Structural elements regulating the photochromicity in a cyanobacteriochrome

The three-dimensional (3D) crystal structures of the GAF3 domain of cyanobacteriochrome Slr1393 (Synechocystis PCC6803) carrying a phycocyanobilin chromophore could be solved in both 15-Z dark-adapted state, Pr, λmax = 649 nm, and 15-E photoproduct, Pg, λmax = 536 nm (resolution, 1.6 and 1.86 Å, respectively). The structural data allowed identifying the large spectral shift of the Pr-to-Pg conversion as resulting from an out-of-plane rotation of the chromophore’s peripheral rings and an outward movement of a short helix formed from a formerly unstructured loop. In addition, a third structure (2.1-Å resolution) starting from the photoproduct crystals allowed identification of elements that regulate the absorption maxima. In this peculiar form, generated during X-ray exposition, protein and chromophore conformation still resemble the photoproduct state, except for the D-ring already in 15-Z configuration and tilted out of plane akin the dark state. Due to its formation from the photoproduct, it might be considered an early conformational change initiating the parental state-recovering photocycle. The high quality and the distinct features of the three forms allowed for applying quantum-chemical calculations in the framework of multiscale modeling to rationalize the absorption maxima changes. A systematic analysis of the PCB chromophore in the presence and absence of the protein environment showed that the direct electrostatic effect is negligible on the spectral tuning. However, the protein forces the outer pyrrole rings of the chromophore to deviate from coplanarity, which is identified as the dominating factor for the color regulation

Assessment of the biogas production potential of renewable resources with near infrared spectroscopy

Die zunehmende Nutzung von Biomasse zur Biogas­gewinnung erfordert die Entwicklung entsprechender Analyseverfahren, mit denen die potentielle Gasausbeute des eingesetzten Pflanzenmaterials bewertet werden kann. Insbesondere in der Energiepflanzenzüchtung werden Schnellmethoden benötigt, die möglichst erntezeitnah und kostengünstig genotypische Unterschiede im Gasbildungspotential an großen Probenserien feststellen können. Die Eignung der Nahinfrarot-Spektroskopie (NIRS) für diese Aufgabe wird an Silomais, Gras und Gras-Leguminosengemischen untersucht. Die Kalibrier-/Validierexperimente werden an frischen und entsprechenden trockenen, vermahlenen Proben durchgeführt. Dabei wird zum Einen die Methanausbeute, die aus Batch-Tests ermittelt wurde, als Referenz genutzt, zum Anderen die berechnete potentielle Methanausbeute, auf der Grundlage der fermentierbaren organischen Trockenmasse. Es wird nachgewiesen, dass sich die gemessene Methanausbeute nicht mit ausreichender Bestimmtheit kalibrieren/validieren lässt. Ursache ist eine zu geringe Wiederholgenauigkeit der Batch-Tests im Verhältnis zur fruchtartenspezifischen Varianz des Methanbildungs­potentials. Dagegen ist mit den Referenzdaten der berechneten potentiellen Methanausbeute die NIR-Kali­brierung/Validierung mit ausreichender Bestimmtheit möglich. Die Vorhersageleistung, die bei frischem Pflanzenmaterial erreicht wird, ist für das Screening geeignet, die die bei trockenem Material erreicht wird, liegt im Bereich üblicher NIR-Laboranalysen.The increasing use of biomass for biogas production requires the development of appropriate analytical processes with which the potential gas yields of the plants used can be evaluated. Particularly in energy plant breeding, quick methods to determine genotypical differences in gas development potential are needed that can be applied on large sample series as close to harvest time as possible and at as low a cost as possible. The suitability of near infrared spectroscopy (NIRS) for this task was studied on silage maize, grass and grass-legume mixtures. The calibration-validation experiments were carried out on fresh and accordingly dried, ground samples, as well as on the basis of two reference data bases on methane yield, one calculated from batch tests and the other calculating potential methane yield on the basis of the fermentable organic dry matter. It is shown that the measured methane yield cannot be calibrated or vali­dated with adequate certainty. The reason for this is the inexactness in the repetitions of the batch tests in relation to the crop type specific variance of the methane production potential. With the reference data on the calculated potential methane yield, in contrast, the NIR calibration/validation can be made with adequate exactness. Here the prediction ability lies within the screening area for fresh plant material and an acceptable level of laboratory analysis for dry material is possible

A RISC-V MCU with adaptive reverse body bias and ultra-low-power retention mode in 22 nm FD-SOI

We present a low-power, energy efficient 32-bit RISC-V microprocessor unit (MCU) in 22 nm FD-SOI. It achieves ultra-low leakage,even at high temperatures, by using an adaptive reverse body biasing aware sign-off approach, a low-power optimized physical implementation, and custom SRAM macros with retention mode. We demonstrate the robustness of the chip with measurements over the full industrial temperature range, from -40 {\deg}C to 125 {\deg}C. Our results match the state of the art (SOTA) with 4.8 uW / MHz at 50 MHz in active mode and surpass the SOTA in ultra-low-power retention mode.Comment: accepted at ISOCC 202

VLSI Implementation of a 2.8 Gevent/s Packet-Based AER Interface with Routing and Event Sorting Functionality

State-of-the-art large-scale neuromorphic systems require sophisticated spike event communication between units of the neural network. We present a high-speed communication infrastructure for a waferscale neuromorphic system, based on application-specific neuromorphic communication ICs in an field programmable gate arrays (FPGA)-maintained environment. The ICs implement configurable axonal delays, as required for certain types of dynamic processing or for emulating spike-based learning among distant cortical areas. Measurements are presented which show the efficacy of these delays in influencing behavior of neuromorphic benchmarks. The specialized, dedicated address-event-representation communication in most current systems requires separate, low-bandwidth configuration channels. In contrast, the configuration of the waferscale neuromorphic system is also handled by the digital packet-based pulse channel, which transmits configuration data at the full bandwidth otherwise used for pulse transmission. The overall so-called pulse communication subgroup (ICs and FPGA) delivers a factor 25–50 more event transmission rate than other current neuromorphic communication infrastructures

Simultaneous Motion Tracking and Joint Stiffness Control of Bidirectional Antagonistic Variable-Stiffness Actuators

Since safe human-robot interaction is naturally linked to compliance in these robots, this requirement presents a challenge for the positioning accuracy. The class of variable- stiffness robots features intrinsically soft contact behavior where the physical stiffness can even be altered during operation. Here we present a control scheme for bidirectional, antagonistic variable-stiffness actuators that achieve high-precision link-side trajectory tracking while simultaneously ensuring compliance during physical contact. Furthermore, the approach enables to regulate the pretension in the antagonism. The theoretical claims are confirmed by formal analyses of passivity during physical interaction and the proof of uniform asymptotic stability of the desired link-side trajectories. Experiments on the forearm joint of the DLR robot David verify the proposed approach

A 16-Channel Fully Configurable Neural SoC With 1.52 μW/Ch Signal Acquisition, 2.79 μW/Ch Real-Time Spike Classifier, and 1.79 TOPS/W Deep Neural Network Accelerator in 22 nm FDSOI

With the advent of high-density micro-electrodes arrays, developing neural probes satisfying the real-time and stringent power-efficiency requirements becomes more challenging. A smart neural probe is an essential device in future neuroscientific research and medical applications. To realize such devices, we present a 22 nm FDSOI SoC with complex on-chip real-time data processing and training for neural signal analysis. It consists of a digitally-assisted 16-channel analog front-end with 1.52 μ W/Ch, dedicated bio-processing accelerators for spike detection and classification with 2.79 μ W/Ch, and a 125 MHz RISC-V CPU, utilizing adaptive body biasing at 0.5 V with a supporting 1.79 TOPS/W MAC array. The proposed SoC shows a proof-of-concept of how to realize a high-level integration of various on-chip accelerators to satisfy the neural probe requirements for modern applications