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Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll-a (chl-a) is a common water quality metric and has been shown to have a relationship with cyanobacteria. The World Health Organization (WHO) recently updated their previous 1999 cyanoHAB guidance values (GVs) to be more practical by basing the GVs on chl-a concentration rather than cyanobacterial counts. This creates an opportunity for widespread cyanoHAB monitoring based on chl-a proxies, with satellite remote sensing (SRS) being a potentially powerful tool. We used Sentinel-2 (S2) and Sentinel-3 (S3) to map chl-a and cyanobacteria, respectively, classified chl-a values according to WHO GVs, and then compared them to cyanotoxin advisories issued by the California Department of Water Resources (DWR) at San Luis Reservoir, key infrastructure in Californias water system. We found reasonably high rates of total agreement between advisories by DWR and SRS, however rates of agreement varied for S2 based on algorithm. Total agreement was 83% for S3, and 52%-79% for S2. False positive and false negative rates for S3 were 12% and 23%, respectively. S2 had 12%-80% false positive rate and 0%-38% false negative rate, depending on algorithm. Using SRS-based chl-a GVs as an early indicator for possible exposure advisories and as a trigger for in situ sampling may be effective to improve public health warnings. Implementing SRS for cyanoHAB monitoring could fill temporal data gaps and provide greater spatial information not available from in situ measurements alone.

Advances in computing, collection, and sharing of Earth Observations (EOs) have significantly improved the potential for integrating EO and water resources models. Inadequate observational data for the systems simulated have been a persistent limitation in developing robust water resources models. Although various EO datasets have been available for decades, they have been under-utilized for water resources modeling. This can be due to sensor and product limitations, including spatial, spectral, and temporal resolutions, and the reluctance of the water resources community to adopt the state-of-art quickly. Motivated by the dual agenda of engaging the water resources community on various aspects of integrating EOs with water resources modeling and understanding the likely factors that limit a deeper integration of EOs in water resources management, we investigated the communities' perception of water resources modeling and EO integration. This paper summarizes the findings of a web-based survey conducted at the annual ASCE-EWRI (American Society of Civil Engineers-Environmental Water Resources Institute) International Water Congress in 2022. The analysis of responses (n = 74) identified limited spatial resolution, atmospheric and cloud interference, and lack of in-situ validation data as the highest perceived barriers to integrating EOs with water resources modeling and management. Perceptions among different groups of participants and even within the groups were different. For example, the perceived barriers often differed between researchers and non-researchers (e.g., policymakers and practitioners). There were differences in perception among the remote-sensing and water resources researchers within the research community. Even among water resources communities, disparities existed between the perceptions of respondents who also identified as knowledgeable about remote sensing and those who didn't. These observations highlighted the need to intentionally develop a convergent group and domain to integrate the disciplines involved and capitalize on the advancements that have improved the EO for water resources management.

BACKGROUND: Although comorbidities are risk factors for recurrent Clostridioides difficile infection (rCDI), many clinical trials exclude patients with medical conditions such as malignancy or immunosuppression. In a phase 3, double-blind, placebo-controlled, randomized trial (ECOSPOR III), fecal microbiota spores, live (VOWST, Seres Therapeutics; hereafter VOS, formerly SER-109), an oral microbiota therapeutic, significantly reduced the risk of rCDI at week 8. We evaluated the efficacy of VOS compared with placebo in patients with comorbidities and other risk factors for rCDI. METHODS: Adults with rCDI were randomized to receive VOS or placebo (4 capsules daily for 3 days) following standard-of-care antibiotics. In this post hoc analysis, the rate of rCDI through week 8 was assessed in VOS-treated participants compared with placebo for subgroups including (i) Charlson comorbidity index (CCI) score category (0, 1-2, 3-4, ≥5); (ii) baseline creatinine clearance (50 to 80, or >80 mL/minute); (iii) number of CDI episodes, inclusive of the qualifying episode (3 and ≥4); (iv) exposure to non-CDI-targeted antibiotics after dosing; and (v) acid-suppressing medication use at baseline. RESULTS: Of 281 participants screened, 182 were randomized (59.9% female; mean age, 65.5 years). Comorbidities were common with a mean overall baseline age-adjusted CCI score of 4.1 (4.1 in the VOS arm and 4.2 in the placebo arm). Across all subgroups analyzed, VOS-treated participants had a lower relative risk of recurrence compared with placebo. CONCLUSIONS: In this post hoc analysis, VOS reduced the risk of rCDI compared with placebo, regardless of baseline characteristics, concomitant medications, or comorbidities.

ObjectiveThe aim of this study was to understand the usability and acceptability of virtual reality (VR) among a racially and ethnically diverse group of patients who experience chronic pain.Materials and methodsUsing the Technology Acceptance Model theory, we conducted semistructured interviews and direct observation of VR use with English-speaking patients who experience chronic pain treated in a public healthcare system (n = 15), using a commercially available VR technology platform. Interviews included questions about current pain management strategies, technology use, experiences and opinions with VR, and motivators for future use.ResultsBefore the study, none of the 15 participants had heard about or used VR for pain management. Common motivators for VR use included a previous history of substance use and having exhausted many other options to manage their pain and curiosity. Most participants had a positive experience with VR and 47% found that the VR modules distracted them from their pain. When attempting the navigation-based usability tasks, most participants (73%-92%) were able to complete them independently.DiscussionVR is a usable tool for diverse patients with chronic pain. Our findings suggest that the usability of VR is not a barrier and perhaps a focus on improving the accessibility of VR in safety-net settings is needed to reduce disparities in health technology use.ConclusionsThe usability and acceptability of VR are rarely studied in diverse patient populations. We found that participants had a positive experience using VR, showed interest in future use, and would recommend VR to family and friends.

African swine fever (ASF), a nonzoonotic, World Organisation for Animal Health (WOAH) notifiable disease, is a devasting hemorrhagic infectious disease of domestic (Sus domesticus) and wild (Sus scrofa) swine populations [...]

BackgroundLifestyle intervention and metformin have been shown to prevent diabetes; however, their efficacy in preventing cardiovascular disease associated with the development of diabetes is unclear. We examined whether these interventions reduced the incidence of major cardiovascular events over a 21-year median follow-up of participants in the DPP trial (Diabetes Prevention Program) and DPPOS (Diabetes Prevention Program Outcomes Study).MethodsDuring DPP, 3234 participants with impaired glucose tolerance were randomly assigned to metformin 850 mg twice daily, intensive lifestyle or placebo, and followed for 3 years. During the next 18-year average follow-up in DPPOS, all participants were offered a less intensive group lifestyle intervention, and unmasked metformin was continued in the metformin group. The primary outcome was the first occurrence of nonfatal myocardial infarction, stroke, or cardiovascular death adjudicated by standard criteria. An extended cardiovascular outcome included the primary outcome or hospitalization for heart failure or unstable angina, coronary or peripheral revascularization, coronary heart disease diagnosed by angiography, or silent myocardial infarction by ECG. ECGs and cardiovascular risk factors were measured annually.ResultsNeither metformin nor lifestyle intervention reduced the primary outcome: metformin versus placebo hazard ratio 1.03 (95% CI, 0.78-1.37; P = 0.81) and lifestyle versus placebo hazard ratio 1.14 (95% CI, 0.87-1.50; P = 0.34). Risk factor adjustment did not change these results. No effect of either intervention was seen on the extended cardiovascular outcome.ConclusionsNeither metformin nor lifestyle reduced major cardiovascular events in DPPOS over 21 years despite long-term prevention of diabetes. Provision of group lifestyle intervention to all, extensive out-of-study use of statin and antihypertensive agents, and reduction in the use of study metformin together with out-of-study metformin use over time may have diluted the effects of the interventions.RegistrationURL: https://www.Clinicaltrialsgov; Unique identifiers: DPP (NCT00004992) and DPPOS (NCT00038727).

M-bridge was a sequential multiple assignment randomized trial (SMART) that aimed to develop a resource-efficient adaptive preventive intervention (API) to reduce binge drinking in first-year college students. The main results of M-bridge suggested no difference, on average, in binge drinking between students randomized to APIs versus assessment-only control, but certain elements of the API were beneficial for at-risk subgroups. This paper extends the main results of M-bridge through an exploratory analysis using Q-learning, a novel algorithm from the computer science literature. Specifically, we sought to further tailor the two aspects of the M-bridge APIs to an individual and test whether deep tailoring offers a benefit over assessment-only control. Q-learning is a method to estimate decision rules that assign optimal treatment (i.e., to minimize binge drinking) based on student characteristics. For the first aspect of the M-bridge API (when to offer), we identified the optimal tailoring characteristic post hoc from a set of 20 candidate variables. For the second (how to bridge), we used a known effect modifier from the trial. The results of our analysis are two rules that optimize (1) the timing of universal intervention for each student based on their motives for drinking and (2) the bridging strategy to indicated interventions (i.e., among those who continue to drink heavily mid-semester) based on mid-semester binge drinking frequency. We estimate that this newly tailored API, if offered to all first-year students, would reduce binge drinking by 1 occasion per 2.5 months (95% CI: decrease of 1.45 to 0.28 occasions, p < 0.01) on average. Our analyses demonstrate a real-world implementation of Q-learning for a substantive purpose, and, if replicable in future trials, our results have practical implications for college campuses aiming to reduce student binge drinking.

Wildfires increase runoff and sediment yields that impact downstream ecosystems. While the effects of wildfire on stream water quality are well documented, oceanic responses to wildfire remain poorly understood. Therefore, this study investigated oceanic responses to the 2018 Woolsey Fire using satellite remote sensing and in situ data analyses. We examined 2016-2020 turbidity plume (n = 192) and 2008-2020 fecal indicator bacteria (FIB, n = 15,015) measurements at variable proximity to the Woolsey Fire. Shifts in coastal water quality were more pronounced in the "inside" region, which drained the burn area. The inside region experienced 2018-2019 plume surface area monthly means that were 10 and 9 times greater than 2016-2017 and 2017-2018 monthly means, respectively. Further, linear regressions showed that 2018-2019 three-day precipitation totals produced plumes of greater surface area. We also noted statistically significant increases in the inside region in 2018-2019 total coliform and Enterococcus monthly means that were 9 and 53 times greater than 2008-2018 monthly means, respectively. These results indicate that sediment and microbial inputs to coastal ecosystems can increase substantially post-wildfire at levels relevant to public and environmental health, and underscore the benefit of considering remote sensing and in situ measurements for water quality monitoring.

ObjectiveTo determine whether metformin or lifestyle modification can lower rates of all-cause and cause-specific mortality in the Diabetes Prevention Program and Diabetes Prevention Program Outcomes Study.Research design and methodsFrom 1996 to 1999, 3,234 adults at high risk for type 2 diabetes were randomized to an intensive lifestyle intervention, masked metformin, or placebo. Placebo and lifestyle interventions stopped in 2001, and a modified lifestyle program was offered to everyone, but unmasked study metformin continued in those originally randomized. Causes of deaths through 31 December 2018 were adjudicated by blinded reviews. All-cause and cause-specific mortality hazard ratios (HRs) were estimated from Cox proportional hazards regression models and Fine-Gray models, respectively.ResultsOver a median of 21 years (interquartile range 20-21), 453 participants died. Cancer was the leading cause of death (n = 170), followed by cardiovascular disease (n = 131). Compared with placebo, metformin did not influence mortality from all causes (HR 0.99 [95% CI 0.79, 1.25]), cancer (HR 1.04 [95% CI 0.72, 1.52]), or cardiovascular disease (HR 1.08 [95% CI 0.70, 1.66]). Similarly, lifestyle modification did not impact all-cause (HR 1.02 [95% CI 0.81, 1.28]), cancer (HR 1.07 [95% CI 0.74, 1.55]), or cardiovascular disease (HR 1.18 [95% CI 0.77, 1.81]) mortality. Analyses adjusted for diabetes status and duration, BMI, cumulative glycemic exposure, and cardiovascular risks yielded results similar to those for all-cause mortality.ConclusionsCancer was the leading cause of mortality among adults at high risk for type 2 diabetes. Although metformin and lifestyle modification prevented diabetes, neither strategy reduced all-cause, cancer, or cardiovascular mortality rates.

BackgroundAsthma is a heterogeneous disease. Clinical blood parameters differ by race/ethnicity and are used to distinguish asthma subtypes and inform therapies. Differences in subtypes may explain population-specific trends in asthma outcomes. However, these differences in racial/ethnic minority pediatric populations are unclear.ObjectiveWe investigated the association of blood parameters and asthma subtypes with asthma outcomes and examined population-specific eligibility for biologic therapies in minority pediatric populations.MethodsUsing data from 2 asthma case-control studies of pediatric minority populations, we performed case-control (N = 3738) and case-only (N = 2743) logistic regressions to quantify the association of blood parameters and asthma subtypes with asthma outcomes. Heterogeneity of these associations was tested using an interaction term between race/ethnicity and each exposure. Differences in therapeutic eligibility were investigated using chi-square tests.ResultsRace/ethnicity modified the association between total IgE and asthma exacerbations. Elevated IgE level was associated with worse asthma outcomes in Puerto Ricans. Allergic asthma was associated with worse outcomes in Mexican Americans, whereas eosinophilic asthma was associated with worse outcomes in Puerto Ricans. A lower proportion of Puerto Ricans met dosing criteria for allergic asthma-directed biologic therapy than other groups. A higher proportion of Puerto Ricans qualified for eosinophilic asthma-directed biologic therapy than African Americans.ConclusionsWe found population-specific associations between blood parameters and asthma subtypes with asthma outcomes. Our findings suggest that eligibility for asthma biologic therapies differs across pediatric racial/ethnic populations. These findings call for more studies in diverse populations for equitable treatment of minority patients with asthma.

This study utilizes satellite data to investigate water quality conditions in the San Francisco Estuary and its upstream delta, the Sacramento-San Joaquin River Delta. To do this, this study derives turbidity from the European Space Agency satellite Sentinel-2 acquired from September 2015 to June 2019 and conducts a rigorous validation with in situ measurements of turbidity from optical sensors at continuous monitoring stations. This validation includes 965 matchup comparisons between satellite and in situ sensor data across 22 stations, yielding R 2 = 0.63 and 0.75 for Nephelometric Turbidity Unit and Formazin Nephelometric Unit (FNU) stations, respectively. This study then applies remote sensing to evaluate patterns in turbidity during the Suisun Marsh Salinity Control Gates Action ("Gates action"), a pilot study designed to increase habitat access and quality for the endangered Delta Smelt. The basic strategy was to direct more freshwater into Suisun Marsh, creating more low salinity habitat that would then have higher (and more suitable) turbidity than upstream river channels. For all seven acquisitions considered from June 29 to September 27, 2018, turbidity conditions in Bays and Sloughs subregions were consistently higher (and more suitable) (26-47 FNU) than what was observed in the upstream River region (13-25 FNU). This overall pattern was observed when comparing images acquired during similar tidal stages and heights.

With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.