with minimal damage. OpenEP displays a highly efficient shared memory implementation by taking advantage of parallel resources; this permits a rapid prediction of optimal EP-based treatment efficiency by pulse number tuning. [ABSTRACT FROM AUTHOR]

It is well-known that no local model—in theory—can simulate the outcome statistics of a Bell-type experiment as long as the detection efficiency is higher than a threshold value. For the Clauser–Horne–Shimony–Holt (CHSH) Bell inequality this theoretical threshold value is η T = 2 (2 - 1) ≈ 0.8284 . On the other hand, Phys. Rev. Lett. 107, 170404 (2011) outlined an explicit practical model that can fake the CHSH inequality for a detection efficiency of up to 0.5. In this work, we close this gap. More specifically, we propose a method to emulate a Bell inequality at the threshold detection efficiency using existing optical detector control techniques. For a Clauser–Horne–Shimony–Holt inequality, it emulates the CHSH violation predicted by quantum mechanics up to η T . For the Garg–Mermin inequality—re-calibrated by incorporating non-detection events—our method emulates its exact local bound at any efficiency above the threshold. This confirms that attacks on secure quantum communication protocols based on Bell violation is a real threat if the detection efficiency loophole is not closed. [ABSTRACT FROM AUTHOR]

The use of a single grey intensity threshold is one of the most straightforward and widely used methods to segment cranial base surface models from a 3D radiographic volume. In this study we used thirty Cone Beam Computer Tomography (CBCT) scans from three different machines and ten CT scans of growing individuals to test the effect of thresholding on the subsequently produced anterior cranial base surface models. From each scan, six surface models were generated using a range of voxel intensity thresholds. The models were then superimposed on a manually selected reference surface model, using an iterative closest point algorithm. Multivariate tests showed significant effects of the machine type, threshold value, and superimposition on the spatial position and the form of the created models. For both, CT and CBCT machines, the distance between the models, as well as the variation within each threshold category, was consistently increasing with the magnitude of difference between thresholds. The present findings highlight the importance of accurate anterior cranial base segmentation for reliable assessment of craniofacial morphology through surface superimposition or similar methods that utilize this anatomical structure as reference. [ABSTRACT FROM AUTHOR]

Coronavirus Disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), continues to persist due to mutations resulting in newer, more infectious variants of concern. We aimed to leverage an ongoing private SARS-CoV-2 testing laboratory's infrastructure to monitor SARS-CoV-2 variants in two large California counties. Study enrollment was offered to adults aged 18 years or older in Los Angeles County and Riverside County who recently tested positive for SARS-CoV-2 with a polymerase chain reaction (PCR) assay. A cycle threshold value less than or equal to 30 cycles was considered a positive test for sequencing purposes. Within 5 days of study enrollment, clinician-monitored, self-collected oral fluid and anterior nares swab specimens were obtained from participants. Specimens were transported and stored at 8 °C or cooler. Samples underwent ribonucleic acid extraction, library preparation, and sequencing. SARS-CoV-2 lineages were identified using sequencing data. Participant and genomic data were analyzed using statistical tools and visualized with toolkits. The study was approved by Advarra Institutional Review Board (Pro00053729). From May 27, 2021 to September 9, 2021, 503 individuals were enrolled and underwent specimen collection. Of the 503 participants, 238 (47.3%) participants were women, 329 (63.6%) participants were vaccinated, and 221 (43.9%) participants were of Hispanic or Spanish origin. Of the cohort, 496 (98.6%) participants had symptoms at the time of collection. Among the 503 samples, 443 (88.1%) nasal specimens and 353 (70.2%) oral specimens yielded positive sequencing results. Over our study period, the prevalence of the Alpha variant of SARS-CoV-2 decreased (initially 23.1% [95% confidence interval (95% CI): 0–0.49%] to 0% [95% CI 0.0–0.0%]) as the prevalence of the Delta variant of SARS-CoV-2 increased (initially 33.3% [95% CI 0.0–100.0%] to 100.0% [95% CI 100.0–100.0%]). A strain that carried mutations of both Delta and Mu was identified. We found that outpatient SARS-CoV-2 variant surveillance could be conducted in a timely and accurate manner. The prevalence of different variants changed over time. A higher proportion of nasal specimens yielded results versus oral specimens. Timely and regional outpatient SARS-CoV-2 variant surveillance could be used for public health efforts to identify changes in SARS-CoV-2 strain epidemiology. [ABSTRACT FROM AUTHOR]

Early detection of circulating tumor cells (CTCs) in a patient's blood is essential to accurate prognosis and effective cancer treatment monitoring. The methods used to detect and separate CTCs should have a high recovery rate and ensure cells viability for post-processing operations, such as cell culture and genetic analysis. In this paper, a novel dielectrophoresis (DEP)-based microfluidic system is presented for separating MDA-MB-231 cancer cells from various subtypes of WBCs with the practical cell viability approach. Three configurations for the sidewall electrodes are investigated to evaluate the separation performance. The simulation results based on the finite-element method show that semi-circular electrodes have the best performance with a recovery rate of nearly 95% under the same operational and geometric conditions. In this configuration, the maximum applied electric field (1.11 × 105 V/m) to separate MDA-MB-231 is lower than the threshold value for cell electroporation. Also, the Joule heating study in this configuration shows that the cells are not damaged in the fluid temperature gradient (equal to 1 K). We hope that such a complete and step-by-step design is suitable to achieve DEP-based applicable cell separation biochips. [ABSTRACT FROM AUTHOR]

Monitoring product temperature during lyophilization is critical, especially during the process development stage, as the final product may be jeopardized if its process temperature exceeds a threshold value. Also, in-situ temperature monitoring of the product gives the capability of creating an optimized closed-loop lyophilization process. While conventional thermocouples can track product temperature, they are invasive, limited to a single-point measurement, and can significantly alter the freezing and drying behavior of the product in the monitored vial. This work has developed a new methodology that combines non-invasive temperature monitoring and comprehensive modeling. It allows the accurate reconstruction of the complete temperature profile of the product inside the vial during the lyophilization process. The proposed methodology is experimentally validated by combining the sensors' wirelessly collected data with the advanced multiphysics simulations. The flexible wireless multi-point temperature sensing probe is produced using micro-manufacturing techniques and attached outside the vial, allowing for accurate extraction of the product temperature. [ABSTRACT FROM AUTHOR]

Being the second most abundant element on earth after oxygen, silicon remains the working horse for key technologies for the years. Novel photonics platform for high-speed data transfer and optical memory demands higher flexibility of the silicon modification, including on-chip and in-bulk inscription regimes. These are deepness, three-dimensionality, controllability of sizes and morphology of created modifications. Mid-IR (beyond 4 µm) ultrafast lasers provide the required control for all these parameters not only on the surface (as in the case of the lithographic techniques), but also inside the bulk of the semiconductor, paving the way to an unprecedented variety of properties that can be encoded via such an excitation. We estimated the deposited energy density as 6 kJ cm−3 inside silicon under tight focusing of mid-IR femtosecond laser radiation, which exceeds the threshold value determined by the specific heat of fusion (~ 4 kJ cm−3). In such a regime, we successfully performed single-pulse silicon microstructuring. Using third-harmonic and near-IR microscopy, and molecular dynamics, we demonstrated that there is a low-density region in the center of a micromodification, surrounded by a "ring" with higher density, that could be an evidence of its micro-void structure. The formation of created micromodification could be controlled in situ using third-harmonic generation microscopy. The numerical simulation indicates that single-shot damage becomes possible due to electrons heating in the conduction band up to 8 eV (mean thermal energy) and the subsequent generation of microplasma with an overcritical density of 8.5 × 1021 cm−3. These results promise to be the foundation of a new approach of deep three-dimensional single-shot bulk micromachining of silicon. [ABSTRACT FROM AUTHOR]

The concentrations of polycyclic aromatic hydrocarbons (PAHs) in aerosol were measured in Shinjuku, which is central Tokyo, Japan, for 10 years from 2007 to 2016. The effects of changes in emission sources and their degradation by reaction with ozone were assessed in this study. There was no significant increasing or decreasing trend of the PAH concentrations during 10 years (P > 0.05). The average selected seven the PAH concentrations (0.88 ng m−3) during 10 years was lower than those in New York and Paris. However, the trend of ozone concentrations is increasing in central Tokyo. This inconsistency raises a question. Did the fact that the ozone concentration was higher than the PAH concentrations promote PAH degradation? To apportion the PAH sources, we used PAH concentration profiles and positive matrix factorization analysis. The contribution of vehicle emissions to the PAHs ranged from 40 to 80%. Ozone concentrations increased by 3.70%/year during 10 years. The theoretical degradation rates of PAHs by ozone, which were calculated using a pseudo-first-order rate equation, suggested that the lifetimes of benzo[a]pyrene (BaP) decreased by 1 min from 2007 to 2016. We investigated the aging of BaP using the profile of the isomer ratios. We found that the aging of BaP at the urban and roadside sites were nearly identical indicating aging regardless of the season. Although the decomposition of BaP is promoted by the photochemical oxidation reaction, this result suggests that a certain threshold value exists as the degree of the decomposition. This degradation of PAH can improve chemical loss processes in air quality model. [ABSTRACT FROM AUTHOR]

To assess the usefulness of a theoretical postural instability discrimination index (PIth) in amyotrophic lateral sclerosis (ALS). Prospective regression analyzes were performed to identify the biomechanical determinants of postural instability unrelated to lower limb motor deficits from gait initiation factors. PIth was constructed using a logit function of biomechanical determinants. Discriminatory performance and performance differences were tested. Backward displacement of the pression center (APAamplitude) and active vertical braking of the mass center (Braking-index) were the biomechanical determinants of postural instability. PIth = − 0.13 × APAamplitude − 0.12 × Braking-index + 5.67, (P < 0.0001, RSquare = 0.6119). OR (APAamplitude) and OR (Braking-index) were 0.878 and 0.887, respectively, i.e., for a decrease of 10 mm in APAamplitude or 10% in Braking-index, the postural instability risk was 11.391 or 11.274 times higher, respectively. PIth had the highest discriminatory performance (AUC 0.953) with a decision threshold value ≥ 0.587, a sensitivity of 90.91%, and a specificity of 83.87%, significantly increasing the sensitivity by 11.11%. PIth, as objective clinical integrator of gait initiation biomechanical processes significantly involved in dynamic postural control, was a reliable and performing discrimination index of postural instability with a significant increased sensitivity, and may be useful for a personalized approach to postural instability in ALS. [ABSTRACT FROM AUTHOR]

Every year, around 28,100 journals publish 2.5 million research publications. Search engines, digital libraries, and citation indexes are used extensively to search these publications. When a user submits a query, it generates a large number of documents among which just a few are relevant. Due to inadequate indexing, the resultant documents are largely unstructured. Publicly known systems mostly index the research papers using keywords rather than using subject hierarchy. Numerous methods reported for performing single-label classification (SLC) or multi-label classification (MLC) are based on content and metadata features. Content-based techniques offer higher outcomes due to the extreme richness of features. But the drawback of content-based techniques is the unavailability of full text in most cases. The use of metadata-based parameters, such as title, keywords, and general terms, acts as an alternative to content. However, existing metadata-based techniques indicate low accuracy due to the use of traditional statistical measures to express textual properties in quantitative form, such as BOW, TF, and TFIDF. These measures may not establish the semantic context of the words. The existing MLC techniques require a specified threshold value to map articles into predetermined categories for which domain knowledge is necessary. The objective of this paper is to get over the limitations of SLC and MLC techniques. To capture the semantic and contextual information of words, the suggested approach leverages the Word2Vec paradigm for textual representation. The suggested model determines threshold values using rigorous data analysis, obviating the necessity for domain expertise. Experimentation is carried out on two datasets from the field of computer science (JUCS and ACM). In comparison to current state-of-the-art methodologies, the proposed model performed well. Experiments yielded average accuracy of 0.86 and 0.84 for JUCS and ACM for SLC, and 0.81 and 0.80 for JUCS and ACM for MLC. On both datasets, the proposed SLC model improved the accuracy up to 4%, while the proposed MLC model increased the accuracy up to 3%. [ABSTRACT FROM AUTHOR]

The understanding of teleost fecundity type (determinate or indeterminate) is essential when deciding which egg production method should be applied to ultimately estimate spawning stock biomass. The fecundity type is, however, unknown or controversial for several commercial stocks, including the Northeast Atlantic mackerel (Scomber scombrus). Aiming at solving this problem, we applied state-of-the-art laboratory methods to document the mackerel fecundity type, including any de novo oocyte recruitment during spawning. Initially, active mackerel spawning females were precisely classified according to their spawning status. The number and size of all phasei-specific oocytes (12 phases), with a special attention to previtellogenic oocytes phases (PVO [PVO2 to PVO4a–c]), were also thoroughly investigated. Examinations of relative fecundity (RFi) clarified that the latest phase of PVOs (PVO4c) are de novo recruited to the cortical alveoli–vitellogenic pool during the spawning period, resulting in a dome-shaped seasonal pattern in RFi. Hence, we unequivocally classify mackerel as a true indeterminate spawner. As PVO4c oocytes were currently identified around 230 µm, mackerel fecundity counts should rather use this diameter as the lower threshold instead of historically 185 µm. Any use of a too low threshold value in this context will inevitably lead to an overestimation of RFi and thereby underestimated spawning stock biomass. [ABSTRACT FROM AUTHOR]

We consider the dynamics of a three-species system incorporating the Allee Effect, focussing on its influence on the emergence of extreme events in the system. First we find that under Allee effect the regular periodic dynamics changes to chaotic. Further, we find that the system exhibits unbounded growth in the vegetation population after a critical value of the Allee parameter. The most significant finding is the observation of a critical Allee parameter beyond which the probability of obtaining extreme events becomes non-zero for all three population densities. Though the emergence of extreme events in the predator population is not affected much by the Allee effect, the prey population shows a sharp increase in the probability of obtaining extreme events after a threshold value of the Allee parameter, and the vegetation population also yields extreme events for sufficiently strong Allee effect. Lastly we consider the influence of additive noise on extreme events. First, we find that noise tames the unbounded vegetation growth induced by Allee effect. More interestingly, we demonstrate that stochasticity drastically diminishes the probability of extreme events in all three populations. In fact for sufficiently high noise, we do not observe any more extreme events in the system. This suggests that noise can mitigate extreme events, and has potentially important bearing on the observability of extreme events in naturally occurring systems. [ABSTRACT FROM AUTHOR]

Contemporary approaches for animal identification use deep learning techniques to recognize coat color patterns and identify individual animals in a herd. However, deep learning algorithms usually require a large number of labeled images to achieve satisfactory performance, which creates the need to manually label all images when automated methods are not available. In this study, we evaluated the potential of a semi-supervised learning technique called pseudo-labeling to improve the predictive performance of deep neural networks trained to identify Holstein cows using labeled training sets of varied sizes and a larger unlabeled dataset. By using such technique to automatically label previously unlabeled images, we observed an increase in accuracy of up to 20.4 percentage points compared to using only manually labeled images for training. Our final best model achieved an accuracy of 92.7% on an independent testing set to correctly identify individuals in a herd of 59 cows. These results indicate that it is possible to achieve better performing deep neural networks by using images that are automatically labeled based on a small dataset of manually labeled images using a relatively simple technique. Such strategy can save time and resources that would otherwise be used for labeling, and leverage well annotated small datasets. [ABSTRACT FROM AUTHOR]

In 2021, only 6.4 million of the 10.6 million people with tuberculosis (TB) were diagnosed and treated for the disease. Although the World Health Organization recommends initial diagnostic testing using a rapid sensitive molecular assay, only 38% of people diagnosed with TB benefited from these, due to barriers including the high cost of available assays. Pooled testing has been used as an approach to increase testing efficiency in many resource-constrained situations, such as the COVID-19 pandemic, but it has not yet been widely adopted for TB diagnostic testing. Here we report a retrospective analysis of routine pooled testing of 10,117 sputum specimens using the Xpert MTB/RIF and Xpert MTB/RIF Ultra assays that was performed from July 2020 to February 2022. Pooled testing saved 48% of assays and enabled rapid molecular testing for 4156 additional people as compared to individual testing, with 6.6% of specimens positive for TB. From an in silico analysis, the positive percent agreement of pooled testing in pools of 3 as compared with individual testing for the Xpert MTB/RIF Ultra assay was estimated as 99.4% (95% CI, 96.6% to 100%). These results support the scale-up of pooled testing for efficient TB diagnosis. [ABSTRACT FROM AUTHOR]