New developments in combating infection from biofilm forming bacteria of orthopedic implants

Orthopedic device related infections (ODRI’s) represent a difficult to treat situation owing to their biofilm based nature. Biofilm infections once established are difficult to eradicate even with an aggressive treatment regimen due to their recalcitrance towards antibiotics and immune attack. The definitive treatment to eradicate the infections once a biofilm has established is surgical excision of the implant and thorough local debridement, but this carries a significant socioeconomic cost, the outcomes for the patient are often poor, and there is a significant risk of recurrence. The aim of the study was to provide a comprehensive insight into the newer anti-biofilm interventions (non-antibiotic approaches) and a better understanding of their mechanism of action essential for improved management of orthopedic implant infections

A prospective study of comparison of scoring systems in trauma patients

Background: Trauma is a neglected area of the society. It is a health problem that is responsible for mortality and disability, predominantly among the young generation. Thereupon, the risk stratification of such patients become essential to avoid the mortality, for which various scoring systems are employed.Methods: A prospective observational study was conducted among the 300 polytrauma patients who presented in a tertiary care institute over a span of one and half year (March 2018 to December 2019). The severity of injuries of each patient was assessed using various scoring systems (GCS, RTS, AIS, ISS, NISS), and accordingly the outcome (mortality and hospital stay) was recorded.Results: Of the total 300 cases of polytrauma, the young men are most commonly afflicted with road traffic injuries as the leading cause. Most patients presented after a latent period of 2-8 hours since injury with predominantly accidental injuries. Total 21% mortality was observed in this study of which 5% patients succumbed early (<24 hours) despite all possible resuscitative efforts. Mortality was associated with lower GCS and RTS scores but higher ISS and NISS scores.Conclusions: All patients should have their GCS and RTS scores computed on admission along with the primary survey as they are good predictors of outcome and can predict salvageable patients from the non-salvageable ones. Both anatomical scores ISS and NISS can significantly predict the need for emergency life-saving surgery within 24 hours of admission

End-to-end Deep Prototype and Exemplar Models for Predicting Human Behavior

Traditional models of category learning in psychology focus on representation at the category level as opposed to the stimulus level, even though the two are likely to interact. The stimulus representations employed in such models are either hand-designed by the experimenter, inferred circuitously from human judgments, or borrowed from pretrained deep neural networks that are themselves competing models of category learning. In this work, we extend classic prototype and exemplar models to learn both stimulus and category representations jointly from raw input. This new class of models can be parameterized by deep neural networks (DNN) and trained end-to-end. Following their namesakes, we refer to them as Deep Prototype Models, Deep Exemplar Models, and Deep Gaussian Mixture Models. Compared to typical DNNs, we find that their cognitively inspired counterparts both provide better intrinsic fit to human behavior and improve ground-truth classification.Comment: 7 pages, 4 figures, 2 tables. Accepted as a paper to the 42nd Annual Meeting of the Cognitive Science Society (CogSci 2020

Impact of comorbidity on patients with COVID-19 in India: A nationwide analysis

BackgroundThe emergence of coronavirus disease (COVID-19) as a global pandemic has resulted in the loss of many lives and a significant decline in global economic losses. Thus, for a large country like India, there is a need to comprehend the dynamics of COVID-19 in a clustered way.ObjectiveTo evaluate the clinical characteristics of patients with COVID-19 according to age, gender, and preexisting comorbidity. Patients with COVID-19 were categorized according to comorbidity, and the data over a 2-year period (1 January 2020 to 31 January 2022) were considered to analyze the impact of comorbidity on severe COVID-19 outcomes.MethodsFor different age/gender groups, the distribution of COVID-19 positive, hospitalized, and mortality cases was estimated. The impact of comorbidity was assessed by computing incidence rate (IR), odds ratio (OR), and proportion analysis.ResultsThe results indicated that COVID-19 caused an exponential growth in mortality. In patients over the age of 50, the mortality rate was found to be very high, ~80%. Moreover, based on the estimation of OR, it can be inferred that age and various preexisting comorbidities were found to be predictors of severe COVID-19 outcomes. The strongest risk factors for COVID-19 mortality were preexisting comorbidities like diabetes (OR: 2.39; 95% confidence interval (CI): 2.31–2.47; p &lt; 0.0001), hypertension (OR: 2.31; 95% CI: 2.23–2.39; p &lt; 0.0001), and heart disease (OR: 2.19; 95% CI: 2.08–2.30; p &lt; 0.0001). The proportion of fatal cases among patients positive for COVID-19 increased with the number of comorbidities.ConclusionThis study concluded that elderly patients with preexisting comorbidities were at an increased risk of COVID-19 mortality. Patients in the elderly age group with underlying medical conditions are recommended for preventive medical care or medical resources and vaccination against COVID-19

Selectivity of mass extinctions: Patterns, processes, and future directions

A central question in the study of mass extinction is whether these events simply intensify background extinction processes and patterns versus change the driving mechanisms and associated patterns of selectivity. Over the past two decades, aided by the development of new fossil occurrence databases, selectivity patterns associated with mass extinction have become increasingly well quantified and their differences from background patterns established. In general, differences in geographic range matter less during mass extinction than during background intervals, while differences in respiratory and circulatory anatomy that may correlate with tolerance to rapid change in oxygen availability, temperature, and pH show greater evidence of selectivity during mass extinction. The recent expansion of physiological experiments on living representatives of diverse clades and the development of simple, quantitative theories linking temperature and oxygen availability to the extent of viable habitat in the oceans have enabled the use of Earth system models to link geochemical proxy constraints on environmental change with quantitative predictions of the amount and biogeography of habitat loss. Early indications are that the interaction between physiological traits and environmental change can explain substantial proportions of observed extinction selectivity for at least some mass extinction events. A remaining challenge is quantifying the effects of primary extinction resulting from the limits of physiological tolerance versus secondary extinction resulting from the loss of taxa on which a given species depended ecologically. The calibration of physiology-based models to past extinction events will enhance their value in prediction and mitigation efforts related to the current biodiversity crisis

Controls on Microbial and Oolitic Carbonate Sedimentation and Stratigraphic Cyclicity Within a Mixed Carbonate-Siliciclastic System: Upper Cambrian Wilberns Formation, Llano Uplift, Mason County, Texas, USA

The upper Cambrian Wilberns Formation in central Texas records deposition on a low-gradient shelf within a mixed carbonate–siliciclastic tidal-flat system that changes offshore to subtidal shelf and open-marine oolitic skeletal shoals with large microbial mounds. Siliciclastic sediment is interpreted to have been delivered to the tidal flat by aeolian processes because of the narrow range in grain size and paucity of clay. Tidal influence is dominant as evidenced by reversing currents and desiccation on the tidal flat, and megaripples with reversing current indicators in offshore shoals. Intraclastic conglomerates were deposited in broad channels on the tidal flats during storm surges. Microbialite deposition is interpreted to be controlled by accommodation favouring amalgamated thin biostromes developed in the tidal flat vs. larger mounds with greater synoptic relief in the offshore, and current energy resulting in preferential elongation of offshore mounds in a NE–SW orientation. Intertidal mounds and biostromes grew in the presence of significant siliciclastic flux and trapped it within their structure, whereas offshore large buildups incorporated little siliciclastic component. Oolite and skeletal grainstone formed in tide agitated shoals associated with large subtidal microbial mounds. Storms extensively recycled and redistributed skeletal and oolitic sands from the offshore shoals across the shelf as thin sand sheets. Spatial mixing of siliciclastic and carbonate sediment occurred across the tidal flat and shelf. Low-frequency and intermediate-frequency stratigraphic cycles were driven by shifts in the shoreline and changes in rate of siliciclastic flux in response to relative sea-level fluctuation. Random facies stacking and the lack of metre-scale cyclicity are interpreted to reflect stratigraphic incompleteness and an episodic signal introduced by storms

Deep Prototype Models to Study Human Categorization Behavior

Inspired by the potential synergy between models of visual categorization from cognitive science and neural classifiers from computer vision, this project proposes a probabilistic framework that integrates the mechanism of cognitively-inspired generative classification into an otherwise discriminative deep learning system. Specifically, we integrate a Gaussian classifier into a Convolutional Neural Network, jointly learning embeddings of images and distributions over embeddings. We dub these models Deep Prototype Models (DPMs), and find that they boost validation accuracy and reduce generalization loss under modest distributional shift. Additionally, we examine their similarity to human categorization behavior across two dimensions – the uncertainty or relative activation across categories for stimuli, and the organization of stimuli within categories. For the former, we employ an existing dataset of full-label distributions of human categorizations, and find that DPMs provide a better fit to human uncertainty behavior. In the latter case, we explore typicality as a method for inferring the structure of categories from human behavior, and collect a novel large-scale dataset of over 350,000 typicality judgements. We find a number of interesting relationships between typicality, response time, and human agreement; and find that standard CNNs correlate better to human typicality judgements than DPMs. The DPMs proposed and typicality dataset collected represent an initial effort to bridge the gap between cognitive modelling and modern deep learning

Deep Prototype Models to Study Human Categorization Behavior

Inspired by the potential synergy between models of visual categorization from cognitive science and neural classifiers from computer vision, this project proposes a probabilistic framework that integrates the mechanism of cognitively-inspired generative classification into an otherwise discriminative deep learning system. Specifically, we integrate a Gaussian classifier into a Convolutional Neural Network, jointly learning embeddings of images and distributions over embeddings. We dub these models Deep Prototype Models (DPMs), and find that they boost validation accuracy and reduce generalization loss under modest distributional shift. Additionally, we examine their similarity to human categorization behavior across two dimensions – the uncertainty or relative activation across categories for stimuli, and the organization of stimuli within categories. For the former, we employ an existing dataset of full-label distributions of human categorizations, and find that DPMs provide a better fit to human uncertainty behavior. In the latter case, we explore typicality as a method for inferring the structure of categories from human behavior, and collect a novel large-scale dataset of over 350,000 typicality judgements. We find a number of interesting relationships between typicality, response time, and human agreement; and find that standard CNNs correlate better to human typicality judgements than DPMs. The DPMs proposed and typicality dataset collected represent an initial effort to bridge the gap between cognitive modelling and modern deep learning

Deep Prototype Models to Study Human Categorization Behavior

Inspired by the potential synergy between models of visual categorization from cognitive science and neural classifiers from computer vision, this project proposes a probabilistic framework that integrates the mechanism of cognitively-inspired generative classification into an otherwise discriminative deep learning system. Specifically, we integrate a Gaussian classifier into a Convolutional Neural Network, jointly learning embeddings of images and distributions over embeddings. We dub these models Deep Prototype Models (DPMs), and find that they boost validation accuracy and reduce generalization loss under modest distributional shift. Additionally, we examine their similarity to human categorization behavior across two dimensions – the uncertainty or relative activation across categories for stimuli, and the organization of stimuli within categories. For the former, we employ an existing dataset of full-label distributions of human categorizations, and find that DPMs provide a better fit to human uncertainty behavior. In the latter case, we explore typicality as a method for inferring the structure of categories from human behavior, and collect a novel large-scale dataset of over 350,000 typicality judgements. We find a number of interesting relationships between typicality, response time, and human agreement; and find that standard CNNs correlate better to human typicality judgements than DPMs. The DPMs proposed and typicality dataset collected represent an initial effort to bridge the gap between cognitive modelling and modern deep learning