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Assessing wetland vulnerability to chronic and episodic physical drivers is fundamental for establishing restoration priorities. We synthesized multiple data sets from E.B. Forsythe National Wildlife Refuge, New Jersey, to establish a wetland vulnerability metric that integrates a range of physical processes, anthropogenic impact and physical/biophysical features. The geospatial data are based on aerial imagery, remote sensing, regulatory information, and hydrodynamic modeling; and include elevation, tidal range, unvegetated to vegetated marsh ratio (UVVR), shoreline erosion, potential exposure to contaminants, residence time, marsh condition change, change in salinity, salinity exposure and sediment concentration. First, we delineated the wetland complex into individual marsh units based on surface contours, and then defined a wetland vulnerability index that combined contributions from all parameters. We applied principal component and cluster analyses to explore the interrelations between the data layers, and separate regions that exhibited common characteristics. Our analysis shows that the spatial variation of vulnerability in this domain cannot be explained satisfactorily by a smaller subset of the variables. The most influential factor on the vulnerability index was the combined effect of elevation, tide range, residence time, and UVVR. Tide range and residence time had the highest correlation, and similar bay-wide spatial variation. Some variables (e.g., shoreline erosion) had no significant correlation with the rest of the variables. The aggregated index based on the complete dataset allows us to assess the overall state of a given marsh unit and quickly locate the most vulnerable units in a larger marsh complex. The application of geospatially complete datasets and consideration of chronic and episodic physical drivers represents an advance over traditional point-based methods for wetland assessment.

Freshwater salinization is a growing global concern impacting human and ecosystem needs with impacts to water availability for human and ecological uses. In the Upper Colorado River Basin (UCRB), dissolved solids in streams compound ongoing water supply challenges to further limit water availability and cause economic damages. Much effort has been dedicated to understanding dissolved solid sources, transport, and management in the region, yet temporal variability in loading from key sources such as irrigated lands and the influence of climate on dissolved solids loading remains unknown. Quantifying the contributions and temporal variability of dissolved solids loads from irrigated lands may benefit salinity management efforts. This study applies a time-varying (dynamic) modeling approach to predict annual dissolved solids loads across the UCRB from 1986 through 2017. Between 66% and 82% of the total accumulated dissolved solids load in the basin is from groundwater (storage and baseflow). Our findings link climate, irrigation, and groundwater, and confirm large storage contributions that have declined slightly with time. Dissolved solids loads increase during wet periods and decrease during dry periods, although the relative contributions of different sources vary little with time. Irrigation enhances loading efficiency relative to unirrigated areas through runoff and groundwater, and can locally be a major source of dissolved solids where irrigation occurs. Results indicate that loads from irrigated areas increase when irrigated area and/or water available for runoff increase. Increased regional aridification over the study period may have contributed to decreasing stream salinity through both quicker surface runoff and lagged groundwater storage processes. Study results may be relevant to salinity management in arid environments where water availability is limited and where irrigation influences salinity loading to streams.

CAR-T cell therapy is an effective cancer therapy for multiple refractory/relapsed hematologic malignancies but is associated with substantial toxicity, including Immune Effector Cell Associated Neurotoxicity Syndrome (ICANS). Improved detection and assessment of ICANS could improve management and allow greater utilization of CAR-T cell therapy, however, an objective, specific biomarker has not been identified. We hypothesized that the severity of ICANS can be quantified based on patterns of abnormal brain activity seen in electroencephalography (EEG) signals. We conducted a retrospective observational study of 120 CAR-T cell therapy patients who had received EEG monitoring. We determined a daily ICANS grade for each patient through chart review. We used visually assessed EEG features and machine learning techniques to develop the Visual EEG-Immune Effector Cell Associated Neurotoxicity Syndrome (VE-ICANS) score and assessed the association between VE-ICANS and ICANS. We also used it to determine the significance and relative importance of the EEG features. We developed the Visual EEG-ICANS (VE-ICANS) grading scale, a grading scale with a physiological basis that has a strong correlation to ICANS severity (R = 0.58 [0.47–0.66]) and excellent discrimination measured via area under the receiver operator curve (AUC = 0.91 for ICANS ≥ 2). This scale shows promise as a biomarker for ICANS which could help to improve clinical care through greater accuracy in assessing ICANS severity.

Communities and ecosystems of northern Utah, USA receive particulate pollution from anthropogenic activity and dust emissions from sources including the Great Salt Lake (“the Lake”) playa. In addition to affecting communities, anthropogenic pollution is delivered to the Lake's playa sediments, which are eroded during dust events. Yet, spatial variability in dust flux and composition and their risks to human health are poorly understood. We analyzed dust in 17 passive samplers proximal to the Lake during fall 2022 for dust flux, the dust fraction of particulate matter, 87Sr/86Sr, and elemental geochemistry. We evaluated spatial patterns of 11 priority pollutant metals and estimated the hypothetical non‐cancer dust and soil ingestion health hazard for six age cohorts. We observed the highest dust fluxes proximal to the Lake's playa. The highest concentrations of and greatest number of metals occurred in and south of Ogden, UT. Sites to the northeast of Farmington Bay had the highest fluxes. Metal concentrations and 87Sr/86Sr suggest that the dust composition near Bountiful represents contributions from anthropogenic sources, whereas the dust composition to the northeast of Farmington Bay reflects the Lake's playa emissions. Evaluations of potential health hazards from dust ingestion suggest that children between birth and 6 years are vulnerable at higher ingestion rates. Thallium, As, Pb, Co and Cr contributed most to the estimated hazard. Among these, As and sometimes Pb are likely derived from the Lake's playa emissions. Thus, suppression of dust emissions from the Lake's playa may decrease possible health risks for children in northern Utah. Plain Language Summary Neighborhoods and natural areas in northern Utah, USA, receive particulate pollution from anthropogenic activities including vehicle emissions, mining and processing, oil refining, and home heating. Particulate pollution also comes from dust blown from a variety of regional sources, including the dry lakebed of Great Salt Lake (hereafter “the Lake”), which has been polluted by decades of anthropogenic activity. Dust deposited in communities contains metals of concern for human health that come from the lakebed and other pollution sources. We collected dust at 17 sites across northern Utah and analyzed the samples for amount, the dust fraction of total material, and geochemistry. We focused our analysis on quantifying the presence of metals of concern and identifying potential dust sources. Metals of concern occurred at higher concentrations in dust south of Ogden relative to the north. Northeast of Farmington Bay, we found higher dust and metals fluxes than the rest of the study area. The potential health hazard for ingestion of dust was highest for children younger than 6. Some of the metals contributing to the health hazard likely come from Great Salt Lakebed sediments. Thus, preventing dust emissions from the lakebed may decrease health risks from dust in northern Utah. Key Points Priority pollutant metals occur in dust in northern Utah with spatially variable composition and flux Priority pollutant metals in dust are attributed to both anthropogenic emissions and dust emissions from the Great Salt Lake playa Children under 6 may be vulnerable to health hazards from ingestion of metals via dust

The Salt Lake Valley, UT, USA, is proximal to the desiccating Great Salt Lake (GSL). Prior work has found that this lakebed/playa contributes metals‐laden dust to snow in the Wasatch and Uinta Mountains. Dust and industrial particulate pollution are also delivered to communities along the Wasatch Front, but their sources, compositions, and fluxes are poorly characterized. In this study, we analyzed the dust deposited in 18 passive samplers positioned near the GSL, in cities in and near the Salt Lake Valley for total dust flux, the <63 µm dust fraction, 87Sr/86Sr, and trace element geochemistry. We compared spatial patterns in metal flux and abundance with community‐level socioeconomic metrics. We observed the highest dust fluxes at sites near the GSL playa. Within the urban corridor, 87Sr/86Sr and trace element relative abundances suggest that most of the dust to which people are regularly exposed may be fugitive dust from local soil materials. The trace metal content of dust deposited along the Wasatch Front exceeded Environmental Protection Agency screening levels and exhibited enrichment relative to both the upper continental crust and the dust collected adjacent to GSL. Sources of metals to dust deposited along the Wasatch Front may include industrial activities like mining, oil refining, as well as past historical pesticide and herbicide applications. Arsenic and vanadium indicated a statistically significant positive correlation with income, whereas lead, thallium, and nickel exhibited higher concentrations in the least wealthy and least white neighborhoods. Plain Language Summary The Salt Lake Valley, UT, USA is near the drying Great Salt Lake. The shrinking lake reveals a playa, composed of fine grained, metal‐contaminated material. Dust storms lofting these sediments may pose a threat to air quality in the nearby cities of Ogden, Bountiful, Salt Lake, and Lehi. We investigated strontium isotopes as well as a suite of trace elements in dust sampled near the playa and within the urban corridor. We found the most, and coarsest dust was deposited outside of the city. The dust from the playa and the dust collected in the city were not similar in terms of trace elements or strontium isotopes, suggesting that much of the dust collected in the city may come from local fugitive dust sources. The dust within the city had many different metals that are known to be associated with anthropogenic and industrial activities, like mining, oil refining, and agriculture. Abundances of some metals, like As and V were higher in wealthier neighborhoods, while abundances of Pb, Tl, and Ni were higher in lower income and more ethnically diverse neighborhoods. Key Points Dust near the Great Salt Lake playa differs from dust collected in nearby cities Metals in city dust can be traced to specific nearby industries Fugitive dust may be an important dust source for human exposure within cities

BackgroundImmune effector cell-associated neurotoxicity syndrome (ICANS) is a clinical and neuropsychiatric syndrome that can occur days to weeks following administration chimeric antigen receptor (CAR) T-cell therapy. Manifestations of ICANS range from encephalopathy and aphasia to cerebral edema and death. Because the onset and time course of ICANS is currently unpredictable, prolonged hospitalization for close monitoring following CAR T-cell infusion is a frequent standard of care.MethodsThis study was conducted at Brigham and Women’s Hospital from April 2015 to February 2020. A cohort of 199 hospitalized patients treated with CAR T-cell therapy was used to develop a combined hidden Markov model and lasso-penalized logistic regression model to forecast the course of ICANS. Model development was done using leave-one-patient-out cross validation.ResultsAmong the 199 patients included in the analysis 133 were male (66.8%), and the mean (SD) age was 59.5 (11.8) years. 97 patients (48.7%) developed ICANS, of which 59 (29.6%) experienced severe grades 3–4 ICANS. Median time of ICANS onset was day 9. Selected clinical predictors included maximum daily temperature, C reactive protein, IL-6, and procalcitonin. The model correctly predicted which patients developed ICANS and severe ICANS, respectively, with area under the curve of 96.7% and 93.2% when predicting 5 days ahead, and area under the curve of 93.2% and 80.6% when predicting the entire future risk trajectory looking forward from day 5. Forecasting performance was also evaluated over time horizons ranging from 1 to 7 days, using metrics of forecast bias, mean absolute deviation, and weighted average percentage error.ConclusionThe forecasting model accurately predicts risk of ICANS following CAR T-cell infusion and the time course ICANS follows once it has begun.Cite Now

Coproduction represents an inclusive approach for developing decision‐support resources because it seeks to integrate scientific knowledge and end‐user needs. Unfortunately, spatial decision support systems (SDSS) coproduction has sometimes resulted in limited utility for end‐users, partially due to scarce SDSS coproduction guidance. To initiate coproduction, we held a series of workshops to co‐design a spatial conservation prioritization tool for sagebrush ecosystems in the western United States. We share four themes derived from participant feedback and our reflections to guide future SDSS codesign efforts. We found end‐user confidence in data inputs and transparency regarding SDSS assumptions generated trust. Workshop participants noted our virtual format, with smaller break‐out groups, effectively facilitated discussions. Ultimately, end‐users appreciated the conservation context provided by regional‐scale SDSS but preferred local‐scale prioritization efforts for site‐level planning. Therefore, we are shifting ongoing co‐design efforts to consider local‐scale tool development, which can scale up to larger geographic extents. Coproduction of spatial decision support systems (SDSS) has sometimes resulted in limited utility for end‐users, partially due to scarce SDSS coproduction guidance. Here, we describe our experience hosting a series of co‐design workshops to advance the development of a SDSS, share themes derived from participant feedback and our reflections, and provide recommendations for future SDSS co‐design efforts. Our experience has motivated us to develop a multiscale SDSS capable of providing both regional conservation context and local‐scale tools that on‐the‐ground managers have confidence in.

To develop an automated, physiologic metric of immune effector cell-associated neurotoxicity syndrome among patients undergoing chimeric antigen receptor-T cell therapy. We conducted a retrospective observational cohort study from 2016 to 2020 at two tertiary care centers among patients receiving chimeric antigen receptor-T cell therapy with a CD19 or B-cell maturation antigen ligand. We determined the daily neurotoxicity grade for each patient during EEG monitoring via chart review and extracted clinical variables and outcomes from the electronic health records. Using quantitative EEG features, we developed a machine learning model to detect the presence and severity of neurotoxicity, known as the EEG immune effector cell-associated neurotoxicity syndrome score. The EEG immune effector cell-associated neurotoxicity syndrome score significantly correlated with the grade of neurotoxicity with a median Spearman's R of 0.69 (95% CI of 0.59-0.77). The mean area under receiving operator curve was greater than 0.85 for each binary discrimination level. The score also showed significant correlations with maximum ferritin (R 0.24, p = 0.008), minimum platelets (R -0.29, p = 0.001), and dexamethasone usage (R 0.42, p < 0.0001). The score significantly correlated with duration of neurotoxicity (R 0.31, p < 0.0001). The EEG immune effector cell-associated neurotoxicity syndrome score possesses high criterion, construct, and predictive validity, which substantiates its use as a physiologic method to detect the presence and severity of neurotoxicity among patients undergoing chimeric antigen receptor T-cell therapy.

Endocrine-disrupting compounds (EDCs), specifically estrogenic endocrine-disrupting compounds, vary in concentration and composition in surface waters under the influence of different landscape sources and landcover gradients. Estrogenic activity in surface waters may lead to adverse effects in aquatic species at both individual and population levels, often observed through the presence of intersex and vitellogenin induction in male fish. In the Chesapeake Bay Watershed, located on the mid-Atlantic coast of the USA, intersex has been observed in several sub-watersheds where previous studies have identified specific landscape sources of EDCs in tandem with observed fish health effects. Previous work in the Potomac River Watershed (PRW), the largest basin within the Chesapeake Bay Watershed, was leveraged to build random forest regression models to predict estrogenic activity at unsampled reaches in both the Potomac River and larger Chesapeake Bay Watersheds (CBW). Model outputs including important variables, partial dependence plots, and predicted values of estrogenic activity at unsampled reaches provide insight into drivers of estrogenic activity at different seasons and scales. Using the US Environmental Protection Agency effects-based threshold of 1.0 ng/L 17 β-estradiol equivalents, catchments predicted to exceed this value were categorized as at risk for adverse effects from exposure to estrogenic compounds and evaluated relative to healthy watersheds and recreation access locations throughout the PRW. Results show immediate catchment scale models are more reliable than upstream models, and the best predictive variables differ by season and scale. A small percentage of healthy watersheds (< 13%) and public access sites were classified as at risk using the “Total” (annual) model in the CBW. This study is the first Potomac River Watershed assessment of estrogenic activity, providing a new foundation for future risk assessment and management design efforts, with additional context provided for the entire Chesapeake Bay Watershed.

Successful Assessment of the Computer Engineering Technology Program M. Abdallah, D. Jones SUNY Polytechnic Institute Abstract ETAC/ABET-accredited programs have demonstrated their excellence during rigorous, objective, periodic reviews conducted by external evaluators. Two main parts of the accreditation process are the ABET student outcomes and ABET program criteria. For Computer Engineering Technology (CET) programs, ETAC/ABET specifies (a-k) student outcomes and (a-e) program criteria. The CET program at SUNY Poly developed eight student outcomes and mapped them to the ABET student outcomes (a-k) and the ABET program criteria (a-e). The CET program has successfully performed two improvement points regarding ABET accreditation. The first point of improvement is regarding the student outcomes number one and the second point is about the ABET program criteria (d). All student outcomes (1-8) use courses at different levels (1xx, 2xx, 3xx, and 4xx) except the student outcome one which used only 100- and 200-level courses for assessment. However, the CET program accepts transfer students from other colleges. Since they take 1xx and 2xx courses elsewhere, they cannot be assessed at SUNY Poly. Therefore, the program has added a forth performance indicator (PI 1.4) to assess student outcome #1 to be able to assess and evaluate all students (transfer and non-transfer). This performance indicator will be very useful in the case of transfer student’s assessment since they may not have any 100- or 200-level course at SUNY Poly. A required core 300 level course is used to evaluate this new performance indicator. In the paper, the eight developed student outcomes are presented as well as their mapping to the ABET student outcomes (a-k). The second point of improvement is about the ABET program criteria. The program developed a mapping between program criteria and CET student outcomes. The mapping was not efficient and it was difficult to provide evidence to prove that the mapping ideally works. Therefore, the program decided to get rid of the mapping and directly assess the ABET program criteria (a-e). In the paper, the five program criteria (a-e) are presented. A rubric is developed to assess each program criterion. Each performance indicator uses a course or more to be assessed to evaluate the program criterion.