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Purpose Inter-individual metabolic differences may be a reason for previously inconsistent results in diet–diabetes associations. We aimed to investigate associations between dietary intake and diabetes for metabolically homogeneous subgroups (‘metabotypes’) in a large cross-sectional study. Methods We used data of 1517 adults aged 38–87 years from the German population-based KORA FF4 study (2013/2014). Dietary intake was estimated based on the combination of a food frequency questionnaire and multiple 24-h food lists. Glucose tolerance status was classified based on an oral glucose tolerance test in participants without a previous diabetes diagnosis using American Diabetes Association criteria. Logistic regression was applied to examine the associations between dietary intake and diabetes for two distinct metabotypes, which were identified based on 16 biochemical and anthropometric parameters. Results A low intake of fruits and a high intake of total meat, processed meat and sugar-sweetened beverages (SSB) were significantly associated with diabetes in the total study population. Stratified by metabotype, associations with diabetes remained significant for intake of total meat (OR 1.67, 95% CI 1.04–2.67) and processed meat (OR 2.23, 95% CI 1.24–4.04) in the metabotypes with rather favorable metabolic characteristics, and for intake of fruits (OR 0.83, 95% CI 0.68–0.99) and SSB (OR:1.21, 95% CI 1.09–1.35) in the more unfavorable metabotype. However, only the association between SSB intake and diabetes differed significantly by metabotype (p value for interaction = 0.01). Conclusions Our findings suggest an influence of metabolic characteristics on diet–diabetes associations, which may help to explain inconsistent previous results. The causality of the observed associations needs to be confirmed in prospective and intervention studies. Electronic supplementary material The online version of this article (10.1007/s00394-019-01988-5) contains supplementary material, which is available to authorized users.

Dietary intake and requirements in nursing home (NH) residents vary individually, but concepts for individualised interventions are currently lacking. Therefore, we present an individualised modular nutritional intervention concept for NH residents with (risk of) malnutrition and describe its application and acceptability. Three enrichment modules—a sweet and a savoury protein cream (40 g, 125 kcal, 10 g protein) and a protein-energy drink (250 mL, 220 kcal, 22 g protein)—were offered to residents of two German NHs single or in combination in five levels of enrichment from level 0 (no enrichment) to 4 (all enrichment modules) to compensate for individual energy and protein deficiencies. Residents with chewing and/or swallowing difficulties received reshaped instead of usual texture-modified meals. The intervention concept was applied to 55 residents (Mean age of 84 ± 8 years, 76.0% female, 25.5% malnutrition). Despite (risk of) malnutrition, 18.2% received no enrichment (level 0). Level 1 was allocated to 10.9%, level 2 to 27.3%, level 3 to 20.0% and level 4 to 23.6% of the residents. 32.7% received reshaped texture-modified meals (RTMM). Participants consuming RTMM were more often assigned to level 4 than residents receiving usual meals (38.8% vs 16.2%). We proposed and successfully applied an individualised modular nutritional intervention concept to NH residents with (risk of) malnutrition. In the next step, the effects of the concept and its transferability to other NHs need to be demonstrated.

In an aging population, support for independent living is increasingly critical for older generations. Currently, sarcopenia is a major cause of frailty, which increases the risk of decreased mobility, falls, morbidity, and mortality and leads to dependence on third parties. Sarcopenia is preventable by consumption of adequate protein. However, many older people do not meet the recommended daily allowance of protein, thereby supporting dependence rather than independent living. Current literature indicates that a protein drink could be an appropriate product for older peoples' protein consumption. We were interested in autonomous persons whose nutritional decisions were still self-determined and thus could preventively influence their personal health. This study evaluated three commercially available protein drinks in three focus groups (n = 25) to gain insight into which aspects influence the perception of commercial protein drinks on community-dwelling older people (age, 76.8 ± 4.9). Findings from the focus groups revealed only aspects, which influenced the perception of commercial protein drinks negatively. Most importantly, the drinks did not comply with relevant aspects when buying (healthy) foods, which where naturalness, freshness, locally grown ingredients, and trust. Furthermore, the target group did not see a need for additional protein consumption. Thus, we identify important aspects to be considered for the development of a target-group-specific protein drink as well as more suitable communication to prevent distrust in order to support independent living for community-dwelling older people.

Background and aims Dyslipidemia is a major risk factor for cardiovascular disease, the leading cause of preventable death worldwide. As a result, a full understanding of the factors influencing dyslipidemia is urgently necessary. Bile acids have been recognized as regulators of lipid metabolism, and neutral sterols may influence serum lipid levels. Therefore, this analysis was conducted to better understand the relationship between bile acids, neutral sterols, and dyslipidemia. Methods We examined cross-sectional associations between selected fecal metabolites and serum lipids or markers of dyslipidemia in 1387 participants of the KORA FF4 study using linear and logistic regression models. Results We found positive associations between fecal bile acids and serum high-density lipoprotein cholesterol, low-density lipoprotein cholesterol (LDL-c), total cholesterol, triglycerides and markers of dyslipidemia, though associations were seen most consistently with triglycerides and hypertriglyceridemia. We also found positive associations between fecal cholesterol and serum LDL-c, total cholesterol, triglycerides, hypertriglyceridemia and high serum total cholesterol, though only associations with triglycerides or hypertriglyceridemia remained significant after applying the Bonferroni correction. Unexpectedly, several fecal plant sterols were positively associated with serum lipids and the associated markers of dyslipidemia. However, many of these associations were no longer statistically significant after adjusting for multiple testing. Conclusions Our results provide insight into the role that bile acids may play in the development or progression of dyslipidemia. However, further confirmation of these results is warranted. Longitudinal and experimental studies are necessary to clarify the mechanisms behind these associations and to determine causality.

Metabolic diversity leads to differences in nutrient requirements and responses to diet and medication between individuals. Using the concept of metabotyping – that is, grouping metabolically similar individuals – tailored and more efficient recommendations may be achieved. The aim of this study was to review the current literature on metabotyping and to explore its potential for better targeted dietary intervention in subjects with and without metabolic diseases. A comprehensive literature search was performed in PubMed, Google and Google Scholar to find relevant articles on metabotyping in humans including healthy individuals, population-based samples and patients with chronic metabolic diseases. A total of thirty-four research articles on human studies were identified, which established more homogeneous subgroups of individuals using statistical methods for analysing metabolic data. Differences between studies were found with respect to the samples/populations studied, the clustering variables used, the statistical methods applied and the metabotypes defined. According to the number and type of the selected clustering variables, the definitions of metabotypes differed substantially; they ranged between general fasting metabotypes, more specific fasting parameter subgroups like plasma lipoprotein or fatty acid clusters and response groups to defined meal challenges or dietary interventions. This demonstrates that the term ‘metabotype’ has a subjective usage, calling for a formalised definition. In conclusion, this literature review shows that metabotyping can help identify subgroups of individuals responding differently to defined nutritional interventions. Targeted recommendations may be given at such metabotype group levels. Future studies should develop and validate definitions of generally valid metabotypes by exploiting the increasingly available metabolomics data sets.

Circulating tumor cells (CTCs) are extremely rare in the blood, yet they account for metastasis. Notably, it was reported that CTC clusters (CTCCs) can be 50-100 times more metastatic than single CTCs, making them particularly salient as a liquid biopsy target. Yet they can split apart and are even rarer, complicating their recovery. Isolation by filtration risks loss when clusters squeeze through filter pores over time, and release of captured clusters can be difficult. Deterministic lateral displacement is continuous but requires channels not much larger than clusters, leading to clogging. Spiral inertial focusing requires large blood dilution factors (or lysis). Here, we report a microfluidic chip that continuously isolates untouched CTC clusters from large volumes of minimally (or undiluted) whole blood. An array of 100 μm-wide channels first concentrates clusters in the blood, and then a similar array transfers them into a small volume of buffer. The microscope-slide-sized PDMS device isolates individually-spiked CTC clusters from >30 mL per hour of whole blood with 80% efficiency into enumeration (fluorescence imaging), and on-chip yield approaches 100% (high speed video). Median blood cell removal (in base-10 logs) is 4.2 for leukocytes, 5.5 for red blood cells, and 4.9 for platelets, leaving less than 0.01% of leukocytes alongside CTC clusters in the product. We also demonstrate that cluster configurations are preserved. Gentle, high throughput concentration and separation of circulating tumor cell clusters from large blood volumes will enable cluster-specific diagnostics and speed the generation of patient-specific CTC cluster lines.

Recent advances in self-supervised learning with instance-level contrastive objectives facilitate unsupervised clustering. However, a standalone datum is not perceiving the context of the holistic cluster, and may undergo sub-optimal assignment. In this paper, we extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation that encodes the context of each data group. Contrastive learning with this representation then rewards the assignment of each datum. To implement this vision, we propose twin-contrast clustering (TCC). We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. On one hand, with the corresponding assignment variables being the weight, a weighted aggregation along the data points implements the set representation of a cluster. We further propose heuristic cluster augmentation equivalents to enable cluster-level contrastive learning. On the other hand, we derive the evidence lower-bound of the instance-level contrastive objective with the assignments. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps. Extensive experiments show that TCC outperforms the state-of-the-art on challenging benchmarks., Comment: NeurIPS 2021

BACKGROUND The COVID-19 outbreak, an event of global concern, has provided scientists the opportunity to use mathematical modeling to run simulations and test theories about the pandemic. OBJECTIVE The aim of this study was to propose a full-scale individual-based model of the COVID-19 outbreak in Lombardy, Italy, to test various scenarios pertaining to the pandemic and achieve novel performance metrics. METHODS The model was designed to simulate all 10 million inhabitants of Lombardy person by person via a simple agent-based approach using a commercial computer. In order to obtain performance data, a collision detection model was developed to enable cluster nodes in small cells that can be processed fully in parallel. Within this collision detection model, an epidemic model based mostly on experimental findings about COVID-19 was developed. RESULTS The model was used to explain the behavior of the COVID-19 outbreak in Lombardy. Different parameters were used to simulate various scenarios relating to social distancing and lockdown. According to the model, these simple actions were enough to control the virus. The model also explained the decline in cases in the spring and simulated a hypothetical vaccination scenario, confirming, for example, the herd immunity threshold computed in previous works. CONCLUSIONS The model made it possible to test the impact of people’s daily actions (eg, maintaining social distance) on the epidemic and to investigate interactions among agents within a social network. It also provided insight on the impact of a hypothetical vaccine.

International audience; Establishing long-term relative bounded motion between orbits in perturbed dynamics is a key challenge in astrodynamics to enable cluster flight with minimum propellant expenditure. In this work, we present an approach that allows for the design of long-term relative bounded motion considering a zonal gravitational model. Entire sets of orbits are obtained via high-order Taylor expansions of Poincarè return maps about reference fixed points. The high-order normal form algorithm is used to determine a change in expansion variables of the map into normal form space, in which the phase space behavior is circular and can be easily parameterized by action–angle coordinates. The action–angle representation of the normal form coordinates is then used to parameterize the original Poincarè return map and average it over a full phase space revolution by a path integral along the angle parameterization. As a result, the averaged nodal period and drift in the ascending node are obtained, for which the bounded motion conditions are straightforwardly imposed. Sets of highly accurate bounded orbits are obtained, extending over several thousand kilometers, and valid for decades.

Vanadium oxide clusters with a mean diameter below 10 nm are investigated by high resolution Scanning Transmission Electron Microscopy (STEM), Electron Energy Loss Spectroscopy (EELS) and UV-vis absorption spectroscopy. The clusters are synthesised by sublimation from bulk vanadium(v) oxide, in combination with a pick-up by superfluid helium droplets. The latter act as reaction chambers which enable cluster growth under fully inert and solvent-free conditions. High-resolution STEM images of deposited vanadium oxide particles allowing for the determination of lattice constants, clearly indicate a dominating presence of V

We are often interested in clustering objects that evolve over time and identifying solutions to the clustering problem for every time step. Evolutionary clustering provides insight into cluster evolution and temporal changes in cluster memberships while enabling performance superior to that achieved by independently clustering data collected at different time points. In this paper we introduce evolutionary affinity propagation (EAP), an evolutionary clustering algorithm that groups data points by exchanging messages on a factor graph. EAP promotes temporal smoothness of the solution to clustering time-evolving data by linking the nodes of the factor graph that are associated with adjacent data snapshots, and introduces consensus nodes to enable cluster tracking and identification of cluster births and deaths. Unlike existing evolutionary clustering methods that require additional processing to approximate the number of clusters or match them across time, EAP determines the number of clusters and tracks them automatically. A comparison with existing methods on simulated and experimental data demonstrates effectiveness of the proposed EAP algorithm., Comment: To be published in IEEE Transactions on Knowledge and Data Engineering

This paper is composed to specify the indicators identified by scholars used for cluster performance, efficiency or competitiveness assessment. Traditional bibliometric technique was chosen to identify the trends in cluster literature. Articles from the Web of Science database were selected according to the times they were cited which allowed systemizing the aspects of the research and making valuable observations. A system of indicators which should get the most attention in cluster research was composed identifying exceptionally important points to discuss and include in the following studies. In the national level clusters usually are not able to compare their performance for there is no system created to serve this purpose. The close analysis of the indicators of cluster performance would enable cluster evaluation tool formation for cluster performance evaluation in national level. DOI: https://doi.org/10.3846/bm.2018.06

The focus of this research is locational resources in overlapping value-adding webs of firms who at the same time may be embedded in global value chains. Through global value chains, regions are both vulnerable to exclusion and hence job loss. However, they also have opportunities to develop value-adding and job generation activities that are integrated into global value chains. The purpose of this chapter is to bring together a strategic firm perspective with cluster management activities. The rationale behind our conceptualisation of locational resources is to enable cluster managers to reduce the complexity of analysing location factors. This gives them criteria to assess the value of resources for cluster firms and shows avenues for sustaining and upgrading them, and placing them within the opportunities offered beyond the region. To achieve this, we bring together the findings of cluster research from a resource-oriented perspective. We contribute to a better understanding of how firms can exploit the resources that they have access to locally and suggest how cluster managers can shape these locational resources.

Life cycle assessment (LCA) is a fundamental tool used in eco-design. However, it can be costly and resource intensive. We take steps toward the automation of the inventory and impact analyses stages of LCA via the proposal and development of a case-based reasoning (CBR) procedure to estimate the ecological effects of a product. The case memory in CBR, which contains representations and ecological effects of known products, is organized using an extension of the function-behavior-structure (FBS) representation for products. The extension includes ecological characteristics and values. We develop similarity metrics to measure the distance between cases in the case memory and the new product. The $k$ -medoids algorithm is used to cluster the case memory, our metrics enable cluster retrieval and case selection, and multiple linear regression analysis is employed for adaptation. Using a database of 100 fans, we test the accuracy of the proposed approach on a cross flow fan not in the database. The method gives ecological effect estimates within 3% of the true values when there are similar fans in the retrieved cluster and about 7% when the retrieved cluster does not contain similar fans.

Hsp70 chaperones and their obligatory J-protein cochaperones function together in many cellular processes. Via cycles of binding to short stretches of exposed amino acids on substrate proteins, Hsp70/J-protein chaperones not only facilitate protein folding but also drive intracellular protein transport, biogenesis of cellular structures, and disassembly of protein complexes. The biogenesis of iron–sulfur (Fe–S) clusters is one of the critical cellular processes that require Hsp70/J-protein action. Fe–S clusters are ubiquitous cofactors critical for activity of proteins performing diverse functions in, for example, metabolism, RNA/DNA transactions, and environmental sensing. This biogenesis process can be divided into two sequential steps: first, the assembly of an Fe–S cluster on a conserved scaffold protein, and second, the transfer of the cluster from the scaffold to a recipient protein. The second step involves Hsp70/J-protein chaperones. Via binding to the scaffold, chaperones enable cluster transfer to recipient proteins. In eukaryotic cells mitochondria have a key role in Fe–S cluster biogenesis. In this review, we focus on methods that enabled us to dissect protein interactions critical for the function of Hsp70/J-protein chaperones in the mitochondrial process of Fe–S cluster bio-genesis in the yeast Saccharomyces cerevisiae.

Increased knowledge of clusters is essential for the understanding of sediment transport behavior and the monitoring and protection of aquatic life. A physical study using graded river gravels is conducted in a laboratory environment. Using photogrammetry and painted gravels, a cluster identification tool (CIT) is developed based on image subtraction between subsequent frames, allowing identification of any stable areas and groups of particles on the bed. This is combined with digital particle tracking (DPT) to present a novel approach for monitoring the formation and disintegration of clusters. Clusters from graded gravels are formed successfully during the experimental stage, allowing investigation into the complex dynamic behavior of cluster formation and disintegration in a simulated natural environment. Various anchor stone arrangements are used in the experiments. However, only about one fifth of the potential anchor stones on the bed surface enable cluster formation. In general, clusters classified as “typical” and “heap” are most common. Inspection of temporal cluster coverage of the test-bed surface shows that the proportion of clusters present on the surface tends to grow with time. Maximum cluster surface coverage of between 5% and 34% is observed. In addition, particles entering and departing from clusters are monitored. Most commonly, particles enter from directly upstream of the cluster, however >20% of particles approach from a direction >20 deg from the streamwise direction. Approximately 35% of all particles directly upstream of a cluster bypass the cluster.

Cluster-based routes help data transmission by acting as backbone paths in wireless ad hoc networks. Some mobile users form a cluster to obtain common multimedia services. However, node mobility does not guarantee multimedia transmission in a network with a dynamic topology. This study first presents a novel measure of cluster-based route's stability according to a prediction of connection probability at a specific time in future, helping transmit multimedia streams across many clusters in a network. The selected route is sufficiently stable to enable cluster-based mobile users to receive time-consuming multimedia streaming in future. Secondly, inter-cluster connectivity maintenance schemes, including forward and backward connectivity methods to protect nonstop multimedia streams, are developed to maintain inter-cluster links. Finally, analytical and evaluation results indicate that the cluster-based route chosen according to the connection probability scheme is more effective than routes chosen by other flat on-demand routing protocols for transmitting multimedia streaming in ad hoc networks.

We describe a comprehensive methodology for discovering service similarity (substitutability) by testing. Our solutions do not rely on the service descriptions provided by their authors and they avoid common information retrieval techniques. Our work addresses a variety of challenges raised throughout the process. These include: (1) the generation of unbiased test samples based on individual domains and their statistical properties; (2) the use of progressive sampling and Rand index convergence to minimize sample size; (3) the classification of services by their input and output structures (single values, sets of values, sequences of values, and tables), and the development of corresponding similarity measures; (4) the optimal alignment of services that have multiple inputs and outputs of the same type; (5) the management of two types of service exceptions (null values); (6) the selection of clustering methods that are most appropriate to the sets of services being clustered; and (7) the caching of tests, results, similarities, clusters and other statistical information to enable cluster evolution. Initial testing with a prototype implementation validated our methodology, yielding high accuracy at surprisingly small test sizes.

This paper presents a discrete time sliding mode control system based upon a discrete reaching rate law and applied to an autonomous 6DoF satellite cluster located at the interior Sun-Earth Lagrange point. Centralized decision processes with distributed action are used to enable cluster dispersal from a central spacecraft and reconfiguration to optimally form an interferometer array dependant upon the number of cluster members, whilst considering collision avoidance.

High performance interconnects such as InfiniBand (IB)have enabled large scale deployments of High Performance Computing (HPC) systems. High performance communication and IO middleware such as MPI and NFS over RDMA have also been redesigned to leverage the performance of these modern interconnects. With the advent of long haul InfiniBand (IB WAN), IB applications now have inter-cluster reaches. While this technology is intended to enable high performance network connectivity across WAN links,it is important to study and characterize the actual performance that the existing IB middleware achieve in these emerging IB WAN scenarios. In this paper, we study and analyze the performance characteristics of the following three HPC middleware: (i)IPoIB (IP traffic over IB), (ii) MPI and (iii) NFS over RDMA. We utilize the Obsidian IB WAN routers for inter-cluster connectivity. Our results show that many of the applications absorb smaller network delays fairly well. However, most approaches get severely impacted in high delay scenarios. Further, communication protocols need to be optimized in higher delay scenarios to improve the performance. In this paper, we propose several such optimizations to improve communication performance. Our experimental results show that techniques such as WAN-aware protocols, transferring data using large messages (message coalescing) and using parallel data streams can improve the communication performance (up to 50%) in high delay scenarios. Overall, these results demonstrate that IB WAN technologies can enable cluster-of-clusters architecture as a feasible platform for HPC systems.

We provide scheduling techniques to enable cluster tools to produce different kinds of wafers at the same time. In this model, the multi-route model, wafers can visit different processing modules in their path. Some of these processing modules may have a limit on how long they allow the wafer to stay after the process is finished. If none of the modules have this timing constraint, we provide a greedy algorithm to schedule the multi-route cluster tool. However, if some modules have a timing constraint, the scheduling problem becomes more complicated, and an exhaustive search in a very large search space must be performed to find the optimal schedule. The exhaustive search may take as long as an hour to come up with the answer, and is not practical. We provide a simulated annealing technique to find a near-optimal schedule. To evaluate each state in the simulated annealing we need to solve a linear programming system. Instead of solving that LP system with conventional methods, we provide a much faster method. This method that uses shortest path algorithm and binary search improves the performance of the simulated annealing significantly. Our experiments showed that we can find a near-optimal solution in less than 2 minutes with this method.

"Concept design and definition of framework conditions to enable Cluster establishment

Vanadium oxide clusters with a mean diameter below 10 nm are investigated by high resolution Scanning Transmission Electron Microscopy (STEM), Electron Energy Loss Spectroscopy (EELS) and UV-vis absorption spectroscopy. The clusters are synthesised by sublimation from bulk vanadium(V) oxide, in combination with a pick-up by superfluid helium droplets. The latter act as reaction chambers which enable cluster growth under fully inert and solvent-free conditions. High-resolution STEM images of deposited vanadium oxide particles allowing for the determination of lattice constants, clearly indicate a dominating presence of V2O5. This finding is further supported by UV-vis absorption spectra of nanoparticles after deposition on fused silica substrates, which indicates that the oxidation state of the material is preserved over the entire process. From the results of the UV-vis measurement, the band gap of the nanosized V2O5 could be determined to be 3.3 eV. The synthesis approach provides a route to clean V2O5 clusters as it does not involve any surfactant or solvents, which is crucial for an unbiased measurement of intrinsic catalyst properties.