Reduced fecundity, associated with severe mental disorders1, places negative selection pressure on risk alleles and may explain, in part, why common variants have not been found that confer risk of disorders such as autism2 schizophrenia3 and mental retardation4. Thus, rare variants may account for a larger fraction of the overall genetic risk than previously assumed. In contrast to rare single nucleotide mutations, rare copy number variations (CNVs) can be detected using genome-wide single nucleotide polymorphism arrays. This has led to the identification of CNVs associated with mental retardation4,5 and autism2. In a genome-wide search for CNVs associating with schizophrenia, we used a population-based sample to identify de novo CNVs by analysing 9,878 transmissions from parents to offspring. The 66 de novo CNVs identified were tested for association in a sample of 1,433 schizophrenia cases and 33,250 controls. Three deletions at 1q21.1, 15q11.2 and 15q13.3 showing nominal association with schizophrenia in the first sample (phase I) were followed up in a second sample of 3,285 cases and 7,951 controls (phase II). All three deletions significantly associate with schizophrenia and related psychoses in the combined sample. The identification of these rare, recurrent risk variants, having occurred independently in multiple founders and being subject to negative selection, is important in itself. CNV analysis may also point the way to the identification of additional and more prevalent risk variants in genes and pathways involved in schizophrenia.
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We present several new variations on the theme of nonnegative matrix factorization (NMF). Considering factorizations of the form X = F G T , we focus on algorithms in which G is restricted to contain nonnegative entries, but allow the data matrix X to have mixed signs, thus extending the applicable range of NMF methods. We also consider algorithms in which the basis vectors of F are constrained to be convex combinations of the data points. This is used for a kernel extension of NMF. We provide algorithms for computing these new factorizations and we provide supporting theoretical analysis. We also analyze the relationships between our algorithms and clustering algorithms, and consider the implications for sparseness of solutions. Finally, we present experimental results that explore the properties of these new methods.
s u m m a r y Results: Of the 249 patients enrolled, the median age was 51 years old, and 126 (50.6%) were male. The duration from onset of symptoms to hospitalization was 4(2-7) days in symptomatic patients. Fever was occurred in 235(94.3%) patients. A total of 215 (86.3%) patients had been discharged after 16(12-20) days hospitalization. The estimated median duration of fever in all the patients with fever was 10 days (95 confidential intervals [CIs]: 8-11 days) after onset of symptoms. Patients who were transferred to intensive care units (ICU) had significantly longer duration of fever as compared to those not in ICU (31 days v.s. 9 days after onset of symptoms, respectively, P < 0.0 0 01). Radiological aggravation of initial image was observed in 163 (65.7%) patients on day 7 after onset of symptoms. 154(94.5%) of these patients showed radiological improvement on day 14. The median duration to negative reverse-transcriptase PCR tests of upper respiratory tract samples was 11 days (95 CIs: 10-12 days). Viral clearance was more likely to be delayed in patients in ICU than those not in ICU ( P < 0.0 0 01). In multivariate logistical analysis, age (Odds ratio [OR] = 1.06) and CD4 T cell count (OR = 0.55 per 100 cells/ul increase) were independently associated with ICU admission. Conclusions: The majority of COVID-19 cases are mild. The clinical progression pattern suggests that early control of viral replication and application of host-directed therapy in later stage is essential to improve the prognosis of CVOID-19.
We report the crystal structure of a new nanocluster formulated as Au28(TBBT)20, where TBBT = 4-tert-butylbenzenethiolate. It exhibits a rod-like Au20 kernel consisting of two interpenetrating cuboctahedra. The kernel is protected by four dimeric "staples" (-SR-Au-SR-Au-SR-) and eight bridging thiolates (-SR-). The unit cell of Au28(TBBT)20 single crystals contains a pair of enantiomers. The origin of chirality is primarily rooted in the rotating arrangement of the four dimeric staples as well as the arrangement of the bridging thiolates (quasi-D2 symmetry). The enantiomers were separated by chiral HPLC and characterized by circular dichroism spectroscopy.
A golden opportunity: the total structure of a Au(36)(SR)(24) nanocluster reveals an unexpected face-centered-cubic tetrahedral Au(28) kernel (magenta). The protecting layer exhibits an intriguing combination of binding modes, consisting of four regular arch-like staples and the unprecedented appearance of twelve bridging thiolates (yellow). This unique protecting network and superatom electronic shell structure confer extreme stability and robustness.
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