Genomic Surveillance of Invasive Meningococcal Disease During a National MenW Outbreak in Australia, 2017-2018.

Background: In Australia, invasive meningococcal disease (IMD) incidence rapidly increased between 2014 and 2017 due to rising serogroup W (MenW) and MenY infections. We aimed to better understand the genetic diversity of IMD during 2017 and 2018 using whole genome sequencing data. Methods: Whole genome sequencing data from 440 Australian IMD isolates collected during 2017 and 2018 and 1737 international MenW:CC11 isolates collected in Europe, Africa, Asia, North America, and South America between 1974 and 2020 were used in phylogenetic analyses; genetic relatedness was determined from single-nucleotide polymorphisms. Results: Australian isolates were as follows: 181 MenW (41%), 144 MenB (33%), 88 MenY (20%), 16 MenC (4%), 1 MenW/Y (0.2%), and 10 nongenogroupable (2%). Eighteen clonal complexes (CCs) were identified, and 3 (CC11, CC23, CC41/44) accounted for 78% of isolates (343/440). These CCs were associated with specific serogroups: CC11 (n = 199) predominated among MenW (n = 181) and MenC (n = 15), CC23 (n = 80) among MenY (n = 78), and CC41/44 (n = 64) among MenB (n = 64). MenB isolates were highly diverse, MenY were intermediately diverse, and MenW and MenC isolates demonstrated the least genetic diversity. Thirty serogroup and CC-specific genomic clusters were identified. International CC11 comparison revealed diversification of MenW in Australia. Conclusions: Whole genome sequencing comprehensively characterized Australian IMD isolates, indexed their genetic variability, provided increased within-CC resolution, and elucidated the evolution of CC11 in Australia.

Oligomeric amyloid beta prevents myelination in a clusterin-dependent manner.

Background: White matter loss is a well-documented phenomenon in Alzheimer's disease (AD) patients that has been recognized for decades. However, the underlying reasons for the failure of oligodendrocyte progenitor cells (OPCs) to repair myelin deficits in these patients remain elusive. A single nucleotide polymorphism (SNP) in Clusterin has been identified as a risk factor for late-onset Alzheimer's disease and linked to a decrease in white matter integrity in healthy adults, but its specific role in oligodendrocyte function and myelin maintenance in Alzheimer's disease pathology remains unclear. Methods: To investigate the impact of Clusterin on OPCs in the context of Alzheimer's disease, we employed a combination of immunofluorescence and transmission electron microscopy techniques, primary culture of OPCs, and an animal model of Alzheimer's disease. Results: Our findings demonstrate that Clusterin, a risk factor for late-onset AD, is produced by OPCs and inhibits their differentiation into oligodendrocytes. Specifically, we observed upregulation of Clusterin in OPCs in the 5xFAD mouse model of AD. We also found that the phagocytosis of debris, including amyloid beta (Aß), myelin, and apoptotic cells leads to the upregulation of Clusterin in OPCs. In vivo experiments confirmed that Aß oligomers stimulate Clusterin upregulation and that OPCs are capable of phagocytosing Aß. Furthermore, we discovered that Clusterin significantly inhibits OPC differentiation and hinders the production of myelin proteins. Finally, we demonstrate that Clusterin inhibits OPC differentiation by reducing the production of IL-9 by OPCs. Conclusion: Our data suggest that Clusterin may play a key role in the impaired myelin repair observed in AD and could serve as a promising therapeutic target for addressing AD-associated cognitive decline.

Assessing the association between antibody status and symptoms of long COVID: A multisite study.

The association between SARS-CoV-2 humoral immunity and post-acute sequelae of COVID-19 (long COVID) remains uncertain. The objective of this population-based cohort study was to assess the association between SARS-CoV-2 seropositivity and symptoms consistent with long COVID. English and Spanish-speaking members ≥ 18 years old with SARS-CoV-2 serologic testing conducted prior to August 2021 were recruited from Kaiser Permanente Southern California and Kaiser Permanente Colorado. Between November 2021 and April 2022, participants completed a survey assessing symptoms, physical health, mental health, and cognitive function consistent with long COVID. Survey results were linked to SARS-CoV-2 antibody (Ab) and viral (RNA) lab results in electronic health records. Weighted descriptive analyses were generated for five mutually exclusive patient groups: (1) +Ab/+RNA; (2) +Ab/- or missing RNA; (3) -Ab/+RNA; (4a) -Ab/-RNA reporting no prior infection; and (4b) -Ab/-RNA reporting prior infection. The proportions reporting symptoms between the +Ab/+RNA and -Ab/+RNA groups were compared, adjusted for covariates. Among 3,946 participants, the mean age was 52.1 years old (SD 15.6), 68.3% were female, 28.4% were Hispanic, and the serologic testing occurred a median of 15 months prior (IQR = 12-18). Three quarters (74.5%) reported having had COVID-19. Among people with laboratory-confirmed COVID-19, there was no association between antibody positivity (+Ab/+RNA vs. -Ab/+RNA) and any symptoms, physical health, mental health, or cognitive function. As expected, physical health, cognitive function, and fatigue were worse, and palpitations and headaches limiting the ability to work were more prevalent among people with laboratory-confirmed prior infection and positive serology (+Ab/+RNA) compared to those without reported or confirmed prior infection and negative serology (-Ab/-RNA/no reported COVID-19). Among people with laboratory-confirmed COVID-19, SARS-CoV-2 serology from practice settings were not associated with long COVID symptoms and health status suggesting limited utility of serology testing for long COVID.

Factors Associated with Non-Completion of a Pediatric Metabolic and Bariatric Surgery Program.

BACKGROUND: Childhood obesity is a devastating disease process disproportionately affecting minority and low-income populations. Though bariatric surgery leads to durable weight loss and reversal of multiple obesity-related comorbidities, only a small fraction of pediatric patients undergoes the procedure. We sought to identify factors associated with non-completion in a pediatric bariatric surgery program. METHODS: Retrospective review of consecutive patients ≤18-years-old referred to an academic adolescent bariatric surgery program between 2017 and 2022 (n = 20 completers, 40 non-completers) was completed. Demographics and medical and psychosocial histories were summarized by completion status. RESULTS: Of the 33% (20/60; 85% female, 30% racial minorities) who successfully completed the program, the median age was 16 years [IQR 16, 17]. The median age of non-completers was 16 years [IQR 15, 17] (55% female, 56% racial minorities). Non-completion was associated with male gender (15% of completers vs 45% of non-completers, p = 0.022), neighborhood income <150% poverty level (0 completers vs 17.5% of non-completers, p = 0.047), and presence of environmental or family stressors (22% of completers vs 65% of non-completers, p = 0.008). Though not statistically significant, non-completers tended to be racial minorities (p = 0.054). CONCLUSIONS: Non-completion of the bariatric surgery pathway was more prevalent among male patients from lower-income neighborhoods with significant environmental or family stressors. These patients also tended to be racial and ethnic minorities. The findings underscore the need for further investigation into barriers to pediatric bariatric surgery. LEVEL OF EVIDENCE: Level III.

Validation of OpenCap: A low-cost markerless motion capture system for lower-extremity kinematics during return-to-sport tasks.

Low-cost markerless motion capture systems offer the potential for 3D measurement of joint angles during human movement. This study aimed to validate a smartphone-based markerless motion capture system's (OpenCap) derived lower extremity kinematics during common return-to-sport tasks, comparing it to an established optoelectronic motion capture system. Athletes with prior anterior cruciate ligament reconstruction (12-18 months post-surgery) performed three movements: a jump-landing-rebound, single-leg hop, and lateral-vertical hop. Kinematics were recorded concurrently with two smartphones running OpenCap's software and with a 10-camera, marker-based motion capture system. Validity of lower extremity joint kinematics was assessed across 437 recorded trials using measures of agreement (coefficient of multiple correlation: CMC) and error (mean absolute error: MAE, root mean squared error: RMSE) across the time series of movement. Agreement was best in the sagittal plane for the knee and hip in all movements (CMC > 0.94), followed by the ankle (CMC = 0.84-0.93). Lower agreement was observed for frontal (CMC = 0.47-0.78) and transverse (CMC = 0.51-0.6) plane motion. OpenCap presented a grand mean error of 3.85° (MAE) and 4.34° (RMSE) across all joint angles and movements. These results were comparable to other available markerless systems. Most notably, OpenCap's user-friendly interface, free software, and small physical footprint have the potential to extend motion analysis applications beyond conventional biomechanics labs, thus enhancing the accessibility for a diverse range of users.

Sequential administration of peripheral nerve blocks and onabotulinumtoxinA for the treatment of chronic migraine and other headache disorders-A retrospective tolerability and safety study.

OBJECTIVE: To determine the tolerability and safety of concurrent peripheral nerve blocks and onabotulinumtoxinA treatment during a single outpatient clinic procedure visit. BACKGROUND: Procedural interventions are available for the treatment of headache disorders. OnabotulinumtoxinA and peripheral nerve blocks are used as alternatives or in addition to oral therapies to reduce the frequency and intensity of migraine attacks. There is currently a lack of safety data focusing on the sequential administration of local anesthetic via peripheral nerve blocks and onabotulinumtoxinA during a single clinical encounter for the treatment of headache. The primary aim of the study was to determine the safety and tolerability of concurrent peripheral nerve blockade and onabotulinumtoxinA injections during a single outpatient clinic procedure visit. We hypothesized that the dual intervention would be safe and well tolerated by patients with chronic migraine and other headache disorders. METHODS: A retrospective chart review was performed using clinical data from patients seen by multiple providers over a 16-month timeframe at one outpatient headache clinic. Patients were identified by procedure codes and those receiving peripheral nerve block(s) and onabotulinumtoxinA injections during a single encounter within the study period were eligible for inclusion. Inclusion criteria were (1) patients 18 years and older who were (2) receiving both peripheral nerve blocks and onabotulinumtoxinA injections for the treatment of chronic migraine. Patients were excluded if they were under age 18, received their procedure outside of the clinic (emergency room, inpatient ward), or were receiving sphenopalatine ganglion blocks. Age- and sex-matched patients who received one procedure, either peripheral nerve blocks or onabotulinumtoxinA, were used for control. The primary outcome of this safety study was the number of adverse events that occurred in the dual intervention group compared to the single intervention control arms. Information regarding adverse events was gathered via retrospective chart review. If an adverse event was recorded, it was then graded by the reviewer utilizing the Common Terminology Criteria for Adverse Events ranging from Grade 1 Mild Event to Grade 5 Death. Additionally, it was noted whether the adverse event led to treatment discontinuation. RESULTS: In total, 375 patients were considered eligible for inclusion in the study. After age and sex matching of controls, 131 patients receiving dual intervention were able to be compared to 131 patients receiving onabotulinumtoxinA alone and 104 patients receiving dual intervention were able to be compared to 104 patients receiving peripheral nerve block(s) alone. The primary endpoint analysis showed no significant difference in total adverse events between dual intervention compared to nerve blocks alone or onabotulinumtoxinA alone. The number of adverse events that led to treatment discontinuation approached but did not reach statistical significance for those receiving dual intervention versus onabotulinumtoxinA alone in the number of adverse events that led to treatment termination (4.6%, 6/131 vs. 0.8%, 1/131, p = 0.065); however, the number of patients who discontinued therapy was not significantly different between those groups (2.3%, 3/131 vs. 0.8%, 1/131; p = 0.314; odds ratio 0.3 [0-3.2]; p = 0.338). CONCLUSIONS: In this retrospective chart review, there was no significant difference in adverse events or therapy discontinuation between patients receiving sequential peripheral nerve block(s) and onabotulinumtoxinA injections versus those receiving either peripheral nerve block(s) or onabotulinumtoxinA injections alone. As a result, we concluded that the combination procedure is likely safe and well tolerated in routine clinical practice.

The prognostic value of artificial intelligence to predict cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter aortic valve replacement.

Aims: Cardiac amyloidosis (CA) is common in patients with severe aortic stenosis (AS) undergoing transcatheter aortic valve replacement (TAVR). Cardiac amyloidosis has poor outcomes, and its assessment in all TAVR patients is costly and challenging. Electrocardiogram (ECG) artificial intelligence (AI) algorithms that screen for CA may be useful to identify at-risk patients. Methods and results: In this retrospective analysis of our institutional National Cardiovascular Disease Registry (NCDR)-TAVR database, patients undergoing TAVR between January 2012 and December 2018 were included. Pre-TAVR CA probability was analysed by an ECG AI predictive model, with >50% risk defined as high probability for CA. Univariable and propensity score covariate adjustment analyses using Cox regression were performed to compare clinical outcomes between patients with high CA probability vs. those with low probability at 1-year follow-up after TAVR. Of 1426 patients who underwent TAVR (mean age 81.0 ± 8.5 years, 57.6% male), 349 (24.4%) had high CA probability on pre-procedure ECG. Only 17 (1.2%) had a clinical diagnosis of CA. After multivariable adjustment, high probability of CA by ECG AI algorithm was significantly associated with increased all-cause mortality [hazard ratio (HR) 1.40, 95% confidence interval (CI) 1.01-1.96, P = 0.046] and higher rates of major adverse cardiovascular events (transient ischaemic attack (TIA)/stroke, myocardial infarction, and heart failure hospitalizations] (HR 1.36, 95% CI 1.01-1.82, P = 0.041), driven primarily by heart failure hospitalizations (HR 1.58, 95% CI 1.13-2.20, P = 0.008) at 1-year follow-up. There were no significant differences in TIA/stroke or myocardial infarction. Conclusion: Artificial intelligence applied to pre-TAVR ECGs identifies a subgroup at higher risk of clinical events. These targeted patients may benefit from further diagnostic evaluation for CA.

Telehealth Experience Among Patients With Limited English Proficiency.

This cross-sectional study assesses the implication of patients' English language skills for telehealth use and visit experience.

CETARS/CAR Practice Guideline on Imaging the Pregnant Trauma Patient.

Imaging of pregnant patients who sustained trauma often causes fear and confusion among patients, their families, and health care professionals regarding the potential for detrimental effects from radiation exposure to the fetus. Unnecessary delays or potentially harmful avoidance of the justified imaging studies may result from this understandable anxiety. This guideline was developed by the Canadian Emergency, Trauma and Acute Care Radiology Society (CETARS) and the Canadian Association of Radiologists (CAR) Working Group on Imaging the Pregnant Trauma Patient, informed by a literature review as well as multidisciplinary expert panel opinions and discussions. The working group included academic subspecialty radiologists, a trauma team leader, an emergency physician, and an obstetriciangynaecologist/maternal fetal medicine specialist, who were brought together to provide updated, evidence-based recommendations for the imaging of pregnant trauma patients, including patient safety aspects (eg, radiation and contrast concerns) and counselling, initial imaging in maternal trauma, specific considerations for the use of fluoroscopy, angiography, and magnetic resonance imaging. The guideline strives to achieve clarity and prevent added anxiety in an already stressful situation of injury to a pregnant patient, who should not be imaged differently.

Reporting on Health Care and Social Service Provider Approaches to Promoting Alcohol Abstinence During Pregnancy.

Despite the known detrimental health effects of alcohol use during pregnancy, there are still health care (HCP) and social service providers (SSP) who do not promote complete abstinence. The purpose of this study was to explore the current practices of HCPs and SSPs when discussing alcohol use during pregnancy, and to understand their rationale for their specific recommendations. An online survey was completed by 1123 HCPs (n = 588) and SSPs (n = 535) that asked them to identify their approach to discussing alcohol and pregnancy. Participants had the option to further explain their current recommendations regarding alcohol use during pregnancy in an open-ended format. Open-ended responses were analyzed using a content analysis approach (n = 156). The majority of respondents recommend abstinence (83.9% of HCPs, n = 493; 78.4% of SSPs, n = 419), while 9.8% of HCPs (n = 57) and 2.2% of SSPs (n = 12) responded that low levels of consumption may be acceptable. HCPs may recommend low levels of consumption based on other international guidelines, limited evidence to suggest that one unit of alcohol is harmful, and as a harm reduction strategy. SSPs stated that they refer clients to HCPs for recommendations related to alcohol consumption, and that they prefer to provide information based on public health guidelines. This exploratory work may inform the development of resources to support HCPs and SSPs to recommend abstinence from alcohol throughout gestation.

CD4-mediated immunity shapes neutrophil-driven tuberculous pathology.

Pulmonary Mycobacterium tuberculosis (Mtb) infection results in highly heterogeneous lesions ranging from granulomas with central necrosis to those primarily comprised of alveolitis. While alveolitis has been associated with prior immunity in human post-mortem studies, the drivers of these distinct pathologic outcomes are poorly understood. Here, we show that these divergent lesion structures can be modeled in C3HeB/FeJ mice and are regulated by prior immunity. Using quantitative imaging, scRNAseq, and flow cytometry, we demonstrate that Mtb infection in the absence of prior immunity elicits dysregulated neutrophil recruitment and necrotic granulomas. In contrast, prior immunity induces rapid recruitment and activation of T cells, local macrophage activation, and diminished late neutrophil responses. Depletion studies at distinct infection stages demonstrated that neutrophils are required for early necrosis initiation and necrosis propagation at chronic stages, whereas early CD4 T cell responses prevent neutrophil feedforward circuits and necrosis. Together, these studies reveal fundamental determinants of tuberculosis lesion structure and pathogenesis, which have important implications for new strategies to prevent or treat tuberculosis.

Stress-induced mucin 13 reductions drive intestinal microbiome shifts and despair behaviors.

Depression is a prevalent psychological condition with limited treatment options. While its etiology is multifactorial, both chronic stress and changes in microbiome composition are associated with disease pathology. Stress is known to induce microbiome dysbiosis, defined here as a change in microbial composition associated with a pathological condition. This state of dysbiosis is known to feedback on depressive symptoms. While studies have demonstrated that targeted restoration of the microbiome can alleviate depressive-like symptoms in mice, translating these findings to human patients has proven challenging due to the complexity of the human microbiome. As such, there is an urgent need to identify factors upstream of microbial dysbiosis. Here we investigate the role of mucin 13 as an upstream mediator of microbiome composition changes in the context of stress. Using a model of chronic stress, we show that the glycocalyx protein, mucin 13, is selectively reduced after psychological stress exposure. We further demonstrate that the reduction of Muc13 is mediated by the Hnf4 transcription factor family. Finally, we determine that deleting Muc13 is sufficient to drive microbiome shifts and despair behaviors. These findings shed light on the mechanisms behind stress-induced microbial changes and reveal a novel regulator of mucin 13 expression.

The epidemiology of enterococci in a tertiary hospital and primary healthcare facilities in Fiji (2019-2022).

OBJECTIVES: We analysed 4 y of laboratory data to characterise the species and determine the antimicrobial susceptibility profiles of enterococci as human pathogens in Fiji. The study also investigated the molecular epidemiology amongst the subset of vancomycin-resistant enterococci (VRE). METHODS: This retrospective study reviewed bacteriological data from Colonial War Memorial Hospital (CWMH) and other healthcare facilities in the Central and Eastern divisions of Fiji. Phenotypic, antimicrobial susceptibility and vanA and vanB PCR testing were performed using locally approved protocols. The first clinical isolates per patient with antimicrobial susceptibility testing results in a single year were included in the analysis. Data was analysed using WHONET software and Microsoft Excel. RESULTS: A total of 1817 enterococcal isolates were reported, 1415 from CWMH and 402 from other healthcare facilities. The majority of isolates, 75% (n = 1362) were reported as undifferentiated Enterococcus spp., 17.8% (n = 324) were specifically identified as Enterococcus faecalis and 6.7% (n = 122) as E. faecium. Overall, 10% of the enterococci isolates were from blood cultures. Among isolates from CWMH, <15% of E. faecium were susceptible to ampicillin, and 17.2% were vancomycin resistant. Overall, 874 enterococcal isolates (including the undifferentiated species) were tested against vancomycin, of which 4.8% (n = 42) were resistance. All of the VRE isolates tested (n = 15) expressed vanA genes. CONCLUSIONS: This study demonstrates the clinical importance of VRE, particularly van A E. faecium in the national referral hospital in Fiji. Enhanced phenotypic and molecular surveillance data are needed to better understand enterococci epidemiology and help guide specific infection prevention and control measures and antibiotic prescribing guidelines.

Developing an interpretation model for body fluid identification.

Criminal investigations, particularly sexual assaults, frequently require the identification of body fluid type in addition to body fluid donor to provide context. In most cases this can be achieved by conventional methods, however, in certain scenarios, alternative molecular methods are required. An example of this is the detection of menstrual fluid and vaginal material, which are not able to be identified using conventional techniques. Endpoint reverse-transcription PCR (RT-PCR) is currently used for this purpose to amplify body fluid specific messenger RNA (mRNA) transcripts in forensic casework. Real-time quantitative reverse-transcription PCR (RT-qPCR) is a similar method but utilises fluorescent markers to generate quantitative results in the form of threshold cycle (Cq) values. Despite the uncertainty surrounding body fluid identification, most interpretation guidelines utilise categorical statements. Probabilistic modelling is more realistic as it reflects biological variation as well as the known performance of the method. This research describes the application of various machine learning models to single-source mRNA profiles obtained by RT-qPCR and assesses their performance. Multinomial logistic regression (MLR), Naïve Bayes (NB), and linear discriminant analysis (LDA) were used to discriminate between the following body fluid categories: saliva, circulatory blood, menstrual fluid, vaginal material, and semen. We identified that the performance of MLR was somewhat improved when the quantitative dataset of the original Cq values was used (overall accuracy of approximately 0.95) rather than presence/absence coded data (overall accuracy of approximately 0.94). This indicates that the quantitative information obtained by RT-qPCR amplification is useful in assigning body fluid class. Of the three classification methods, MLR performed the best. When we utilised receiver operating characteristic curves to observe performance by body fluid class, it was clear that all methods found difficulty in classifying menstrual blood samples. Future work will involve the modelling of body fluid mixtures, which are common in samples analysed as part of sexual assault investigations.

Reduction in Diabetes-Related Hospitalizations and Medical Costs After Dexcom G6 Continuous Glucose Monitor Initiation in People with Type 2 Diabetes Using Intensive Insulin Therapy.

INTRODUCTION: Some people with type 2 diabetes (T2D) require intensive insulin therapy to manage their diabetes. This can increase the risk of diabetes-related hospitalizations. We hypothesize that initiation of real-time continuous glucose monitoring (RT-CGM), which continuously measures a user's glucose values and provides threshold- and trend-based alerts, will reduce diabetes-related emergency department (ED) and inpatient hospitalizations and concomitant costs. METHODS: A retrospective analysis of US healthcare claims data using Optum's de-identified Clinformatics® Data Mart database was performed. The cohort consisted of commercially insured, CGM-naïve individuals with T2D who initiated Dexcom G6 RT-CGM system between August 1, 2018, and March 31, 2021. Twelve months of continuous health plan enrollment before and after RT-CGM initiation was required to capture baseline and follow-up rates of diabetes-related hospitalizations and associated healthcare resource utilization (HCRU) costs. Analyses were performed for claims with a diabetes-related diagnosis code in either (1) any position or (2) first or second position on the claim. RESULTS: A total of 790 individuals met the inclusion criteria. The average age was 52.8 (10.5) [mean (SD)], 53.3% were male, and 76.3% were white. For claims with a diabetes-related diagnosis code in any position, the number of individuals with ≥ 1 ED visit decreased by 30.0% (p = 0.01) and with ≥ 1 inpatient visit decreased by 41.5% (p < 0.0001). The number of diabetes-related visits and average number of visits per person similarly decreased by at least 31.4%. Larger relative decreases were observed for claims with a diabetes-related diagnosis code in the first or second position on the claim. Total diabetes-related costs expressed as per-person-per-month (PPPM) decreased by $341 PPPM for any position and $330 PPPM for first or second position. CONCLUSION: Initiation of Dexcom G6 among people with T2D using intensive insulin therapy was associated with a significant reduction in diabetes-related ED and inpatient visits and related HCRU costs. Expanded use of RT-CGM could augment these benefits and result in further cost reductions.

A Data-Driven Approach to Estimate Human Center of Mass State During Perturbed Locomotion Using Simulated Wearable Sensors.

Center of mass (COM) state, specifically in a local reference frame (i.e., relative to center of pressure), is an important variable for controlling and quantifying bipedal locomotion. However, this metric is not easily attainable in real time during human locomotion experiments. This information could be valuable when controlling wearable robotic exoskeletons, specifically for stability augmentation where knowledge of COM state could enable step placement planners similar to bipedal robots. Here, we explored the ability of simulated wearable sensor-driven models to rapidly estimate COM state during steady state and perturbed walking, spanning delayed estimates (i.e., estimating past state) to anticipated estimates (i.e., estimating future state). We used various simulated inertial measurement unit (IMU) sensor configurations typically found on lower limb exoskeletons and a temporal convolutional network (TCN) model throughout this analysis. We found comparable COM estimation capabilities across hip, knee, and ankle exoskeleton sensor configurations, where device type did not significantly influence error. We also found that anticipating COM state during perturbations induced a significant increase in error proportional to anticipation time. Delaying COM state estimates significantly increased accuracy for velocity estimates but not position estimates. All tested conditions resulted in models with R2 > 0.85, with a majority resulting in R2 > 0.95, emphasizing the viability of this approach. Broadly, this preliminary work using simulated IMUs supports the efficacy of wearable sensor-driven deep learning approaches to provide real-time COM state estimates for lower limb exoskeleton control or other wearable sensor-based applications, such as mobile data collection or use in real-time biofeedback.

The Evolution of Agrarian Landscapes in the Tropical Andes.

Changes in land-use practices have been a central element of human adaptation to Holocene climate change. Many practices that result in the short-term stabilization of socio-natural systems, however, have longer-term, unanticipated consequences that present cascading challenges for human subsistence strategies and opportunities for subsequent adaptations. Investigating complex sequences of interaction between climate change and human land-use in the past-rather than short-term causes and effects-is therefore essential for understanding processes of adaptation and change, but this approach has been stymied by a lack of suitably-scaled paleoecological data. Through a high-resolution paleoecological analysis, we provide a 7000-year history of changing climate and land management around Lake Acopia in the Andes of southern Peru. We identify evidence of the onset of pastoralism, maize cultivation, and possibly cultivation of quinoa and potatoes to form a complex agrarian landscape by c. 4300 years ago. Cumulative interactive climate-cultivation effects resulting in erosion ended abruptly c. 2300 years ago. After this time, reduced sedimentation rates are attributed to the construction and use of agricultural terraces within the catchment of the lake. These results provide new insights into the role of humans in the manufacture of Andean landscapes and the incremental, adaptive processes through which land-use practices take shape.

Using generative AI to investigate medical imagery models and datasets.

BACKGROUND: AI models have shown promise in performing many medical imaging tasks. However, our ability to explain what signals these models have learned is severely lacking. Explanations are needed in order to increase the trust of doctors in AI-based models, especially in domains where AI prediction capabilities surpass those of humans. Moreover, such explanations could enable novel scientific discovery by uncovering signals in the data that aren't yet known to experts. METHODS: In this paper, we present a workflow for generating hypotheses to understand which visual signals in images are correlated with a classification model's predictions for a given task. This approach leverages an automatic visual explanation algorithm followed by interdisciplinary expert review. We propose the following 4 steps: (i) Train a classifier to perform a given task to assess whether the imagery indeed contains signals relevant to the task; (ii) Train a StyleGAN-based image generator with an architecture that enables guidance by the classifier ("StylEx"); (iii) Automatically detect, extract, and visualize the top visual attributes that the classifier is sensitive towards. For visualization, we independently modify each of these attributes to generate counterfactual visualizations for a set of images (i.e., what the image would look like with the attribute increased or decreased); (iv) Formulate hypotheses for the underlying mechanisms, to stimulate future research. Specifically, present the discovered attributes and corresponding counterfactual visualizations to an interdisciplinary panel of experts so that hypotheses can account for social and structural determinants of health (e.g., whether the attributes correspond to known patho-physiological or socio-cultural phenomena, or could be novel discoveries). FINDINGS: To demonstrate the broad applicability of our approach, we present results on eight prediction tasks across three medical imaging modalities-retinal fundus photographs, external eye photographs, and chest radiographs. We showcase examples where many of the automatically-learned attributes clearly capture clinically known features (e.g., types of cataract, enlarged heart), and demonstrate automatically-learned confounders that arise from factors beyond physiological mechanisms (e.g., chest X-ray underexposure is correlated with the classifier predicting abnormality, and eye makeup is correlated with the classifier predicting low hemoglobin levels). We further show that our method reveals a number of physiologically plausible, previously-unknown attributes based on the literature (e.g., differences in the fundus associated with self-reported sex, which were previously unknown). INTERPRETATION: Our approach enables hypotheses generation via attribute visualizations and has the potential to enable researchers to better understand, improve their assessment, and extract new knowledge from AI-based models, as well as debug and design better datasets. Though not designed to infer causality, importantly, we highlight that attributes generated by our framework can capture phenomena beyond physiology or pathophysiology, reflecting the real world nature of healthcare delivery and socio-cultural factors, and hence interdisciplinary perspectives are critical in these investigations. Finally, we will release code to help researchers train their own StylEx models and analyze their predictive tasks of interest, and use the methodology presented in this paper for responsible interpretation of the revealed attributes. FUNDING: Google.