University of Southern California

This study investigated how corporate social responsibility, adherence to social missions, organisation–public relationships outcomes, and corporate reputation influence the purchase intentions for social enterprises’ products. Through a survey of Taiwanese consumers (N = 507) in online channels, results suggested that corporate social responsibility and adherence to social missions indirectly influenced purchase intentions through organisation−public relationships and corporate reputation. However, the direct effect of corporate social responsibility on organisation–public relationship outcomes was nonsignificant. Adherence to social missions acted as a more important factor than corporate social responsibility in generating purchase intentions, indicating the importance of social missions in the social enterprise context.

International relations scholarship assumes that states weigh the costs and benefits of treaty ratification. In human rights, the worse a particular state's record, the higher the presumptive costs of ratification and the lower the likelihood of ratification. But prior work neglects variation in the extent of obligation that different treaties create. In this article, we argue and demonstrate that (1) human rights treaties differ substantially in the scope and scale of the obligations they contain, (2) this variation can be measured, and (3) it matters for ratification. Treaties that create a larger number of demanding obligations imply greater potential costs of compliance for states. The larger the number of demanding obligations, the more grounds various actors will have to challenge a state's practices. We analyze innovative data on treaty obligations and commitments for the ten core global human rights treaties to test our propositions, and we find strong support.

Background: First responders have experienced increased levels of stress, anxiety, and depression due to job-related pressures associated with the COVID-19 pandemic. However, little is known about the factors associated with first responder drug and alcohol use during this time. Methods: We conducted a nationwide survey of first responders (n = 2801) to understand the relationship between work pressures, workplace support strategies, and problematic substance use during the early stages of the COVID-19 pandemic. We employed structural equation modeling to analyze whether burnout mediated these relationships. Results: Descriptive statistics showed that 60.8 % reported no concerns with substance use. While general workplace support strategies were negatively associated with problematic substance use, specific COVID-related strategies, such as providing compensation during quarantine, were positively associated with problematic substance use. Burnout fully mediated relationships between workplace support strategies and problematic substance use. Finally, providing spaces at work to decompress was negatively associated with problematic substance use and burnout. Conclusion: Although work pressures increased burnout and problematic substance use among first responders, general workplace support strategies (e.g., decompression spaces) reduced problematic substance use while some COVID-related strategies (e.g. compensation during quarantine) increased problematic substance use. Policy interventions to address problematic substance use should focus on providing spaces for first responders to decompress at work, as well as implementing strategies (e.g., access to mental health services, time off) to reduce burnout. However, organizations should be mindful that not all interventions will have their intended impact and some interventions may unintentionally contribute to negative employee outcomes.

Mass extinctions have fundamentally altered the structure of the biosphere throughout Earth's history. The ecological severity of mass extinctions is well studied in marine ecosystems by categorizing marine taxa into functional groups based on ‘ecospace’ approaches, but the ecological response of terrestrial ecosystems to mass extinctions is less well understood due to the lack of a comparable methodology. Here, we present a new terrestrial ecospace framework that categorizes fauna into functional groups as defined by tiering, motility and feeding traits. We applied the new terrestrial and traditional marine ecospace analyses to data from the Paleobiology Database across the end-Triassic mass extinction—a time of catastrophic global warming—to compare changes between the marine and terrestrial biospheres. We found that terrestrial functional groups experienced higher extinction severity, that taxonomic and functional richness are more tightly coupled in the terrestrial, and that the terrestrial realm continued to experience high ecological dissimilarity in the wake of the extinction. Although signals of extinction severity and ecological turnover are sensitive to the quality of the terrestrial fossil record, our findings suggest greater ecological pressure from the end-Triassic mass extinction on terrestrial ecosystems than marine ecosystems, contributing to more prolonged terrestrial ecological flux.

The changing thermal state of permafrost is an important indicator of climate change in northern high latitude ecosystems. The seasonally thawed soil active layer thickness (ALT) overlying permafrost may be deepening as a consequence of enhanced polar warming and widespread permafrost thaw in northern permafrost regions (NPR). The associated increase in ALT may have cascading effects on ecological and hydrological processes that impact climate feedback. However, past NPR studies have only provided a limited understanding of the spatially continuous patterns and trends of ALT due to a lack of long-term high spatial resolution ALT data across the NPR. Using a suite of observational biophysical variables and machine learning (ML) techniques trained with available in situ ALT network measurements (n = 2966 site-years), we produced annual estimates of ALT at 1-km resolution over the NPR from 2003 to 2020. Our ML-derived ALT dataset showed high accuracy (R2 = 0.97) and low bias when compared with in situ ALT observations. We found the ALT distribution to be most strongly affected by local soil properties, followed by topographic elevation and land surface temperatures. Pair-wise site-level evaluation between our data-driven ALT with Circumpolar Active Layer Monitoring (CALM) data indicated that about 80% of sites had a deepening ALT trend from 2003 to 2020. Based on our long-term gridded ALT data, about 65% of the NPR showed a deepening ALT trend, while the entire NPR showed a mean deepening trend of 0.11 ± 0.35 cm/yr [25% - 75% quantile: (-0.035, 0.204) cm/yr]. The estimated ALT trends were also sensitive to fire disturbance. Our new gridded ALT product provides an observationally constrained, updated understanding of the progression of thawing and the thermal state of permafrost in the NPR, as well as the underlying environmental drivers of these trends.

Blood culture contamination (BCC) is the presence of specific commensal and environmental organisms cultivated from a single blood culture set out of a blood culture series and that do not represent true bacteremia. BCC can impact quality of care and lead to negative outcomes, unnecessary antibiotic exposure, prolonged hospital stays, and substantial costs. As part of the laboratory’s quality management plan, microbiology laboratory personnel are tasked with monitoring BCC rates, preparing BCC rate reports, and providing feedback to the appropriate committees within their healthcare system. The BCC rate is calculated by the laboratory using pre-set criteria. However, pre-set criteria are not universally defined and depend on the individual institution’s patient population and practices. This mini-review provides practical recommendations on elaborating BCC rate reports, the parameters to define for the pre-set criteria, how to collect and interpret the data, and additional analysis to include in a BCC report.

Importance Early anhydramnios during pregnancy, resulting from fetal bilateral renal agenesis, causes lethal pulmonary hypoplasia in neonates. Restoring amniotic fluid via serial amnioinfusions may promote lung development, enabling survival. Objective To assess neonatal outcomes of serial amnioinfusions initiated before 26 weeks’ gestation to mitigate lethal pulmonary hypoplasia. Design, Setting, and Participants Prospective, nonrandomized clinical trial conducted at 9 US fetal therapy centers between December 2018 and July 2022. Outcomes are reported for 21 maternal-fetal pairs with confirmed anhydramnios due to isolated fetal bilateral renal agenesis without other identified congenital anomalies. Exposure Enrolled participants initiated ultrasound-guided percutaneous amnioinfusions of isotonic fluid before 26 weeks’ gestation, with frequency of infusions individualized to maintain normal amniotic fluid levels for gestational age. Main Outcomes and Measures The primary end point was postnatal infant survival to 14 days of life or longer with dialysis access placement. Results The trial was stopped early based on an interim analysis of 18 maternal-fetal pairs given concern about neonatal morbidity and mortality beyond the primary end point despite demonstration of the efficacy of the intervention. There were 17 live births (94%), with a median gestational age at delivery of 32 weeks, 4 days (IQR, 32-34 weeks). All participants delivered prior to 37 weeks’ gestation. The primary outcome was achieved in 14 (82%) of 17 live-born infants (95% CI, 44%-99%). Factors associated with survival to the primary outcome included a higher number of amnioinfusions ( P = .01), gestational age greater than 32 weeks ( P = .005), and higher birth weight ( P = .03). Only 6 (35%) of the 17 neonates born alive survived to hospital discharge while receiving peritoneal dialysis at a median age of 24 weeks of life (range, 12-32 weeks). Conclusions and Relevance Serial amnioinfusions mitigated lethal pulmonary hypoplasia but were associated with preterm delivery. The lower rate of survival to discharge highlights the additional mortality burden independent of lung function. Additional long-term data are needed to fully characterize the outcomes in surviving neonates and assess the morbidity and mortality burden. Trial Registration ClinicalTrials.gov Identifier: NCT03101891

Staphylococcus saprophyticus is the leading Gram-positive cause of uncomplicated urinary tract infections. Recent reports of increasing antimicrobial resistance (AMR) in S. saprophyticus warrant investigation of its understudied resistance patterns. Here, we characterized a diverse collection of S. saprophyticus ( n = 275) using comparative whole genome sequencing. We performed a phylogenetic analysis of core genes (1,646) to group our S. saprophyticus and investigated the distributions of antibiotic resistance genes (ARGs). S. saprophyticus isolates belonged to two previously characterized lineages, and 14.91% (41/275) demonstrated multidrug resistance. We compared antimicrobial susceptibility phenotypes of our S. saprophyticus with the presence of different ARGs and gene alleles. 29.8% (82/275) carried staphylococcal cassette chromosome mobile elements, among which 25.6% (21/82) were mecA ⁺ . Penicillin resistance was associated with the presence of mecA or blaZ . The mecA gene could serve as a marker to infer cefoxitin and oxacillin resistance of S. saprophyticus , but the absence of this gene is not predictive of susceptibility. Utilizing computational modeling, we found several genes were associated with cefoxitin and oxacillin resistance in mecA ⁻ isolates, some of which have predicted functions in stress response and cell wall synthesis. Furthermore, phenotype association analysis indicates ARGs against non-β-lactams reported in other staphylococci may serve as resistance determinants of S. saprophyticus . Lastly, we observed that two ARGs [ erm and erm (44)v ], carried by bacteriophages, were correlated with high phenotypic non-susceptibility against erythromycin (11/11 and 10/10) and clindamycin (11/11 and 10/10). The AMR-correlated genetic elements identified in this work can help to refine resistance prediction of S. saprophyticus during antibiotic treatment. IMPORTANCE Staphylococcus saprophyticus is the second most common bacteria associated with urinary tract infections (UTIs) in women. The antimicrobial treatment regimen for uncomplicated UTI is normally nitrofurantoin, trimethoprim-sulfamethoxazole (TMP-SMX), or a fluoroquinolone without routine susceptibility testing of S. saprophyticus recovered from urine specimens. However, TMP-SMX-resistant S. saprophyticus has been detected recently in UTI patients, as well as in our cohort. Herein, we investigated the understudied resistance patterns of this pathogenic species by linking genomic antibiotic resistance gene (ARG) content to susceptibility phenotypes. We describe ARG associations with known and novel SCC mec configurations as well as phage elements in S. saprophyticus , which may serve as intervention or diagnostic targets to limit resistance transmission. Our analyses yielded a comprehensive database of phenotypic data associated with the ARG sequence in clinical S. saprophyticus isolates, which will be crucial for resistance surveillance and prediction to enable precise diagnosis and effective treatment of S. saprophyticus UTIs.

Kirchhoff-Love shells are commonly used in many branches of engineering, including in computer graphics, but have so far been simulated only under limited nonlinear material options. We derive the Kirchhoff-Love thin-shell mechanical energy for an arbitrary 3D volumetric hyperelastic material, including isotropic materials, anisotropic materials, and materials whereby the energy includes both even and odd powers of the principal stretches. We do this by starting with any 3D hyperelastic material, and then analytically computing the corresponding thin-shell energy limit. This explicitly identifies and separates in-plane stretching and bending terms, and avoids numerical quadrature. Thus, in-plane stretching and bending are shown to originate from one and the same process (volumetric elasticity of thin objects), as opposed to from two separate processes as done traditionally in cloth simulation. Because we can simulate materials that include both even and odd powers of stretches, we can accommodate standard mesh distortion energies previously employed for 3D solid simulations, such as Symmetric ARAP and Co-rotational materials. We relate the terms of our energy to those of prior work on Kirchhoff-Love thin-shells in computer graphics that assumed small in-plane stretches, and demonstrate the visual difference due to the presence of our exact stretching and bending terms. Furthermore, our formulation allows us to categorize all distinct hyperelastic Kirchhoff-Love thin-shell energies. Specifically, we prove that for Kirchhoff-Love thin-shells, the space of all hyperelastic materials collapses to two-dimensional hyperelastic materials. This observation enables us to create an interface for the design of thin-shell Kirchhoff-Love mechanical energies, which in turn enables us to create thin-shell materials that exhibit arbitrary stiffness profiles under large deformations.

Capturing material properties of real-world elastic solids is both challenging and highly relevant to many applications in computer graphics, robotics and related fields. We give a non-intrusive, in-situ and inexpensive approach to measure the nonlinear elastic energy density function of man-made materials and biological tissues. We poke the elastic object with 3d-printed rigid cylinders of known radii, and use a precision force meter to record the contact force as a function of the indentation depth, which we measure using a force meter stand, or a novel unconstrained laser setup. We model the 3D elastic solid using the Finite Element Method (FEM), and elastic energy using a compressible Valanis-Landel material that generalizes Neo-Hookean materials by permitting arbitrary tensile behavior under large deformations. We then use optimization to fit the nonlinear isotropic elastic energy so that the FEM contact forces and indentations match their measured real-world counterparts. Because we use carefully designed cubic splines, our materials are accurate in a large range of stretches and robust to inversions, and are therefore "animation-ready" for computer graphics applications. We demonstrate how to exploit radial symmetry to convert the 3D elastostatic contact problem to the mathematically equivalent 2D problem, which vastly accelerates optimization. We also greatly improve the theory and robustness of stretch-based elastic materials, by giving a simple and elegant formula to compute the tangent stiffness matrix, with rigorous proofs and singularity handling. We also contribute the observation that volume compressibility can be estimated by poking with rigid cylinders of different radii, which avoids optical cameras and greatly simplifies experiments. We validate our method by performing full 3D simulations using the optimized materials and confirming that they match real-world forces, indentations and real deformed 3D shapes. We also validate it using a "Shore 00" durometer, a standard device for measuring material hardness.

We propose a variational technique to optimize for generalized barycentric coordinates that offers additional control compared to existing models. Prior work represents barycentric coordinates using meshes or closed-form formulae, in practice limiting the choice of objective function. In contrast, we directly parameterize the continuous function that maps any coordinate in a polytope's interior to its barycentric coordinates using a neural field. This formulation is enabled by our theoretical characterization of barycentric coordinates, which allows us to construct neural fields that parameterize the entire function class of valid coordinates. We demonstrate the flexibility of our model using a variety of objective functions, including multiple smoothness and deformation-aware energies; as a side contribution, we also present mathematically-justified means of measuring and minimizing objectives like total variation on discontinuous neural fields. We offer a practical acceleration strategy, present a thorough validation of our algorithm, and demonstrate several applications.

Uncertainty in safety-critical cyber-physical systems can be modeled using a finite number of parameters or parameterized input signals. Given a system specification in Signal Temporal Logic (STL), we would like to verify that for all (infinite) values of the model parameters/input signals, the system satisfies its specification. Unfortunately, this problem is undecidable in general. Statistical model checking (SMC) offers a solution by providing guarantees on the correctness of CPS models by statistically reasoning on model simulations. We propose a new approach for statistical verification of CPS models for user-provided distribution on the model parameters. Our technique uses model simulations to learn surrogate models , and uses conformal inference to provide probabilistic guarantees on the satisfaction of a given STL property. Additionally, we can provide prediction intervals containing the quantitative satisfaction values of the given STL property for any user-specified confidence level. We compare this prediction interval with the interval we get using risk estimation procedures. We also propose a refinement procedure based on Gaussian Process (GP)-based surrogate models for obtaining fine-grained probabilistic guarantees over sub-regions in the parameter space. This in turn enables the CPS designer to choose assured validity domains in the parameter space for safety-critical applications. Finally, we demonstrate the efficacy of our technique on several CPS models.

INTRODUCTION This study examines how receiving a dementia diagnosis influences social relationships by race and ethnicity. METHODS Using data from the Health and Retirement Study (10 waves; 7,159 observations) of adults 70 years and older predicted to have dementia using Gianattasio‐Power scores (91% accuracy), this study assessed changes in social support, engagement, and networks after a dementia diagnosis. We utilized quasi‐experimental methods to estimate treatment effects and subgroup analyses by race/ethnicity. RESULTS A diagnostic label significantly increased the likelihood of gaining social support but reduced social engagement and one measure of social networks. With some exceptions, the results were similar by race and ethnicity. DISCUSSION Results suggest that among older adults with assumed dementia, being diagnosed by a doctor may influence social relationships in both support‐seeking and socially withdrawn ways. This suggests that discussing services and supports at the time of diagnosis is important for healthcare professionals.

Introduction Early detection of ST-segment elevation myocardial infarction (STEMI) on the prehospital electrocardiogram (ECG) improves patient outcomes. Current software algorithms optimize sensitivity but have a high false-positive rate. The authors propose an algorithm to improve the specificity of STEMI diagnosis in the prehospital setting. Methods A dataset of prehospital ECGs with verified outcomes was used to validate an algorithm to identify true and false-positive software interpretations of STEMI. Four criteria implicated in prior research to differentiate STEMI true positives were applied: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. The test characteristics were calculated and regression analysis was used to examine the association between the number of criteria included and test characteristics. Results There were 44,611 cases available. Of these, 1,193 were identified as STEMI by the software interpretation. Applying all four criteria had the highest positive likelihood ratio of 353 (95% CI, 201-595) and specificity of 99.96% (95% CI, 99.93-99.98), but the lowest sensitivity (14%; 95% CI, 11-17) and worst negative likelihood ratio (0.86; 95% CI, 0.84-0.89). There was a strong correlation between increased positive likelihood ratio (r ² = 0.90) and specificity (r ² = 0.85) with increasing number of criteria. Conclusions Prehospital ECGs with a high probability of true STEMI can be accurately identified using these four criteria: heart rate <130, QRS <100, verification of ST-segment elevation, and absence of artifact. Applying these criteria to prehospital ECGs with software interpretations of STEMI could decrease false-positive field activations, while also reducing the need to rely on transmission for physician over-read. This can have significant clinical and quality implications for Emergency Medical Services (EMS) systems.