Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a pseudo-multimodal object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approach. Our code and pre-trained models are made available at https://github.com/tdchaitanya/MMTODComment: Accepted at Perception Beyond Visible Spectrum Workshop, CVPR 201

Borrow from Anywhere: Pseudo Multi-modal Object Detection in Thermal Imagery

Can we improve detection in the thermal domain by borrowing features from rich domains like visual RGB? In this paper, we propose a ‘pseudo-multimodal’ object detector trained on natural image domain data to help improve the performance of object detection in thermal images. We assume access to a large-scale dataset in the visual RGB domain and relatively smaller dataset (in terms of instances) in the thermal domain, as is common today. We propose the use of well-known image-to-image translation frameworks to generate pseudo-RGB equivalents of a given thermal image and then use a multi-modal architecture for object detection in the thermal image. We show that our framework outperforms existing benchmarks without the explicit need for paired training examples from the two domains. We also show that our framework has the ability to learn with less data from thermal domain when using our approac

Outcome analysis of upper and lower limb motor functions after anterior cervical discectomy and fusion for degenerative cervical disc disease

Background: Anterior cervical discectomy and fusion (ACDF) is the most commonly performed surgical procedure for symptomatic cervical disc disease. In this study, we analysed the upper and lower limb motor functions after ACDF for disc prolapse in patients with degenerative cervical disc disease. Methods: One hundred consecutive adult patients who underwent ACDF for single or two-level cervical disc prolapse during the study period (October 2015 to October 2017) were included in the study. Results: Preoperative motor deficits in limbs were noted in 73% (73/100) of the patients. Enhance recovery of motor deficits was noted in 72.6% (53/73) of these patients and persisting motor deficits in the remaining patients (20/73- 27.4%). Five patients (5/27- 18.5%) without any preoperative motor deficits developed motor deficits after ACDF. Detailed pre and postoperative (at the time of discharge) motor power (graded by MRC grade) in all 4 limbs (Shoulder abduction/adduction/flexion/extension, elbow flexion/extension, wrist flexion/extension, hip abduction/adduction/flexion/extension, knee flexion/extension, ankle flexion/extension) was recorded. Statistically significant improvement in motor power (as recorded at the time of discharge) was noted in all the tested muscle groups after ACDF. Conclusion: Early improvement in preoperative motor deficits can be expected in the majority of the patients with cervical PIVD following ACDF

Development of benchmark system for charging control investigation

To address the emerging threat of climate change, consumers must transition to sustainable transportation. The electrification of the transport sector through e-mobility poses new challenges and uncertainties for grid operators as shown in Figure 1. Without efficient prior measures, grid development problems will inevitably arise, causing a need for costly grid expansions. To ensure a technically and economically successful transition to electric vehicles, grid operators need modern, digital tools that enable the investigation of a variety of future scenarios. At present, these tools only exist in a simulation environment, where multiple assumptions are made to obtain feasible results. This poses a high risk, as operators must design and maintain distribution grids in advance and based on clear-cut scenarios

When Should Asymptomatic Persons Be Tested for COVID-19?

On August 24, 2020, the Centers for Disease Control and Prevention (CDC) updated its website to highlight that asymptomatic individuals, even those with exposure to a COVID-19 positive contact, do not necessarily need to be tested unless they have medical conditions associated with increased risk of severe illness from COVID-19. The CDC subsequently updated its guidance on September 19, 2020 to support testing of asymptomatic persons, including close contacts of persons with documented SARS-CoV-2 infection. In this editorial, the American Society for Microbiology Clinical and Public Health Microbiology Committee's Subcommittee on Laboratory Practices comments on testing of asymptomatic individuals relative to current medical knowledge of the virus and mitigation measures. Specific points are provided concerning such testing when undertaking contact tracing and routine surveillance. Limitations to consider when testing asymptomatic persons are covered, including the need to prioritize testing of contacts of positive COVID-19 cases. We urge the CDC to consult with primary stakeholders of COVID-19 testing when making such impactful changes in testing guidance

A Semi-Physiologically Based Pharmacokinetic Model Describing the Altered Metabolism of Midazolam Due to Inflammation in Mice

This is the author's accepted manuscript.Purpose To investigate influence of inflammation on metabolism and pharmacokinetics (PK) of midazolam (MDZ) and construct a semi-physiologically based pharmacokinetic (PBPK) model to predict PK in mice with inflammatory disease. Methods Glucose-6-phosphate isomerase (GPI)-mediated inflammation was used as a preclinical model of arthritis in DBA/1 mice. CYP3A substrate MDZ was selected to study changes in metabolism and PK during the inflammation. The semi-PBPK model was constructed using mouse physiological parameters, liver microsome metabolism, and healthy animal PK data. In addition, serum cytokine, and liver-CYP (cytochrome P450 enzymes) mRNA levels were examined. Results The in vitro metabolite formation rate was suppressed in liver microsomes prepared from the GPI-treated mice as compared to the healthy mice. Further, clearance of MDZ was reduced during inflammation as compared to the healthy group. Finally, the semi-PBPK model was used to predict PK of MDZ after GPI-mediated inflammation. IL-6 and TNF-α levels were elevated and liver-cyp3a11 mRNA was reduced after GPI treatment. Conclusion The semi-PBPK model successfully predicted PK parameters of MDZ in the disease state. The model may be applied to predict PK of other drugs under disease conditions using healthy animal PK and liver microsomal data as inputs

Comprehensive Pan-Genomic Characterization of Adrenocortical Carcinoma

SummaryWe describe a comprehensive genomic characterization of adrenocortical carcinoma (ACC). Using this dataset, we expand the catalogue of known ACC driver genes to include PRKAR1A, RPL22, TERF2, CCNE1, and NF1. Genome wide DNA copy-number analysis revealed frequent occurrence of massive DNA loss followed by whole-genome doubling (WGD), which was associated with aggressive clinical course, suggesting WGD is a hallmark of disease progression. Corroborating this hypothesis were increased TERT expression, decreased telomere length, and activation of cell-cycle programs. Integrated subtype analysis identified three ACC subtypes with distinct clinical outcome and molecular alterations which could be captured by a 68-CpG probe DNA-methylation signature, proposing a strategy for clinical stratification of patients based on molecular markers

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages