Large-scale Nonlinear Variable Selection via Kernel Random Features

We propose a new method for input variable selection in nonlinear regression. The method is embedded into a kernel regression machine that can model general nonlinear functions, not being a priori limited to additive models. This is the first kernel-based variable selection method applicable to large datasets. It sidesteps the typical poor scaling properties of kernel methods by mapping the inputs into a relatively low-dimensional space of random features. The algorithm discovers the variables relevant for the regression task together with learning the prediction model through learning the appropriate nonlinear random feature maps. We demonstrate the outstanding performance of our method on a set of large-scale synthetic and real datasets.Comment: Final version for proceedings of ECML/PKDD 201

Response of Methicillin-Resistant Staphylococcus aureus to Amicoumacin A

Amicoumacin A exhibits strong antimicrobial activity against methicillin-resistant Staphylococcus aureus (MRSA), hence we sought to uncover its mechanism of action. Genome-wide transcriptome analysis of S. aureus COL in response to amicoumacin A showed alteration in transcription of genes specifying several cellular processes including cell envelope turnover, cross-membrane transport, virulence, metabolism, and general stress response. The most highly induced gene was lrgA, encoding an antiholin-like product, which is induced in cells undergoing a collapse of Δψ. Consistent with the notion that LrgA modulates murein hydrolase activity, COL grown in the presence of amicoumacin A showed reduced autolysis, which was primarily caused by lower hydrolase activity. To gain further insight into the mechanism of action of amicoumacin A, a whole genome comparison of wild-type COL and amicoumacin A-resistant mutants isolated by a serial passage method was carried out. Single point mutations generating codon substitutions were uncovered in ksgA (encoding RNA dimethyltransferase), fusA (elongation factor G), dnaG (primase), lacD (tagatose 1,6-bisphosphate aldolase), and SACOL0611 (a putative glycosyl transferase). The codon substitutions in EF-G that cause amicoumacin A resistance and fusidic acid resistance reside in separate domains and do not bring about cross resistance. Taken together, these results suggest that amicoumacin A might cause perturbation of the cell membrane and lead to energy dissipation. Decreased rates of cellular metabolism including protein synthesis and DNA replication in resistant strains might allow cells to compensate for membrane dysfunction and thus increase cell survivability

Hybrid multicriteria fuzzy classification of network traffic patterns, anomalies, and protocols

© 2017, Springer-Verlag London Ltd., part of Springer Nature. Traffic classification in computer networks has very significant roles in network operation, management, and security. Examples include controlling the flow of information, allocating resources effectively, provisioning quality of service, detecting intrusions, and blocking malicious and unauthorized access. This problem has attracted a growing attention over years and a number of techniques have been proposed ranging from traditional port-based and payload inspection of TCP/IP packets to supervised, unsupervised, and semi-supervised machine learning paradigms. With the increasing complexity of network environments and support for emerging mobility services and applications, more robust and accurate techniques need to be investigated. In this paper, we propose a new supervised hybrid machine-learning approach for ubiquitous traffic classification based on multicriteria fuzzy decision trees with attribute selection. Moreover, our approach can handle well the imbalanced datasets and zero-day applications (i.e., those without previously known traffic patterns). Evaluating the proposed methodology on several benchmark real-world traffic datasets of different nature demonstrated its capability to effectively discriminate a variety of traffic patterns, anomalies, and protocols for unencrypted and encrypted traffic flows. Comparing with other methods, the performance of the proposed methodology showed remarkably better classification accuracy

Neuropsychological predictors of conversion from mild cognitive impairment to Alzheimer’s disease: a feature selection ensemble combining stability and predictability

Background Predicting progression from Mild Cognitive Impairment (MCI) to Alzheimer’s Disease (AD) is an utmost open issue in AD-related research. Neuropsychological assessment has proven to be useful in identifying MCI patients who are likely to convert to dementia. However, the large battery of neuropsychological tests (NPTs) performed in clinical practice and the limited number of training examples are challenge to machine learning when learning prognostic models. In this context, it is paramount to pursue approaches that effectively seek for reduced sets of relevant features. Subsets of NPTs from which prognostic models can be learnt should not only be good predictors, but also stable, promoting generalizable and explainable models. Methods We propose a feature selection (FS) ensemble combining stability and predictability to choose the most relevant NPTs for prognostic prediction in AD. First, we combine the outcome of multiple (filter and embedded) FS methods. Then, we use a wrapper-based approach optimizing both stability and predictability to compute the number of selected features. We use two large prospective studies (ADNI and the Portuguese Cognitive Complaints Cohort, CCC) to evaluate the approach and assess the predictive value of a large number of NPTs. Results The best subsets of features include approximately 30 and 20 (from the original 79 and 40) features, for ADNI and CCC data, respectively, yielding stability above 0.89 and 0.95, and AUC above 0.87 and 0.82. Most NPTs learnt using the proposed feature selection ensemble have been identified in the literature as strong predictors of conversion from MCI to AD. Conclusions The FS ensemble approach was able to 1) identify subsets of stable and relevant predictors from a consensus of multiple FS methods using baseline NPTs and 2) learn reliable prognostic models of conversion from MCI to AD using these subsets of features. The machine learning models learnt from these features outperformed the models trained without FS and achieved competitive results when compared to commonly used FS algorithms. Furthermore, the selected features are derived from a consensus of methods thus being more robust, while releasing users from choosing the most appropriate FS method to be used in their classification task.PTDC/EEI-SII/1937/2014; SFRH/BD/95846/2013; SFRH/BD/118872/2016info:eu-repo/semantics/publishedVersio

Long- and short-range correlations and their event-scale dependence in high-multiplicity pp collisions at 1as = 13 TeV

Two-particle angular correlations are measured in high-multiplicity proton-proton collisions at s = 13 TeV by the ALICE Collaboration. The yields of particle pairs at short-( 06\u3b7 3c 0) and long-range (1.6 < | 06\u3b7| < 1.8) in pseudorapidity are extracted on the near-side ( 06\u3c6 3c 0). They are reported as a function of transverse momentum (pT) in the range 1 < pT< 4 GeV/c. Furthermore, the event-scale dependence is studied for the first time by requiring the presence of high-pT leading particles or jets for varying pT thresholds. The results demonstrate that the long-range \u201cridge\u201d yield, possibly related to the collective behavior of the system, is present in events with high-pT processes as well. The magnitudes of the short- and long-range yields are found to grow with the event scale. The results are compared to EPOS LHC and PYTHIA 8 calculations, with and without string-shoving interactions. It is found that while both models describe the qualitative trends in the data, calculations from EPOS LHC show a better quantitative agreement for the pT dependency, while overestimating the event-scale dependency. [Figure not available: see fulltext.

Clinical and organizational factors associated with mortality during the peak of first COVID-19 wave: the global UNITE-COVID study

Purpose: To accommodate the unprecedented number of critically ill patients with pneumonia caused by coronavirus disease 2019 (COVID-19) expansion of the capacity of intensive care unit (ICU) to clinical areas not previously used for critical care was necessary. We describe the global burden of COVID-19 admissions and the clinical and organizational characteristics associated with outcomes in critically ill COVID-19 patients. Methods: Multicenter, international, point prevalence study, including adult patients with SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) and a diagnosis of COVID-19 admitted to ICU between February 15th and May 15th, 2020. Results: 4994 patients from 280 ICUs in 46 countries were included. Included ICUs increased their total capacity from 4931 to 7630 beds, deploying personnel from other areas. Overall, 1986 (39.8%) patients were admitted to surge capacity beds. Invasive ventilation at admission was present in 2325 (46.5%) patients and was required during ICU stay in 85.8% of patients. 60-day mortality was 33.9% (IQR across units: 20%–50%) and ICU mortality 32.7%. Older age, invasive mechanical ventilation, and acute kidney injury (AKI) were associated with increased mortality. These associations were also confirmed specifically in mechanically ventilated patients. Admission to surge capacity beds was not associated with mortality, even after controlling for other factors. Conclusions: ICUs responded to the increase in COVID-19 patients by increasing bed availability and staff, admitting up to 40% of patients in surge capacity beds. Although mortality in this population was high, admission to a surge capacity bed was not associated with increased mortality. Older age, invasive mechanical ventilation, and AKI were identified as the strongest predictors of mortality

Co-infection and ICU-acquired infection in COIVD-19 ICU patients: a secondary analysis of the UNITE-COVID data set

Background: The COVID-19 pandemic presented major challenges for critical care facilities worldwide. Infections which develop alongside or subsequent to viral pneumonitis are a challenge under sporadic and pandemic conditions; however, data have suggested that patterns of these differ between COVID-19 and other viral pneumonitides. This secondary analysis aimed to explore patterns of co-infection and intensive care unit-acquired infections (ICU-AI) and the relationship to use of corticosteroids in a large, international cohort of critically ill COVID-19 patients.Methods: This is a multicenter, international, observational study, including adult patients with PCR-confirmed COVID-19 diagnosis admitted to ICUs at the peak of wave one of COVID-19 (February 15th to May 15th, 2020). Data collected included investigator-assessed co-infection at ICU admission, infection acquired in ICU, infection with multi-drug resistant organisms (MDRO) and antibiotic use. Frequencies were compared by Pearson's Chi-squared and continuous variables by Mann-Whitney U test. Propensity score matching for variables associated with ICU-acquired infection was undertaken using R library MatchIT using the "full" matching method.Results: Data were available from 4994 patients. Bacterial co-infection at admission was detected in 716 patients (14%), whilst 85% of patients received antibiotics at that stage. ICU-AI developed in 2715 (54%). The most common ICU-AI was bacterial pneumonia (44% of infections), whilst 9% of patients developed fungal pneumonia; 25% of infections involved MDRO. Patients developing infections in ICU had greater antimicrobial exposure than those without such infections. Incident density (ICU-AI per 1000 ICU days) was in considerable excess of reports from pre-pandemic surveillance. Corticosteroid use was heterogenous between ICUs. In univariate analysis, 58% of patients receiving corticosteroids and 43% of those not receiving steroids developed ICU-AI. Adjusting for potential confounders in the propensity-matched cohort, 71% of patients receiving corticosteroids developed ICU-AI vs 52% of those not receiving corticosteroids. Duration of corticosteroid therapy was also associated with development of ICU-AI and infection with an MDRO.Conclusions: In patients with severe COVID-19 in the first wave, co-infection at admission to ICU was relatively rare but antibiotic use was in substantial excess to that indication. ICU-AI were common and were significantly associated with use of corticosteroids

The First Bromeligenous Species of Dendropsophus (Anura: Hylidae) from Brazil\u27s Atlantic Forest

We describe a new treefrog species of Dendropsophus collected on rocky outcrops in the Brazilian Atlantic Forest. Ecologically, the new species can be distinguished from all known congeners by having a larval phase associated with rainwater accumulated in bromeliad phytotelms instead of temporary or lentic water bodies. Phylogenetic analysis based on molecular data confirms that the new species is a member of Dendropsophus; our analysis does not assign it to any recognized species group in the genus. Morphologically, based on comparison with the 96 known congeners, the new species is diagnosed by its small size, framed dorsal color pattern, and short webbing between toes IV-V. The advertisement call is composed of a moderate-pitched two-note call (~5 kHz). The territorial call contains more notes and pulses than the advertisement call. Field observations suggest that this new bromeligenous species uses a variety of bromeliad species to breed in, and may be both territorial and exhibit male parental care