An interpretable time series machine learning method for varying forecast and nowcast lengths in wastewater-based epidemiology.

Wastewater-based epidemiology has emerged as a viable tool for monitoring disease prevalence in a population. This paper details a time series machine learning (TSML) method for predicting COVID-19 cases from wastewater and environmental variables. The TSML method utilizes a number of techniques to create an interpretable, hypothesis-driven framework for machine learning that can handle different nowcast and forecast lengths. Some of the techniques employed include:•Feature engineering to construct interpretable features, like site-specific lead times, hypothesized to be potential predictors of COVID-19 cases.•Feature selection to identify features with the best predictive performance for the tasks of nowcasting and forecasting.•Prequential evaluation to prevent data leakage while evaluating the performance of the machine learning algorithm.

Effect of Physical Properties on Mechanical Behaviors of Sandstone under Uniaxial and Triaxial Compressions.

Mechanical properties of sandstone, such as compressive strength and young's modulus, are commonly used in the design of geotechnical structures and numerical simulation of underground reservoirs using models such as the digital groundwater, equivalent porous medium, and Discrete Fracture Network (DFN) models. A better understanding of the mechanical behaviors of sandstone under different loading conditions is imperative when assessing the stability of geotechnical structures. This paper highlights the effect of the physical properties (i.e., porosity, mean grain size) and environmental conditions (i.e., water content and confining stress) on uniaxial compressive strength, triaxial compressive strength, and young's modulus of sandstone. A series of uniaxial and triaxial compression experiments are conducted on sandstone formations from Wyoming. In addition, experimental data on sandstones from the literature are compiled and integrated into this study. Prediction equations for the compressive strengths and young's modulus of sandstone are established based on commonly available physical properties and known environmental conditions. The results show that the mean Uniaxial Compressive Strength (UCS) decreases as the porosity, water content, and mean grain size increase. Furthermore, a predictive empirical relationship for the triaxial compressive strength is established under different confinements and porosity. The relationship suggests that the mean peak compressive strength increases at a higher confinement and decreases at a higher porosity. The results and recommendations provide a useful framework for evaluating the strength and deformation of most sandstone.

A time series based machine learning strategy for wastewater-based forecasting and nowcasting of COVID-19 dynamics.

Monitoring COVID-19 infection cases has been a singular focus of many policy makers and communities. However, direct monitoring through testing has become more onerous for a number of reasons, such as costs, delays, and personal choices. Wastewater-based epidemiology (WBE) has emerged as a viable tool for monitoring disease prevalence and dynamics to supplement direct monitoring. The objective of this study is to intelligently incorporate WBE information to nowcast and forecast new weekly COVID-19 cases and to assess the efficacy of such WBE information for these tasks in an interpretable manner. The methodology consists of a time-series based machine learning (TSML) strategy that can extract deeper knowledge and insights from temporal structured WBE data in the presence of other relevant temporal variables, such as minimum ambient temperature and water temperature, to boost the capability for predicting new weekly COVID-19 case numbers. The results confirm that feature engineering and machine learning can be utilized to enhance the performance and interpretability of WBE for COVID-19 monitoring, along with identifying the different recommended features to be applied for short-term and long-term nowcasting and short-term and long-term forecasting. The conclusion of this research is that the proposed time-series ML methodology performs as well, and sometimes better, than simple predictions that assume available and accurate COVID-19 case numbers from extensive monitoring and testing. Overall, this paper provides an insight into the prospects of machine learning based WBE to the researchers and decision-makers as well as public health practitioners for predicting and preparing the next wave of COVID-19 or the next pandemic.

Role of cathepsin K in the expression of mechanical hypersensitivity following intra-plantar inflammation.

Persistent/chronic inflammatory pain involves multiple pathophysiological mechanisms and is far more complex than acute/momentary pain. Current therapeutics for chronic inflammatory pain are often not effective because the etiology responsible for the pain is not addressed by traditional pharmacological treatments. Cathepsin K is a cysteine protease that has mostly been studied in the context of bone and joint disorders. Previous work by others has shown that inhibition of cathepsin K activity reduces osteoarthritis-associated nociception in joints. However, the role of cathepsin K in cutaneous inflammation is understudied. We assessed the effectiveness of genetic deletion or pharmacological inhibition of cathepsin K in male mice on the expression of nocifensive behaviors after formalin injection or mechanical and thermal hypersensitivity after injection of complete Freund's adjuvant (CFA) into the mouse hind paw. Our data demonstrate that cathepsin K knockout mice (Ctsk-/-) have a reduction in nocifensive behaviors in the formalin test. In addition, Ctsk-/- do not develop mechanical hypersensitivity after CFA injection for up to 7 days. Moreover, we found that inhibition of cathepsin K reduced mechanical hypersensitivity after CFA injection and mRNA levels, protein levels, and cathepsin K activity levels were elevated after CFA injection. Based upon our data, cathepsin K is indicated to play a role in the expression of chemically-induced cutaneous hypersensitivity, as Ctsk-/- mice do not develop mechanical hypersensitivity and show a reduction in nocifensive behaviors. Further research is needed to determine whether attenuating cathepsin K activity may generate a clinically relevant therapeutic.

Bayesian hierarchical modelling of traffic barrier crash severity.

Severe vehicle crashes have resulted in a large-scale social and economic loss. As a result, the reduction of those crashes has become one of the key objectives of policy makers. Although traffic barriers have been utilized to reduce the run-of-road crash severity, still those crashes account for a high number of severe crashes. Previous studies of traffic barrier crashes often either ignore the heterogeneity across different traffic barrier types, or just focus on specific types of traffic barriers. Thus, this study developed a Bayesian Hierarchical model (BHM) to identify the contributory factors impacting the severity of traffic barrier crashes while accounting for that heterogeneity. The assessment of model fit, inter-class correlation (ICC) coefficient, and deviance information criterion (DIC) all favoured the use of the BHM. Besides accounting for the heterogeneity between barrier types, the interaction across variables shoulder width and traffic barrier height were incorporated into the analysis. Due to the differences in traffic barrier design and vehicle performance across different roadway classifications, only Wyoming interstate traffic barrier were considered. Results indicated that there is an important interaction term between traffic barrier height and shoulder width so that the impact of these two predictors should not be separated. In addition, having a citation record, negotiating a curve, being a female driver, non-speed compliance, alcohol involvement, and showing emotional signs at the time of crash were factors increasing the severity of traffic barrier crashes. On the other hand, having a turn before hitting a traffic barrier, being a younger driver, and driving in adverse weather conditions were factors that significantly decrease the severity of traffic barrier crashes.

Examination of the severity of two-lane highway traffic barrier crashes using the mixed logit model.

INTRODUCTION: Vehicles in transport sometimes leave the travel lane and encroach onto natural or artificial objects on the roadsides. These types of crashes are called run-off the road crashes, which account for a large proportion of fatalities and severe crashes to vehicle occupants. In the United States, there are about one million such crashes, with roadside features leading to one third of all road fatalities. Traffic barriers could be installed to keep vehicles on the roadways and to prevent vehicles from colliding with obstacles such as trees, boulder, and walls. The installation of traffic barriers would be warranted if the severity of colliding with the barrier would be less severe than colliding with other fix objects on the sides of the roadway. However, injuries and fatalities do occur when vehicle collide with traffic barriers. A comprehensive analysis of traffic barrier features is lacking due to the absence of traffic barrier features data. Previous research has focused on simulation studies or only a general evaluation of traffic barriers, without accounting for different traffic barrier features. METHOD: This study is conducted using an extensive traffic barrier features database for the purpose of investigating the impact of different environmental and traffic barrier geometry on this type of crash severity. This study only included data related to two-lane undivided roadway systems, which did not involve median barrier crashes. Crash severity is modeled using a mixed binary logistic regression model in which some parameters are fixed and some are random. RESULTS: The results indicated that the effects of traffic barrier height, traffic barrier offset, and shoulder width should not be separated, but rather considered as interactions that impact crash severity. Rollover, side slope height, alcohol involvement, road surface conditions, and posted speed limit are some factors that also impact the severity of these crashes. The effects of gender, truck traffic count, and time of a day were found to be best modeled with random parameters in this study. The effects of these risk factors are discussed in this paper. PRACTICAL APPLICATIONS: Results from this study could provide new guidelines for the design of traffic barriers based upon the identified roadway and traffic barrier characteristics.

Structural and Functional Plasticity within the Nucleus Accumbens and Prefrontal Cortex Associated with Time-Dependent Increases in Food Cue-Seeking Behavior.

Urges to consume food can be driven by stimuli in the environment that are associated with previous food experience. Identifying adaptations within brain reward circuits that facilitate cue-induced food seeking is critical for understanding and preventing the overconsumption of food and subsequent weight gain. Utilizing electrophysiological, biochemical, and DiI labeling, we examined functional and structural changes in the nucleus accumbens (NAc) and prefrontal cortex (PFC) associated with time-dependent increases in food craving ('incubation of craving'). Rats self-administered 60% high fat or chow 45 mg pellets and were then tested for incubation of craving either 1 or 30 days after training. High fat was chosen for comparison to determine whether palatability differentially affected incubation and/or plasticity. Rats showed robust incubation of craving for both food rewards, although responding for cues previously associated with high fat was greater than chow at both 1 and 30 days. In addition, previous experience with high-fat consumption reduced dendritic spine density in the PFC at both time points. In contrast, incubation was associated with an increase in NAc spine density and α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-mediated transmission at 30 days in both groups. Finally, incubation of craving for chow and high fat was accompanied by an increase in calcium-permeable and calcium-impermeable AMPARs, respectively. Our results suggest that incubation of food craving alters brain reward circuitry and macronutrient composition specifically induces cortical changes in a way that may facilitate maladaptive food-seeking behaviors.

Comparison of sample preparation methods and evaluation of intra- and intersample reproducibility in bacteria MALDI-MS profiling.

A study is presented on the reproducibility of mass spectral profiles of the whole bacterium E. coli resulting from laser sampling at different regions within and between matrix-assisted laser desorption ionization (MALDI) samples deposited onto the plate. Samples were prepared with different deposition methods and using different MALDI matrices. The three most common matrices used in MALDI-mass spectrometry (MS) bacteria profiling, α-cyano-4-hydroxycinnamic acid (CHCA), sinapinic acid (SA), and ferulic acid (FA), were compared in this study along with two pipet-based sample deposition methods, dried-droplet and premix. Sample variability was determined by analysis of variances (ANOVA), principal component analysis (PCA), and multivariate ANOVA (MANOVA). For the two pipet-based sample deposition methods tested in this study, the intrasample variability (most commonly referred to as "spot-to-spot" reproducibility) was of the same magnitude as the intersample variability for all MALDI matrices tested. By incorporating a spray nebulizer sample deposition method to produce uniform sample/matrix mixtures onto the MALDI plate, we demonstrate that the crystalline morphology of the MALDI sample greatly influences the intrasample reproducibility (i.e., spot-to-spot) of the resulting whole cell MALDI-MS profiles. Overall, for the pipet-based deposition methods, results showed that the smallest variability in bacteria MALDI mass spectral profiles was obtained from samples deposited using the premix method, regardless of the MALDI matrix used, with the best reproducibility obtained with the CHCA matrix. It is concluded that a sample preparation strategy that reduces or eliminates the MALDI matrix morphology heterogeneity can reduce variability (i.e., spot-to-spot) of the bacteria mass spectral profiles by up to 90%.

Identification of problems developing an ultrasensitive immunoassay for the ante mortem detection of the infectious isoform of the CWD-associated prion protein.

Ante-mortem assays exist for some Transmission Spongiform Encephalopathies (TSE). These assays facilitate our understanding of disease pathology and epidemiology; however, the limitations of these ante-mortem assays include the inability to quantify protein amount, poor sensitivity, and/or limited robustness. Here, we utilize a bioinformatics approach to report on problems associated with developing a more sensitive immunoassay for TSEs including: 1) the lack of specific and sufficiently sensitive antibodies for the infectious isoform(s) of PrP(res), 2) problems associated with serial titration of PrP(res), and 3) the distribution of PrP(res) particle sizes. Overcoming these problems require more sophisticated antibody design and a creative engineering of an ultrasensitive protein assay systems for PrP(res).