Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage

We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. The method exhibits a roughly linear runtime scaling over real-world networks ranging from 1000 to 100 million nodes. In a test on a social network with 1.8 billion edges, the algorithm finds the largest clique in about 20 minutes. Our method employs a branch and bound strategy with novel and aggressive pruning techniques. For instance, we use the core number of a vertex in combination with a good heuristic clique finder to efficiently remove the vast majority of the search space. In addition, we parallelize the exploration of the search tree. During the search, processes immediately communicate changes to upper and lower bounds on the size of maximum clique, which occasionally results in a super-linear speedup because vertices with large search spaces can be pruned by other processes. We apply the algorithm to two problems: to compute temporal strong components and to compress graphs.Comment: 11 page

DETEKSI GLUKOSA DALAM URIN ORANGUTAN SUMATERA (PONGO ABELII) MENGGUNAKAN STRIPTEST SEMIKUANTITATIF DI TAMAN HEWAN PEMATANG SIANTAR

Penelitian ini bertujuan mengetahui ada tidaknya glukosa dalam uron orangutan sumatera (Pongo abelii) sebagai penunjang diagnosa di Taman Hewan Pematang Siantar, Sumatera Utara. Pengoleksian urin terhadap 4 ekor orangutan sumatera di dalam kandang yang dilakukan pada pagi hari yaitu saat orangutan bangun tidur atau sebelum pemberian pakan orangutan. Pengulangan uji dilakukan 3 kali selama 10 hari pada bulan Januari 2015. Setelah pengoleksian urin kemudian dilakukan pemeriksaan dengan cara mencelupkan stripstest pada 5-10 ml urin selama 30 detik. Analisis data menggunakan metode deskriptif kualitatif dengan hasil bersifat semikuantitatif melalui pembacaan nilai glukosa pada stripstest yang memiliki skala perubahan warna yaitu : negatif, positif 1 (100 mg/dL), positif 2 (250 mg/dL), positif 3 (500 mg/dL), dan positif 4 (1000 mg/dL). Hasil penelitian menunjukkan bahwa dari 4 sampel urin orangutan sumatera tidak terdeteksi adanya glukosa dalam urin

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

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

Challenges for Food Security in Eritrea -- A Descriptive and Qualitative Analysis

Food security is about ensuring that all people at all times have both physical and economic access to the basic food they need. In a number of African countries chronic malnutrition and transitory food insecurity are pervasive. Like most African countries, Eritrea is also a victim of the problem of food insecurity. Based on this historical and recurrent food insecurity in Eritrea, an attempt is made in this paper to assess the possible causes of food insecurity in the country. Furthermore, the paper captures the available food security policy proposals of Eritrea and eventually draws conclusions and extends possible recommendations and policy remedies suited to the country

Higher-order reverse automatic differentiation with emphasis on the third-order

It is commonly assumed that calculating third order information is too expensive for most applications. But we show that the directional derivative of the Hessian ( D3f(x)⋅d ) can be calculated at a cost proportional to that of a state-of-the-art method for calculating the Hessian matrix. We do this by first presenting a simple procedure for designing high order reverse methods and applying it to deduce several methods including a reverse method that calculates D3f(x)⋅d . We have implemented this method taking into account symmetry and sparsity, and successfully calculated this derivative for functions with a million variables. These results indicate that the use of third order information in a general nonlinear solver, such as Halley–Chebyshev methods, could be a practical alternative to Newton’s method. Furthermore, high-order sensitivity information is used in methods for robust aerodynamic design. An efficient high-order differentiation tool could facilitate the use of similar methods in the design of other mechanical structures

Characterization of Anaplasma marginale subsp. centrale strains by use of msp1aS genotyping reveals a wildlife reservoir

Bovine anaplasmosis caused by the intraerythrocytic rickettsial pathogen Anaplasma marginale is endemic in South Africa. Anaplasma marginale subspecies centrale also infects cattle; however, it causes a milder form of anaplasmosis and is used as a live vaccine against A. marginale. There has been less interest in the epidemiology of A. marginale subsp. centrale, and, as a result, there are few reports detecting natural infections of this organism. When detected in cattle, it is often assumed that it is due to vaccination, and in most cases, it is reported as coinfection with A. marginale without characterization of the strain. A total of 380 blood samples from wild ruminant species and cattle collected from biobanks, national parks, and other regions of South Africa were used in duplex real-time PCR assays to simultaneously detect A. marginale and A. marginale subsp. centrale. PCR results indicated high occurrence of A. marginale subsp. centrale infections, ranging from 25 to 100% in national parks. Samples positive for A. marginale subsp. centrale were further characterized using the msp1aS gene, a homolog of msp1 of A. mar-ginale, which contains repeats at the 5= ends that are useful for genotyping strains. A total of 47 Msp1aS repeats were identified, which corresponded to 32 A. marginale subsp. centrale genotypes detected in cattle, buffalo, and wildebeest. RepeatAnalyzer was used to examine strain diversity. Our results demonstrate a diversity of A. marginale subsp. centrale strains from cattle and wildlife hosts from South Africa and indicate the utility of msp1aS as a genotypic marker for A. marginale subsp. centrale strain diversity.http://jcm.asm.org2017-04-30hb2017Veterinary Tropical Disease