Advancing Transportation Equity and Safety Through Autonomous Vehicles.

Motor vehicle crashes are a leading cause of death in the United States, and disproportionately impact communities of color. Replacing human control with automated vehicles (AVs) holds the potential to reduce crashes and save lives. The benefits of AVs, including automated shuttles, buses, or cars could extend beyond safety to include improvements in congestion, reductions in emissions, and increased access to mobility, particularly for vulnerable populations. However, AVs have not attained the level of public trust that has been expected, given their potential to save lives and increase access to mobility. Public opinion surveys have highlighted safety and security concerns as reasons for this lack of confidence. In this study, we present the findings of an experiment we conducted to actively shift mindsets on AVs toward advancing health equity. We demonstrate through a nationally representative sample of 2265 U.S. adults that the public support for AVs can be improved by expanding their scope of application to include advancing social benefit. The survey began with questions on respondent's support for AVs based on a priori knowledge and beliefs. Consistent with prior surveys, baseline support (strong support and some degree of support) was low at 26.4% (95% confidence interval 24.0-29.0). After introducing information about how AVs could be used to provide mobility for older adults, those with limited income, or the vision-impaired, respondents were asked to reassess their support for AVs. Support significantly increased to include the majority of respondents. By prioritizing the deployment of AVs to serve individuals and communities in greatest need of mobility, AVs would not only demonstrate compelling social value by reducing disparities but would also gain widespread public support among the U.S. public.

Machine Learning in Cardiovascular Risk Prediction and Precision Preventive Approaches.

PURPOSE OF REVIEW: In this review, we sought to provide an overview of ML and focus on the contemporary applications of ML in cardiovascular risk prediction and precision preventive approaches. We end the review by highlighting the limitations of ML while projecting on the potential of ML in assimilating these multifaceted aspects of CAD in order to improve patient-level outcomes and further population health. RECENT FINDINGS: Coronary artery disease (CAD) is estimated to affect 20.5 million adults across the USA, while also impacting a significant burden at the socio-economic level. While the knowledge of the mechanistic pathways that govern the onset and progression of clinical CAD has improved over the past decade, contemporary patient-level risk models lag in accuracy and utility. Recently, there has been renewed interest in combining advanced analytic techniques that utilize artificial intelligence (AI) with a big data approach in order to improve risk prediction within the realm of CAD. By virtue of being able to combine diverse amounts of multidimensional horizontal data, machine learning has been employed to build models for improved risk prediction and personalized patient care approaches. The use of ML-based algorithms has been used to leverage individualized patient-specific data and the associated metabolic/genomic profile to improve CAD risk assessment. While the tool can be visualized to shift the paradigm toward a patient-specific care, it is crucial to acknowledge and address several challenges inherent to ML and its integration into healthcare before it can be significantly incorporated in the daily clinical practice.

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications.

Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.

The Impact of the COVID-19 Pandemic on Respiratory Illness Admissions at a Single Academic Institution in Arkansas.

BACKGROUND: The first reported COVID-19 case in Arkansas was on 11 March 2020, two months after the first reported case in the United States. We sought to analyze rates of respiratory illness and influenza tests during the 2019/2020 influenza season compared to pre-pandemic years to assess whether there were higher rates of respiratory illness than expected, which may suggest undiagnosed COVID-19 cases. METHODS: Using data collected from the data warehouse of the largest hospital in Arkansas, ICD-9 and ICD-10 codes related to respiratory illness were identified for 1 October to 1 May 2017-2020. RESULTS: We identified 25,747 patients admitted with respiratory illness during the study. We found no significant difference in the rate of monthly admissions with respiratory illness between seasons (p = 0.14). We saw a significant increase in the number of influenza tests ordered in 2019/2020 (p < 0.01). CONCLUSIONS: The rate of hospitalizations with respiratory illness did not significantly increase during the 2019/2020 season; however, influenza testing increased without a statistically significant difference in positivity rate. The increase in ordered influenza tests indicates an increased clinical suspicion, which may suggest a rise in pre-hospital viral illness associated with COVID-19.

Isolated Acute Lateral Compartment Syndrome in an Adolescent Athlete: A Case Report.

CASE: A 17-year-old adolescent boy presented with anterolateral, right leg pain and numbness of his right foot 2 days after participating in football practice. He denied a traumatic event, and radiographs were negative for fracture. His imaging and physical examination raised suspicion for acute compartment syndrome (ACS). Single-incision fasciotomy with anterior and lateral compartment release was performed. The peroneus longus muscle was detached at the musculotendinous junction. The peroneus longus was then debrided and transferred to the peroneus brevis. CONCLUSION: Atraumatic ACS, although rare, is a diagnostic challenge. Prompt recognition of this atypical presentation is important for proper treatment.

Engineering Pseudomonas putida for improved utilization of syringyl aromatics.

Lignin is a largely untapped source for the bioproduction of value-added chemicals. Pseudomonas putida KT2440 has emerged as a strong candidate for bioprocessing of lignin feedstocks due to its resistance to several industrial solvents, broad metabolic capabilities, and genetic amenability. Here we demonstrate the engineering of P. putida for the ability to metabolize syringic acid, one of the major products that comes from the breakdown of the syringyl component of lignin. The rational design was first applied for the construction of strain Sy-1 by overexpressing a native vanillate demethylase. Subsequent adaptive laboratory evolution (ALE) led to the generation of mutations that achieved robust growth on syringic acid as a sole carbon source. The best mutant showed a 30% increase in the growth rate over the original engineered strain. Genomic sequencing revealed multiple mutations repeated in separate evolved replicates. Reverse engineering of mutations identified in agmR, gbdR, fleQ, and the intergenic region of gstB and yadG into the parental strain recaptured the improved growth of the evolved strains to varied extent. These findings thus reveal the ability of P. putida to utilize lignin more fully as a feedstock and make it a more economically viable chassis for chemical production.

Machine-learning from Pseudomonas putida KT2440 transcriptomes reveals its transcriptional regulatory network.

Bacterial gene expression is orchestrated by numerous transcription factors (TFs). Elucidating how gene expression is regulated is fundamental to understanding bacterial physiology and engineering it for practical use. In this study, a machine-learning approach was applied to uncover the genome-scale transcriptional regulatory network (TRN) in Pseudomonas putida KT2440, an important organism for bioproduction. We performed independent component analysis of a compendium of 321 high-quality gene expression profiles, which were previously published or newly generated in this study. We identified 84 groups of independently modulated genes (iModulons) that explain 75.7% of the total variance in the compendium. With these iModulons, we (i) expand our understanding of the regulatory functions of 39 iModulon associated TFs (e.g., HexR, Zur) by systematic comparison with 1993 previously reported TF-gene interactions; (ii) outline transcriptional changes after the transition from the exponential growth to stationary phases; (iii) capture group of genes required for utilizing diverse carbon sources and increased stationary response with slower growth rates; (iv) unveil multiple evolutionary strategies of transcriptome reallocation to achieve fast growth rates; and (v) define an osmotic stimulon, which includes the Type VI secretion system, as coordination of multiple iModulon activity changes. Taken together, this study provides the first quantitative genome-scale TRN for P. putida KT2440 and a basis for a comprehensive understanding of its complex transcriptome changes in a variety of physiological states.

Dynamic community detection reveals transient reorganization of functional brain networks across a female menstrual cycle.

Sex steroid hormones have been shown to alter regional brain activity, but the extent to which they modulate connectivity within and between large-scale functional brain networks over time has yet to be characterized. Here, we applied dynamic community detection techniques to data from a highly sampled female with 30 consecutive days of brain imaging and venipuncture measurements to characterize changes in resting-state community structure across the menstrual cycle. Four stable functional communities were identified, consisting of nodes from visual, default mode, frontal control, and somatomotor networks. Limbic, subcortical, and attention networks exhibited higher than expected levels of nodal flexibility, a hallmark of between-network integration and transient functional reorganization. The most striking reorganization occurred in a default mode subnetwork localized to regions of the prefrontal cortex, coincident with peaks in serum levels of estradiol, luteinizing hormone, and follicle stimulating hormone. Nodes from these regions exhibited strong intranetwork increases in functional connectivity, leading to a split in the stable default mode core community and the transient formation of a new functional community. Probing the spatiotemporal basis of human brain-hormone interactions with dynamic community detection suggests that hormonal changes during the menstrual cycle result in temporary, localized patterns of brain network reorganization.

Review of Salvage Therapies for Periprosthetic Joint Infection After Total Knee Arthroplasty.

Periprosthetic joint infection after knee arthroplasty is exceptionally challenging to manage and can result in significant morbidity and mortality for the patient. When irrigation and debridement, polyethylene exchange, and one- or two-stage exchange fail to clear the infection, the surgeon is left with two primary salvage therapies: knee arthrodesis and amputation. The decision between these two treatments is difficult and requires an open conversation with the patient about their desire and expectations. The purpose of this review article is to give an overview of knee arthrodesis and amputation after periprosthetic joint infection about the knee as well as provide two case examples to highlight these two management strategies. (Journal of Surgical Orthopaedic Advances 30(4):231-234, 2021).

Variation and Variability in Drosophila Grooming Behavior.

Behavioral differences can be observed between species or populations (variation) or between individuals in a genetically similar population (variability). Here, we investigate genetic differences as a possible source of variation and variability in Drosophila grooming. Grooming confers survival and social benefits. Grooming features of five Drosophila species exposed to a dust irritant were analyzed. Aspects of grooming behavior, such as anterior to posterior progression, were conserved between and within species. However, significant differences in activity levels, proportion of time spent in different cleaning movements, and grooming syntax were identified between species. All species tested showed individual variability in the order and duration of action sequences. Genetic diversity was not found to correlate with grooming variability within a species: melanogaster flies bred to increase or decrease genetic heterogeneity exhibited similar variability in grooming syntax. Individual flies observed on consecutive days also showed grooming sequence variability. Standardization of sensory input using optogenetics reduced but did not eliminate this variability. In aggregate, these data suggest that sequence variability may be a conserved feature of grooming behavior itself. These results also demonstrate that large genetic differences result in distinguishable grooming phenotypes (variation), but that genetic heterogeneity within a population does not necessarily correspond to an increase in the range of grooming behavior (variability).

Rationale and design of an evidence-based tool to guide preoperative evaluation and management.

BACKGROUND: In the United States, over-testing and over-treatment are recognised causes of excess cost and patient harm. Healthcare value, defined as health outcomes achieved relative to the costs of care, has become a focus to improve the quality and affordability of healthcare. AIM: To describe the rationale for, and development of a standardised clinical preoperative decision-support tool.Program description: An evidence-based, preoperative clinical decision tool was developed to guide preoperative testing and management of high-risk medications.Program evaluation: Patient data before and after implementation of the tool will be analysed to determine its effectiveness in reducing preoperative testing. DISCUSSION: Preoperative testing is an area that presents an opportunity to increase healthcare value and decrease healthcare spending. Guidelines are available to standardise preoperative assessment but their adoption and acceptance into practice has been slow. To systematise preoperative assessment within our healthcare system, we reviewed current published literature and guidelines and synthesised them into an electronic, evidence-based, decision-support tool. After distribution of the tool to clinicians in our healthcare system, we will assess its impact on healthcare value, costs and outcomes. We believe that an evidence-based preoperative tool, seamlessly and efficiently integrated into clinician workflow, can improve preoperative patient care.

Control of ecological outcomes through deliberate parameter changes in a model of the gut microbiome.

The generalized Lotka-Volterra (gLV) equations are a mathematical proxy for ecological dynamics. We focus on a gLV model of the gut microbiome, in which the evolution of the gut microbial state is determined in part by pairwise interspecies interaction parameters that encode environmentally mediated resource competition between microbes. We develop an in silico method that controls the steady-state outcome of the system by adjusting these interaction parameters. This approach is confined to a bistable region of the gLV model. In this method, a dimensionality reduction technique called steady-state reduction (SSR) is first used to generate a two-dimensional (2D) gLV model that approximates the high-dimensional dynamics on the 2D subspace spanned by the two steady states. Then a bifurcation analysis of the 2D model analytically determines parameter modifications that drive an initial condition to a target steady state. This parameter modification of the reduced 2D model guides parameter modifications of the original high-dimensional model, resulting in a change of steady-state outcome in the high-dimensional model. This control method, called SSR-guided parameter change (SPARC), bypasses the computational challenge of directly determining parameter modifications in the original high-dimensional system. SPARC could guide the development of indirect bacteriotherapies, which seek to change microbial compositions by deliberately modifying gut environmental variables such as gut acidity or macronutrient availability.

Direct biosynthesis of adipic acid from lignin-derived aromatics using engineered Pseudomonas putida KT2440.

Lignin is one largely untapped natural resource that can be exploited as a raw material for the bioproduction of value-added chemicals. Meanwhile, the current petroleum-based process for the production of adipic acid faces sustainability challenges. Here we report the successful engineering of Pseudomonas putida KT2440 strain for the direct biosynthesis of adipic acid from lignin-derived aromatics. The devised bio-adipic acid route features an artificial biosynthetic pathway that is connected to the endogenous aromatics degradation pathway of the host at the branching point, 3-ketoadipoyl-CoA, by taking advantage of the unique carbon skeleton of this key intermediate. Studies of the metabolism of 3-ketoadipoyl-CoA led to the discovery of crosstalk between two aromatics degradation pathways in KT2440. This knowledge facilitated the formulation and implementation of metabolic engineering strategies to optimize the carbon flux into the biosynthesis of adipic acid. By optimizing pathway expression and cultivation conditions, an engineered strain AA-1 produced adipic acid at 0.76 g/L and 18.4% molar yield under shake-flask conditions and 2.5 g/L and 17.4% molar yield under fermenter-controlled conditions from common aromatics that can be derived from lignin. This represents the first example of the direct adipic acid production from model compounds of lignin depolymerization.

High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities.

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.

Drosophila melanogaster grooming possesses syntax with distinct rules at different temporal scales.

Mathematical modeling of behavioral sequences yields insight into the rules and mechanisms underlying sequence generation. Grooming in Drosophila melanogaster is characterized by repeated execution of distinct, stereotyped actions in variable order. Experiments demonstrate that, following stimulation by an irritant, grooming progresses gradually from an early phase dominated by anterior cleaning to a later phase with increased walking and posterior cleaning. We also observe that, at an intermediate temporal scale, there is a strong relationship between the amount of time spent performing body-directed grooming actions and leg-directed actions. We then develop a series of data-driven Markov models that isolate and identify the behavioral features governing transitions between individual grooming bouts. We identify action order as the primary driver of probabilistic, but non-random, syntax structure, as has previously been identified. Subsequent models incorporate grooming bout duration, which also contributes significantly to sequence structure. Our results show that, surprisingly, the syntactic rules underlying probabilistic grooming transitions possess action duration-dependent structure, suggesting that sensory input-independent mechanisms guide grooming behavior at short time scales. Finally, the inclusion of a simple rule that modifies grooming transition probabilities over time yields a generative model that recapitulates the key features of observed grooming sequences at several time scales. These discoveries suggest that sensory input guides action selection by modulating internally generated dynamics. Additionally, the discovery of these principles governing grooming in D. melanogaster demonstrates the utility of incorporating temporal information when characterizing the syntax of behavioral sequences.

Metabolic engineering of Escherichia coli for the de novo stereospecific biosynthesis of 1,2-propanediol through lactic acid.

1,2-propanediol (1,2-PDO) is an industrial chemical with a broad range of applications, such as the production of alkyd and unsaturated polyester resins. It is currently produced as a racemic mixture from nonrenewable petroleum-based feedstocks. We have reported a novel artificial pathway for the biosynthesis of 1,2-PDO via lactic acid isomers as the intermediates. The pathway circumvents the cytotoxicity issue caused by methylglyoxal intermediate in the naturally existing pathway. Successful E. coli bioconversion of lactic acid to 1,2-PDO was shown in previous report. Here, we demonstrated the engineering of E. coli host strains for the de novo biosynthesis of 1,2-PDO through this pathway. Under fermenter-controlled conditions, the R-1,2-PDO was produced at 17.3 g/L with a molar yield of 42.2% from glucose, while the S-isomer was produced at 9.3 g/L with a molar yield of 23.2%. The optical purities of the two isomers were 97.5% ee (R) and 99.3% ee (S), respectively. To the best of our knowledge, these are the highest titers of 1,2-PDO biosynthesized by either natural producer or engineered microbial strains that are published in peer-reviewed journals.

Deciphering molecular details in the assembly of alpha-type carboxysome.

Bacterial microcompartments (BMCs) are promising natural protein structures for applications that require the segregation of certain metabolic functions or molecular species in a defined microenvironment. To understand how endogenous cargos are packaged inside the protein shell is key for using BMCs as nano-scale reactors or delivery vesicles. In this report, we studied the encapsulation of RuBisCO into the α-type carboxysome from Halothiobacillus neapolitan. Our experimental data revealed that the CsoS2 scaffold proteins engage RuBisCO enzyme through an interaction with the small subunit (CbbS). In addition, the N domain of the large subunit (CbbL) of RuBisCO interacts with all shell proteins that can form the hexamers. The binding affinity between the N domain of CbbL and one of the major shell proteins, CsoS1C, is within the submicromolar range. The absence of the N domain also prevented the encapsulation of the rest of the RuBisCO subunits. Our findings complete the picture of how RuBisCOs are encapsulated into the α-type carboxysome and provide insights for future studies and engineering of carboxysome as a protein shell.

Epigenetic deregulation of the LMP1/LMP2 locus of Epstein-Barr virus by mutation of a single CTCF-cohesin binding site.

The chromatin regulatory factors CTCF and cohesin have been implicated in the coordinated control of multiple gene loci in Epstein-Barr virus (EBV) latency. We have found that CTCF and cohesin are highly enriched at the convergent and partially overlapping transcripts for the LMP1 and LMP2A genes, but it is not yet known how CTCF and cohesin may coordinately regulate these transcripts. We now show that genetic disruption of this CTCF binding site (EBVΔCTCF166) leads to a deregulation of LMP1, LMP2A, and LMP2B transcription in EBV-immortalized B lymphocytes. EBVΔCTCF166 virus-immortalized primary B lymphocytes showed a decrease in LMP1 and LMP2A mRNA and a corresponding increase in LMP2B mRNA. The reduction of LMP1 and LMP2A correlated with a loss of euchromatic histone modification H3K9ac and a corresponding increase in heterochromatic histone modification H3K9me3 at the LMP2A promoter region in EBVΔCTCF166. Chromosome conformation capture (3C) revealed that DNA loop formation with the origin of plasmid replication (OriP) enhancer was eliminated in EBVΔCTCF166. We also observed that the EBV episome copy number was elevated in EBVΔCTCF166 and that this was not due to increased lytic cycle activity. These findings suggest that a single CTCF binding site controls LMP2A and LMP1 promoter selection, chromatin boundary function, DNA loop formation, and episome copy number control during EBV latency.

The N-DRC forms a conserved biochemical complex that maintains outer doublet alignment and limits microtubule sliding in motile axonemes.

The nexin-dynein regulatory complex (N-DRC) is proposed to coordinate dynein arm activity and interconnect doublet microtubules. Here we identify a conserved region in DRC4 critical for assembly of the N-DRC into the axoneme. At least 10 subunits associate with DRC4 to form a discrete complex distinct from other axonemal substructures. Transformation of drc4 mutants with epitope-tagged DRC4 rescues the motility defects and restores assembly of missing DRC subunits and associated inner-arm dyneins. Four new DRC subunits contain calcium-signaling motifs and/or AAA domains and are nearly ubiquitous in species with motile cilia. However, drc mutants are motile and maintain the 9 + 2 organization of the axoneme. To evaluate the function of the N-DRC, we analyzed ATP-induced reactivation of isolated axonemes. Rather than the reactivated bending observed with wild-type axonemes, ATP addition to drc-mutant axonemes resulted in splaying of doublets in the distal region, followed by oscillatory bending between pairs of doublets. Thus the N-DRC provides some but not all of the resistance to microtubule sliding and helps to maintain optimal alignment of doublets for productive flagellar motility. These findings provide new insights into the mechanisms that regulate motility and further highlight the importance of the proximal region of the axoneme in generating flagellar bending.

Incidence and Symptoms of High Altitude Illness in South Pole Workers: Antarctic Study of Altitude Physiology (ASAP).

INTRODUCTION: Each year, the US Antarctic Program rapidly transports scientists and support personnel from sea level (SL) to the South Pole (SP, 2835 m) providing a unique natural laboratory to quantify the incidence of acute mountain sickness (AMS), patterns of altitude related symptoms and the field effectiveness of acetazolamide in a highly controlled setting. We hypothesized that the combination of rapid ascent (3 hr), accentuated hypobarism (relative to altitude), cold, and immediate exertion would increase altitude illness risk. METHODS: Medically screened adults (N = 246, age = 37 ± 11 yr, 30% female, BMI = 26 ± 4 kg/m(2)) were recruited. All underwent SL and SP physiological evaluation, completed Lake Louise symptom questionnaires (LLSQ, to define AMS), and answered additional symptom related questions (eg, exertional dyspnea, mental status, cough, edema and general health), during the 1st week at altitude. Acetazolamide, while not mandatory, was used by 40% of participants. RESULTS: At SP, the barometric pressure resulted in physiological altitudes that approached 3400 m, while T °C averaged -42, humidity 0.03%. Arterial oxygen saturation averaged 89% ± 3%. Overall, 52% developed LLSQ defined AMS. The most common symptoms reported were exertional dyspnea-(87%), sleeping difficulty-(74%), headache-(66%), fatigue-(65%), and dizziness/lightheadedness-(46%). Symptom severity peaked on days 1-2, yet in >20% exertional dyspnea, fatigue and sleep problems persisted through day 7. AMS incidence was similar between those using acetazolamide and those abstaining (51 vs. 52%, P = 0.87). Those who used acetazolamide tended to be older, have less altitude experience, worse symptoms on previous exposures, and less SP experience. CONCLUSION: The incidence of AMS at SP tended to be higher than previously reports in other geographic locations at similar altitudes. Thus, the SP constitutes a more intense altitude exposure than might be expected considering physical altitude alone. Many symptoms persist, possibly due to extremely cold, arid conditions and the benefits of acetazolamide appeared negligible, though it may have prevented more severe symptoms in higher risk subjects.