HAWKS: Evolving Challenging Benchmark Sets for Cluster Analysis

Comprehensive benchmarking of clustering algorithms is rendered difficult by two key factors: (i) the elusiveness of a unique mathematical definition of this unsupervised learning approach and (ii) dependencies between the generating models or clustering criteria adopted by some clustering algorithms and indices for internal cluster validation. Consequently, there is no consensus regarding the best practice for rigorous benchmarking, and whether this is possible at all outside the context of a given application. Here, we argue that synthetic datasets must continue to play an important role in the evaluation of clustering algorithms, but that this necessitates constructing benchmarks that appropriately cover the diverse set of properties that impact clustering algorithm performance. Through our framework, HAWKS, we demonstrate the important role evolutionary algorithms play to support flexible generation of such benchmarks, allowing simple modification and extension. We illustrate two possible uses of our framework: (i) the evolution of benchmark data consistent with a set of hand-derived properties and (ii) the generation of datasets that tease out performance differences between a given pair of algorithms. Our work has implications for the design of clustering benchmarks that sufficiently challenge a broad range of algorithms, and for furthering insight into the strengths and weaknesses of specific approaches

Sensitive SERS nanotags for use with 1550 nm (retina-safe) laser excitation

Chalcogenopyrylium nanotags demonstrate an unprecedented SERS performance with a retina safe, 1550 nm laser excitation. These unique nanotags consisting of chalcogenopyrylium dyes and 100 nm gold nanoparticles produce exceptional SERS signals with picomolar detection limits obtained at this extremely red-shifted and eye-safe laser excitation

Core excitations across the neutron shell gap in ²⁰⁷Tl

The single closed-neutron-shell, one proton-hole nucleus 207Tl was populated in deep-inelastic collisions of a 208Pb beam with a 208Pb target. The yrast and near-yrast level scheme has been established up to high excitation energy, comprising an octupol

Sensitive SERS nanotags for use with a hand-held 1064 nm Raman spectrometer

This is the first report of the use of a hand-held 1064 nm Raman spectrometer combined with red shifted surface enhanced Raman scattering (SERS) nanotags to provide an unprecedented performance in the short-wave infrared (SWIR) region. A library consisting of 17 chalcogenopyrylium nanotags produce extraordinary SERS responses with femtomolar detection limits being obtained using the portable instrument. This is well beyond previous SERS detection limits at this far red shifted wavelength and opens up new options for SERS sensors in the SWIR region of the electromagnetic spectrum (between 950-1700 nm)

Concentrations and sources of Cd, Cr, Cu, Fe, Ni, Pb and Zn in soil of the Mitidja plain, Algeria

peer reviewedIn the last few decades, the Mitidja plain has undergone economic development which may have altered the concentrations of some trace metals in its soils. Therefore, this study was aimed at investigating the concentrations and sources of Cd, Cr, Cu, Fe, Ni, Pb and Zn in 180 composite topsoil samples using a combination of environmental quality indicators and multivariate statistical approaches with a geographic information system (GIS). Based on spatial distribution maps, various hot-spots have been identified. Enrichment factors (EFs) indicated that Cd, Cu, Pb and Zn were from anthropogenic sources, while Ni and Cr were controlled mainly by natural lithogenic source. Multivariate statistical analyses were in agreement, except for Cu which was classified as coming from both anthropogenic and lithogenic sources. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group

Octupole transitions in the 208Pb region

The 208Pb region is characterised by the existence of collective octupole states. Here we populated such states in 208Pb + 208Pb deep-inelastic reactions. γ-ray angular distribution measurements were used to infer the octupole character of several E3 transitions. The octupole character of the 2318 keV 17− → 14+ in 208Pb, 2485 keV 19/2 − → 13/2 + in 207Pb, 2419 keV 15/2 − → 9/2 + in 209Pb and 2465 keV 17/2 + → 11/2 − in 207Tl transitions was demonstrated for the first time. In addition, shell model calculations were performed using two different sets of two-body matrix elements. Their predictions were compared with emphasis on collective octupole states.This work is supported by the Science and Technology Facilities Council (STFC), UK, US Department of Energy, Office of Nuclear Physics, under Contract No. DEAC02-06CH11357 and DE-FG02-94ER40834, NSF grant PHY-1404442

Risk factors for human brucellosis in northern Tanzania

Little is known about the epidemiology of human brucellosis in sub-Saharan Africa. This hampers prevention and control efforts at the individual and population levels. To evaluate risk factors for brucellosis in northern Tanzania, we conducted a study of patients presenting with fever to two hospitals in Moshi, Tanzania. Serum taken at enrollment and at 4–6 week follow-up was tested by Brucella microagglutination test. Among participants with a clinically compatible illness, confirmed brucellosis cases were defined as having a ≥ 4-fold rise in agglutination titer between paired sera or a blood culture positive for Brucella spp., and probable brucellosis cases were defined as having a single reciprocal titer ≥ 160. Controls had reciprocal titers < 20 in paired sera. We collected demographic and clinical information and administered a risk factor questionnaire. Of 562 participants in the analysis, 50 (8.9%) had confirmed or probable brucellosis. Multivariable analysis showed that risk factors for brucellosis included assisting goat or sheep births (Odds ratio [OR] 5.9, 95% confidence interval [CI] 1.4, 24.6) and having contact with cattle (OR 1.2, 95% CI 1.0, 1.4). Consuming boiled or pasteurized dairy products was protective against brucellosis (OR 0.12, 95% CI 0.02, 0.93). No participants received a clinical diagnosis of brucellosis from their healthcare providers. The under-recognition of brucellosis by healthcare workers could be addressed with clinician education and better access to brucellosis diagnostic tests. Interventions focused on protecting livestock keepers, especially those who assist goat or sheep births, are needed

Risk factors for human acute leptospirosis in northern Tanzania

Introduction: Leptospirosis is a major cause of febrile illness in Africa but little is known about risk factors for human infection. We conducted a cross-sectional study to investigate risk factors for acute leptospirosis and Leptospira seropositivity among patients with fever attending referral hospitals in northern Tanzania. Methods: We enrolled patients with fever from two referral hospitals in Moshi, Tanzania, 2012–2014, and performed Leptospira microscopic agglutination testing on acute and convalescent serum. Cases of acute leptospirosis were participants with a four-fold rise in antibody titers, or a single reciprocal titer ≥800. Seropositive participants required a single titer ≥100, and controls had titers <100 in both acute and convalescent samples. We administered a questionnaire to assess risk behaviors over the preceding 30 days. We created cumulative scales of exposure to livestock urine, rodents, and surface water, and calculated odds ratios (OR) for individual behaviors and for cumulative exposure variables. Results: We identified 24 acute cases, 252 seropositive participants, and 592 controls. Rice farming (OR 14.6), cleaning cattle waste (OR 4.3), feeding cattle (OR 3.9), farm work (OR 3.3), and an increasing cattle urine exposure score (OR 1.2 per point) were associated with acute leptospirosis. Conclusions: In our population, exposure to cattle and rice farming were risk factors for acute leptospirosis. Although further data is needed, these results suggest that cattle may be an important source of human leptospirosis. Further investigation is needed to explore the potential for control of livestock Leptospira infection to reduce human disease

Rice Production in Sri Lanka

Crop Production/Industries,

Stillbirth risk prediction using machine learning for a large cohort of births from Western Australia, 1980–2015

Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric history, lack of validation, and restriction to a single classifier (logistic regression). Consequently, predictive performance remains low, and risk quantification has not been adopted into antenatal practice. The study population consisted of all births to women in Western Australia from 1980 to 2015, excluding terminations. After all exclusions there were 947,025 livebirths and 5,788 stillbirths. Predictive models for stillbirth were developed using multiple machine learning classifiers: regularised logistic regression, decision trees based on classification and regression trees, random forest, extreme gradient boosting (XGBoost), and a multilayer perceptron neural network. We applied 10-fold cross-validation using independent data not used to develop the models. Predictors included maternal socio-demographic characteristics, chronic medical conditions, obstetric complications and family history in both the current and previous pregnancy. In this cohort, 66% of stillbirths were observed for multiparous women. The best performing classifier (XGBoost) predicted 45% (95% CI: 43%, 46%) of stillbirths for all women and 45% (95% CI: 43%, 47%) of stillbirths after the inclusion of previous pregnancy history. Almost half of stillbirths could be potentially identified antenatally based on a combination of current pregnancy complications, congenital anomalies, maternal characteristics, and medical history. Greatest sensitivity is achieved with addition of current pregnancy complications. Ensemble classifiers offered marginal improvement for prediction compared to logistic regression