Later prehistoric vegetation dynamics and Bronze Age agriculture at Hobbister, Orkney, Scotland

The Bronze Age in Britain was a time of major social and cultural changes, reflected in the division of the landscape into field systems and the establishment of new belief systems and ritual practices. Several hypotheses have been advanced to explain these changes, and assessment of many of them is dependent on the availability of detailed palaeoenvironmental data from the sites concerned. This paper explores the development of a later prehistoric landscape in Orkney, where a Bronze Age field system and an apparently ritually-deposited late Bronze Age axe head are located in an area of deep blanket peat from which high-resolution palaeoenvironmental sequences have been recovered. There is no indication that the field system was constructed to facilitate agricultural intensification, and it more likely reflects a cultural response to social fragmentation associated with a more dispersed settlement pattern. There is evidence for wetter conditions during the later Bronze Age, and the apparent votive deposit may reflect the efforts of the local population to maintain community integrity during a time of perceptible environmental change leading to loss of farmland. The study emphasises the advantages of close integration of palaeoenvironmental and archaeological data for interpretation of prehistoric human activity. The palaeoenvironmental data also provide further evidence for the complexity of prehistoric woodland communities in Orkney, hinting at greater diversity than is often assumed. Additionally, differing dates for woodland decline in the two sequences highlight the dangers of over-extrapolation from trends observed in a single pollen profile, even at a very local scale.

Classification the geographical origin of corn distillers dried grains with solubles by near infrared reflectance spectroscopy combined with chemometrics: A feasibility study

In this study, 137 corn distillers dried grains with solubles (DDGS) samples from a range of different geographical origins (Jilin Province of China, Heilongjiang Province of China, USA and Europe) were collected and analysed. Different near infrared spectrometers combined with different chemometric packages were used in two independent laboratories to investigate the feasibility of classifying geographical origin of DDGS. Base on the same dataset, one laboratory developed a partial least square discriminant analysis model and another laboratory developed an orthogonal partial least square discriminant analysis model. Results showed that both models could perfectly classify DDGS samples from different geographical origins. These promising results encourage the development of larger scale efforts to produce datasets which can be used to differentiate the geographical origin of DDGS and such efforts are required to provide higher level food security measures on a global scale.

The latest LGM culmination of the Garda Glacier (Italian Alps) and the onset of glacial termination. Age of glacial collapse and vegetation chronosequence

In the deglacial sequence of the largest end moraine system of the Italian Alps, we focused on the latest culmination of the Last Glacial Maximum, before a sudden downwasting of the piedmontane lobe occupying the modern lake basin. We obtained a robust chronology for this culmination and for the subsequent deglacial history by cross-radiocarbon dating of a proximal fluvioglacial plain and of a deglacial continuous lake sedimentation. We used reworked dinocysts to locate sources of glacial abrasion and to mark the input of glacial meltwater until depletion. The palynological record from postglacial lake sediments provided the first vegetation chronosequence directly reacting to the early Lateglacial withdrawal so far documented in the Alps.

Glacier collapse occurred soon after 17.46 +/- 0.2 ka cal BP, which is, the Manerba advance culmination. Basin deglaciation of several overdeepened foreland piedmont lakes on southern and northern sides of the Alps appears to be synchronous at millennial scale and near-synchronous with large-scale glacial retreat at global scale. The pioneering succession shows a first afforestation step at a median modeled age of 64 years after deglaciation, while rapid tree growth lagged 7 centuries. Between 16.4 +/- 0.16 and 15.5 +/- 0.16 ka cal BP, a regressive phase interrupted forest growth marking a Lateglacial phase of continental-dry climate predating GI-1. This event, spanning the most advanced phases of North-Atlantic H1, is consistently radiocarbon-framed at three deglacial lake records so far investigated on the Italian side of the Alps. Relationships with the Gschnitz stadial from the Alpine record of Lateglacial advances are discussed

Root Cause Analysis by a Combined Sparse Classification and Monte Carlo Approach

Classification methods with embedded feature selection capability are very appealing for the analysis of complex processes since they allow the analysis of root causes even when the number of input variables is high. In this work, we investigate the performance of three techniques for classification within a Monte Carlo strategy with the aim of root cause analysis. We consider the naive bayes classifier and the logistic regression model with two different implementations for controlling model complexity, namely, a LASSO-like implementation with a L1 norm regularization and a fully Bayesian implementation of the logistic model, the so called relevance vector machine. Several challenges can arise when estimating such models mainly linked to the characteristics of the data: a large number of input variables, high correlation among subsets of variables, the situation where the number of variables is higher than the number of available data points and the case of unbalanced datasets. Using an ecological and a semiconductor manufacturing dataset, we show advantages and drawbacks of each method, highlighting the superior performance in term of classification accuracy for the relevance vector machine with respect to the other classifiers. Moreover, we show how the combination of the proposed techniques and the Monte Carlo approach can be used to get more robust insights into the problem under analysis when faced with challenging modelling conditions.

Does chemical composition of individual Scots pine trees determine the biodiversity of their associated ground vegetation?

Despite plant secondary metabolites being major determinants of species interactions and ecosystem processes, their role in the maintenance of biodiversity has received little attention. In order to investigate the relationship between chemical and biological diversity in a natural ecosystem, we considered the impact of chemical diversity in individual Scots pine trees (Pinus sylvestris) on species richness of associated ground vegetation. Scots pine trees show substantial genetically determined constitutive variation between individuals in concentrations of a group of secondary metabolites, the monoterpenes. When the monoterpenes of particular trees were assessed individually, there was no relationship with species richness of associated ground flora. However, the chemical diversity of monoterpenes of individual trees was significantly positively associated with the species richness of the ground vegetation beneath each tree, mainly the result of an effect among the non-woody vascular plants. This correlation suggests that the chemical diversity of the ecosystem dominant species has an important role in shaping the biodiversity of the associated plant community. The extent and significance of this effect, and its underlying processes require further investigation.

An ultra-short-term wind power prediction method using “offline classification and optimization, online model matching” based on time series features

The applicability of ultra-short-term wind power prediction (USTWPP) models is reviewed. The USTWPP method proposed extracts featrues from historical data of wind power time series (WPTS), and classifies every short WPTS into one of several different subsets well defined by stationary patterns. All the WPTS that cannot match any one of the stationary patterns are sorted into the subset of nonstationary pattern. Every above WPTS subset needs a USTWPP model specially optimized for it offline. For on-line application, the pattern of the last short WPTS is recognized, then the corresponding prediction model is called for USTWPP. The validity of the proposed method is verified by simulations.

Holocene vegetation change in relation to fire and volcanic events in Jilin, Northeastern China

Volatiles erupted from large-scale explosive volcanic activities have a significant impact on climate and environmental changes. As an important ecological factor, the occurrence of fire is affected by vegetation cover, and fire can feed back into both vegetation and climatic change. The causes of fire events are diverse; and can include volcanic eruptions. The amount of charcoal in sediment sequences is related to the frequency and intensity of fire, and hence under good preservation conditions fire history can be reconstructed from fossil charcoal abundance. Until now, little research on the role of fire has been carried out in northeastern China. In this study, through research on charcoal and tephra shards from Gushantun and Hanlongwan, Holocene vegetation change in relation to fire and volcanic events in Jilin, Northeastern China, was investigated. Where tephra shards are present in Gushantun it is associated with low level of both conifers and broadleaved trees, and is also associated with a pronounced charcoal peak. This suggests forest cover was greatly reduced from a fire caused by an eruption of the Tianchi volcano. We also detected one tephra layer in Hanlongwan, which also has the almost same depth with low level forest pollen values and one charcoal peak. This was caused probably by an eruption of the Jinlongdingzi volcano.

Abstract 1905: Defining a therapeutic classification of breast cancer by actionable targets

Breast cancer remains a frequent cause of female cancer death despite the great strides in elucidation of biological subtypes and their reported clinical and prognostic significance. We have defined a general cohort of breast cancers in terms of putative actionable targets, involving growth and proliferative factors, the cell cycle, and apoptotic pathways, both as single biomarkers across a general cohort and within intrinsic molecular subtypes.

We identified 293 patients treated with adjuvant chemotherapy. Additional hormonal therapy and trastuzumab was administered depending on hormonal and HER2 status respectively. We performed immunohistochemistry for ER, PR, HER2, MM1, CK5/6, p53, TOP2A, EGFR, IGF1R, PTEN, p-mTOR and e-cadherin. The cohort was classified into luminal (62%) and non-luminal (38%) tumors as well as luminal A (27%), luminal B HER2 negative (22%) and positive (12%), HER2 enriched (14%) and triple negative (25%). Patients with luminal tumors and co-overexpression of TOP2A or IGF1R loss displayed worse overall survival (p=0.0251 and p=0.0008 respectively). Non-luminal tumors had much greater heterogeneous expression profiles with no individual markers of prognostic significance. Non-luminal tumors were characterised by EGFR and TOP2A overexpression, IGF1R, PTEN and p-mTOR negativity and extreme p53 expression.

Our results indicate that only a minority of intrinsic subtype tumors purely express single novel actionable targets. This lack of pure biomarker expression is particular prevalent in the triple negative subgroup and may allude to the mechanism of targeted therapy inaction and myriad disappointing trial results. Utilising a combinatorial biomarker approach may enhance studies of targeted therapies providing additional information during design and patient selection while also helping decipher negative trial results.

A New Android Malware Detection Method Using Bayesian Classification

Mobile malware has been growing in scale and complexity as smartphone usage continues to rise. Android has surpassed other mobile platforms as the most popular whilst also witnessing a dramatic increase in malware targeting the platform. A worrying trend that is emerging is the increasing sophistication of Android malware to evade detection by traditional signature-based scanners. As such, Android app marketplaces remain at risk of hosting malicious apps that could evade detection before being downloaded by unsuspecting users. Hence, in this paper we present an effective approach to alleviate this problem based on Bayesian classification models obtained from static code analysis. The models are built from a collection of code and app characteristics that provide indicators of potential malicious activities. The models are evaluated with real malware samples in the wild and results of experiments are presented to demonstrate the effectiveness of the proposed approach.