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General chemistry II is often a challenging course for first-year STEM students, and many students rely on partial credit to achieve success in the course. However, assigning liberal partial credit to students' work can also disincentivize developing proficiency with the material. Here, we present an alternative specifications-based grading scheme for a general chemistry II lecture delivered as a flipped class. Students evaluated with a specifications-based grading scheme had higher average end-of-semester grades, with a greater proportion of students receiving A's and A-'s than the students in the traditional grading scheme. Performance on standardized ACS final exams decreased slightly in courses using specifications-based grading. Additionally, we present the results of end-of-semester surveys, which revealed that students under the specifications-based grading system agreed more with positive statements related to student-instructor interactions, including getting feedback and asking for help. These results suggest that specifications-based grading may lead to more positive outcomes in the course and may encourage students to interact with their instructor more often.

Cis-regulatory elements (CREs) interact with trans regulators to orchestrate gene expression, but how transcriptional regulation is coordinated in multi-gene loci has not been experimentally defined. We sought to characterize the CREs controlling dynamic expression of the adjacent costimulatory genes CD28, CTLA4 and ICOS, encoding regulators of T cell-mediated immunity. Tiling CRISPR interference (CRISPRi) screens in primary human T cells, both conventional and regulatory subsets, uncovered gene-, cell subset- and stimulation-specific CREs. Integration with CRISPR knockout screens and assay for transposase-accessible chromatin with sequencing (ATAC-seq) profiling identified trans regulators influencing chromatin states at specific CRISPRi-responsive elements to control costimulatory gene expression. We then discovered a critical CCCTC-binding factor (CTCF) boundary that reinforces CRE interaction with CTLA4 while also preventing promiscuous activation of CD28. By systematically mapping CREs and associated trans regulators directly in primary human T cell subsets, this work overcomes longstanding experimental limitations to decode context-dependent gene regulatory programs in a complex, multi-gene locus critical to immune homeostasis.

SLITs comprise a family of secreted proteins that function as ligands for Roundabout (ROBO) receptors. Previous research showed that ROBO1 promotes the differentiation of milk-producing alveolar cells by inhibiting Notch signaling in mammary luminal cells. Here, we show enhanced alveolar development and increased milk production in Slit2-/-;Slit3-/- knockout mammary gland epithelia. This result can also be achieved by intraperitoneal delivery of recombinant ROBO1 extracellular domain fragment, ROBO1-5Ig-Fc, which sequesters SLITs. Together, our phenotypic studies suggest that SLITs restrict alveologenesis and lactogenesis by inhibiting ROBO1.

Abstract Reference genomes of cattle and sheep have lacked contiguous assemblies of the sex-determining Y chromosome. Here, we assemble complete and gapless telomere to telomere (T2T) Y chromosomes for these species. We find that the pseudo-autosomal regions are similar in length, but the total chromosome size is substantially different, with the cattle Y more than twice the length of the sheep Y. The length disparity is accounted for by expanded ampliconic region in cattle. The genic amplification in cattle contrasts with pseudogenization in sheep suggesting opposite evolutionary mechanisms since their divergence 19MYA. The centromeres also differ dramatically despite the close relationship between these species at the overall genome sequence level. These Y chromosomes have been added to the current reference assemblies in GenBank opening new opportunities for the study of evolution and variation while supporting efforts to improve sustainability in these important livestock species that generally use sire-driven genetic improvement strategies.

Abstract Background Parental psychological distress is a well-known risk factor for developmental psychopathology, with longer term parental distress associated with worse youth mental health. Neurotoxicant exposure during pregnancy is a risk factor for both poor maternal and youth mental health. The impact of one class of pollutant, polycyclic aromatic hydrocarbons (PAH), on long-term trajectories of maternal distress and youth self-reported mental health symptoms in adolescence has been understudied. Methods PAH exposure was measured by DNA adducts in maternal blood sampled during the third trimester of pregnancy. Maternal distress, operationalized as maternal demoralization, was measured at 11 timepoints (prenatal to child age 16). Adolescent mental health symptoms were measured at age 13–15. Follow up analyses examined a subset of measures available at age 15–20 years. Structural equation modeling examined associations between PAH exposure during pregnancy and latent growth metrics of maternal distress, and between maternal distress (intercept and slope) and youth mental health symptoms in a prospective longitudinal birth cohort (N = 564 dyads). Results Higher prenatal PAH exposure was associated with higher concurrent maternal distress. Prenatal maternal distress was associated with adolescent’s self-reported anxiety, depression, and externalizing problems. On average, maternal distress declined over time; a slower decline in mother’s distress across the course of the child’s life was associated with greater self-reported anxiety and externalizing problems in youth. Conclusions Our findings are consistent with an intergenerational framework of environmental effects on mental health: PAH exposure during pregnancy affects maternal mental health, which in turn influences mental health outcomes for youth well into adolescence. Future research is necessary to elucidate the possible social and biological mechanisms (e.g., parenting, epigenetics) underlying the intergenerational transmission of the negative effects of pollution on mental health in caregiver-child dyads.

Brain age, a measure of biological aging in the brain, has been linked to psychiatric illness, principally in adult populations. Components of socioeconomic status (SES) associate with differences in brain structure and psychiatric risk across the lifespan. This study aimed to investigate the influence of SES on brain aging in childhood and adolescence, a period of rapid neurodevelopment and peak onset for many psychiatric disorders. We reanalyzed data from the Healthy Brain Network to examine the influence of SES components (occupational prestige, public assistance enrollment, parent education, and household income-to-needs ratio [INR]) on relative brain age (RBA). Analyses included 470 youth (5-17 years; 61.3% men), self-identifying as White (55%), African American (15%), Hispanic (9%), or multiracial (17.2%). Household income was 3.95 ± 2.33 (mean ± SD) times the federal poverty threshold. RBA quantified differences between chronological age and brain age using covariation patterns of morphological features and total volumes. We also examined associations between RBA and psychiatric symptoms (Child Behavior Checklist [CBCL]). Models covaried for sex, scan location, and parent psychiatric diagnoses. In a linear regression, lower RBA is associated with lower parent occupational prestige (p = 0.01), lower public assistance enrollment (p = 0.03), and more parent psychiatric diagnoses (p = 0.01), but not parent education or INR. Lower parent occupational prestige (p = 0.02) and lower RBA (p = 0.04) are associated with higher CBCL anxious/depressed scores. Our findings underscore the importance of including SES components in developmental brain research. Delayed brain aging may represent a potential biological pathway from SES to psychiatric risk.

Abstract Background Managing genetic diversity is critically important for maintaining species fitness. Excessive homozygosity caused by the loss of genetic diversity can have detrimental effects on the reproduction and production performance of a breed. Analysis of genetic diversity can facilitate the identification of signatures of selection which may contribute to the specific characteristics regarding the health, production and physical appearance of a breed or population. In this study, breeds with well-characterized traits such as fine wool production (Rambouillet, N = 745), parasite resistance (Katahdin, N = 581) and environmental hardiness (Dorper, N = 265) were evaluated for inbreeding, effective population size (N e ), runs of homozygosity (ROH) and Wright’s fixation index (FST) outlier approach to identify differential signatures of selection at 36,113 autosomal single nucleotide polymorphisms (SNPs). Results Katahdin sheep had the largest current N e at the most recent generation estimated with both the GONe and NeEstimator software. The most highly conserved ROH Island was identified in Rambouillet with a signature of selection on chromosome 6 containing 202 SNPs called in an ROH in 50 to 94% of the individuals. This region contained the DCAF16, LCORL and NCAPG genes that have been previously reported to be under selection and have biological roles related to milk production and growth traits. The outlier regions identified through the FST comparisons of Katahdin with Rambouillet and Dorper contained genes with known roles in milk production and mastitis resistance or susceptibility, and the FST comparisons of Rambouillet with Katahdin and Dorper identified genes related to wool growth, suggesting these traits have been under natural or artificial selection pressure in these populations. Genes involved in the cytokine-cytokine receptor interaction pathways were identified in all FST breed comparisons, which indicates the presence of allelic diversity between these breeds in genomic regions controlling cytokine signaling mechanisms. Conclusions In this paper, we describe signatures of selection within diverse and economically important U.S. sheep breeds. The genes contained within these signatures are proposed for further study to understand their relevance to biological traits and improve understanding of breed diversity.

Abstract Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system and a leading cause of neurological disability in young adults. Clinical presentation and disease course are highly heterogeneous. Typically, disease progression occurs over time and is characterized by the gradual accumulation of disability. The risk of developing MS is driven by complex interactions between genetic and environmental factors, including the gut microbiome. How the commensal gut microbiota impacts disease severity and progression over time remains unknown. In a longitudinal study, disability status and associated clinical features in 58 MS patients were tracked over 4.2 ± 0.98 years, and the baseline fecal gut microbiome was characterized via 16S amplicon sequencing. Progressor status, defined as patients with an increase in Expanded Disability Status Scale (EDSS), were correlated with features of the gut microbiome to determine candidate microbiota associated with risk of MS disease progression. We found no overt differences in microbial community diversity and overall structure between MS patients exhibiting disease progression and non-progressors. However, a total of 41 bacterial species were associated with worsening disease, including a marked depletion in Akkermansia, Lachnospiraceae, and Oscillospiraceae, with an expansion of Alloprevotella, Prevotella-9, and Rhodospirillales. Analysis of the metabolic potential of the inferred metagenome from taxa associated with progression revealed enrichment in oxidative stress-inducing aerobic respiration at the expense of microbial vitamin K2 production (linked to Akkermansia), and a depletion in SCFA metabolism (linked to Oscillospiraceae). Further, as a proof of principle, statistical modeling demonstrated that microbiota composition and clinical features were sufficient to predict disease progression. Additionally, we found that constipation, a frequent gastrointestinal comorbidity among MS patients, exhibited a divergent microbial signature compared with progressor status. These results demonstrate a proof of principle for the utility of the gut microbiome for predicting disease progression in MS in a small well-defined cohort. Further, analysis of the inferred metagenome suggested that oxidative stress, vitamin K2, and SCFAs are associated with progression, warranting future functional validation and mechanistic study.

Abstract Lewy body (LB) diseases, characterized by the aggregation of misfolded α-synuclein proteins, exhibit notable clinical heterogeneity. This may be due to variations in accumulation patterns of LB neuropathology. Here we apply a data-driven disease progression model to regional neuropathological LB density scores from 814 brain donors with Lewy pathology. We describe three inferred trajectories of LB pathology that are characterized by differing clinicopathological presentation and longitudinal antemortem clinical progression. Most donors (81.9%) show earliest pathology in the olfactory bulb, followed by accumulation in either limbic (60.8%) or brainstem (21.1%) regions. The remaining donors (18.1%) initially exhibit abnormalities in brainstem regions. Early limbic pathology is associated with Alzheimer’s disease-associated characteristics while early brainstem pathology is associated with progressive motor impairment and substantial LB pathology outside of the brain. Our data provides evidence for heterogeneity in the temporal spread of LB pathology, possibly explaining some of the clinical disparities observed in Lewy body disease.

A key organismal response to overnutrition involves the development of new adipocytes through the process of adipogenesis. Preadipocytes sense changes in the systemic nutrient status and metabolites can directly modulate adipogenesis. We previously identified a role of de novo nucleotide biosynthesis in adipogenesis induction, whereby inhibition of nucleotide biosynthesis suppresses the expression of the transcriptional regulators PPARγ and C/EBPα. Here, we set out to identify the global transcriptomic changes associated with the inhibition of nucleotide biosynthesis. Through RNA sequencing (RNAseq), we discovered that mitochondrial signatures were the most altered in response to inhibition of nucleotide biosynthesis. Blocking nucleotide biosynthesis induced rounded mitochondrial morphology, and altered mitochondrial function, and metabolism, reducing levels of tricarboxylic acid cycle intermediates, and increasing fatty acid oxidation (FAO). The loss of mitochondrial function induced by suppression of nucleotide biosynthesis was rescued by exogenous expression of PPARγ. Moreover, inhibition of FAO restored PPARγ expression, mitochondrial protein expression, and adipogenesis in the presence of nucleotide biosynthesis inhibition, suggesting a regulatory role of nutrient oxidation in differentiation. Collectively, our studies shed light on the link between substrate oxidation and transcription in cell fate determination.

Terrestrial open water evaporation is difficult to measure both in situ and remotely yet is critical for understanding changes in reservoirs, lakes, and inland seas from human management and climatically altered hydrological cycling. Multiple satellite missions and data systems (e.g., ECOSTRESS, OpenET) now operationally produce evapotranspiration (ET), but the open water evaporation data produced over millions of water bodies are algorithmically produced differently than the main ET data and are often overlooked in evaluation. Here, we evaluated the open water evaporation algorithm, AquaSEBS, used by ECOSTRESS and OpenET against 19 in situ open water evaporation sites from around the world using MODIS and Landsat data, making this one of the largest open water evaporation validations to date. Overall, our remotely sensed open water evaporation retrieval captured some variability and magnitude in the in situ data when controlling for high wind events (instantaneous: r2 = 0.71; bias = 13% of mean; RMSE = 38% of mean). Much of the instantaneous uncertainty was due to high wind events (u > mean daily 7.5 m·s-1) when the open water evaporation process shifts from radiatively-controlled to atmospherically-controlled; not accounting for high wind events decreases instantaneous accuracy significantly (r2 = 0.47; bias = 36% of mean; RMSE = 62% of mean). However, this sensitivity minimizes with temporal integration (e.g., daily RMSE = 1.2-1.5 mm·day-1). To benchmark AquaSEBS, we ran a suite of 11 machine learning models, but found that they did not significantly improve on the process-based formulation of AquaSEBS suggesting that the remaining error is from a combination of the in situ evaporation measurements, forcing data, and/or scaling mismatch; the machine learning models were able to predict error well in and of itself (r2 = 0.74). Our results provide confidence in the remotely sensed open water evaporation data, though not without uncertainty, and a foundation by which current and future missions may build such operational data.

Radical-directed dissociation (RDD) is a fragmentation technique in which a radical created by selective 213/266 nm photodissociation of a carbon-iodine bond is reisolated and collisionally activated. In previous RDD experiments, collisional activation was effected by ion-trap collision-induced dissociation (CID). Higher-energy collisional dissociation (HCD) differs from CID both in terms of how ions are excited and in the number, type, or abundance of fragments that are observed. In this paper, we explore the use of HCD for activation in RDD experiments. While RDD-CID favors fragments produced from radical-directed pathways such as a/z-ions and side chain losses regardless of the activation energy employed, RDD-HCD spectra vary considerably as a function of activation energy, with lower energies favoring RDD while higher energies favor products resulting from cleavage directed by mobile protons (b/y-ions). RDD-HCD therefore affords more tunable fragmentation based on the HCD energy provided. Importantly, the abundance of radical products decreases as a function of increasing HCD energy, confirming that RDD generally proceeds via lower-energy barriers relative to mobile-proton-driven dissociation. The dominance of b/y-ions at higher energies for RDD-HCD can therefore be explained by the higher survivability of fragments not containing the radical after the initial or subsequent dissociation events. Furthermore, these results confirm previous suspicions that HCD spectra differ from CID spectra due to multiple dissociation events.

Abstract: The Medicare Consumer Assessment of Healthcare Providers and Systems (CAHPS®) surveys collect standardized information about patient experiences of care from nationally representative samples of people with Medicare to support consumers’ enrollment choices and enable the Centers for Medicare & Medicaid Services to monitor care quality and incentivize high quality patient-centered care. Since 2007, protocols for data collection, analysis, and reporting have evolved to address expanded Medicare coverage options and a shift from a single survey vendor to a model in which health plans hire approved vendors to administer the survey. During that time, response rates for all types of surveys have declined; increasing effort has gone toward increasing survey participation, especially among people whose preferred language is not English. In this paper, we describe the history, goals, and current use of the Medicare CAHPS surveys. We also summarize key methodological issues, such as sample design, field implementation and data cleaning, adjustment, scoring, and report production. Additionally, we discuss issues that may arise more generally in managing a large, annual national survey that has direct impact on policy, and consider how a long-running survey of this nature may need to evolve to reflect changes in health care delivery and promote standardization in survey administration while maintaining survey content.

Purpose: To evaluate the time course of biodegradation of an intracameral, biodegradable, sustained-release bimatoprost implant that lowers intraocular pressure without the need for daily eye drops. Methods: In 2 identically designed, randomized, phase 3 clinical trials, adults with open-angle glaucoma or ocular hypertension and open iridocorneal angles inferiorly in the study eye were administered 10- or 15-μg bimatoprost implant (day 1 and weeks 16 and 32) or twice-daily topical timolol 0.5%. Implants were assessed on gonioscopy throughout the studies. Investigators reported whether implants were visible, estimated the size of visible implants relative to their initial size at implantation, and reported the implant location. Data for 10-μg implant placed on day 1 were pooled from both studies for analysis. Results: A total of 372 patients received the 10-μg bimatoprost implant. The degree of implant biodegradation at each follow-up time point was variable among patients. The implant frequently swelled during the initial phase of biodegradation from 6 to 28 weeks. Accelerated biodegradation occurred between 31 and 52 weeks, resulting in 82% of implants absent or ≤25% of initial size by 52 weeks. By month 20, 95% of implants had biodegraded to absent or ≤25% of initial size. The implant was predominantly located inferiorly in the iridocorneal angle. Conclusions: Bimatoprost implant biodegradation in phase 3 studies showed some degree of variability among patients. Clinically significant implant biodegradation was observed in the majority of patients by 12 months. Clinical studies are in progress to further understand implant biodegradation and the ideal timing for implant re-administration. ClinicalTrials.gov NCT02247804; ClinicalTrials.gov NCT02250651.

BackgroundCognitive reserve and resilience are terms used to explain interindividual variability in maintenance of cognitive health in response to adverse factors, such as brain pathology in the context of aging or neurodegenerative disorders. There is substantial interest in identifying tractable substrates of resilience to potentially leverage this phenomenon into intervention strategies. One way of operationalizing cognitive resilience that has gained popularity is the residual method: regressing cognition on an adverse factor and using the residual as a measure of resilience. This method is attractive because it provides a statistical approach that is an intuitive match to the reserve/resilience conceptual framework. However, due to statistical properties of the regression equation, the residual approach has qualities that complicate its interpretation as an index of resilience and make it statistically inappropriate in certain circumstances.Methods and resultsWe describe statistical properties of the regression equation to illustrate why the residual is highly correlated with the cognitive score from which it was derived. Using both simulations and real data, we model common applications of the approach by creating a residual score (global cognition residualized for hippocampal volume) in individuals along the AD spectrum. We demonstrate that in most real-life scenarios, the residual measure of cognitive resilience is highly correlated with cognition, and the degree of this correlation depends on the initial relationship between the adverse factor and cognition. Subsequently, any association between this resilience metric and an external variable may actually be driven by cognition, rather than by an operationalized measure of resilience. We then assess several strategies proposed as potential solutions to this problem, such as including both the residual and original cognitive measure in a model. However, we conclude these solutions may be insufficient, and we instead recommend against "pre-regression" strategies altogether in favor of using statistical moderation (e.g., interactions) to quantify resilience.ConclusionsCaution should be taken in the use and interpretation of the residual-based method of cognitive resilience. Rather than identifying resilient individuals, we encourage building more complete models of cognition to better identify the specific adverse and protective factors that influence cognitive decline.