Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Accuracy of the Indonesian child development pre-screening questionnaire

Background Early stimulation, detection and intervention are important for child development and are recommended in the early years of childhood for optimal results. The Indonesian child development pre-screening questionnaire, Kuesioner Pra Skrining Perkembangan (KPSP), has been widely used in public health centers (PHC) and community health centers (CHC) in the country. Howevei; the accuracy of this test has not been adequately assessed. Objective To assess the diagnostic value of KPSP as a prescreening tool for child development compared to that of the Denver II developmental screening test. Methods We conducted a KPSP diagnostic study, using the Denver II test as a gold standard for comparison. Subjects were children aged 3 to 60 months. They were recruited from one of three settings: hospital, community (child care centers) or schools (kindergarten). Results Of 210 children recruited, 182 were included in our study. The overall sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy of KPSP were 68.8%, 86.6%, 64.7%, 88.6% and 81.9%, respectively. The comparison of diagnostic value based on age groups showed better results in the 3 - 24 month group than that of the older group. Sensitivity, specificity and accuracy of the younger group vs. the older group were 92.3% vs. 60.0%, 78.6% vs. 87.5% and 85.2% vs. 81.3%, respectively. Conclusion The accuracy of KPSP compared to Denver II test was good for the 3 - 24 month age group. However, this tool should be revised for the older age group. [Paediatr lndones. 2012;52:6-9]

Dictionary Learning-Cooperated Matrix Decomposition for Hyperspectral Target Detection

Hyperspectral target detection is one of the most challenging tasks in remote sensing due to limited spectral information. Many algorithms based on matrix decomposition (MD) are proposed to promote the separation of the background and targets, but they suffer from two problems: (1) Targets are detected with the criterion of reconstruction residuals, and the imbalanced number of background and target atoms in union dictionary may lead to misclassification of targets. (2) The detection results are susceptible to the quality of the apriori target spectra, thus obtaining inferior performance because of the inevitable spectral variability. In this paper, we propose a matrix decomposition-based detector named dictionary learning-cooperated matrix decomposition (DLcMD) for hyperspectral target detection. The procedure of DLcMD is two-fold. First, the low rank and sparse matrix decomposition (LRaSMD) is exploited to separate targets from the background due to its insensitivity to the imbalanced number of background and target atoms, which can reduce the misclassification of targets. Inspired by dictionary learning, the target atoms are updated during LRaSMD to alleviate the impact of spectral variability. After that, a binary hypothesis model specifically designed for LRaSMD is proposed, and a generalized likelihood ratio test (GLRT) is performed to obtain the final detection result. Experimental results on five datasets have shown the reliability of the proposed method. Especially in the Los Angeles-II dataset, the area under the curve (AUC) value is nearly 16% higher than the average value of the other seven detectors, which reveals the superiority of DLcMD in hyperspectral target detection

Preparation and Characterization of Vancomycin-Loaded Electrospun Rana chensinensis

Collagen was extracted from abandoned Rana chensinensis skin in northeastern China via an acid enzymatic extraction method for the use of drug carriers. In this paper we demonstrated two different nanofiber-vancomycin (VCM) systems, that is, VCM blended nanofibers and core-shell nanofibers with VCM in the core. Rana chensinensis skin collagen (RCSC) and poly(L-lactide) (PLLA) (3 : 7) were blended in 1,1,1,3,3,3-hexafluoroisopropanol (HFIP) at a concentration of 10% (g/mL) to fabricate coaxial and blend nanofibers, respectively. Coaxial and blend electrospun RCSC/PLLA nanofibers containing VCM (5 wt%) were evaluated for the local and temporal delivery of VCM. The nanofiber scaffolds were characterized by environmental scanning electron microscope (ESEM), transmission electron microscopy (TEM), Fourier transform infrared spectra (FTIR), differential scanning calorimeter (DSC), water contact angle (WCA), and mechanical tests. The drug release of VCM in these two systems was compared by using UV spectrophotometer. The empirical result indicated that both the blend and coaxial RCSC/PLLA scaffolds followed sustained control release for a period of 80 hours, but the coaxial nanofiber might be a potential drug delivery material for its better mechanical properties and sustained release effect

Preparation and Characterization of Vancomycin-Loaded Electrospun Rana chensinensis Skin Collagen/Poly(L-lactide) Nanofibers for Drug Delivery

Collagen was extracted from abandoned Rana chensinensis skin in northeastern China via an acid enzymatic extraction method for the use of drug carriers. In this paper we demonstrated two different nanofiber-vancomycin (VCM) systems, that is, VCM blended nanofibers and core-shell nanofibers with VCM in the core. Rana chensinensis skin collagen (RCSC) and poly(L-lactide) (PLLA) (3 : 7) were blended in 1,1,1,3,3,3-hexafluoroisopropanol (HFIP) at a concentration of 10% (g/mL) to fabricate coaxial and blend nanofibers, respectively. Coaxial and blend electrospun RCSC/PLLA nanofibers containing VCM (5 wt%) were evaluated for the local and temporal delivery of VCM. The nanofiber scaffolds were characterized by environmental scanning electron microscope (ESEM), transmission electron microscopy (TEM), Fourier transform infrared spectra (FTIR), differential scanning calorimeter (DSC), water contact angle (WCA), and mechanical tests. The drug release of VCM in these two systems was compared by using UV spectrophotometer. The empirical result indicated that both the blend and coaxial RCSC/PLLA scaffolds followed sustained control release for a period of 80 hours, but the coaxial nanofiber might be a potential drug delivery material for its better mechanical properties and sustained release effect

Clinical Factors Associated with Progression and Prolonged Viral Shedding in COVID-19 Patients: A Multicenter Study

Coronavirus disease 2019 (COVID-19) is a global pandemic associated with a high mortality. Our study aimed to determine the clinical risk factors associated with disease progression and prolonged viral shedding in patients with COVID-19. Consecutive 564 hospitalized patients with confirmed COVID-19 between January 17, 2020 and February 28, 2020 were included in this multicenter, retrospective study. The effects of clinical factors on disease progression and prolonged viral shedding were analyzed using logistic regression and Cox regression analyses. 69 patients (12.2%) developed severe or critical pneumonia, with a higher incidence in the elderly and in individuals with underlying comorbidities, fever, dyspnea, and laboratory and imaging abnormalities at admission. Multivariate logistic regression analysis indicated that older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02-1.06), hypertension without receiving angiotensinogen converting enzyme inhibitors or angiotensin receptor blockers (ACEI/ARB) therapy (OR, 2.29; 95% CI, 1.14-4.59), and chronic obstructive pulmonary disease (OR, 7.55; 95% CI, 2.44-23.39) were independent risk factors for progression to severe or critical pneumonia. Hypertensive patients without receiving ACEI/ARB therapy showed higher lactate dehydrogenase levels and computed tomography (CT) lung scores at about 3 days after admission than those on ACEI/ARB therapy. Multivariate Cox regression analysis revealed that male gender (hazard ratio [HR], 1.22; 95% CI, 1.02-1.46), receiving lopinavir/ritonavir treatment within 7 days from illness onset (HR, 0.75; 95% CI, 0.63-0.90), and receiving systemic glucocorticoid therapy (HR, 1.79; 95% CI, 1.46-2.21) were independent factors associated with prolonged viral shedding. Our findings presented several potential clinical factors associated with developing severe or critical pneumonia and prolonged viral shedding, which may provide a rationale for clinicians in medical resource allocation and early intervention