Community detection is a central problem of network data analysis. Given a network, the goal of community detection is to partition the network nodes into a small number of clusters, which could ...often help reveal interesting structures. The present paper studies community detection in Degree-Corrected Block Models (DCBMs). We first derive asymptotic minimax risks of the problem for a misclassification proportion loss under appropriate conditions. The minimax risks are shown to depend on degree-correction parameters, community sizes and average within and between community connectivities in an intuitive and interpretable way. In addition, we propose a polynomial time algorithm to adaptively perform consistent and even asymptotically optimal community detection in DCBMs.
This paper considers estimation of sparse covariance matrices and establishes the optimal rate of convergence under a range of matrix operator norm and Bregman divergence losses. A major focus is on ...the derivation of a rate sharp minimax lower bound. The problem exhibits new features that are significantly different from those that occur in the conventional nonparametric function estimation problems. Standard techniques fail to yield good results, and new tools are thus needed. We first develop a lower bound technique that is particularly well suited for treating "two-directional" problems such as estimating sparse covariance matrices. The result can be viewed as a generalization of Le Cam's method in one direction and Assouad's Lemma in another. This lower bound technique is of independent interest and can be used for other matrix estimation problems. We then establish a rate sharp minimax lower bound for estimating sparse covariance matrices under the spectral norm by applying the general lower bound technique. A thresholding estimator is shown to attain the optimal rate of convergence under the spectral norm. The results are then extended to the general matrix l w operator norms for 1 ≤ w ≤ ∞. In addition, we give a unified result on the minimax rate of convergence for sparse covariance matrix estimation under a class of Bregman divergence losses.
Many polymorphisms in dopamine genes are reported to affect cognitive, imaging, or clinical phenotypes. It is often inferred or assumed that such associations are causal, mediated by a direct effect ...of the polymorphism on the gene product itself. However, the supporting evidence is not always clear.
We conducted systematic reviews and meta-analyses to assess the empirical evidence for functional polymorphisms in genes encoding dopaminergic enzymes (COMT, DBH, DDC, MAOA, MAOB, and TH), dopamine receptors (DRD1, DRD2, DRD3, DRD4, and DRD5), the dopamine transporter (DAT), and vesicular transporters (VMAT1 and VMAT2). We defined functionality as an effect of the polymorphism on the expression, abundance, activity, or affinity of the gene product.
We screened 22,728 articles and identified 255 eligible studies. We found robust and medium to large effects for polymorphisms in 4 genes. For catechol-O-methyltransferase (COMT), the Val158Met polymorphism (rs4680) markedly affected enzyme activity, protein abundance, and protein stability. Dopamine β-hydroxylase (DBH) activity was associated with rs1611115, rs2519152, and the DBH–STR polymorphism. Monoamine oxidase A (MAOA) activity was associated with a 5′ VNTR polymorphism. Dopamine D2 receptor (DRD2) binding was influenced by the Taq1A (rs1800497) polymorphism, and rs1076560 affected DRD2 splicing.
Some widely studied dopaminergic polymorphisms clearly and substantially affect the abundance or activity of the encoded gene product. However, for other polymorphisms, evidence of such an association is negative, inconclusive, or lacking. These findings are relevant when selecting polymorphisms as “markers” of dopamine function, and for interpreting the biological plausibility of associations between these polymorphisms and aspects of brain function or dysfunction.
Spectral clustering is one of the most popular algorithms to group high- dimensional data. It is easy to implement and computationally efficient. Despite its popularity and successful applications, ...its theoretical properties have not been fully understood. In this paper, we show that spectral clustering is minimax optimal in the Gaussian mixture model with isotropic covariance matrix, when the number of clusters is fixed and the signal-to-noise ratio is large enough. Spectral gap conditions are widely assumed in the literature to analyze spectral clustering. On the contrary, these conditions are not needed to establish optimality of spectral clustering in this paper.
Recent reports have demonstrated a decline in bacterial bloodstream infections (BSIs) following adherence to central line insertion practices; however, declines have been less evident for BSIs due to ...Candida species.
We conducted active, population-based laboratory surveillance for candidemia in metropolitan Atlanta, GA and Baltimore, MD over a 5-year period. We calculated annual candidemia incidence and antifungal drug resistance rates.
We identified 3,848 candidemia cases from 2008-2013. Compared with 2008, candidemia incidence per 100,000 person-years decreased significantly by 2013 in both locations (GA: 14.1 to 9.5, p<0.001; MD: 30.9 to 14.4, p<0.001). A total of 3,255 cases (85%) had a central venous catheter (CVC) in place within 2 days before the BSI culture date. In both locations, the number of CVC-associated cases declined (GA: 473 to 294; MD: 384 to 151). Candida albicans (CA, 36%) and Candida glabrata (CG, 27%) were the most common species recovered. In both locations, the proportion of cases with fluconazole resistance decreased (GA: 8.0% to 7.1%, -10%; MD: 6.6% to 4.9%, -25%), while the proportion of cases with an isolate resistant to an echinocandin increased (GA: 1.2% to 2.9%, +147%; MD: 2.0% to 3.5%, +77%). Most (74%) echinocandin-resistant isolates were CG; 17 (<1%) isolates were resistant to both drug categories (multidrug resistant MDR, 16/17 were CG). The proportion of CG cases with MDR Candida increased from 1.8% to 2.6%.
We observed a significant decline in the incidence of candidemia over a five-year period, and increases in echinocandin-resistant and MDR Candida. Efforts to strengthen infection control practices may be preventing candidemia among high-risk patients. Further surveillance for resistant Candida is warranted.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The Gaussian graphical model, a popular paradigm for studying relationship among variables in a wide range of applications, has attracted great attention in recent years. This paper considers a ...fundamental question: When is it possible to estimate low-dimensional parameters at parametric squareroot rate in a large Gaussian graphical model? A novel regression approach is proposed to obtain asymptotically efficient estimation of each entry of a precision matrix under a sparseness condition relative to the sample size. When the precision matrix is not sufficiently sparse, or equivalently the sample size is not sufficiently large, a lower bound is established to show that it is no longer possible to achieve the parametric rate in the estimation of each entry. This lower bound result, which provides an answer to the delicate sample size question, is established with a novel construction of a subset of sparse precision matrices in an application of Le Cam's lemma. Moreover, the proposed estimator is proven to have optimal convergence rate when the parametric rate cannot be achieved, under a minimal sample requirement. The proposed estimator is applied to test the presence of an edge in the Gaussian graphical model or to recover the support of the entire model, to obtain adaptive rate-optimal estimation of the entire precision matrix as measured by the matrix lq operator norm and to make inference in latent variables in the graphical model. All of this is achieved under a sparsity condition on the precision matrix and a side condition on the range of its spectrum. This significantly relaxes the commonly imposed uniform signal strength condition on the precision matrix, irrepresentability condition on the Hessian tensor operator of the covariance matrix or the l₁ constraint on the precision matrix. Numerical results confirm our theoretical findings. The ROC curve of the proposed algorithm, Asymptotic Normal Thresholding (ANT), for support recovery significantly outperforms that of the popular GLasso algorithm.
Naturally occurring retinoids (retinol, retinal, retinoic acid, retinyl esters) are a subclass of β-apocarotenoids, defined by the length of the polyene side chain. Provitamin A carotenoids are ...metabolically converted to retinal (β-apo-15-carotenal) by the enzyme β-carotene-15,15'-dioxygenase (BCO1) that catalyzes the oxidative cleavage of the central C=C double bond. A second enzyme β-carotene-9'-10'-dioxygenase cleaves the 9',10' bond to yield β-apo-10'-carotenal and β-ionone. Chemical oxidation of the other double bonds leads to the generation of other β-apocarotenals. Like retinal, some of these β-apocarotenals are metabolically oxidized to the corresponding β-apocarotenoic acids or reduced to the β-apocarotenols, which in turn are esterified to β-apocarotenyl esters. Other metabolic fates such as 5,6-epoxidation also occur as for retinoids. Whether the same enzymes are involved remains to be understood. β-Apocarotenoids occur naturally in plant-derived foods and, therefore, are present in the diet of animals and humans. However, the levels of apocarotenoids are relatively low, compared with those of the parent carotenoids. Moreover, human studies show that there is little intestinal absorption of intact β-apocarotenoids. It is possible that they are generated in vivo under conditions of oxidative stress. The β-apocarotenoids are structural analogs of the naturally occurring retinoids. As such, they may modulate retinoid metabolism and signaling. In deed, those closest in size to the C-20 retinoids-namely, β-apo-14'-carotenoids (C-22) and β-apo-13-carotenone (C-18) bind with high affinity to purified retinoid receptors and function as retinoic acid antagonists in transactivation assays and in retinoic acid induction of target genes. The possible pathophysiologic relevance in human health remains to be determined.
Alopecia areata (AA) is a common autoimmune alopecia with heterogeneous severity and distribution. Previous studies found conflicting results about AA epidemiology.
To determine the prevalence, ...incidence, and predictors of AA, alopecia totalis, alopecia ophiasis, and alopecia universalis.
A systematic review of all published cohort and cross-sectional studies that analyzed AA and its subtypes. MEDLINE, Embase, LILACS, Scopus, Cochrane Library, and GREAT were searched. At least 2 reviewers performed study title/abstract review and data extraction. Random-effects meta-analysis was used because of significant heterogeneity (I2 = 99.97%).
Ninety-four studies met the inclusion criteria. The pooled prevalence (95% confidence interval, N) of AA overall was 2.11% (1.82-2.42, N = 302,157,365), with differences of population-based (0.75% 0.49-1.06%, N = 301,173,403) and clinic-based (3.47% 3.01-3.96, N = 983,962) studies. The prevalences of alopecia totalis, ophiasis, and universalis were 0.08% (0.04-0.13, N = 1,088,149), 0.02% (0.00-0.06, N = 1,075,203), and 0.03% (0.01-0.06, N = 1,085,444), respectively. AA prevalence (95% confidence interval) increased over time (<2000: 1.02% 0.85-1.22; 2000-2009: 1.76% 1.51-2.03; >2009: 3.22% 2.59-3.92; P < .0001) and differed by region. AA prevalence was significantly lower in adults (1.47% 1.18-1.80) than children (1.92% 1.31-2.65; P < .0001).
AA affects 2% of the global population. AA prevalence is lower in adults than children, is increasing over time, and significantly differs by region.
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