Impacts of Surface Characteristics and Dew Point on the Blue-Light (BL405) Inactivation of Viruses.

The increased prevalence of multidrug-resistant organisms (MDROs), healthcare associated infections (HAIs), and the recent COVID-19 pandemic has caused the photoinactivation industry to explore alternative wavelengths. Blue light (BL405) has gained significant interest as it is much less harmful to the skin and eyes than traditional germicidal wavelengths; therefore, in theory, it can be used continuously with human exposure. At present, the viricidal effects of BL405 are largely unknown as the literature predominately addresses bacterial disinfection performed with this wavelength. This work provides novel findings to the industry, reporting on the virucidal effects of BL405 on surfaces. This research utilizes three surfaces: ceramic, PTFE, and stainless steel. The efficacy of BL405 inactivation varied by surface type, which was due to surface characteristics, such as the contact angle, porosity, zeta potential, and reflectivity. Additionally, the effect of the dew point on BL405 inactivation efficacy was determined. This research is the first to study the effects of the dew point on the virucidal effectiveness of BL405 surface inactivation. The effects of the dew point were significant for all surfaces and the control experiments. The high-dew-point conditions (18 °C) yielded higher levels of BL405 inactivation and viral degradation for the experiments and controls, respectively.

Virus Behavior after UV254 Treatment of Materials with Different Surface Properties.

The COVID-19 pandemic highlighted the limitations in scientific and engineering understanding of applying germicidal UV to surfaces. This study combines surface characterization, viral retention, and the related UV dose response to evaluate the effectiveness of UV254 as a viral inactivation technology on five surfaces: aluminum, ceramic, Formica laminate, PTFE and stainless steel. Images of each surface were determined using SEM (Scanning Electron Microscopy), which produced a detailed characterization of the surfaces at a nanometer scale. From the SEM images, the surface porosity of each material was calculated. Through further analysis, it was determined that surface porosity, surface roughness, contact angle, and zeta potential correlate to viral retention on the material. The imaging revealed that the aluminum surface, after repeated treatment, is highly oxidized, increasing surface area and surface porosity. These interactions are important as they prevent the recovery of MS-2 without exposure to UV254. The dose response curve for PTFE was steeper than ceramic, Formica laminate and stainless steel, as inactivation to the detection limit was achieved at 25 mJ/cm2. These findings are consistent with well-established literature indicating UV reflectivity of PTFE is maximized. Statistical testing reinforced that the efficacy of UV254 for surface inactivation varies by surface type.

MarZIC: A Marginal Mediation Model for Zero-Inflated Compositional Mediators with Applications to Microbiome Data.

BACKGROUND: The human microbiome can contribute to pathogeneses of many complex diseases by mediating disease-leading causal pathways. However, standard mediation analysis methods are not adequate to analyze the microbiome as a mediator due to the excessive number of zero-valued sequencing reads in the data and that the relative abundances have to sum to one. The two main challenges raised by the zero-inflated data structure are: (a) disentangling the mediation effect induced by the point mass at zero; and (b) identifying the observed zero-valued data points that are not zero (i.e., false zeros). METHODS: We develop a novel marginal mediation analysis method under the potential-outcomes framework to address the issues. We also show that the marginal model can account for the compositional structure of microbiome data. RESULTS: The mediation effect can be decomposed into two components that are inherent to the two-part nature of zero-inflated distributions. With probabilistic models to account for observing zeros, we also address the challenge with false zeros. A comprehensive simulation study and the application in a real microbiome study showcase our approach in comparison with existing approaches. CONCLUSIONS: When analyzing the zero-inflated microbiome composition as the mediators, MarZIC approach has better performance than standard causal mediation analysis approaches and existing competing approach.

Quantifying the population burden of musculoskeletal disorders, including impact on sickness absence: analysis of national Scottish data.

Objectives: Musculoskeletal disorders (MSDs) account for the greatest burden of years lived with disability globally. To prevent disability, good-quality services need to be commissioned, appropriate for local need. We analysed data collected systematically from a new musculoskeletal service serving 70% of the population of Scotland to evaluate: age- and sex-specific occurrence; anatomical distribution; and impact and effect on work ability. Methods: A new centralized telephone-based triage for people with musculoskeletal disorders was set up in Scotland in 2015. Available to most of the population aged >16 years (>3 million people), data were collected systematically into a database detailing: anatomical site, nature of onset, duration, impact/risk (modified STarT score), deprivation level and, for those in employment, sickness absence. Results: Data were available from 219 314 new callers, 2015-18. Calls were more frequently from women (60%), increased with age until the eighth decade, and 66% reported symptoms that had been present for >6 weeks. Callers were more likely to be living in more deprived areas in each age band between 20 and 64 years and tended to have higher-impact symptoms. The majority (53%) of callers were in employment, and 19% of these were off sick because of their symptoms. Sickness absence was more common among those with highest impact/risk scores from deprived areas with more acute symptoms. Discussion: Large-scale systematic data collection for MSDs emphasizes the size and impact of the burden among adults aged >16 years. A socio-economic gradient is evident in terms of prevalence and impact of MSDs, particularly for sickness absence.

Increased DNA methylation, cellular senescence and premature epigenetic aging in guinea pigs and humans with tuberculosis.

BACKGROUND: Tuberculosis (TB) is the archetypical chronic infection, with patients having months of symptoms before diagnosis. In the two years after successful therapy, survivors of TB have a three-fold increased risk of death. METHODS: Guinea pigs were infected with Mycobacterium tuberculosis (Mtb) for 45 days, followed by RRBS DNA methylation analysis. In humans, network analysis of differentially expressed genes across three TB cohorts were visualized at the pathway-level. Serum levels of inflammation were measured by ELISA. Horvath (DNA methylation) and RNA-seq biological clocks were used to investigate shifts in chronological age among humans with TB. RESULTS: Guinea pigs with TB demonstrated DNA hypermethylation and showed system-level similarity to humans with TB (p-value = 0.002). The transcriptome in TB in multiple cohorts was enriched for DNA methylation and cellular senescence. Senescence associated proteins CXCL9, CXCL10, and TNF were elevated in TB patients compared to healthy controls. Humans with TB demonstrate 12.7 years (95% CI: 7.5, 21.9) and 14.38 years (95% CI: 10.23-18.53) of cellular aging as measured by epigenetic and gene expression based cellular clocks, respectively. CONCLUSIONS: In both guinea pigs and humans, TB perturbs epigenetic processes, promoting premature cellular aging and inflammation, a plausible means to explain the long-term detrimental health outcomes after TB.

What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics.

Genetic data have become increasingly complex within the past decade, leading researchers to pursue increasingly complex questions, such as those involving epistatic interactions and protein prediction. Traditional methods are ill-suited to answer these questions, but machine learning (ML) techniques offer an alternative solution. ML algorithms are commonly used in genetics to predict or classify subjects, but some methods evaluate which features (variables) are responsible for creating a good prediction; this is called feature importance. This is critical in genetics, as researchers are often interested in which features (e.g., SNP genotype or environmental exposure) are responsible for a good prediction. This allows for the deeper analysis beyond simple prediction, including the determination of risk factors associated with a given phenotype. Feature importance further permits the researcher to peer inside the black box of many ML algorithms to see how they work and which features are critical in informing a good prediction. This review focuses on ML methods that provide feature importance metrics for the analysis of genetic data. Five major categories of ML algorithms: k nearest neighbors, artificial neural networks, deep learning, support vector machines, and random forests are described. The review ends with a discussion of how to choose the best machine for a data set. This review will be particularly useful for genetic researchers looking to use ML methods to answer questions beyond basic prediction and classification.

Implementation of collaborative care for depressive disorder treatment among accountable care organizations.

ABSTRACT: Collaborative care - primary care models combining care management, consulting behavioral health clinicians, and registries to target mental health treatment - is a cost-effective depression treatment model, but little is known about uptake of collaborative care in a national setting. Alternative payment models such as accountable care organizations (ACOs), in which ACOs are responsible for quality and cost for defined patient populations, may encourage collaborative care use.Determine prevalence of collaborative care implementation among ACOs and whether ACO structure or contract characteristics are associated with implementation.Cross-sectional analysis of 2017-2018 National Survey of ACOs (NSACO). Overall, 55% of ACOs returned a survey (69% of Medicare, 36% of non-Medicare ACOs); 48% completed at least half of core survey questions. We used logistic regression to examine the association between implementation of core collaborative care components - care management, a consulting mental health clinician, and a patient registry to track mental health symptoms - and ACO characteristics.Four hundred five National Survey of ACOs respondents answering questions on collaborative care implementation.Only 17% of ACOs reported implementing all collaborative care components. Most reported using care managers (71%) and consulting mental health clinicians (58%), =just 26% reported using patient registries. After adjusting for multiple ACO characteristics, ACOs responsible for mental health care quality measures were 15 percentage points (95% CI 5-23) more likely to implement collaborative care.Most ACOs are not utilizing behavioral health collaborative care. Including mental health care quality measures in payment contracts may facilitate implementation of this cost-effective model. Improving provider capacity to track and target depression treatment with patient registries is warranted as payment contracts focus on treatment outcomes.

Distribution and fate of per- and polyfluoroalkyl substances (PFAS) in wastewater treatment facilities.

Anthropogenic compounds known as per- and polyfluoroalkyl substances (PFAS) represent a major class of contaminants of emerging concern composed of nearly 5000 chemicals. Many PFAS are persistent, bioaccumulative and toxic, and their widespread use makes their environmental distribution a growing concern. Wastewater treatment facilities (WWTFs) are a conduit of PFAS to the environment, integrating common household products from municipal sewage, industrial wastewater sources, septic materials, and firefighting wastewaters in effluent and sludge. This study investigated the distribution and fate of twenty-four PFAS within six New Hampshire municipal WWTFs applying a range of biological and disinfection unit processes. PFAS quantification was conducted using two approaches: (1) liquid chromatography with tandem mass spectrometry (LC-MS/MS) of 24 known compounds and (2) a total oxidizable precursor assay (TOP assay) followed by LC-MS/MS to determine the total oxidizable PFAS concentration. Of the 24 PFAS analyzed, up to 7 and 12 constituents were detected in influent and effluent of WWTFs, respectively, with concentrations ranging from 30 to 128 ng L-1 in March. Effluent ΣPFAS concentration increased during July, with concentrations between 70 and 198 ng L-1 for the same detected constituents. Short-chain PFAS were dominant in both influent and effluent, while long-chain compounds dominated in WWTF sludge. The increase in terminal end-products after oxidation by the TOP assay indicates the presence of unquantified PFAS precursors in both influent and effluent. A significantly lower proportion of oxidizable PFAS precursors were detected in July influent and effluent relative to March, indicating a possible role of season or temperature on microbial transformation of these compounds prior to reaching WWTFs and during treatment. These results provide new insight into PFAS distribution and fate during two seasons in New England municipal WWTFs.

Comparison of Juvenile Feed Protocols on Growth and Spawning in Zebrafish.

Over the past 2 decades, zebrafish, Danio rerio, have become a mainstream laboratory animal model, yet zebrafish husbandry practices remain far from standardized. Feeding protocols play a critical role in the health, wellbeing, and productivity of zebrafish laboratories, yet they vary significantly between facilities. In this study, we compared our current feeding protocol for juvenile zebrafish (30 dpf to 75 dpf), a 3:1mixture of fish flake and freeze-dried krill fed twice per day with live artemia twice per day (FKA), to a diet of Gemma Micro 300 fed once per day with live artemia once per day (GMA). Our results showed that juvenile EK wild-type zebrafish fed GMA were longer and heavier than juveniles fed FKA. As compared with FKA-fed juveniles, fish fed GMA as juveniles showed better reproductive performance as measured by spawning success, fertilization rate, and clutch size. As adults, fish from both feeding protocols were acclimated to our standard adult feeding protocol, and the long-term effects of juvenile diet were assessed. At 2 y of age, the groups showed no difference in mortality or fecundity. Reproductive performance is a crucial aspect of zebrafish research, as much of the research focuses on the developing embryo. Here we show that switching juvenile zebrafish from a mixture of flake and krill to Gemma Micro 300 improves reproductive performance, even with fewer feedings of live artemia, thus simplifying husbandry practices.

The fate and removal of pharmaceuticals and personal care products within wastewater treatment facilities discharging to the Great Bay Estuary.

Pharmaceuticals and personal care products (PPCPs) are contaminants of emerging concern that derive primarily in the water environment from combined sewer overflows and discharges from industrial and municipal wastewater treatment facilities (WWTFs). Due to incomplete removal during wastewater treatment, PPCP impacts to aquatic ecosystems are a major concern. The Great Bay Estuary (New Hampshire, USA) is an important ecological, commercial, and recreational resource where upstream WWTFs have recently been under pressure to reduce nitrogen loading to the estuary and consequently upgrade treatment systems. Therefore, we investigated the distribution and abundance of 18 PPCPs and three flame retardants within the Great Bay Estuary and WWTFs discharging to the estuary to examine how WWTF type influenced PPCP removal. All 21 analytes were frequently detected at µg/L to ng/L concentrations in influent and effluent and ng/kg in sludge. WWTFs with enhanced nutrient removal and longer solids retention times correlated to higher PPCP removal, indicating facility upgrades may have benefits related to PPCP removal. Understanding PPCP fate during treatment and in downstream waters informs our ability to assess the environmental and ecological impacts of PPCPs on estuarine resources and develop mitigation strategies to better protect marine ecosystems from emerging contaminant exposure. PRACTITIONER POINTS: PPCP removal positively correlated with solids retention time and varied by treatment facility and compound. Upgrade of WWTFs for biological nitrogen removal may also increase PPCP removal. Surface water fluoxetine concentrations may present an ecological risk to the Great Bay Estuary.

A New Standard for Advance Care Planning (ACP) Conversations in the Hospital: Results from a Delphi Panel.

BACKGROUND: Fewer than half of the US population has an advance healthcare directive. Hospitalizations offer a key opportunity for clinicians to engage patients in advance care planning (ACP) conversations. Guidelines suggest screening for the presence of "serious illness" but do not further specify how to prioritize the 12.4 million patients hospitalized each year. OBJECTIVE: To establish a normative standard for prioritizing hospitalized patients for ACP conversations. DESIGN AND SETTING: A modified Delphi study, with three iterative rounds of online surveys. PARTICIPANTS: Multi-disciplinary group of US-based clinicians with research and practical expertise in ACP. MAIN MEASURES: Indirect and direct elicitation of short-term and 1-year risk of mortality that prompt experts to prioritize ACP conversations for hospitalized adults. MAIN RESULTS: Fifty-seven of 108 (52%) candidate panelists completed round 1, and 47 completed rounds 2 and 3. Panelists were primarily physicians (84%), with significant experience (mean years 23 [SD 9.8]), who either taught (55%) and/or performed research about ACP (55%). In round 1, > 70% of panelists agreed that all hospitalized adults ≥ 65 years should have an ACP conversation before discharge, but disagreed about the timing and content of the conversation. By round 3, > 70% of participants agreed that patients with either high (> 10%) short-term or high (≥ 34%) 1-year risk of mortality should have a goals of care conversation (i.e., focused on preferences for near-term treatment), while patients with low (≤ 10%) short-term and low (< 19%) 1-year risk of mortality warranted an ACP conversation (i.e., focused on preferences for future care) before discharge. LIMITATIONS: Use of case vignettes to elicit clinician judgment; response rate. CONCLUSIONS: Panelists agreed that clinicians should have an ACP conversation with all hospitalized adults over 65 years in an ACP conversation, adjusting the content and timing of the conversation conditional on the patient's risk of short-term and 1-year mortality.

Graph literacy matters: Examining the association between graph literacy, health literacy, and numeracy in a Medicaid eligible population.

OBJECTIVES: Graphic display formats are often used to enhance health information. Yet limited attention has been paid to graph literacy in people of lower education and lower socioeconomic status (SES). This study aimed to: 1) examine the relationship between graph literacy, numeracy, health literacy and sociodemographic characteristics in a Medicaid-eligible population 2) determine the impact of graph literacy on comprehension and preference for different visual formats. METHODS: We conducted a cross-sectional online survey among people in the US on Medicaid, and of presumed lower education and SES. RESULTS: The mean graph literacy score among 436 participants was 1.47 (SD 1.05, range: 0 to 4). Only graph literacy was significantly associated with overall comprehension (p < .001). Mean comprehension scores were highest for the table format (1.91), closely followed by bar graph (1.85) and icon array (1.80). Information comprehension was aligned with preference scores. CONCLUSIONS: Graph literacy in a Medicaid-eligible population was lower than previous estimates in the US. Tables were better understood, with icon arrays yielding the lowest score. Preferences aligned with comprehension. PRACTICE IMPLICATIONS: It may be necessary to reconsider the use of graphic display formats when designing information for people with lower educational levels. Further research is needed.

Regulation of stem cell function and neuronal differentiation by HERV-K via mTOR pathway.

Stem cells are capable of unlimited proliferation but can be induced to form brain cells. Factors that specifically regulate human development are poorly understood. We found that human stem cells expressed high levels of the envelope protein of an endogenized human-specific retrovirus (HERV-K, HML-2) from loci in chromosomes 12 and 19. The envelope protein was expressed on the cell membrane of the stem cells and was critical in maintaining the stemness via interactions with CD98HC, leading to triggering of human-specific signaling pathways involving mammalian target of rapamycin (mTOR) and lysophosphatidylcholine acyltransferase (LPCAT1)-mediated epigenetic changes. Down-regulation or epigenetic silencing of HML-2 env resulted in dissociation of the stem cell colonies and enhanced differentiation along neuronal pathways. Thus HML-2 regulation is critical for human embryonic and neurodevelopment, while it's dysregulation may play a role in tumorigenesis and neurodegeneration.

KMN-159, a novel EP4 receptor selective agonist, stimulates osteoblastic differentiation in cultured whole rat bone marrow.

KMN-159 is the lead compound from a series of novel difluorolactam prostanoid EP4 receptor agonists aimed at inducing local bone formation while avoiding the inherent side effects of systemic EP4 activation. KMN-159 is a potent, selective small molecule possessing pharmacokinetic properties amenable to local administration. Unfractionated rat bone marrow cells (BMCs) were treated once at plating with escalating doses of KMN-159 (1 pM to 10 µM). The resulting elevated alkaline phosphatase (ALP) levels measured 9 days post-dose are consistent with increased osteoblastic differentiation and exposure to KMN-159 at low nanomolar concentrations for as little as 30 min was sufficient to induce complete osteoblast differentiation of the BMCs from both sexes and regardless of age. ALP induction was blocked by an EP4 receptor antagonist but not by EP1 or EP2 receptor antagonists and was not induced by EP2 or EP3 receptor agonists. Addition of BMCs to plates coated with KMN-159 24 days earlier resulted in ALP activation, highlighting the chemical stability of the compound. The expression of phenotype markers such as ALP, type I collagen, and osteocalcin was significantly elevated throughout the osteoblastic differentiation timecourse initiated by KMN-159 stimulation. An increased number of tartrate-resistant acid phosphatase-positive cells was observed KMN-159 or PGE2 treated BMCs but only in the presence of exogenous receptor activator of nuclear factor kappa-Β ligand (RANKL). No change in the number of adipocytes was observed. KMN-159 also increased bone healing in a rat calvarial defect model with a healing rate equivalent to recombinant human bone morphogenetic protein-2. Our studies show that KMN-159 is able to stimulate osteoblastic differentiation with a very short time of exposure, supporting its potential as a therapeutic candidate for augmenting bone mass.

Difluoromethylene at the γ-Lactam α-Position Improves 11-Deoxy-8-aza-PGE1 Series EP4 Receptor Binding and Activity: 11-Deoxy-10,10-difluoro-8-aza-PGE1 Analog (KMN-159) as a Potent EP4 Agonist.

A series of small-molecule full agonists of the prostaglandin E2 type 4 (EP4) receptor have been generated and evaluated for binding affinity and cellular potency. KMN-80 and its gem-difluoro analog KMN-159 possess high selectivity relative to other prostanoid receptors. Difluoro substitution is positioned alpha to the lactam ring carbonyl and results in KMN-159's fivefold increase in potency versus KMN-80. The two analogs exhibit electronic and conformational variations, including altered nitrogen hybridization and lactam ring puckering, that may drive the observed difluoro-associated increased potency within this four-compound series.

The Vegetation Red Edge Biosignature Through Time on Earth and Exoplanets.

The high reflection of land vegetation in the near-infrared, the vegetation red edge (VRE), is often cited as a spectral biosignature for surface vegetation on exoplanets. The VRE involves only a few percentage change in reflectivity for a disk-integrated observation of present-day Earth. Here we show that the strength of Earth's VRE has increased over the past ∼500 million years of land plant evolution and may continue to increase as solar luminosity increases and the planet warms, until either vegetation coverage is reduced, or the planet's atmosphere becomes opaque to light reflected off the surface. Early plants such as mosses and liverworts, which dominated the land 500-400 million years ago, produce a weaker VRE, approximately half as strong as that of modern vegetation. We explore how the changes in land plants, as well as geological changes such as ice coverage during ice ages and interglacial periods, influence the detectability of the VRE through Earth's geological past. Our results show that the VRE has varied through the evolutionary history of land plants on Earth and could continue to change into the future if hotter climate conditions became dominant, encouraging the spread of vegetation. Our findings suggest that older and hotter Earth-like planets are good targets for the search for a VRE signature. In addition, hot exoplanets and dry exoplanets with some water could be the best targets for a successful vegetation biosignature detection. As well as a strong red edge, lower cloud fractions and low levels of atmospheric water vapor on such planets could make it easier to detect surface features in general.

Life cycle environmental and economic implications of small drinking water system upgrades to reduce disinfection byproducts.

Many of the small drinking water systems in the US that utilize simple filtration and chlorine disinfection or chlorine disinfection alone are facing disinfection byproduct (DBP) noncompliance issues, which need immediate upgrades. In this study, four potential upgrade scenarios, namely the GAC, ozone, UV30, and UV186 scenarios, were designed for a typical small drinking water systems and compared in terms of embodied energy, carbon footprint, and life cycle cost. These scenarios are designed to either reduce the amount of DBP precursors using granular activated carbon filtration (the GAC scenario) or ozonation (the ozone scenario), or replace the chlorine disinfection with the UV disinfection at different intensities followed by chloramination (the UV30 and UV186 scenarios). The UV30 scenario was found to have the lowest embodied energy (417 GJ/year) and life cycle cost ($0.25 million US dollars), while the GAC scenario has the lowest carbon footprint (21 Mg CO2e/year). The UV186 scenario consistently presents the highest environmental and economic impacts. The major contributors of the economic and environmental impacts of individual scenarios also differ. Energy and/or material consumptions during the operation phase dominate the environmental impacts of the four scenarios, while the infrastructure investments have a noticeable contribution to the economic costs. The results are sensitive to changes in water quality. An increase of raw water quality, i.e., an increase in organic precursor content, could potentially result in the ozone scenario being the least energy intensive scenario, while a decrease of water quality could greatly reduce the overall competitiveness of the GAC scenario.

The optimal crowd learning machine.

BACKGROUND: Any family of learning machines can be combined into a single learning machine using various methods with myriad degrees of usefulness. RESULTS: For making predictions on an outcome, it is provably at least as good as the best machine in the family, given sufficient data. And if one machine in the family minimizes the probability of misclassification, in the limit of large data, then Optimal Crowd does also. That is, the Optimal Crowd is asymptotically Bayes optimal if any machine in the crowd is such. CONCLUSIONS: The only assumption needed for proving optimality is that the outcome variable is bounded. The scheme is illustrated using real-world data from the UCI machine learning site, and possible extensions are proposed.

Comparison of parametric and machine methods for variable selection in simulated Genetic Analysis Workshop 19 data.

Current findings from genetic studies of complex human traits often do not explain a large proportion of the estimated variation of these traits due to genetic factors. This could be, in part, due to overly stringent significance thresholds in traditional statistical methods, such as linear and logistic regression. Machine learning methods, such as Random Forests (RF), are an alternative approach to identify potentially interesting variants. One major issue with these methods is that there is no clear way to distinguish between probable true hits and noise variables based on the importance metric calculated. To this end, we are developing a method called the Relative Recurrency Variable Importance Metric (r2VIM), a RF-based variable selection method. Here, we apply r2VIM to the unrelated Genetic Analysis Workshop 19 data with simulated systolic blood pressure as the phenotype. We compare the number of "true" functional variants identified by r2VIM with those identified by linear regression analyses that use a Bonferroni correction to calculate a significance threshold. Our results show that r2VIM performed comparably to linear regression. Our findings are proof-of-concept for r2VIM, as it identifies a similar number of functional and nonfunctional variants as a more commonly used technique when the optimal importance score threshold is used.