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Single-molecule imaging provides a powerful way to study biochemical processes in live cells, yet it remains challenging to track single molecules while simultaneously detecting their interactions. Here, we describe a novel property of rhodamine dyes, proximity-assisted photoactivation (PAPA), in which one fluorophore (the ‘sender’) can reactivate a second fluorophore (the ‘receiver’) from a dark state. PAPA requires proximity between the two fluorophores, yet it operates at a longer average intermolecular distance than Förster resonance energy transfer (FRET). We show that PAPA can be used in live cells both to detect protein–protein interactions and to highlight a subpopulation of labeled protein complexes in which two different labels are in proximity. In proof-of-concept experiments, PAPA detected the expected correlation between androgen receptor self-association and chromatin binding at the single-cell level. These results establish a new way in which a photophysical property of fluorophores can be harnessed to study molecular interactions in single-molecule imaging of live cells.

Abstract This contribution summarizes the current state of research regarding so‐called sensor‐integrating machine elements as an enabler of digitalization in mechanical engineering and——if available—their application in industry. The focus is on the methodical aspects of the development of these machine elements in general as well as specific sensor‐integrating machine elements that are either already in use or currently under development. Developmental aspects include the robust design of initially evaluated concepts for sensor‐integrating machine elements as well as their modularization. Smart materials with sensory functions are included in the analysis as well as the differentiation with regard to add‐on sensors. The aim of the authors interlinked by a special research program funded by the German Research Foundation (DFG) is to facilitate the exchange with other researchers with the help of the comprehensive overview given in this contribution. The contribution concludes with a brief discussion of open challenges, such as the energy supply and data transfer in rotating systems and also data security.

Abstract This retrospective observational study evaluated racial disparities among Black and White patients with multiple myeloma (MM). We included patients from a longitudinal de-identified EHR-derived database who had ≥2 visits recorded on or after 1/1/2011, documented treatment, and race listed as White or Black. Black patients (n = 1172) were more likely female (54.8%/42.9%) and younger (

Abstract Over the past 13 years, there have been advances in characterizing the patient experience in oncology trials, primarily using patient-reported outcomes (PROs). This review aims to provide details on the PRO measures and analyses used in multiple myeloma (MM) registrational trials. We identified registrational trials supporting MM indications from 2007 to 2020 from FDA databases. Trial protocols, statistical analysis plans, and clinical study reports were reviewed for PRO measures used, collection methods, statistical analyses, baseline and instrument completion definitions, and thresholds for clinical meaningfulness. Twenty-five trials supporting 20 MM indications were identified; 17 (68%) contained submitted PRO data. Of the 17 trials, 14 were randomized controlled trials and the remainder were single-arm trials. All but one trial were open label trials. Seven trials collected data electronically and five in paper format. The majority of trials evaluated at least two PRO measures (82%) with two trials (12%) utilizing four measures. Nine unique PRO measures were used, most commonly the EORTC QLQ-30 (87%), EQ-5D (65%), and QLQ-MY20 (47%). All 17 (100%) trials provided descriptive summaries, 10 (59%) carried out longitudinal mixed model analysis, 9 (53%) conducted responder analysis, and 2 (12%) did a basic inferential test. We noted substantial heterogeneity in terms of PRO collection methods, measures, definitions, and analyses, which may hinder the ability to effectively capture and interpret patient experience in future MM clinical trials. Further research is needed to determine the most appropriate approaches for statistical and analytical methodologies for PRO data in MM trials.

This paper deals with the real-time simulation of a power tool battery pack on a mechatronic powertrain test bench. The ability of an easy-to-use model for quick and iterative test runs mainly depends on the effort of parameterization. For this purpose, an easily parameterizable battery model is required. The battery model used is based on the current state of research and simulates the battery's behavior with an adequate precision. The suggested parameterization allows building the model without the necessity of experimental investigation. Three different procedures for model parameterization were used and compared with the real battery behavior. In conclusion, this paper shows a good tradeoff between precision and an easy way to handle a battery model for testing mechatronic powertrains.

The design of reliable systems is a key challenge in product engineering [...]

Using hydrogen fuel cells for power systems, temperature conditions are important for efficient and reliable operations, especially in low-temperature environments. A heating system with an electrical energy buffer is therefore required for reliable operation. There is a research gap in finding an appropriate control strategy regarding energy efficiency and reliable operations for different environmental conditions. This paper investigates heating strategies for the subfreezing start of a fuel cell for portable applications at an early development stage to enable frontloading in product engineering. The strategies were investigated by simulation and experiment. A prototype for such a system was built and tested for subfreezing start-ups and non-subfreezing start-ups. This was done by heating the fuel cell system with different control strategies to test their efficiency. It was found that operating strategies to heat up the fuel cell system can ensure a more reliable and energy-efficient operation. The heating strategy needs to be adjusted according to the ambient conditions, as this influences the required heating energy, efficiency, and reliable operation of the system. A differentiation in the control strategy between subfreezing and non-subfreezing temperatures is recommended due to reliability reasons.

For the control of dynamic systems such as an Electro-Hydraulic Actuator (EHA), there is a need to optimize the control based on simulations, since a prototype or a physical system is usually not available during system design. In consequence, no system identification can be performed. Therefore, it is unclear how well a simulation model of an EHA can be used for multicriteria optimization of the position control due to the uncertain model quality. To evaluate the suitability for control optimization, the EHA is modeled and parameterized as a grey-box model using existing parameters independent of test bench experiments. A method for multi-objective optimization of a controller is used to optimize the position control of the EHA. Finally, the step responses are compared with the test bench. The evaluation of the step responses for different loads and control parameters shows similar behavior between the simulation model and the physical system on the test bench, although the essential phenomena could not be reproduced. This means that the model quality achieved by modeling is suitable as an indication for the optimization of the control by simulation without a physical system.

Repetitive overhead work with a heavy load increases the risk for work-related shoulder disorders. Occupational exoskeletons supporting arm elevation are potential solutions to reduce that risk by lowering the physical strains on the shoulder. Many studies have reported a reduction in shoulder stress in various overhead tasks by using such exoskeletons. However, the support demand can vary in each phase of motion as well as in each individual task. This paper presents a laboratory study with five participants to evaluate the influence of the support level of an active shoulder exoskeleton in different motion phases (e.g., arm lifting, screw-in, and arm lowering of two overhead tasks) to identify the potential optimization of support at each phase. Results show that the support level of the exoskeleton should be adapted to the motion phase of the two chosen tasks. A higher support force is desired for the screw phase compared to the arm lifting and lowering phases, and the support level needs to be reduced immediately for arm lowering after the screw phase. The time for switching the support levels can be recognized by the electric current of the screwdriver.

Impaired muscle regeneration has repeatedly been described after anterior cruciate ligament reconstruction (ACL-R). The results of recent studies provided some evidence for negative alterations in knee extensor muscles after ACL-R causing persisting strength deficits in spite of the regain of muscle mass. Accordingly, we observed that 12 weeks of concentric/eccentric quadriceps strength training with eccentric overload (CON/ECC+) induced a significantly greater hypertrophy of the atrophied quadriceps muscle after ACL-R than conventional concentric/eccentric quadriceps strength training (CON/ECC). However, strength deficits persisted and there was an unexpected increase in the proportion of slow type I fibers instead of the expected shift towards a faster muscle phenotype after CON/ECC+. In order to shed further light on muscle recovery after ACL-R, the steady-state levels of 84 marker mRNAs were analyzed in biopsies obtained from the vastus lateralis muscle of 31 subjects before and after 12 weeks of CON/ECC+ (n = 18) or CON/ECC strength training (n = 13) during rehabilitation after ACL-R using a custom RT2 Profiler PCR array. Significant (p < 0.05) changes were detected in the expression of 26 mRNAs, several of them involved in muscle wasting/atrophy. A different pattern with regard to the strength training mode was observed for 16 mRNAs, indicating an enhanced hypertrophic stimulus, mechanical sensing or fast contractility after CON/ECC+. The effects of the type of autograft (quadriceps, QUAD, n = 19, or semitendinosus tendon, SEMI, n = 12) were reflected in the lower expression of 6 mRNAs involved in skeletal muscle hypertrophy or contractility in QUAD. In conclusion, the greater hypertrophic stimulus and mechanical stress induced by CON/ECC+ and a beginning shift towards a faster muscle phenotype after CON/ECC+ might be indicated by significant gene expression changes as well as still ongoing muscle wasting processes and a negative impact of QUAD autograft.

Validation is important for a high product quality of drive components. An X-in-the-Loop test bench enables the integration of scaled prototypes through coupling systems and scaling models even before serial parts are available. In the context of X-in-the-loop investigations, it is still unclear whether a scaling model enables the early investigation of geometry variants in powertrain subsystems. In this paper, scaled geometry experiments taking into account the interacting system are considered to evaluate the scaling model in terms of early investigation of geometry variants. The aim of this paper is the functional investigation of geometrically scaled drive components by integrating scaled prototypes in an X-in-the-Loop test bench. Using an overload clutch with detents, component variants of different size levels are investigated in scaled experiments with a scaling model. The results confirm possibilities of X-in-the-Loop integration of scaled prototypes and their investigation on geometrically scaled drive components. The investigations show, therefore, the opportunities of integrating scaled drive components through the scaling model to support the investigation of geometry variants before serial parts are available. Scaled geometry investigations considering the interacting system can, thus, support product development.

Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development.

Image analysis tools for cancer, such as automatic nuclei segmentation, are impacted by the inherent variation contained in pathology image data. Convolutional neural networks (CNN), demonstrate success in generalizing to variable data, illustrating great potential as a solution to the problem of data variability. In some CNN-based segmentation works for digital pathology, authors apply color normalization (CN) to reduce color variability of data as a preprocessing step prior to prediction, while others do not. Both approaches achieve reasonable performance and yet, the reasoning for utilizing this step has not been justified. It is therefore important to evaluate the necessity and impact of CN for deep learning frameworks, and its effect on downstream processes. In this paper, we evaluate the effect of popular CN methods on CNN-based nuclei segmentation frameworks.

Abstract Background There are over 700 known arboviruses and at least 80 immunologically distinct types that cause disease in humans. Arboviruses are transmitted among vertebrates by biting insects, chiefly mosquitoes and ticks. These viruses are widely distributed throughout the world, depending on the presence of appropriate hosts (birds, horses, domestic animals, humans) and vectors. Mosquito-borne arboviruses present some of the most important examples of emerging and resurgent diseases of global significance. Methods A strategy has been developed by which host-range mutants of Dengue virus can be constructed by generating deletions in the transmembrane domain (TMD) of the E glycoprotein. The host-range mutants produced and selected favored growth in the insect hosts. Mouse trials were conducted to determine if these mutants could initiate an immune response in an in vivo system. Results The DV2 E protein TMD defined as amino acids 452SWTMKILIGVIITWIG467 was found to contain specific residues which were required for the production of this host-range phenotype. Deletion mutants were found to be stable in vitro for 4 sequential passages in both host cell lines. The host-range mutants elicited neutralizing antibody above that seen for wild-type virus in mice and warrant further testing in primates as potential vaccine candidates. Conclusions Novel host-range mutants of DV2 were created that have preferential growth in insect cells and impaired infectivity in mammalian cells. This method for creating live, attenuated viral mutants that generate safe and effective immunity may be applied to many other insect-borne viral diseases for which no current effective therapies exist.