Search

Abstract Nanophase mixtures, leveraging the complementary strengths of each component, are vital for composites to overcome limitations posed by single elemental materials. Among these, metal-elastomer nanophases are particularly important, holding various practical applications for stretchable electronics. However, the methodology and understanding of nanophase mixing metals and elastomers are limited due to difficulties in blending caused by thermodynamic incompatibility. Here, we present a controlled method using kinetics to mix metal atoms with elastomeric chains on the nanoscale. We find that the chain migration flux and metal deposition rate are key factors, allowing the formation of reticular nanophases when kinetically in-phase. Moreover, we observe spontaneous structural evolution, resulting in gyrified structures akin to the human brain. The hybridized gyrified reticular nanophases exhibit strain-invariant metallic electrical conductivity up to 156% areal strain, unparalleled durability in organic solvents and aqueous environments with pH 2–13, and high mechanical robustness, a prerequisite for environmentally resilient devices.

Abstract Objective To evaluate musculoskeletal ultrasound (MSUS) as a screening tool for rheumatoid arthritis (RA) and osteoarthritis (OA) patients in a rheumatology-screening program. Patients and methods To raise awareness for rheumatic diseases, a mobile rheumatology office was deployed in different cities of Germany (“Rheuma-Truck”). Standardized questionnaire assessment, testing for rheumatoid factor and citrullinated peptide antibodies and medical student driven MSUS of the clinically dominant hand/foot including wrist, MCP-II, -III, -V, PIP-II, -III, MTP-II and -V were offered free of charge to the population. In case of suspicious results, a rheumatologist was consulted. Results In MSUS, 192 of 560 selected volunteers (aged 18–89, mean 52.7 years; 72.9% female) had suspicious findings including synovitis or erosions primarily affecting the MTP-II (11.8%), dorsal wrist (8.9%), and MCP-II (7%). 354 of the 560 volunteers further visited a rheumatologist of whom 76 were diagnosed with RA. According to the ‘US7 Score’, a sum scores ≥ 5 was significantly predictive for RA (odds ratio (OR) 5.06; confidence interval (CI) 0.83–35.32). 313 volunteers displayed signs of OA including osteophytes, while MCP-II (36.2%), MCP-III (14.8%), and the wrist (10.5%) were mostly affected. Diagnosis of RA was favoured over OA if the wrist (OR 4.2; CI 1.28–13.95), MTP-II (OR 1.62; CI 1.0–2.6), and MCP-V (OR 2.0; CI 1.0–3.8) were involved. Conclusion Medical student driven MSUS by the ‘US7 Score’ can facilitate diagnosis of RA in rheumatology-screening programs due to the level of the score and the affected joints. A high rate of unknown OA signs was detected by MSUS. A mobile rheumatology office displays an opportunity to screen patients for RA and OA.

ObjectiveTo compare physician and patient assessments of global disease activity in anti-neutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) and to identify associated factors.MethodsGlobal disease activity scores (0–10 points) were retrospectively analyzed from physicians and patients with AAV at each outpatient visit from 2010 to 2020. We compared the scores and performed a linear regression with a random effects to identify associated factors.ResultsPatients (n = 143, 1,291 pairs, 52% female) had a mean 64 (±15) years of age and a mean disease duration of 9 (±7) years. Patients and physicians global disease activity assessments showed a moderate correlation (Pearson R 0.31, CI [0.23–0.52], p

Abstract Optimizing new drug therapies remains a challenge for clinical development, despite the use of ever more sophisticated quantitative methodologies. Although conceptually simple, the idea of finding the right treatment at the right dose for the right patient to ensure an appropriate balance of risks and benefits is challenging and requires a multidisciplinary approach. In this paper, we present a framework developed as a tool for organizing knowledge and facilitating collaboration in development teams.

The introduction of Vehicular Ad-Hoc Networks (VANETs) enables great potential for improving road traffic flow and especially active safety applications such as cooperative adaptive cruise control (CACC). Such applications not only rely on continuous broadcast of vehicle state information (beacons) of all vehicles, but also have strict real-time requirements. Regarding automotive E/E architectures this continuous broadcasting adds heavy internal E/E data traffic that needs to be processed in real-time by Electronic Control Units (ECUs). In this work we address this issue by proposing a novel cluster-based message evaluation methodology to significantly reduce internal E/E network traffic by discarding irrelevant messages. The approach is only depending on information received over beacons. It combines a vehicle clustering strategy as well as network and vehicle state monitoring capabilities in order to correctly evaluate messages under real-time constraints. The proposed methodology is modeled inside an abstract ECU. It is evaluated by simulating a model-based CACC application under different traffic scenarios. It is shown that a significant reduction of messages is achievable, while still guaranteeing accident-free behavior of CACC.

SRAM-based fingerprinting uses deviations in power-up behaviour caused by the CMOS fabrication process to identify distinct devices. This method is a promising technique for unique identification of physical devices. In the case of SRAM-based hardware reconfigurable devices such as FPGAs, the integrated SRAM cells are often initialized automatically at power-up, sweeping potential identification data. We demonstrate an approach to utilize unused parts of configuration memory space for device identification. Based on a total of over 200,000 measurements on nine Xilinx Virtex-5 FPGAs, we show that the retrieved values have promising properties with respect to consistency on one device, variety between different devices, and stability considering temperature variation and aging.

Novel embedded applications are characterized by increasing requirements on processing performance as well as the demand for communication between several or many devices. Networked Multiprocessor System-on-Chips (MPSoCs) are a possible solution to cope with this increasing complexity. Such systems require a detailed exploration on both architectures and system design. An approach that allows investigating interdependencies between system and network domain is the cooperative execution of system design tools with a network simulator. Within previous work, synchronization mechanisms have been developed for parallel system simulation and system/network co-simulation using the high level architecture (HLA). Within this contribution, a methodology is presented that extends previous work with further building blocks towards a construction kit for system/network co-simulation. The methodology facilitates flexible assembly of components and adaptation to the specific needs of use cases in terms of performance and accuracy. Underlying concepts and made extensions are discussed in detail. Benefits are substantiated by means of various benchmarks.

Growing ubiquity and safety relevance of embedded systems strengthen the need to protect their functionality against malicious attacks. Communication and system authentication by digital signature schemes is a major issue in securing such systems. This contribution presents a complete ECDSA signature processing system over prime fields for bit lengths of up to 256 on reconfigurable hardware. By using dedicated hardware implementation, the performance can be improved by up to two orders of magnitude compared to microcontroller implementations. The flexible system is tailored to serve as an autonomous subsystem providing authentication transparent for any application. Integration into a vehicle-to-vehicle communication system is shown as an application example.

Protein-protein interactions take place at defined binding interfaces. One protein may bind two or more proteins at different interfaces at the same time. So far it has been commonly accepted that non-overlapping interfaces allow a given protein to bind other proteins simultaneously while no collisions occur between the binding protein structures. To test this assumption, we performed a comprehensive analysis of structural protein interactions to detect potential collisions. Our results did not indicate cases of biologically relevant collisions in the Protein Data Bank of protein structures. However, we discovered a number of collisions that originate from alternative protein conformations or quaternary structures due to different experimental conditions.

We assess the variability of protein function in protein sequence and structure space. Various regions in this space exhibit considerable difference in the local conservation of molecular function. We analyze and capture local function conservation by means of logistic curves. Based on this analysis, we propose a method for predicting molecular function of a query protein with known structure but unknown function. The prediction method is rigorously assessed and compared with a previously published function predictor. Furthermore, we apply the method to 500 functionally unannotated PDB structures and discuss selected examples. The proposed approach provides a simple yet consistent statistical model for the complex relations between protein sequence, structure, and function. The GOdot method is available online (http://godot.bioinf.mpi-inf.mpg.de).

HIV-1 cell entry commonly uses, in addition to CD4, one of the chemokine receptors CCR5 or CXCR4 as coreceptor. Knowledge of coreceptor usage is critical for monitoring disease progression as well as for supporting therapy with the novel drug class of coreceptor antagonists. Predictive methods for inferring coreceptor usage based on the third hypervariable (V3) loop region of the viral gene coding for the envelope protein gp120 can provide us with these monitoring facilities while avoiding expensive phenotypic tests. All simple heuristics (such as the 11/25 rule) as well as statistical learning methods proposed to date predict coreceptor usage based on sequence features of the V3 loop exclusively. Here, we show, based on a recently resolved structure of gp120 with an untruncated V3 loop, that using structural information on the V3 loop in combination with sequence features of V3 variants improves prediction of coreceptor usage. In particular, we propose a distance-based descriptor of the spatial arrangement of physicochemical properties that increases discriminative performance. For a fixed specificity of 0.95, a sensitivity of 0.77 was achieved, improving further to 0.80 when combined with a sequence-based representation using amino acid indicators. This compares favorably with the sensitivities of 0.62 for the traditional 11/25 rule and 0.73 for a prediction based on sequence information as input to a support vector machine and constitutes a statistically significant improvement. A detailed analysis and interpretation of structural features important for classification shows the relevance of several specific hydrogen-bond donor sites and aliphatic side chains to coreceptor specificity towards CCR5 or CXCR4. Furthermore, an analysis of side chain orientation of the specificity-determining residues suggests a major role of one side of the V3 loop in the selection of the coreceptor. The proposed method constitutes the first approach to an improved prediction of coreceptor usage based on an original integration of structural bioinformatics methods with statistical learning.