Mummies and skeletons from the Coptic monastery complex Deir el-Bachit in Thebes-west, Egypt

Deir el-Bachit is the largest known Coptic monastery complex in Thebes-West. It dates to the Late Antiquity period between the 6th and the beginning of the 10th century AD. So far, at least 26 individuals from the site were analysed anthropologically. 22 of them were excavated directly at the necropolis, the other 4 are special burials that were found at other locations nearby.Most individuals from the necropolis are male adults. There are two categories of human remains: “mummified” and “skeletonised”. The differences are probably due to social stratification. A substance similar to bitumen was found at the mummies. At that time, resin containing oils and bitumen were normally not used any more. One of the Special burials was an approximately three years old child which was found enclosed within a wall. Another special burial was a juvenile or young adult female who was found in the vault of an abandoned granary. The female was most likely pregnant and fell victim to a violent crime. This is indicated by the bones of a six months old foetus and an intravital skull fracture. She was no contemporary from the time the monastery was cultivated but was later deposited in this area.

Estimation of sex and age of "virtual skeletons"--a feasibility study

This article presents a feasibility study with the objective of investigating the potential of multi-detector computed tomography (MDCT) to estimate the bone age and sex of deceased persons. To obtain virtual skeletons, the bodies of 22 deceased persons with known age at death were scanned by MDCT using a special protocol that consisted of high-resolution imaging of the skull, shoulder girdle (including the upper half of the humeri), the symphysis pubis and the upper halves of the femora. Bone and soft-tissue reconstructions were performed in two and three dimensions. The resulting data were investigated by three anthropologists with different professional experience. Sex was determined by investigating three-dimensional models of the skull and pelvis. As a basic orientation for the age estimation, the complex method according to Nemeskéri and co-workers was applied. The final estimation was effected using additional parameters like the state of dentition, degeneration of the spine, etc., which where chosen individually by the three observers according to their experience. The results of the study show that the estimation of sex and age is possible by the use of MDCT. Virtual skeletons present an ideal collection for anthropological studies, because they are obtained in a non-invasive way and can be investigated ad infinitum.

SNA--a toolbox for the stoichiometric analysis of metabolic networks

BACKGROUND: Despite recent algorithmic and conceptual progress, the stoichiometric network analysis of large metabolic models remains a computationally challenging problem. RESULTS: SNA is a interactive, high performance toolbox for analysing the possible steady state behaviour of metabolic networks by computing the generating and elementary vectors of their flux and conversions cones. It also supports analysing the steady states by linear programming. The toolbox is implemented mainly in Mathematica and returns numerically exact results. It is available under an open source license from: http://bioinformatics.org/project/?group_id=546. CONCLUSION: Thanks to its performance and modular design, SNA is demonstrably useful in analysing genome scale metabolic networks. Further, the integration into Mathematica provides a very flexible environment for the subsequent analysis and interpretation of the results.

Dual-energy X-ray absorptiometry for measuring total bone mineral content in the rat: study of accuracy and precision

Sequential studies of osteopenic bone disease in small animals require the availability of non-invasive, accurate and precise methods to assess bone mineral content (BMC) and bone mineral density (BMD). Dual-energy X-ray absorptiometry (DXA), which is currently used in humans for this purpose, can also be applied to small animals by means of adapted software. Precision and accuracy of DXA was evaluated in 10 rats weighing 50-265 g. The rats were anesthetized with a mixture of ketamine-xylazine administrated intraperitoneally. Each rat was scanned six times consecutively in the antero-posterior incidence after repositioning using the rat whole-body software for determination of whole-body BMC and BMD (Hologic QDR 1000, software version 5.52). Scan duration was 10-20 min depending on rat size. After the last measurement, rats were sacrificed and soft tissues were removed by dermestid beetles. Skeletons were then scanned in vitro (ultra high resolution software, version 4.47). Bones were subsequently ashed and dissolved in hydrochloric acid and total body calcium directly assayed by atomic absorption spectrophotometry (TBCa[chem]). Total body calcium was also calculated from the DXA whole-body in vivo measurement (TBCa[DXA]) and from the ultra high resolution measurement (TBCa[UH]) under the assumption that calcium accounts for 40.5% of the BMC expressed as hydroxyapatite. Precision error for whole-body BMC and BMD (mean +/- S.D.) was 1.3% and 1.5%, respectively. Simple regression analysis between TBCa[DXA] or TBCa[UH] and TBCa[chem] revealed tight correlations (n = 0.991 and 0.996, respectively), with slopes and intercepts which were significantly different from 1 and 0, respectively.(ABSTRACT TRUNCATED AT 250 WORDS)

Electron transport through catechol-functionalized molecular rods

The charge transport properties of a catechol-type dithiol-terminated oligo-phenylene-ethynylene was investigated by cyclic voltammetry (CV) and by the scanning tunnelling microscopy break junction technique (STM-BJ). Single molecule charge transport experiments demonstrated the existence of high and low conductance regions. The junction conductance is rather weakly dependent on the redox state of the bridging molecule. However, a distinct dependence of junction formation probability and of relative stretching distances of the catechol- and quinone-type molecular junctions is observed. Substitution of the central catechol ring with alkoxy-moieties and the combination with a topological analysis of possible π-electron pathways through the respective molecular skeletons lead to a working hypothesis, which could rationalize the experimentally observed conductance characteristics of the redox-active nanojunctions.

SLA-driven predictive orchestration for distributed cloud-based mobile services

We describe a system for performing SLA-driven management and orchestration of distributed infrastructures composed of services supporting mobile computing use cases. In particular, we focus on a Follow-Me Cloud scenario in which we consider mobile users accessing cloud-enable services. We combine a SLA-driven approach to infrastructure optimization, with forecast-based performance degradation preventive actions and pattern detection for supporting mobile cloud infrastructure management. We present our system's information model and architecture including the algorithmic support and the proposed scenarios for system evaluation.

Adaptive Algorithms for Personalized Diabetes Treatment

Dynamic systems, especially in real-life applications, are often determined by inter-/intra-variability, uncertainties and time-varying components. Physiological systems are probably the most representative example in which population variability, vital signal measurement noise and uncertain dynamics render their explicit representation and optimization a rather difficult task. Systems characterized by such challenges often require the use of adaptive algorithmic solutions able to perform an iterative structural and/or parametrical update process towards optimized behavior. Adaptive optimization presents the advantages of (i) individualization through learning of basic system characteristics, (ii) ability to follow time-varying dynamics and (iii) low computational cost. In this chapter, the use of online adaptive algorithms is investigated in two basic research areas related to diabetes management: (i) real-time glucose regulation and (ii) real-time prediction of hypo-/hyperglycemia. The applicability of these methods is illustrated through the design and development of an adaptive glucose control algorithm based on reinforcement learning and optimal control and an adaptive, personalized early-warning system for the recognition and alarm generation against hypo- and hyperglycemic events.

A framework for benchmarking of homogenisation algorithm performance on the global scale

The International Surface Temperature Initiative (ISTI) is striving towards substantively improving our ability to robustly understand historical land surface air temperature change at all scales. A key recently completed first step has been collating all available records into a comprehensive open access, traceable and version-controlled databank. The crucial next step is to maximise the value of the collated data through a robust international framework of benchmarking and assessment for product intercomparison and uncertainty estimation. We focus on uncertainties arising from the presence of inhomogeneities in monthly mean land surface temperature data and the varied methodological choices made by various groups in building homogeneous temperature products. The central facet of the benchmarking process is the creation of global-scale synthetic analogues to the real-world database where both the "true" series and inhomogeneities are known (a luxury the real-world data do not afford us). Hence, algorithmic strengths and weaknesses can be meaningfully quantified and conditional inferences made about the real-world climate system. Here we discuss the necessary framework for developing an international homogenisation benchmarking system on the global scale for monthly mean temperatures. The value of this framework is critically dependent upon the number of groups taking part and so we strongly advocate involvement in the benchmarking exercise from as many data analyst groups as possible to make the best use of this substantial effort.

A Possible Case of Möller-Barlow Disease in Northwestern Switzerland (7th Century)

The excavation site Reigoldswil is located at 550 m above sea level on the Jura chain hillside in north-western Switzerland. The mountains divide the Rhine valley from an agriculturally rich region. The origin of the village lies in the early medieval time. Until now the skeletons of one cemetery have been morphologically studied. Around 216 individuals were excavated from under the foundation walls of a church and in the open field. They date to the 7/8th up to the 10th century. The striking part is the high amount of subadult (0-18 years) individuals with 58% (n=126). One of these children, an approximately 1.5 year old toddler from the 7th century, was buried in a stone cist. Its bones show morphological traces like porotic lesions of the greater wings of the sphenoidale, the squama, the mandibule and the scapula as new bone formation on both femora and tibiae. These signs could be an indicator for Möller-Barlow disease (Ortner 2003, Brickley and Ives 2008, Stark in press). As scurvy is associated with an insufficient intake of vitamin C, malnutrition must be assumed. A reason might be the geographic location or/and a harsh climat with crop failure and famine the first settler had to face. Besides the morphological diagnose amino acids of the bone collagen have been analyzed (Kramis et. al.). Further examinations, such as radiocarbon dating and stable isotope ratios (C, N, O, S) to specify nutrition, are planned.

Multi-Environment Model Estimation for Motility Analysis of Caernorhabditis elegans

The nematode Caenorhabditis elegans is a well-known model organism used to investigate fundamental questions in biology. Motility assays of this small roundworm are designed to study the relationships between genes and behavior. Commonly, motility analysis is used to classify nematode movements and characterize them quantitatively. Over the past years, C. elegans' motility has been studied across a wide range of environments, including crawling on substrates, swimming in fluids, and locomoting through microfluidic substrates. However, each environment often requires customized image processing tools relying on heuristic parameter tuning. In the present study, we propose a novel Multi-Environment Model Estimation (MEME) framework for automated image segmentation that is versatile across various environments. The MEME platform is constructed around the concept of Mixture of Gaussian (MOG) models, where statistical models for both the background environment and the nematode appearance are explicitly learned and used to accurately segment a target nematode. Our method is designed to simplify the burden often imposed on users; here, only a single image which includes a nematode in its environment must be provided for model learning. In addition, our platform enables the extraction of nematode ‘skeletons’ for straightforward motility quantification. We test our algorithm on various locomotive environments and compare performances with an intensity-based thresholding method. Overall, MEME outperforms the threshold-based approach for the overwhelming majority of cases examined. Ultimately, MEME provides researchers with an attractive platform for C. elegans' segmentation and ‘skeletonizing’ across a wide range of motility assays.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon’s implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike’s preceding ISI. As we show, the EIF’s exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron’s ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational theories about UP states during slow wave sleep and present possible extensions of the model in the context of spike-frequency adaptation.