WTEN: An advanced coupled tensor factorization strategy for learning from imbalanced data

© Springer International Publishing AG 2016. Learning from imbalanced and sparse data in multi-mode and high-dimensional tensor formats efficiently is a significant problem in data mining research. On one hand,Coupled Tensor Factorization (CTF) has become one of the most popular methods for joint analysis of heterogeneous sparse data generated from different sources. On the other hand,techniques such as sampling,cost-sensitive learning,etc. have been applied to many supervised learning models to handle imbalanced data. This research focuses on studying the effectiveness of combining advantages of both CTF and imbalanced data learning techniques for missing entry prediction,especially for entries with rare class labels. Importantly,we have also investigated the implication of joint analysis of the main tensor and extra information. One of our major goals is to design a robust weighting strategy for CTF to be able to not only effectively recover missing entries but also perform well when the entries are associated with imbalanced labels. Experiments on both real and synthetic datasets show that our approach outperforms existing CTF algorithms on imbalanced data

The transport and fate of microplastic fibres in the Antarctic: The role of multiple global processes

This is the final version. Available from Frontiers Media via the DOI in this record. Understanding the transport and accumulation of microplastics is useful to determine the relative risk they pose to global biodiversity. The exact contribution of microplastic sources is hard to elucidate; therefore, investigating the Antarctic Weddell Sea, an area known for its remoteness and little human presence (i.e. limited pollution sources), will help us to better understand microplastic transportation. Here, we investigate the presence of microplastics in a range of Antarctic sample media including air, seawater, and sediment. We hypothesised that multiple transportation processes including atmospheric and oceanic vectors determine the presence of microplastics in the Antarctic. Using techniques including Polarised Light Microscopy and Raman Spectrometry, we identified mostly fibres and categorised them based on their optical and chemical properties. A total of 47 individual microplastic categories (45 of which were fibres) were identified in the air, seawater, and sediment samples. The majority of categories did not overlap multiple media (42/47); however, four fibre categories were present in both air and water samples, and another fibre category was found in all three media (category 27). We suggest that the large variety of fibres identified and the overlap of fibre categories among media indicates that the pollution may result from multiple diffuse sources and transportation pathways. Additionally, our Air Mass Back Trajectory analyses demonstrates that microplastic fibres are being transported by air masses or wind, and strongly suggests that they are transported to the Antarctic from southern South America. We also propose that fibres may be transported into the Antarctic in subsurface waters, and as pollution was identified in our sediment and additional sea ice samples, we suggest that the coastal and Antarctic deep sea may be a sink for microplastic fibres. The results shown here from a remote, near-pristine system, further highlight the need for a global response to the plastic pollution crisis.Flotilla FoundationFlotilla FoundationSouth African National Research FoundationUniversity of Cape TownUniversity Research CouncilAfrican Academy of Sciences/Royal SocietyNekton Foundatio

6-Sulphated Chondroitins Have a Positive Influence on Axonal Regeneration

Chondroitin sulphate proteoglycans (CSPGs) upregulated in the glial scar inhibit axon regeneration via their sulphated glycosaminoglycans (GAGs). Chondroitin 6-sulphotransferase-1 (C6ST-1) is upregulated after injury leading to an increase in 6-sulphated GAG. In this study, we ask if this increase in 6-sulphated GAG is responsible for the increased inhibition within the glial scar, or whether it represents a partial reversion to the permissive embryonic state dominated by 6-sulphated glycosaminoglycans (GAGs). Using C6ST-1 knockout mice (KO), we studied post-injury changes in chondroitin sulphotransferase (CSST) expression and the effect of chondroitin 6-sulphates on both central and peripheral axon regeneration. After CNS injury, wild-type animals (WT) showed an increase in mRNA for C6ST-1, C6ST-2 and C4ST-1, but KO did not upregulate any CSSTs. After PNS injury, while WT upregulated C6ST-1, KO showed an upregulation of C6ST-2. We examined regeneration of nigrostriatal axons, which demonstrate mild spontaneous axon regeneration in the WT. KO showed many fewer regenerating axons and more axonal retraction than WT. However, in the PNS, repair of the median and ulnar nerves led to similar and normal levels of axon regeneration in both WT and KO. Functional tests on plasticity after the repair also showed no evidence of enhanced plasticity in the KO. Our results suggest that the upregulation of 6-sulphated GAG after injury makes the extracellular matrix more permissive for axon regeneration, and that the balance of different CSs in the microenvironment around the lesion site is an important factor in determining the outcome of nervous system injury

A new framework for sign language alphabet hand posture recognition using geometrical features through artificial neural network (part 1)

Hand pose tracking is essential in sign languages. An automatic recognition of performed hand signs facilitates a number of applications, especially for people with speech impairment to communication with normal people. This framework which is called ASLNN proposes a new hand posture recognition technique for the American sign language alphabet based on the neural network which works on the geometrical feature extraction of hands. A user’s hand is captured by a three-dimensional depth-based sensor camera; consequently, the hand is segmented according to the depth analysis features. The proposed system is called depth-based geometrical sign language recognition as named DGSLR. The DGSLR adopted in easier hand segmentation approach, which is further used in segmentation applications. The proposed geometrical feature extraction framework improves the accuracy of recognition due to unchangeable features against hand orientation compared to discrete cosine transform and moment invariant. The findings of the iterations demonstrate the combination of the extracted features resulted to improved accuracy rates. Then, an artificial neural network is used to drive desired outcomes. ASLNN is proficient to hand posture recognition and provides accuracy up to 96.78% which will be discussed on the additional paper of this authors in this journal

Multi-step time series prediction intervals using neuroevolution

Multi-step time series forecasting (TSF) is a crucial element to support tactical decisions (e.g., designing production or marketing plans several months in advance). While most TSF research addresses only single-point prediction, prediction intervals (PIs) are useful to reduce uncertainty related to important decision making variables. In this paper, we explore a large set of neural network methods for multi-step TSF and that directly optimize PIs. This includes multi-step adaptations of recently proposed PI methods, such as lower--upper bound estimation (LUBET), its ensemble extension (LUBEXT), a multi-objective evolutionary algorithm LUBE (MLUBET) and a two-phase learning multi-objective evolutionary algorithm (M2LUBET). We also explore two new ensemble variants for the evolutionary approaches based on two PI coverage--width split methods (radial slices and clustering), leading to the MLUBEXT, M2LUBEXT, MLUBEXT2 and M2LUBEXT2 methods. A robust comparison was held by considering the rolling window procedure, nine time series from several real-world domains and with different characteristics, two PI quality measures (coverage error and width) and the Wilcoxon statistic. Overall, the best results were achieved by the M2LUBET neuroevolution method, which requires a reasonable computational effort for time series with a few hundreds of observations.This article is a result of the project NORTE-01- 0247-FEDER-017497, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). We would also like to thank the anonymous reviewers for their helpful suggestionsinfo:eu-repo/semantics/publishedVersio

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Association of the MAOA promoter uVNTR polymorphism with suicide attempts in patients with major depressive disorder

<p>Abstract</p> <p>Background</p> <p>The MAOA uVNTR polymorphism has been documented to affect the MAOA gene at the transcriptional level and is associated with aggressive impulsive behaviors, depression associated with suicide (depressed suicide), and major depressive disorder (MDD). We hypothesized that the uVNTR polymorphism confers vulnerability to MDD, suicide or both. The aim of this study was to explore the association between the MAOA uVNTR and depressed suicide, using multiple controls.</p> <p>Methods</p> <p>Four different groups were included: 432 community controls, 385 patients with MDD who had not attempted suicide, 96 community subjects without mental disorders who had attempted suicide, and 109 patients with MDD who had attempted suicide. The MAOA uVNTR polymorphism was genotyped by a PCR technique. The symptom profiles and personal characteristics in each group were also compared.</p> <p>Results</p> <p>The MAOA 4R allele was more frequent in males with MDD than in male community controls (χ²= 4.182, p = 0.041). Logistic regression analysis showed that, among the depressed subjects, those younger in age, more neurotic or who smoked had an increased risk of suicide (β = -0.04, p = 0.002; β = 0.15, p = 0.017; β = 0.79, p = 0.031, respectively). Moreover, among those who had attempted suicide, those younger in age, with more paternal overprotection, and more somatic symptoms were more likely to be in the MDD group than in the community group (β = -0.11, p < 0.001; β = 0.15, p = 0.026; β = 1.11, p < 0.001). Structural equation modeling (SEM) showed that nongenetic factors, such as age, paternal overprotection, and somatic symptoms, were associated with MDD, whereas depressed suicide were associated with severity of depression, personality traits, age, marital status, and inversely associated with anxiety symptoms. However, depression did not affect suicidal behavior in the community group.</p> <p>Conclusion</p> <p>The MAOA 4R allele is associated with enhanced vulnerability to suicide in depressed males, but not in community subjects. The MAOA 4R allele affects vulnerability to suicide through the mediating factor of depressive symptoms. Further large-scale studies are needed to verify the psychopathology of the relationships among MAOA uVNTR polymorphism, symptom profiles, and suicidal behavior.</p

Wolfram Syndrome: New Mutations, Different Phenotype

BACKGROUND: Wolfram Syndrome (WS) is an autosomal recessive neurodegenerative disorder characterized by Diabetes Insipidus, Diabetes Mellitus, Optic Atrophy, and Deafness identified by the acronym "DIDMOAD". The WS gene, WFS1, encodes a transmembrane protein called Wolframin, which recent evidence suggests may serve as a novel endoplasmic reticulum calcium channel in pancreatic β-cells and neurons. WS is a rare disease, with an estimated prevalence of 1/550.000 children, with a carrier frequency of 1/354. The aim of our study was to determine the genotype of WS patients in order to establish a genotype/phenotype correlation. METHODOLOGY/PRINCIPAL FINDINGS: We clinically evaluated 9 young patients from 9 unrelated families (6 males, 3 females). Basic criteria for WS clinical diagnosis were coexistence of insulin-treated diabetes mellitus and optic atrophy occurring before 15 years of age. Genetic analysis for WFS1 was performed by direct sequencing. Molecular sequencing revealed 5 heterozygous compound and 3 homozygous mutations. All of them were located in exon 8, except one in exon 4. In one proband only an heterozygous mutation (A684V) was found. Two new variants c.2663 C>A and c.1381 A>C were detected. CONCLUSIONS/SIGNIFICANCE: Our study increases the spectrum of WFS1 mutations with two novel variants. The male patient carrying the compound mutation [c.1060_1062delTTC]+[c.2663 C>A] showed the most severe phenotype: diabetes mellitus, optic atrophy (visual acuity 5/10), deafness with deep auditory bilaterally 8000 Hz, diabetes insipidus associated to reduced volume of posterior pituitary and pons. He died in bed at the age of 13 years. The other patient carrying the compound mutation [c.409_424dup16]+[c.1381 A>C] showed a less severe phenotype (DM, OA)

Extreme Conservation Leads to Recovery of the Virunga Mountain Gorillas

As wildlife populations are declining, conservationists are under increasing pressure to measure the effectiveness of different management strategies. Conventional conservation measures such as law enforcement and community development projects are typically designed to minimize negative human influences upon a species and its ecosystem. In contrast, we define “extreme” conservation as efforts targeted to deliberately increase positive human influences, including veterinary care and close monitoring of individual animals. Here we compare the impact of both conservation approaches upon the population growth rate of the critically endangered Virunga mountain gorillas (Gorilla beringei beringei), which increased by 50% since their nadir in 1981, from approximately 250 to nearly 400 gorillas. Using demographic data from 1967–2008, we show an annual decline of 0.7%±0.059% for unhabituated gorillas that received intensive levels of conventional conservation approaches, versus an increase 4.1%±0.088% for habituated gorillas that also received extreme conservation measures. Each group of habituated gorillas is now continuously guarded by a separate team of field staff during daylight hours and receives veterinary treatment for snares, respiratory disease, and other life-threatening conditions. These results suggest that conventional conservation efforts prevented a severe decline of the overall population, but additional extreme measures were needed to achieve positive growth. Demographic stochasticity and socioecological factors had minimal impact on variability in the growth rates. Veterinary interventions could account for up to 40% of the difference in growth rates between habituated versus unhabituated gorillas, with the remaining difference likely arising from greater protection against poachers. Thus, by increasing protection and facilitating veterinary treatment, the daily monitoring of each habituated group contributed to most of the difference in growth rates. Our results argue for wider consideration of extreme measures and offer a startling view of the enormous resources that may be needed to conserve some endangered species