An Investigation into the Application of Acceleration Responses' Trendline for Bridge Damage Detection Using Quadratic Regression.

It has been proven that structural damage can be successfully identified using trendlines of structural acceleration responses. In previous numerical and experimental studies, the Savitzky-Golay filter and moving average filter were adjusted to determine suitable trendlines and locate structural damage in a simply supported bridge. In this study, the quadratic regression technique was studied and employed to calculate the trendlines of the bridge acceleration responses. The normalized energies of the resulting trendlines were then used as a damage index to identify the location and severity of the structural bridge damage. An ABAQUS model of a 25 m simply supported bridge under a truckload with different velocities was used to verify the accuracy of the proposed method. The structural damage was numerically modeled as cracks at the bottom of the bridge, so the stiffness at the damage positions was decreased accordingly. Four different velocities from 1 m/s to 8 m/s were used. The proposed method can identify structural damage in noisy environments without monitoring the dynamic modal parameters. Moreover, the accuracy of the newly proposed trendline-based method was increased compared to the previous method. For velocities up to 4 m/s, the damage in all single- and multiple-damage scenarios was successfully identified. For the velocity of 8 m/s, the damage in some scenarios was not located accurately. Additionally, it should be noted that the proposed method can be categorized as an online, quick, and baseline-free structural damage-detection method.

Pesticides and insulin resistance-related metabolic diseases: Evidences and mechanisms.

The use of pesticides in the past century has lot helped humankind in improving crops' field and general hygiene level. Nevertheless, there has been countless evidences on the toxic effects of pesticides on the living systems. The link of exposure to pesticides with different human chronic diseases in the context of carcinogenicity, neurotoxicity, developmental toxicity, etc., have been evaluated in various types of studies. There are also some evidences on the link of exposure to pesticides with higher incidence of metabolic diseases associated with insulin resistance like diabetes, obesity, metabolic syndrome, hypertension, polycystic ovary syndrome and chronic kidney diseases. Physiologically, weakening intracellular insulin signaling is considered as a compensatory mechanism for cells to cope with cellular stresses like xenobiotic effects, oxidative stress and inflammatory responses, but it can pathologically lead to a defective cycle with lowered sensitivity of the cells to insulin which happens in metabolic disorders. In this work, the data related to metabolic toxicity of pesticides categorized in the mentioned metabolic diseases with a focus on the effects of pesticides on insulin signaling pathway and the mechanisms of development of insulin resistance will be systematically reviewed and presented.

Frequency, Predictors, and Outcomes of the Potential Drug-Drug Interactions in the ICUs of Teaching Hospitals in Ardabil, Northwest of Iran During 2019-2020.

Introduction: Drug-drug interactions (DDIs) can reduce therapeutic efficacy and increase the duration and cost of hospitalization so that patients are sometimes exposed to significant complications and even death. Patients in the intensive care unit (ICU) are at higher risk of DDIs for a variety of reasons, including impaired absorption, decreased metabolism, and renal failure. The main objective of this study was to evaluate frequency, clinical ranking and risk factors of potential DDIs in the ICUs of 3 teaching hospitals in Ardabil. Methods: In this descriptive-analytical cross-sectional study, drug prescriptions 355 patients admitted to the ICUs were studied. Patient information including age, sex, diagnosis, number of prescribers, number of drugs, length of stay, and status of patients' discharge (recovery or death) were recorded and checked using the online software up to date and the book Drug Interaction Facts. Finally, the data were statistically analyzed using the SPSS software. Results: The number of patients studied was 355. The mean age of the patients were 51.88 ± 23.22 years, and on average, 8.45 drugs had been prescribed for each patient. The total number of DDIs was 1597 among which class X was 1.4%, class D was 26.2%, and class C was 67.7%. Four hundred ninety-seven unique pairs of DDIs were identified. Age, number of prescribed drugs and length of stay in ICU were associated with prevalence of DDIs. Age and number of drugs were also identified as the risk factors of patients' discharge caused by death. Conclusion: DDIs can complicate health state of patients in ICUs and may increase the length of hospital stay. Setting up computerized systems to alert drug interactions in hospital wards and pharmacotherapeutic intervention by clinical pharmacist can minimize DDIs.

Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review.

BACKGROUND: This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia. METHODS: We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines. RESULTS: Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia. CONCLUSION: Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.

Effect of Cell Geometry on the Mechanical Properties of 3D Voronoi Tessellation.

Irregular 3D biological scaffolds have been widely observed in nature. Therefore, in the current work, new designs are proposed for lightweight 3D scaffolds based on Voronoi tessellation with high porosity. The proposed designs are inspired by nature, which has undoubtedly proven to be the best designer. Thus, the Rhinoceros 7/Grasshopper software was used to design three geometric models for both normal and elongated Voronoi structures: homogeneous, gradient I, and gradient II. Then, stereolithography (SLA) additive manufacturing was utilized to fabricate biopolymeric materials. Finally, a compression test was carried out to study and compare the mechanical properties of the designed samples. The gradient I cylinder show the highest Young's modulus. For the Homogeneous and gradient II cylinders, elongated Voronoi structures show superior mechanical properties and energy absorption compared to normal Voronoi designs. Hence, these designs are promising topologies for future applications.

Comparison of Physical/Chemical Properties of Prussian Blue Thin Films Prepared by Different Pulse and DC Electrodeposition Methods.

Prussian Blue (PB) thin films were prepared by DC chronoamperometry (CHA), symmetric pulse, and non-symmetric pulse electrodeposition techniques. The formation of PB was confirmed by infrared spectroscopy (FTIR), energy-dispersive X-ray spectroscopy (EDX) and UV-Vis transmission measurements. X-ray diffraction (XRD) shows the stabilization of the insoluble form of PB. From scanning electron microscopy (SEM) studies, an increase in porosity is obtained for the shorter pulse widths, which tends to improve the total charge exchange and electrochemical stability of the films. While the film prepared by CHA suffered a degradation of 82% after 260 cycles, the degradation reduced to 24% and 34% for the samples prepared by the symmetric and non-symmetric pulse methods, respectively. Additionally, in the non-symmetric pulse film, the improvement in the charge exchange reached ~522% after 260 cycles. According to this study, the deposition time distribution affects the physical/chemical properties of PB films. These results then render pulse electrodeposition methods especially suitable to produce high-quality thin films for electrochemical devices, based on PB.

The Influence of Microstructure on the Flexural Properties of 3D Printed Zirconia Part via Digital Light Processing Technology.

In recent years, additive manufacturing of ceramics is becoming of increasing interest due to the possibility of the fabrication of complex shaped parts. However, the fabrication of a fully dense bulk ceramic part without cracks and defects is still challenging. In the presented work, the digital light processing method was introduced for fabricating zirconia parts. The flexural properties of the printed zirconia were systematically investigated via a three-point bending test with the digital image correlation method, scanning electron microscopy observation and fractography analysis. Due to the anisotropy of the sample, the bending deformation behaviors of the zirconia samples in the parallel and vertical printing directions were significantly different. The flexural strength and the related elastic modulus of the samples under vertical loading were higher than that of the parallel loading, as the in-plane strength is higher than that of the interlayer strength. The maximum horizontal strain always appeared at the bottom center before the failure for the parallel loading case; while the maximum horizontal strain for the vertical loading moved upward from the bottom center to the top center. There was a clear dividing line between the minimum perpendicular strain and the maximum perpendicular strain of the samples under parallel loading; however, under vertical loading, the perpendicular strain declined from the bottom to the top along the crack path. The surrounding dense part of the sintered sample (a few hundred microns) was mainly composed of large and straight cracks between printing layers, whereas the interior contained numerous small winding cracks. The intense cracks inside the sample led to a low flexural property compared to other well-prepared zirconia samples, which the inadequate additive formulations would be the main reason for the generation of cracks. A better understanding of the additive formulation (particularly the dispersant) and the debinding-sintering process are necessary for future improvement.

Spatial aggregation choice in the era of digital and administrative surveillance data.

Traditional disease surveillance is increasingly being complemented by data from non-traditional sources like medical claims, electronic health records, and participatory syndromic data platforms. As non-traditional data are often collected at the individual-level and are convenience samples from a population, choices must be made on the aggregation of these data for epidemiological inference. Our study seeks to understand the influence of spatial aggregation choice on our understanding of disease spread with a case study of influenza-like illness in the United States. Using U.S. medical claims data from 2002 to 2009, we examined the epidemic source location, onset and peak season timing, and epidemic duration of influenza seasons for data aggregated to the county and state scales. We also compared spatial autocorrelation and tested the relative magnitude of spatial aggregation differences between onset and peak measures of disease burden. We found discrepancies in the inferred epidemic source locations and estimated influenza season onsets and peaks when comparing county and state-level data. Spatial autocorrelation was detected across more expansive geographic ranges during the peak season as compared to the early flu season, and there were greater spatial aggregation differences in early season measures as well. Epidemiological inferences are more sensitive to spatial scale early on during U.S. influenza seasons, when there is greater heterogeneity in timing, intensity, and geographic spread of the epidemics. Users of non-traditional disease surveillance should carefully consider how to extract accurate disease signals from finer-scaled data for early use in disease outbreaks.

Neurotoxicity of pesticides in the context of CNS chronic diseases.

Following the introduction and application of pesticides in human life, they have always been along with health concerns both in acute poisoning and chronic toxicities. Neurotoxicity of pesticides in chronic exposures has been known as one of the most important human health problems, as most of these chemicals act through interacting with some elements of nervous system. Pesticide-induced neurotoxicity can be defined in different categories of neurological disorders including neurodegenerative (Alzheimer, Parkinson, amyotrophic lateral sclerosis, multiple sclerosis), neurodevelopmental (attention deficit hyperactivity disorder, autism spectrum disorders, developmental delay, and intellectual disability), neurobehavioral and neuropsychiatric (depression/suicide attempt, anxiety/insomnia, and cognitive impairment) disorders some of which are among the most debilitating human health problems. In this review, neurotoxicity of pesticides in the mentioned categories and sub-categories of neurological diseases have been systematically presented in relation to different route of exposures including general, occupational, environmental, prenatal, postnatal, and paternal.

Moderate-Intensity Physical Activity, Music and Art Activities Preserved Cognitive Health in Older Adults: An Argument for Social Prescribing Solution.

Introduction: Rates of dementia are projected to increase over the coming years as global populations age. Without a treatment to slow the progression of dementia, many health policies are focusing on preventing dementia by slowing the rate of cognitive decline with age. However, it is unclear which lifestyle changes in old age meaningfully reduce the rate of cognitive decline associated with aging. Objectives: Use existing, multi-year longitudinal health data to determine if engagement in a variety of different lifestyle activities can slow the rate of cognitive decline as older adults age. Method: Data from the English Longitudinal Study of Aging was analyzed using a quasi-experimental, efficient matched-pair design inspired by the clinical trial methodology. Changes in short-term memory scores were assessed over a multi-year interval for groups who undertook one of 11 different lifestyle activities, compared to control groups matched across confounding socioeconomic and lifestyle factors. Results: Two factors, moderate-intensity physical activity and learning activities, resulted in significant positive impact on cognitive function. Conclusion: Our analysis brings cognitive benefit arguments in favor of two lifestyle activities, moderate-intensity physical activity and learning activities, while rejecting other factors advanced by the literature such as vigorous-intensity physical activity. Those findings justify and encourage the development of new lifestyle health programs by health authorities and bring forward the new health system solution, social prescribing.

Spatio-temporal modeling for confirmed cases of lyme disease in Virginia.

Epidemiological data often include characteristics such as spatial and/or temporal dependencies and excess zero counts, which pose modeling challenges. Excess zeros in such data may arise from imperfect detection and/or relative rareness of the disease in a given location. Here, we studied the spatio-temporal variation in annual Lyme disease cases in Virginia from 2001-2016 and modeled the disease with a spatio-temporal hierarchical Bayesian model. Using observed ecological and environmental covariates, we constructed a predictive model for the disease spread over space and time, including spatial and temporal random effects. We considered several different models and found that the negative binomial hurdle model performs the best for such epidemiological data. Among the various ecological predictors, the North-South (V component) of winds and relative humidity significantly contributed to predicting the Lyme cases. Our model results provide important insights on the spread of the disease in Virginia and the proposed modeling framework offers epidemiologists and health policymakers a useful tool for improving disease preparedness and control plans for the future.

Environmental Correlates of Lyme Disease Emergence in Southwest Virginia, 2005-2014.

Lyme disease is the most common tick-borne disease in North America. Though human infection is mostly transmitted in a limited geography, the range has expanded in recent years. One notable area of recent expansion is in the mountainous region of southwestern Virginia. The ecological factors that facilitate or constrain the range of human Lyme disease in this region remain uncertain. To evaluate this further, we obtained ecological data, including remotely sensed data on forest structure and vegetation, weather data, and elevation. These data were aggregated within the census block groups of a 9,153 km2 area around the cities of Blacksburg and Roanoke, VA, an area with heterogeneous Lyme disease transmission. In this geographic area, 755 individuals were reported to have Lyme disease in the 10 yr from 2006 to 2015, and these cases were aggregated by block group. A zero-inflated negative binomial model was used to evaluate which environmental variables influenced the abundance of Lyme disease cases. Higher elevation and higher vegetation density had the greatest effect size on the abundance of Lyme disease. Measures of forest edge, forest integrity, temperature, and humidity were not associated with Lyme disease cases. Future southward expansion of Lyme disease into the southeastern states may be most likely in ecologically similar mountainous areas.

Migratory strategy drives species-level variation in bird sensitivity to vegetation green-up.

Animals and plants are shifting the timing of key life events in response to climate change, yet despite recent documentation of escalating phenological change, scientists lack a full understanding of how and why phenological responses vary across space and among species. Here, we used over 7 million community-contributed bird observations to derive species-specific, spatially explicit estimates of annual spring migration phenology for 56 bird species across eastern North America. We show that changes in the spring arrival of migratory birds are coarsely synchronized with fluctuations in vegetation green-up and that the sensitivity of birds to plant phenology varied extensively. Bird arrival responded more synchronously with vegetation green-up at higher latitudes, where phenological shifts over time are also greater. Critically, species' migratory traits explained variation in sensitivity to green-up, with species that migrate more slowly, arrive earlier and overwinter further north showing greater responsiveness to earlier springs. Identifying how and why species vary in their ability to shift phenological events is fundamental to predicting species' vulnerability to climate change. Such variation in sensitivity across taxa, with long-distance neotropical migrants exhibiting reduced synchrony, may help to explain substantial declines in these species over the last several decades.

A Bayesian approach for estimating age-adjusted rates for low-prevalence diseases over space and time.

Age-adjusted rates are frequently used by epidemiologists to compare disease incidence and mortality across populations. In small geographic regions, age-adjusted rates computed directly from the data are subject to considerable variability and are generally unreliable. Therefore, we desire an approach that accounts for the excessive number of zero counts in disease mapping datasets, which are naturally present for low-prevalence diseases and are further innated when stratifying by age group. Bayesian modeling approaches are naturally suited to employ spatial and temporal smoothing to produce more stable estimates of age-adjusted rates for small areas. We propose a Bayesian hierarchical spatio-temporal hurdle model for counts and demonstrate how age-adjusted rates can be estimated from the hurdle model. We perform a simulation study to evaluate the performance of the proposed model vs a traditional Poisson model on datasets with varying characteristics. The approach is illustrated using two applications to cancer mortality at the county level.

A fast and fully-automated deep-learning approach for accurate hemorrhage segmentation and volume quantification in non-contrast whole-head CT.

This project aimed to develop and evaluate a fast and fully-automated deep-learning method applying convolutional neural networks with deep supervision (CNN-DS) for accurate hematoma segmentation and volume quantification in computed tomography (CT) scans. Non-contrast whole-head CT scans of 55 patients with hemorrhagic stroke were used. Individual scans were standardized to 64 axial slices of 128 × 128 voxels. Each voxel was annotated independently by experienced raters, generating a binary label of hematoma versus normal brain tissue based on majority voting. The dataset was split randomly into training (n = 45) and testing (n = 10) subsets. A CNN-DS model was built applying the training data and examined using the testing data. Performance of the CNN-DS solution was compared with three previously established methods. The CNN-DS achieved a Dice coefficient score of 0.84 ± 0.06 and recall of 0.83 ± 0.07, higher than patch-wise U-Net (< 0.76). CNN-DS average running time of 0.74 ± 0.07 s was faster than PItcHPERFeCT (> 1412 s) and slice-based U-Net (> 12 s). Comparable interrater agreement rates were observed between "method-human" vs. "human-human" (Cohen's kappa coefficients > 0.82). The fully automated CNN-DS approach demonstrated expert-level accuracy in fast segmentation and quantification of hematoma, substantially improving over previous methods. Further research is warranted to test the CNN-DS solution as a software tool in clinical settings for effective stroke management.

Finite-Element analysis of a lateral femoro-tibial impact on the total knee arthroplasty.

BACKGROUND AND OBJECTIVE: Total knee arthroplasty (TKA) is a routine surgery performed to treat patients with severe knee osteoarthritis. The success of a TKA depends strongly on the initial stability of the prosthetic components and its long-term osseointegration due to the optimal distribution of mechanical stresses in the surrounding bones under the effect of the different biomechanical loads applied to the Femur-TKA-Tibia system. The purpose of this study is to analyze the level and the distribution of the induced stresses in a Femur-TKA-Tibia system subjected to combined triaxial forces, which mimic a femoral mechanical shock. METHODS: In this study, complex TKA system implanted in both femoral and tibial bones has been analyzed numerically with a three-dimensional finite-element method. A virtual model is designed to examine in silico the effect of the combined triaxial forces acting on this prosthesis in femoral region. Anatomical three-dimensional finite-element models of both femoral and tibial bones were constructed to calculate the interfacial stresses around the TKA components. The 3D finite-element processing program ABAQUS was used to perform the analysis. RESULTS: The stresses propagated in the bone regions adjacent to the TKA osseointegrated components, and the decreased in their magnitude to the outer region. These stresses reached the highest level in the cortical bone areas that are right next to the proximal upper attachment portions of the TKA osseointegrated components. The magnitude of the stresses in the tibial component is higher than that in the femoral component. Finally, it is very important to emphasize the role of the polyethylene articulating spacer in the shock absorption of bone support sections. Thus, this component should be preserved mechanically from the impact of high shocks in order to maintain healthy TKA systems. CONCLUSIONS: Optimizing TKA model by controlling the biomechanical stresses distributed within its both components and supporting bones is a valid approach to achieving favorable long-term outcomes. The 3D finite-element analysis provides an effective pre-operative method for planning patient-specific TKA prostheses, and for designing future models that preserves the biomechanical function of the Femur-TKA-Tibia system.

Effect of MgO Addition on the Mechanical and Dynamic Properties of Zirconia Toughened Alumina (ZTA) Ceramics.

Zirconia toughened alumina (ZTA) is a promising advanced ceramic material for a wide range of applications that are subjected to dynamic loading. Therefore, the investigation of dynamic compressive strength, fracture toughness and hardness is essential for ZTA ceramics. However, the relationship between these mechanical properties in ZTA has not yet been established. An example of this relationship is demonstrated using ZTA samples added with MgO prepared through conventional sintering. The microstructure and mechanical properties of ZTA composites were characterized. The hardness of ZTA composites increased for ≤0.7 wt.% MgO due to the pinning effect of MgO and decrease of the porosity in the microstructure. Oppositely, the fracture toughness of ZTA composites continuously decreased due to the size reduction of Al2O3 grains. This is the main reason of deteriorate of dynamic compressive strength more than 0.2 wt.% of MgO addition. Therefore, the SHPB test shows the improvement of the dynamic compressive strength only up to a tiny amount (0.2 wt.% of MgO addition) into ZTA ceramics.

Automation of CT-based haemorrhagic stroke assessment for improved clinical outcomes: study protocol and design.

INTRODUCTION: Haemorrhagic stroke is of significant healthcare concern due to its association with high mortality and lasting impact on the survivors' quality of life. Treatment decisions and clinical outcomes depend strongly on the size, spread and location of the haematoma. Non-contrast CT (NCCT) is the primary neuroimaging modality for haematoma assessment in haemorrhagic stroke diagnosis. Current procedures do not allow convenient NCCT-based haemorrhage volume calculation in clinical settings, while research-based approaches are yet to be tested for clinical utility; there is a demonstrated need for developing effective solutions. The project under review investigates the development of an automatic NCCT-based haematoma computation tool in support of accurate quantification of haematoma volumes. METHODS AND ANALYSIS: Several existing research methods for haematoma volume estimation are studied. Selected methods are tested using NCCT images of patients diagnosed with acute haemorrhagic stroke. For inter-rater and intrarater reliability evaluation, different raters will analyse haemorrhage volumes independently. The efficiency with respect to time of haematoma volume assessments will be examined to compare with the results from routine clinical evaluations and planimetry assessment that are known to be more accurate. The project will target the development of an enhanced solution by adapting existing methods and integrating machine learning algorithms. NCCT-based information of brain haemorrhage (eg, size, volume, location) and other relevant information (eg, age, sex, risk factor, comorbidities) will be used in relation to clinical outcomes with future project development. Validity and reliability of the solution will be examined for potential clinical utility. ETHICS AND DISSEMINATION: The project including procedures for deidentification of NCCT data has been ethically approved. The study involves secondary use of existing data and does not require new consent of participation. The team consists of clinical neuroimaging scientists, computing scientists and clinical professionals in neurology and neuroradiology and includes patient representatives. Research outputs will be disseminated following knowledge translation plans towards improving stroke patient care. Significant findings will be published in scientific journals. Anticipated deliverables include computer solutions for improved clinical assessment of haematoma using NCCT.

Deploying digital health data to optimize influenza surveillance at national and local scales.

The surveillance of influenza activity is critical to early detection of epidemics and pandemics and the design of disease control strategies. Case reporting through a voluntary network of sentinel physicians is a commonly used method of passive surveillance for monitoring rates of influenza-like illness (ILI) worldwide. Despite its ubiquity, little attention has been given to the processes underlying the observation, collection, and spatial aggregation of sentinel surveillance data, and its subsequent effects on epidemiological understanding. We harnessed the high specificity of diagnosis codes in medical claims from a database that represented 2.5 billion visits from upwards of 120,000 United States healthcare providers each year. Among influenza seasons from 2002-2009 and the 2009 pandemic, we simulated limitations of sentinel surveillance systems such as low coverage and coarse spatial resolution, and performed Bayesian inference to probe the robustness of ecological inference and spatial prediction of disease burden. Our models suggest that a number of socio-environmental factors, in addition to local population interactions, state-specific health policies, as well as sampling effort may be responsible for the spatial patterns in U.S. sentinel ILI surveillance. In addition, we find that biases related to spatial aggregation were accentuated among areas with more heterogeneous disease risk, and sentinel systems designed with fixed reporting locations across seasons provided robust inference and prediction. With the growing availability of health-associated big data worldwide, our results suggest mechanisms for optimizing digital data streams to complement traditional surveillance in developed settings and enhance surveillance opportunities in developing countries.

Spatial and Spatio-Temporal Models for Modeling Epidemiological Data with Excess Zeros.

Epidemiological data often include excess zeros. This is particularly the case for data on rare conditions, diseases that are not common in specific areas or specific time periods, and conditions and diseases that are hard to detect or on the rise. In this paper, we provide a review of methods for modeling data with excess zeros with focus on count data, namely hurdle and zero-inflated models, and discuss extensions of these models to data with spatial and spatio-temporal dependence structures. We consider a Bayesian hierarchical framework to implement spatial and spatio-temporal models for data with excess zeros. We further review current implementation methods and computational tools. Finally, we provide a case study on five-year counts of confirmed cases of Lyme disease in Illinois at the county level.