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Abstract Over the past several decades, strategic planning has become ubiquitous in higher education. Yet, there are varying perceptions of its overall value. Perceptions of value are critically important as they can influence the effectiveness and breadth of participation in the strategic planning process, and such participation is widely held to be critical for success. We investigated these issues using an on-line survey distributed to faculty, chairs, and deans at public colleges and universities in the United States. We found that, compared to both faculty and chairs, deans had a more positive assessment of whether the benefits of the process were greater than the costs and were significantly more familiar with strategic plans. On the other hand, faculty indicated strategic plans had less effect on how they do their jobs, while chairs held a negative view as to whether the process led to meaningful evaluation and change. Interestingly, there were no significant differences between responses from baccalaureate, master’s, and doctoral institutions. Although shared governance in the strategic planning process is widely endorsed, our work suggests that new approaches may be necessary to achieve the goal of an effective strategic planning process.

Abstract Background Changes in body temperature anticipate labor onset in numerous mammals, yet this concept has not been explored in humans. We investigated if continuous body temperature exhibits similar changes in women and whether these changes may be linked to hormonal status. Finally, we developed a deep learning model using temperature patterning to provide a daily forecast of time to labor onset. Methods We evaluated patterns in continuous skin temperature data in 91 (n = 54 spontaneous labors) pregnant women using a wearable smart ring. In a subset of 28 pregnancies, we examined daily steroid hormone samples leading up to labor to analyze relationships among hormones and body temperature trajectory. Finally, we applied an autoencoder long short-term memory (AE-LSTM) deep learning model to provide a novel daily estimation of days until labor onset. Results Features of temperature change leading up to labor were associated with urinary hormones and labor type. Spontaneous labors exhibited greater estriol to α-pregnanediol ratio, as well as lower body temperature and more stable circadian rhythms compared to pregnancies that did not undergo spontaneous labor. Skin temperature data from 54 pregnancies that underwent spontaneous labor between 34 and 42 weeks of gestation were included in training the AE-LSTM model, and an additional 37 pregnancies that underwent artificial induction of labor or Cesarean without labor were used for further testing. The input to the pipeline was 5-min skin temperature data from a gestational age of 240 days until the day of labor onset. During cross-validation AE-LSTM average error (true – predicted) dropped below 2 days at 8 days before labor, independent of gestational age. Labor onset windows were calculated from the AE-LSTM output using a probabilistic distribution of model error. For these windows AE-LSTM correctly predicted labor start for 79% of the spontaneous labors within a 4.6-day window at 7 days before true labor, and 7.4-day window at 10 days before true labor. Conclusion Continuous skin temperature reflects progression toward labor and hormonal change during pregnancy. Deep learning using continuous temperature may provide clinically valuable tools for pregnancy care.

Abstract Prostate cancer treatment resistance is a significant challenge facing the field. Genomic and transcriptomic profiling have partially elucidated the mechanisms through which cancer cells escape treatment, but their relation toward the tumor microenvironment (TME) remains elusive. Here we present a comprehensive transcriptomic landscape of the prostate TME at multiple points in the standard treatment timeline employing single-cell RNA-sequencing and spatial transcriptomics data from 120 patients. We identify club-like cells as a key epithelial cell subtype that acts as an interface between the prostate and the immune system. Tissue areas enriched with club-like cells have depleted androgen signaling and upregulated expression of luminal progenitor cell markers. Club-like cells display a senescence-associated secretory phenotype and their presence is linked to increased polymorphonuclear myeloid-derived suppressor cell (PMN-MDSC) activity. Our results indicate that club-like cells are associated with myeloid inflammation previously linked to androgen deprivation therapy resistance, providing a rationale for their therapeutic targeting.

Mixed reality refers to the integration of virtual reality into the real-world environment. This digital content can be interacted with in real time. The emergence of mixed reality technology has been made possible by the introduction of head-mounted displays, which are being utilized across multiple surgical specialties. In upper extremity surgery, mixed reality has widespread applications in trauma, corrective surgery, arthroplasty, arthroscopy, and oncology. Preoperatively, mixed reality allows for complex 3D planning. Intraoperatively, surgeons can access this 3D data in a sterile environment. While in its infant stages, mixed reality is likely to become a powerful tool for intraoperative guidance and navigation. Mixed reality can change the paradigm of communication, as it allows the sharing of visual data from the surgeon’s perspective, enabling remote assistance and participation.

Background: The purpose of this study was to assess impingement-free internal rotation (IR) in a virtual reverse shoulder arthroplasty simulation using a Statistical Shape Model based on scapula size. Methods: A database of over 10,000 scapulae utilized for preoperative planning for shoulder arthroplasty was analyzed with a Statistical Shape Model to obtain 5 scapula sizes including the mean and 2 standard deviations. For each scapula model, one glenosphere size (33-42 mm) was selected as the best fit based on consensus among 3 shoulder surgeons. Virtual implantation variables included 1) lateral offset (0-12 mm in 2-mm increments), 2) inferior eccentricity (0, 2.5, 5, and 7.5 mm), and 3) posterior eccentricity (0, 2.5, and 5 mm). The neck shaft angle was fixed at 135° with an inlay design humeral prosthesis. IR at the side (IR0) and in abduction (IRABD) were then simulated. Results: Maximum impingement-free IR0 was reached with increasing inferior offset in combination with increasing lateralization. Lateralization was the most important variable in increasing impingement-free IRABD. Maximum IRABD was reached at 4-6 mm of lateralization with smaller scapula (−2 to 0 standard deviation). Increasing lateralization up to 12 mm continues to increase IRABD for larger-sized scapula (+1 to +2 standard deviation). Optimal inferior offset and lateralization to maximize IR did have a small loss of external rotation in neutral abduction. There was no loss of external rotation in 60° of abduction. Conclusion: In a virtual model, the glenosphere position required to maximize IR varied by scapula size. For smaller scapulae, maximum IR0 was reached with a combination of 2.5-mm inferior offset and 0- 4 mm of lateralization. For larger scapulae, maximum IR0 was reached with a combination of 2.5 mm of inferior offset and 4 mm of lateralization. The amount of lateralization required to maximize IRABD also varies by scapula size. Maximum IRABD was reached in smaller scapula with 4-6 mm of lateralization and at least 12 mm of lateralization in larger scapula. These findings may be applied in the clinical decision-making process knowing that impingement-free IR and IRABD can be maximized with combinations of inferior offset and lateralization based on scapula size with minimal effect on external rotation and external rotation in 60° of abduction.

Abstract Brain microvascular dysfunction is an important feature of Alzheimer’s disease (AD). To better understand the brain microvascular molecular signatures of AD, we processed and analyzed isolated human brain microvessels by data-independent acquisition liquid chromatography with tandem mass spectrometry (DIA LC–MS/MS) to generate a quantitative dataset at the peptide and protein level. Brain microvessels were isolated from parietal cortex grey matter using protocols that preserve viability for downstream functional studies. Our cohort included 23 subjects with clinical and neuropathologic concordance for Alzheimer’s disease, and 21 age-matched controls. In our analysis, we identified 168 proteins whose abundance was significantly increased, and no proteins that were significantly decreased in AD. The most highly increased proteins included amyloid beta, tau, midkine, SPARC related modular calcium binding 1 (SMOC1), and fatty acid binding protein 7 (FABP7). Additionally, Gene Ontology (GO) enrichment analysis identified the enrichment of increased proteins involved in cellular detoxification and antioxidative responses. A systematic evaluation of protein functions using the UniProt database identified groupings into common functional themes including the regulation of cellular proliferation, cellular differentiation and survival, inflammation, extracellular matrix, cell stress responses, metabolism, coagulation and heme breakdown, protein degradation, cytoskeleton, subcellular trafficking, cell motility, and cell signaling. This suggests that AD brain microvessels exist in a stressed state of increased energy demand, and mount a compensatory response to ongoing oxidative and cellular damage that is associated with AD. We also used public RNAseq databases to identify cell-type enriched genes that were detected at the protein level and found no changes in abundance of these proteins between control and AD groups, indicating that changes in cellular composition of the isolated microvessels were minimal between AD and no-AD groups. Using public data, we additionally found that under half of the proteins that were significantly increased in AD microvessels had concordant changes in brain microvascular mRNA, implying substantial discordance between gene and protein levels. Together, our results offer novel insights into the molecular underpinnings of brain microvascular dysfunction in AD.

ABSTRACT: The purpose of this experiment was to determine if nicotinic acid (NA) effects on dairy cows and rumen microbial characteristics are forage-type dependent (corn silage, CS; grass silage, GS). Four late-lactation (DIM = 225 ± 12 d) Holstein cows were used in a 4 × 4 Latin square design with a 2 × 2 factorial arrangement of treatments. The main effects were a CS (66.10% CS) based diet or a GS (79.59%) based diet with or without 12 g/d NA. Each experimental period lasted for 28 d. Milk production and milk components, blood metabolites, apparent total-tract nutrient digestibilities, minutes rumen pH were below 5.8 as an indicator of ruminal acidosis, and body temperature changes were analyzed as indicators of heat stress. Nicotinic acid supplementation did not improve apparent total-tract nutrient digestibility. Feeding a GS-based diet improved NDF and hemicellulose digestibility. Feeding a CS-based diet increased the apparent total-tract digestibility of fat, and minutes rumen pH below 5.8 for a greater proportion of the time. The CS-based diet also improved milk yield, milk fat and protein yields, and ECM yield; however, somatic cell count and BHB were also increased. Supplementing NA tended to decrease nonesterified fatty acids, especially when combined with GS where DMI was low. There was a trend for the total protozoa population to increase when GS and NA were fed but decreased when CS and NA were fed. Nicotinic acid tended to decrease rumen protozoal populations of Dasytricha, but increased populations of Ophryoscolex and Diplodiniinae with GS diets and decreased with CS diets. Entodiniinae were increased with CS but NA had no effect. Body temperature was increased when a CS-based diet was fed when compared with a GS-based diet. More research is needed to determine how NA can affect rumen microbial protein synthesis and what kind of diets will provide the optimum effect.

ABSTRACT: Our objectives were to develop and evaluate an integrated system consisting of a lateral flow immunoassay (LFIA) and an electronic portable imaging device for determination of pregnancy status of cows based on plasma concentrations of pregnancy-specific protein B (PSPB). Experiment 1 was conducted to test the performance of the LFIA for PSPB (PSPB-LFIA), whereas experiment 2 was conducted to evaluate the performance of the integrated system, including both the LFIA and imaging device. The PSPB-LFIA strips were made of nitrocellulose membrane with polystreptavidin, anti-mouse antibody, europium anti-PSPB conjugates, and biotin PSPB. After adding buffer and plasma in a 96-well plate, strips were dipped to initiate flow and were read in a fluorescence microscope to estimate PSPB concentrations based on the test-to-control line signal (T/C ratio). The T/C ratio of standards was linearly associated with PSPB (R2 = 0.99 in both experiments) concentrations. To test the ability to identify pregnant cows of the PSPB-LFIA only or the integrated system, plasma samples were collected and transrectal ultrasonography (TUS) was conducted 29 to 35 d after AI in lactating Holstein cows (experiment 1: n = 83; experiment 2: n = 205). A cow was considered pregnant (Preg) if concentrations of PSPB in plasma obtained by ELISA were ≥2 ng/mL or if an embryo was visible by TUS. In experiment 1, the accuracy of the PSPB-LFIA compared with ELISA was 92.7% (91.2% sensitivity [Se]; 96.1% specificity [Sp]; 98.1% positive predictive value [PPV]; 83.3% negative predictive value [NPV]) and compared with TUS was 90.4% (100% Se; 78.9% Sp; 84.9% PPV; 100% NPV). The agreement between LFIA and ELISA (kappa = 0.84; 95% CI 0.71–0.96) or LFIA and TUS (kappa = 0.80; 95% CI 0.67–0.93) as methods to classify cows as Preg or nonpregnant (Non-Preg) was high. In experiment 2, the accuracy of the PSPB-LFIA compared with ELISA was 96.1% (93.8% Se; 100% Sp; 100% PPV; 90.5% NPV) and compared with TUS was 92.2% (99.0% Se; 84.7% Sp; 87.6% PPV; 98.8% NPV). The agreement between LFIA and ELISA (kappa = 0.92; 95% CI 0.86–0.97) or LFIA and TUS (kappa = 0.84; 95% CI 0.77–0.92) as methods to classify cows as Preg or Non-Preg was high. We conclude that a system integrating a fluorescence-based LFIA and an optical reader was effective for classifying cows as pregnant or not pregnant based on estimations of plasma concentrations of PSPB. This novel system serves as a platform for further development of on-farm pregnancy testing tools based on measurement of biomarkers of pregnancy in bodily fluids of cattle.

Abstract Recent failures translating preclinical behavioral treatment effects to positive clinical trial results in humans with Fragile X Syndrome (FXS) support refocusing attention on biological pathways and associated measures, such as electroencephalography (EEG), with strong translational potential and small molecule target engagement. This study utilized guided machine learning to test promising translational EEG measures (resting power and auditory chirp oscillatory variables) in a large heterogeneous sample of individuals with FXS to identify best performing EEG variables for reliably separating individuals with FXS, and genetically-mediated subgroups within FXS, from typically developing controls. Best performing variables included resting relative frontal theta power, all combined posterior-head resting power bands, posterior peak alpha frequency (PAF), combined PAF across all measured regions, combined theta, alpha, and gamma power during the chirp, and all combined chirp oscillatory variables. Sub-group analyses for resting EEG best discriminated non-mosaic FXS males via frontal theta resting relative power (AUC = 0.8759), even with data reduced to a 20-channel clinical montage (AUC = 0.9062). In the chirp task, FXS females and non-mosaic males were nearly perfectly discriminated by combined theta, alpha, and gamma power (AUC = 0.9444) and a combination of all variables (AUC = 0.9610), respectively. Results support use of resting and auditory oscillatory tasks to reliably identify neural deficit in FXS, and to identify specific translational targets for genetically-mediated sub-groups, supporting potential points for stratification.

Abstract Fragile X Syndrome (FXS) is an X-linked disorder leading to the loss of expression of FMR1-protein product, FMRP. The absence or deficiency of FMRP is thought to result in the characteristic FXS phenotypes, including intellectual disability. Identifying the relationship between FMRP levels and IQ may be critical to better understand underlying mechanisms and advance treatment development and planning. A sample of 143 individuals with FXS (69% male), aged 8–50 years, completed IQ testing and blood draw via venipuncture to determine the relationship between Deviation IQ scores and FMRP levels as well as the distribution of Deviation IQ scores. In both males and females with FXS, higher FMRP levels were associated with higher Deviation IQ. However, this relationship was no longer significant when only examining full mutation, fully-methylated males. Yet, both the full and restricted male samples showed a downward shifted but otherwise normal distribution of Deviation IQ scores. Our findings support and extend previous studies establishing molecular markers of disease severity in FXS as well as provide novel evidence of a “FXS IQ standard curve”. This latter finding suggests inter-individual variation in Deviation IQ in FXS, especially among males, may be driven by similar factors known to impact cognitive outcomes in typically-developing individuals. Thus, future work aimed at understanding the mechanisms by which FMRP loss leads to intellectual disability should revisit the biological/genetic, socio-environmental, and epigenetic factors contributing to inter-individual variation in IQ in FXS.

The reliability of cognitive demand measures in controlled laboratory settings is well-documented; however, limited research has directly established their stability under real-life and high-stakes conditions, such as operating automated technology on actual highways. Partially automated vehicles have advanced to become an everyday mode of transportation, and research on driving these advanced vehicles requires reliable tools for evaluating the cognitive demand on motorists to sustain optimal engagement in the driving process. This study examined the reliability of five cognitive demand measures, while participants operated partially automated vehicles on real roads across four occasions. Seventy-one participants (aged 18-64 years) drove on actual highways while their heart rate, heart rate variability, electroencephalogram (EEG) alpha power, and behavioral performance on the Detection Response Task were measured simultaneously. Findings revealed that EEG alpha power had excellent test-retest reliability, heart rate and its variability were good, and Detection Response Task reaction time and hit-rate had moderate reliabilities. Thus, the current study addresses concerns regarding the reliability of these measures in assessing cognitive demand in real-world automation research, as acceptable test-retest reliabilities were found across all measures for drivers across occasions. Despite the high reliability of each measure, low intercorrelations among measures were observed, and internal consistency was better when cognitive demand was estimated as a multi-factorial construct. This suggests that they tap into different aspects of cognitive demand while operating automation in real life. The findings highlight that a combination of psychophysiological and behavioral methods can reliably capture multi-faceted cognitive demand in real-world automation research.

In this study, we analyzed student participation and success in a college-level Calculus I course that utilized standards-based grading. By measuring the level to which students participate in this class structure, we were able to use a clustering algorithm that revealed multiple groupings of students that were distinct based on activity throughout the semester. Additionally, we analyzed student progress, defined as the number of graded activities successfully completed each week. We found that students who progressed steadily throughout the semester, and thus had lower variability in the number of completed activities per week, tended to receive a higher overall grade. Students whose progress was less consistent, and thus exhibited higher variability in weekly activities completed, tended to receive a lower grade. Overall, this shows a relationship between understanding the expectations in standards-based grading and succeeding in a course.

The purpose of this phenomenological, qualitative, temporal study was to examine the effects of a semester-long physical theater class on body schema (body posture, awareness, confidence, expression) among eight college students. Individual, semi-structured interviews were conducted twice to qualitatively collect the study's data. Two themes emerged from the qualitative/phenomenological data analysis, showcasing how the participants' body schema improved. Based on the first theme, the students emphasized the importance of physical theater in improving body posture, awareness, confidence and comfort during physical theater and their daily functions. Practically learning the body schema of their character was key to excellence in performance. According to the second theme, the participants learned how to express emotions using their whole body and not only their face. They also managed to switch emotions naturally and corporeally. Experts in Kinesiology and Performing Arts should promote embodied, holistic, playful, and physically demanding movement programs for improved body schema.

Literacy instruction has an important role in augmentative and alternative communication (AAC) because spelling and writing support precise communication. Unfortunately, few students with extensive support needs and complex communication needs (ESN/CCN) develop literacy skills at or above the first grade reading-level. Given that learning to read and write begins with extensive emergent literacy learning opportunities, limited skills may result from insufficient opportunity to learn (OTL) emergent literacy skills. This study examined the instruction provided in early U.S. childhood classrooms to students with ESN/CCN to understand OTL during this critical period. This study used a concurrent embedded mixed methods design integrating a survey of teachers of students with ESN/CCN (n = 26) with a qualitative multiple case study (n = 2). Results suggest the time teachers are devoting to instruction is not translating into increased OTL for individual students. Solutions that may increase learning opportunities and implications for future research are discussed.

Recently, the science of reading has garnered much attention in elementary schools across the U.S. The science of reading is a body of research on learning to read that has been accumulated through systematic inquiry. Seth A. Parsons and Joy Dangora Erickson argue that the way the science of reading is being implemented is missing a key ingredient: motivation. There is an expansive research base on the importance of motivation for learning. Using practical guidelines, they advocate infusing the science of reading with research on reading motivation to design instruction that optimally supports young learners.