Undersampled Hyperspectral Image Reconstruction Based on Surfacelet Transform

Hyperspectral imaging is a crucial technique for military and environmental monitoring. However, limited equipment hardware resources severely affect the transmission and storage of a huge amount of data for hyperspectral images. This limitation has the potentials to be solved by compressive sensing (CS), which allows reconstructing images from undersampled measurements with low error. Sparsity and incoherence are two essential requirements for CS. In this paper, we introduce surfacelet, a directional multiresolution transform for 3D data, to sparsify the hyperspectral images. Besides, a Gram-Schmidt orthogonalization is used in CS random encoding matrix, two-dimensional and three-dimensional orthogonal CS random encoding matrixes and a patch-based CS encoding scheme are designed. The proposed surfacelet-based hyperspectral images reconstruction problem is solved by a fast iterative shrinkage-thresholding algorithm. Experiments demonstrate that reconstruction of spectral lines and spatial images is significantly improved using the proposed method than using conventional three-dimensional wavelets, and growing randomness of encoding matrix can further improve the quality of hyperspectral data. Patch-based CS encoding strategy can be used to deal with large data because data in different patches can be independently sampled

Analysis of gut microbiotal diversity in healthy young adults in Sunan County, Gansu Province, China

ObjectiveTo examine gut microbiotal diversity in the Han Chinese and Yugur populations of Sunan County, Gansu Province, living in the same environmental conditions, and to analyze possible causes of differences in diversity.MethodsWe selected 28 people, ages 18–45 years old, all of whom were third-generation pure Yugur or Han Chinese from Sunan County. Fresh fecal samples were collected, and total bacterial deoxyribonucleic acid (DNA) was extracted. We performed 16S ribosomal ribonucleic acid (16S rRNA) high-throughput sequencing (HTS) and bioinformatics to study the relationships among between gut microbiota structure, genetics, and dietary habits in Yugur and Han Chinese subjects.ResultsWe found 350 differential operational taxonomic units (OTUs) in Han Chinese and Yugur gut microbiota, proving that gut microbiota differed between the two populations. That were less abundant among Yugurs than Han Chinese were Prevotella_9 and Alloprevotella. That were more abundant among Yugurs than Han Chinese were Anaerostipes and Christensenellaceae_R-7_group. And they were significantly associated with a high-calorie diet In addition. we found differences in predicted gut microbiota structural functions (The main functions were metabolic and genetic information) between the two populations.ConclusionYugur subjects demonstrated differences in gut microbiotal structure from Han Chinese subjects, and this difference influenced by dietary and may be influenced by genetic influences. This finding will provide a fundamental basis for further study of the relationships among gut microbiota, dietary factors, and disease in Sunan County

Efficacy and safety of tigecycline monotherapy vs. imipenem/cilastatin in Chinese patients with complicated intra-abdominal infections: a randomized controlled trial

<p>Abstract</p> <p>Background</p> <p>Tigecycline, a first-in-class broad-spectrum glycylcycline antibiotic, has broad-spectrum in vitro activity against bacteria commonly encountered in complicated intra-abdominal infections (cIAIs), including aerobic and facultative Gram-positive and Gram-negative bacteria and anaerobic bacteria. In the current trial, tigecycline was evaluated for safety and efficacy vs. imipenem/cilastatin in hospitalized Chinese patients with cIAIs.</p> <p>Methods</p> <p>In this phase 3, multicenter, open-label study, patients were randomly assigned to receive IV tigecycline or imipenem/cilastatin for ≤2 weeks. The primary efficacy endpoints were clinical response at the test-of-cure visit (12-37 days after therapy) for the microbiologic modified intent-to-treat and microbiologically evaluable populations. Because the study was not powered to demonstrate non-inferiority between tigecycline and imipenem/cilastatin, no formal statistical analysis was performed. Two-sided 95% confidence intervals (CIs) were calculated for the response rates in each treatment group and for differences between treatment groups for descriptive purposes.</p> <p>Results</p> <p>One hundred ninety-nine patients received ≥1 dose of study drug and comprised the modified intent-to-treat population. In the microbiologically evaluable population, 86.5% (45 of 52) of tigecycline- and 97.9% (47 of 48) of imipenem/cilastatin-treated patients were cured at the test-of-cure assessment (12-37 days after therapy); in the microbiologic modified intent-to-treat population, cure rates were 81.7% (49 of 60) and 90.9% (50 of 55), respectively. The overall incidence of treatment-emergent adverse events was 80.4% for tigecycline vs. 53.9% after imipenem/cilastatin therapy (<it>P </it>< 0.001), primarily due to gastrointestinal-related events, especially nausea (21.6% vs. 3.9%; <it>P </it>< 0.001) and vomiting (12.4% vs. 2.0%; <it>P </it>= 0.005).</p> <p>Conclusions</p> <p>Clinical cure rates for tigecycline were consistent with those found in global cIAI studies. The overall safety profile was also consistent with that observed in global studies of tigecycline for treatment of cIAI, as well as that observed in analyses of Chinese patients in those studies; no novel trends were observed.</p> <p>Trial Registration</p> <p>ClinicalTrials.gov NCT00136201</p

NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image

This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole- scene hyperspectral (HS) information from a 3-channel RGB image. As in the previous challenge, two tracks were provided: (i) a "Clean" track where HS images are estimated from noise-free RGBs, the RGB images are themselves calculated numerically using the ground-truth HS images and supplied spectral sensitivity functions (ii) a "Real World" track, simulating capture by an uncalibrated and unknown camera, where the HS images are recovered from noisy JPEG-compressed RGB images. A new, larger-than-ever, natural hyperspectral image data set is presented, containing a total of 510 HS images. The Clean and Real World tracks had 103 and 78 registered participants respectively, with 14 teams competing in the final testing phase. A description of the proposed methods, alongside their challenge scores and an extensive evaluation of top performing methods is also provided. They gauge the state-of-the-art in spectral reconstruction from an RGB image

The Health Consequences of Social Mobility in Contemporary China

Although numerous studies have shown the importance of an individual&#8217;s socioeconomic status on his or her self-rated health status, less well-known is whether self-perceived class mobility, a measure highly correlated with an individual&#8217;s de facto social class and past mobility experiences, affects self-rated health. In this paper, we attempt to fill the gap by examining how perception of class mobility is associated with self-rated health. Using eight waves of Chinese General Social Survey data spanning the years 2005 to 2015, we conducted an analysis at the micro (individual) level and the macro (provincial) level. Analyses at both levels yielded consistent results. At the individual level, we employed ordered logistic regression and found that the perception of experiencing downward mobility was associated with significantly lower self-rated health in both rural and urban areas compared with those who consider themselves to be upwardly mobile or immobile. At the provincial level, the findings from static panel analysis further revealed that there is a positive relationship between the self-perceived class mobility and self-rated health level

A global perspective on the convergence of hypervirulence and carbapenem resistance in Klebsiella pneumoniae

Hypervirulence and carbapenem resistance have emerged as two distinct evolutionary directions for Klebsiella pneumoniae, which pose a great threat in clinical settings. Multiple virulence factors contribute to hypervirulence, and the mechanisms of carbapenem resistance are complicated. However, more and more K. pneumoniae strains have been identified in recent years integrating both phenotypes, resulting in devastating clinical outcomes. Hypervirulent and carbapenem-resistant K. pneumoniae (CR-hvKP) emerged in the early 2010s and thereafter have become increasingly prevalent. CR-hvKP are primarily prevalent in Asia, especially China, but are reported all over the world. Mechanisms for the emergence of CR-hvKP can be summarised by three patterns: (i) carbapenem-resistant K. pneumoniae (CRKP) acquiring a hypervirulent phenotype; (ii) hypervirulent K. pneumoniae (hvKP) acquiring a carbapenem-resistant phenotype; and (iii) K. pneumoniae acquiring both a carbapenem resistance and hypervirulence hybrid plasmid. With their global dissemination, continued surveillance of the emergence of CR-hvKP should be more highly prioritised

Underwater Image Restoration Based on a Parallel Convolutional Neural Network

Restoring degraded underwater images is a challenging ill-posed problem. The existing prior-based approaches have limited performance in many situations due to the reliance on handcrafted features. In this paper, we propose an effective convolutional neural network (CNN) for underwater image restoration. The proposed network consists of two paralleled branches: a transmission estimation network (T-network) and a global ambient light estimation network (A-network); in particular, the T-network employs cross-layer connection and multi-scale estimation to prevent halo artifacts and to preserve edge features. The estimates produced by these two branches are leveraged to restore the clear image according to the underwater optical imaging model. Moreover, we develop a new underwater image synthesizing method for building the training datasets, which can simulate images captured in various underwater environments. Experimental results based on synthetic and real images demonstrate that our restored underwater images exhibit more natural color correction and better visibility improvement against several state-of-the-art methods