Does Haze Removal Help CNN-based Image Classification?

Hazy images are common in real scenarios and many dehazing methods have been developed to automatically remove the haze from images. Typically, the goal of image dehazing is to produce clearer images from which human vision can better identify the object and structural details present in the images. When the ground-truth haze-free image is available for a hazy image, quantitative evaluation of image dehazing is usually based on objective metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). However, in many applications, large-scale images are collected not for visual examination by human. Instead, they are used for many high-level vision tasks, such as automatic classification, recognition and categorization. One fundamental problem here is whether various dehazing methods can produce clearer images that can help improve the performance of the high-level tasks. In this paper, we empirically study this problem in the important task of image classification by using both synthetic and real hazy image datasets. From the experimental results, we find that the existing image-dehazing methods cannot improve much the image-classification performance and sometimes even reduce the image-classification performance

Preparation and biomedical application of a non-polymer coated superparamagnetic nanoparticle

We report the preparation of a non-polymer coated superparamagnetic nanoparticle that is stable and biocompatible both in vitro and in vivo. The non-polymer, betaine, is a natural methylating agent in mammalian liver with active surface property. Upon systemic administration, the nanoparticle has preferential biodistribution in mammalian liver and exhibits good reduction of relaxivity time and negative enhancement for the detection of hepatoma nodules in rats using MRI. Our data demonstrate that the non-polymer coated superparamagnetic nanoparticle should have potential applications in biomedicine

Chinese herbal compound for multidrug-resistant or extensively drug-resistant bacterial pneumonia: a meta-analysis and trial sequential analysis with association rule mining to identify core herb combinations

Purpose: Antibiotic-resistant bacterial pneumonia poses a significant therapeutic challenge. In China, Chinese herbal compound (CHC) is commonly used to treat bacterial pneumonia. We aimed to evaluate the efficacy and safety of CHC and identify core herb combinations for the treatment of multidrug-resistant or extensively drug-resistant bacterial pneumonia.Methods: Stata 16 and TSA 0.9.5.10 beta software were used for meta-analysis and trial sequential analysis (TSA), respectively. Exploring the sources of heterogeneity through meta-regression and subgroup analysis.Results: Thirty-eight studies involving 2890 patients were included in the analyses. Meta-analysis indicated that CHC combined with antibiotics improved the response rate (RR = 1.24; 95% CI: 1.19–1.28; p < 0.0001) and microbiological eradication (RR = 1.41; 95% CI: 1.27–1.57; p < 0.0001), lowered the white blood cell count (MD = −2.09; 95% CI: −2.65 to −1.53; p < 0.0001), procalcitonin levels (MD = −0.49; 95% CI: −0.59 to −0.40; p < 0.0001), C-reactive protein levels (MD = −11.80; 95% CI: −15.22 to −8.39; p < 0.0001), Clinical Pulmonary Infection Scores (CPIS) (MD = −1.97; 95% CI: −2.68 to −1.26; p < 0.0001), and Acute Physiology and Chronic Health Evaluation (APACHE)-II score (MD = −4.08; 95% CI: −5.16 to −3.00; p < 0.0001), shortened the length of hospitalization (MD = −4.79; 95% CI: −6.18 to −3.40; p < 0.0001), and reduced the number of adverse events. TSA indicated that the response rate and microbiological eradication results were robust. Moreover, Scutellaria baicalensis Georgi, Fritillaria thunbergii Miq, Lonicera japonica Thunb, and Glycyrrhiza uralensis Fisch were identified as core CHC prescription herbs.Conclusion: Compared with antibiotic treatment, CHC + antibiotic treatment was superior in improving response rate, microbiological eradication, inflammatory response, CPIS, and APACHE-II score and shortening the length of hospitalization. Association rule analysis identified four core herbs as promising candidates for treating antibiotic-resistant bacterial pneumonia. However, large-scale clinical studies are still required.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/, identifier CRD42023410587

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

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

Multidifferential study of identified charged hadron distributions in ZZ-tagged jets in proton-proton collisions at s=\sqrt{s}=13 TeV

Jet fragmentation functions are measured for the first time in proton-proton collisions for charged pions, kaons, and protons within jets recoiling against a $Z$ boson. The charged-hadron distributions are studied longitudinally and transversely to the jet direction for jets with transverse momentum 20 $< p_{\textrm{T}} < 100$ GeV and in the pseudorapidity range $2.5 < \eta < 4$. The data sample was collected with the LHCb experiment at a center-of-mass energy of 13 TeV, corresponding to an integrated luminosity of 1.64 fb$^{-1}$. Triple differential distributions as a function of the hadron longitudinal momentum fraction, hadron transverse momentum, and jet transverse momentum are also measured for the first time. This helps constrain transverse-momentum-dependent fragmentation functions. Differences in the shapes and magnitudes of the measured distributions for the different hadron species provide insights into the hadronization process for jets predominantly initiated by light quarks.Comment: All figures and tables, along with machine-readable versions and any supplementary material and additional information, are available at https://cern.ch/lhcbproject/Publications/p/LHCb-PAPER-2022-013.html (LHCb public pages