Examining the Potential of Pharmacies to Expand Pre-Exposure Prophylaxis Access Along Georgia's Fixed-Route Public Transit: A Geospatial Analysis.

BACKGROUND: Despite accounting for more than half of new Human Immunodeficiency Virus diagnoses in the United States, the South has fewer than 30% of all pre-exposure prophylaxis users. Pre-exposure prophylaxis access geospatial analyses have focused on drive time but analyses along public transit routes have not been evaluated. Given the proximity to pharmacists and pharmacies, involvement in pre-exposure prophylaxis services may increase access and uptake of this preventative health need. OBJECTIVE: The objectives were to compare the rate of pre-exposure prophylaxis uptake between Georgia counties with and without public transit, to assess the geospatial accessibility of services along public transit, and to evaluate the potential impact of expanding pre-exposure prophylaxis services to community pharmacies. METHODS: Pre-exposure prophylaxis uptake rates between counties with and without public transit were compared using the Mann-Whitney U test. Geospatial analysis was performed using ArcGIS Pro and Geoda. The Pearson correlation coefficient was used to determine the relationship between pre-exposure prophylaxis uptake rates and population and county characteristics. Spatial analysis was completed to uncover predictors for pre-exposure prophylaxis uptake rates. Increased access to pre-exposure prophylaxis along public transit was calculated by reporting the number of counties that would experience at least a 50% increase in pre-exposure prophylaxis access through community pharmacies. RESULTS: Pre-exposure prophylaxis uptake is significantly higher in Georgia counties with versus without public transit (P < 0.001). Pre-exposure prophylaxis rate is positively correlated with the accessibility of community pharmacies and pre-exposure prophylaxis clinics along fixed-route public transit (R2 = 0.524). Among pre-exposure prophylaxis clinics, 44% are inaccessible by public transit alone. Community pharmacies are significantly more widely distributed and accessible along public transit routes than pre-exposure prophylaxis clinics. CONCLUSION: Transportation remains a barrier to accessing pre-exposure prophylaxis. Georgia community pharmacies along public transit may serve as a solution to pre-exposure prophylaxis care access barriers.

Temporal Action Segmentation: An Analysis of Modern Techniques.

Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid growth of TAS techniques in recent years, no systematic survey has been conducted in these sectors. This survey analyzes and summarizes the most significant contributions and trends. In particular, we first examine the task definition, common benchmarks, types of supervision, and prevalent evaluation measures. In addition, we systematically investigate two essential techniques of this topic, i.e., frame representation and temporal modeling, which have been studied extensively in the literature. We then conduct a thorough review of existing TAS works categorized by their levels of supervision and conclude our survey by identifying and emphasizing several research gaps.

Contrastive Video Question Answering via Video Graph Transformer.

We propose to perform video question answering (VideoQA) in a Contrastive manner via a Video Graph Transformer model (CoVGT). CoVGT's uniqueness and superiority are three-fold: 1) It proposes a dynamic graph transformer module which encodes video by explicitly capturing the visual objects, their relations and dynamics, for complex spatio-temporal reasoning. 2) It designs separate video and text transformers for contrastive learning between the video and text to perform QA, instead of multi-modal transformer for answer classification. Fine-grained video-text communication is done by additional cross-modal interaction modules. 3) It is optimized by the joint fully- and self-supervised contrastive objectives between the correct and incorrect answers, as well as the relevant and irrelevant questions respectively. With superior video encoding and QA solution, we show that CoVGT can achieve much better performances than previous arts on video reasoning tasks. Its performances even surpass those models that are pretrained with millions of external data. We further show that CoVGT can also benefit from cross-modal pretraining, yet with orders of magnitude smaller data. The results demonstrate the effectiveness and superiority of CoVGT, and additionally reveal its potential for more data-efficient pretraining.

C2F-TCN: A Framework for Semi- and Fully-Supervised Temporal Action Segmentation.

Temporal action segmentation tags action labels for every frame in an input untrimmed video containing multiple actions in a sequence. For the task of temporal action segmentation, we propose an encoder-decoder style architecture named C2F-TCN featuring a "coarse-to-fine" ensemble of decoder outputs. The C2F-TCN framework is enhanced with a novel model agnostic temporal feature augmentation strategy formed by the computationally inexpensive strategy of the stochastic max-pooling of segments. It produces more accurate and well-calibrated supervised results on three benchmark action segmentation datasets. We show that the architecture is flexible for both supervised and representation learning. In line with this, we present a novel unsupervised way to learn frame-wise representation from C2F-TCN. Our unsupervised learning approach hinges on the clustering capabilities of the input features and the formation of multi-resolution features from the decoder's implicit structure. Further, we provide first semi-supervised temporal action segmentation results by merging representation learning with conventional supervised learning. Our semi-supervised learning scheme, called "Iterative-Contrastive-Classify (ICC)", progressively improves in performance with more labeled data. The ICC semi-supervised learning in C2F-TCN, with 40% labeled videos, performs similar to fully supervised counterparts.

Bias-Compensated Integral Regression for Human Pose Estimation.

In human and hand pose estimation, heatmaps are a crucial intermediate representation for a body or hand keypoint. Two popular methods to decode the heatmap into a final joint coordinate are via an argmax, as done in heatmap detection, or via softmax and expectation, as done in integral regression. Integral regression is learnable end-to-end, but has lower accuracy than detection. This paper uncovers an induced bias from integral regression that results from combining the softmax and the expectation operation. This bias often forces the network to learn degenerately localized heatmaps, obscuring the keypoint's true underlying distribution and leads to lower accuracies. Training-wise, by investigating the gradients of integral regression, we show that the implicit guidance of integral regression to update the heatmap makes it slower to converge than detection. To counter the above two limitations, we propose Bias Compensated Integral Regression (BCIR), an integral regression-based framework that compensates for the bias. BCIR also incorporates a Gaussian prior loss to speed up training and improve prediction accuracy. Experimental results on both the human body and hand benchmarks show that BCIR is faster to train and more accurate than the original integral regression, making it competitive with state-of-the-art detection methods.

Mechanistic Studies of Aziridine Formation Catalyzed by Mononuclear Non-Heme Iron Enzymes.

Aziridines are compounds with a nitrogen-containing three-membered ring. When it is incorporated into natural products, the reactivity of the strained ring often drives the biological activities of aziridines. Despite its importance, the enzymes and biosynthetic strategies deployed to install this reactive moiety remain understudied. Herein, we report the use of in silico methods to identify enzymes with potential aziridine-installing (aziridinase) functionality. To validate candidates, we reconstitute enzymatic activity in vitro and demonstrate that an iron(IV)-oxo species initiates aziridine ring closure by the C-H bond cleavage. Furthermore, we divert the reaction pathway from aziridination to hydroxylation using mechanistic probes. This observation, isotope tracing experiments using H218O and 18O2, and quantitative product analysis, provide evidence for the polar capture of a carbocation species by the amine in the pathway to aziridine installation.

The Recent Developments of ADCs with the Tubulysins as the Payloads.

As a novel bio-targeting antitumor agent, an antibody-drug conjugate (ADC) combines the high selectivity of monoclonal antibody and potent cytotoxicity of drug or payload. It can expand the scope of clinical application of small molecule drugs. Tubulysin and its bio-precursor pretubulysin (PT) are potent tubulin-binding antitumor drugs. Due to the excellent antitumoral, antimetastatic, antiangiogenic, and anti-multidrug resistance properties, Tubulysins or PT is believed to be a promising cancer therapeutic approach. Currently, the modifications of tubulysin are centering on the C-11 acetoxyl and N,O-acetal groups, and numerous promising payloads are identified. There are at least 5 sites to introduce appropriate drug linkers in tubulysin and PT for connecting the antibodies. The possible sites of attachment are located in Mep, Tuv, or Tup parts. Cleavage and non-cleavage linkers are used in these ADCs. The chemical reactions involved in the final conjugation of antibody and linkerpayload (LP) are cysteine, lysine, site-specific, and click chemistry reactions. In this article, the recent development of ADCs with tubulysins as the payloads is reviewed, with the hope of providing a reference and future strategies for developing new ADSs.

Transferring Knowledge From Text to Video: Zero-Shot Anticipation for Procedural Actions.

Can we teach a robot to recognize and make predictions for activities that it has never seen before? We tackle this problem by learning models for video from text. This paper presents a hierarchical model that generalizes instructional knowledge from large-scale text corpora and transfers the knowledge to video. Given a portion of an instructional video, our model recognizes and predicts coherent and plausible actions multiple steps into the future, all in rich natural language. To demonstrate the capabilities of our model, we introduce the Tasty Videos Dataset V2, a collection of 4022 recipes for zero-shot learning, recognition and anticipation. Extensive experiments with various evaluation metrics demonstrate the potential of our method for generalization, given limited video data for training models.

Failure to initiate trauma team activation for patients who meet the criteria in a level 1 paediatric trauma centre: which patients are missing out?

BACKGROUND: Trauma team activation (TTA) is paramount in the early hospital management of trauma patients. This study aimed to evaluate factors which contribute to failure to activate the Trauma team for management of paediatric trauma. METHODS: A retrospective cohort study of Emergency Department (ED) presentations at the paediatric major trauma hospital in Adelaide, South Australia was conducted over a 16-month period. Data from the hospital's trauma registry, individual case files and digital medical records were evaluated to determine factors that were associated with no TTA. RESULTS: During the study period, 617 trauma patients who met Level 1 or Level 2 TTA criteria attended the trauma centre. For 29 (4.7%) of these patients, there was no TTA. Predictors of no TTA included sustaining abdomen and/or pelvis injuries compared to limb injuries (unadjusted odds ratio [OR] = 10.59, 95% confidence interval [CI] 1.98-56.69, P = 0.006), sustaining non-accidental injury (NAI) versus an injury with vehicle involvement (OR = 30.13, 95% CI 6.43-141.21, P < 0.001), and arriving via emergency medical retrieval service compared to private vehicle (OR = 14.23, 95% CI 3.94-51.36, P < 0.001). No patients transferred directly to Paediatric Intensive Care Unit (PICU), or High Dependency Unit (HDU) received an appropriate TTA. CONCLUSION: Multiple factors were associated with no TTA in paediatric trauma patients. The results highlight that even in PICU and HDU admissions and transfer patients, vigilant clarification of mechanism of injury and potential for occult injuries should be undertaken to ensure appropriate TTA and improve patient outcome.

3D Printed Acetabular Components for Complex Revision Arthroplasty.

Recent studies have shown high early failure rates with Cup Cage constructs in complex revision surgery for Paprosky 3B acetabular defects. As a result, the use of 3D printed custom-made acetabular components has become more common. In this case series, we present two cases that demonstrate the latest advancement in 3D printed implants for severe acetabular bone loss. The follow up was 3 and 7 years. Neither patient has undergone revision surgery of the acetabular component to date. One patient sustained a femoral peri-prosthetic fracture requiring plate fixation. This case study demonstrates that 3D printed implants have excellent intraoperative and immediate postoperative outcomes in revision surgery for severe acetabular bone defects.

Geospatial indicators of exposure, sensitivity, and adaptive capacity to assess neighbourhood variation in vulnerability to climate change-related health hazards.

BACKGROUND: Although the frequency and magnitude of climate change-related health hazards (CCRHHs) are likely to increase, the population vulnerabilities and corresponding health impacts are dependent on a community's exposures, pre-existing sensitivities, and adaptive capacities in response to a hazard's impact. To evaluate spatial variability in relative vulnerability, we: 1) identified climate change-related risk factors at the dissemination area level; 2) created actionable health vulnerability index scores to map community risks to extreme heat, flooding, wildfire smoke, and ground-level ozone; and 3) spatially evaluated vulnerability patterns and priority areas of action to address inequity. METHODS: A systematic literature review was conducted to identify the determinants of health hazards among populations impacted by CCRHHs. Identified determinants were then grouped into categories of exposure, sensitivity, and adaptive capacity and aligned with available data. Data were aggregated to 4188 Census dissemination areas within two health authorities in British Columbia, Canada. A two-step principal component analysis (PCA) was then used to select and weight variables for each relative vulnerability score. In addition to an overall vulnerability score, exposure, adaptive capacity, and sensitivity sub-scores were computed for each hazard. Scores were then categorised into quintiles and mapped. RESULTS: Two hundred eighty-one epidemiological papers met the study criteria and were used to identify 36 determinant indicators that were operationalized across all hazards. For each hazard, 3 to 5 principal components explaining 72 to 94% of the total variance were retained. Sensitivity was weighted much higher for extreme heat, wildfire smoke and ground-level ozone, and adaptive capacity was highly weighted for flooding vulnerability. There was overall varied contribution of adaptive capacity (16-49%) across all hazards. Distinct spatial patterns were observed - for example, although patterns varied by hazard, vulnerability was generally higher in more deprived and more outlying neighbourhoods of the study region. CONCLUSIONS: The creation of hazard and category-specific vulnerability indices (exposure, adaptive capacity and sensitivity sub-scores) supports evidence-based approaches to prioritize public health responses to climate-related hazards and to reduce inequity by assessing relative differences in vulnerability along with absolute impacts. Future studies can build upon this methodology to further understand the spatial variation in vulnerability and to identify and prioritise actionable areas for adaptation.

Culture Time Needed to Scale up Infrapatellar Fat Pad Derived Stem Cells for Cartilage Regeneration: A Systematic Review.

Adipose tissue is a rich source of stem cells, which are reported to represent 2% of the stromal vascular fraction (SVF). The infrapatellar fat pad (IFP) is a unique source of tissue, from which human adipose-derived stem cells (hADSCs) have been shown to harbour high chondrogenic potential. This review aims to calculate, based on the literature, the culture time needed before an average knee articular cartilage defect can be treated using stem cells obtained from arthroscopically or openly harvested IFP. Firstly, a systematic literature review was performed to search for studies that included the number of stem cells isolated from the IFP. Subsequent analysis was conducted to identify the amount of IFP tissue harvestable, stem cell count and the overall yield based on the harvesting method. We then determined the minimum time required before treating an average-sized knee articular cartilage defect with IFP-derived hADSCs by using our newly devised equation. The amount of fat tissue, the SVF cell count and the stem cell yield are all lower in arthroscopically harvested IFP tissue compared to that collected using arthrotomy. As an extrapolation, we show that an average knee defect can be treated in 20 or 17 days using arthroscopically or openly harvested IFP-derived hADSCs, respectively. In summary, the systematic review conducted in this study reveals that there is a higher amount of fat tissue, SVF cell count and overall yield (cells/volume or cells/gram) associated with open (arthrotomy) compared to arthroscopic IFP harvest. In addition to these review findings, we demonstrate that our novel framework can give an indication about the culture time needed to scale up IFP-derived stem cells for the treatment of articular cartilage defects based on harvesting method.

Hypoxia-inducible factor 2α is a negative regulator of osteoblastogenesis and bone mass accrual.

Osteoblasts, which are the bone-forming cells, operate in a hypoxic environment. The transcription factors hypoxia-inducible factor-1α (HIF1) and HIF2 are key mediators of the cellular response to hypoxia. Both are expressed in osteoblasts. HIF1 is known to be a positive regulator of bone formation. Conversely, the role of HIF2 in the control osteoblast biology is still poorly understood. In this study, we used mouse genetics to demonstrate that HIF2 is an inhibitor of osteoblastogenesis and bone mass accrual. Moreover, we provided evidence that HIF2 impairs osteoblast differentiation at least in part, by upregulating the transcription factor Sox9. Our findings constitute a paradigm shift, as activation of the hypoxia-signaling pathway has traditionally been associated with increased bone formation through HIF1. Inhibiting HIF2 could thus represent a therapeutic approach for the treatment of the low bone mass observed in chronic diseases, osteoporosis, or aging.

Paper #16: HIF-1α critical to Somitogenesis and interacts with the Notch pathway leading to vertebral malformations.

HIF-1α is critical to somitogenesis. Lack of HIF-1α disrupts the Notch signaling pathway which leads to vertebral malformations seen in congenital scoliosis.

Hough forests for object detection, tracking, and action recognition.

Abstract­The paper introduces Hough forests, which are random forests adapted to perform a generalized Hough transform in an efficient way. Compared to previous Hough-based systems such as implicit shape models, Hough forests improve the performance of the generalized Hough transform for object detection on a categorical level. At the same time, their flexibility permits extensions of the Hough transform to new domains such as object tracking and action recognition. Hough forests can be regarded as task-adapted codebooks of local appearance that allow fast supervised training and fast matching at test time. They achieve high detection accuracy since the entries of such codebooks are optimized to cast Hough votes with small variance and since their efficiency permits dense sampling of local image patches or video cuboids during detection. The efficacy of Hough forests for a set of computer vision tasks is validated through experiments on a large set of publicly available benchmark data sets and comparisons with the state-of-the-art.

Color aids late but not early stages of rapid natural scene recognition.

Color has an unresolved role in natural scene recognition. Whereas rapid serial visual presentation paradigms typically find no advantage for colored over grayscale scenes, color seems to play a decisive role for recognition memory. The distinction between detection and memorization has not been addressed directly in one paradigm. Here we asked ten observers to detect animals in 2-s 20 Hz sequences. Each sequence consisted of two 1-s segments, one of grayscale images and one of colored; each segment contained one or no target, totaling zero, one, or two targets per sequence. In one-target sequences, hit rates were virtually the same for targets appearing in the first or second segment, as well as for grayscale and colored targets, though observers were more confident about detecting colored targets. In two-target sequences, observers preferentially reported the second of two identical targets, in comparison to categorically related (same-species animals) or unrelated (different-species animals) targets. Observers also showed a strong preference for reporting colored targets, though only when targets were of different species. Our findings suggest that color has little effect on detection, but is used in later stages of processing. We may speculate that color ensures preferential access to or retrieval from memory when distinct items must be rapidly remembered.