Range Of Seconds Research Articles

Impact of proprioceptive cervical dizziness in chronic neck pain syndromes on gait and stance during active head-turn challenges

An earlier observational study described selected patients with acute neck pain syndrome, who experienced short bursts of cervical vertigo elicited by rapid head movements. The current study on a larger cohort of 20 patients with chronic or frequently recurring neck pain syndrome and age-matched controls focused on two major questions: (1) Can head movements in subjects with exacerbations of neck pain and restrictions of neck mobility also elicit bursts of vertigo? (2) What is the impact on postural balance measured by analysis of body sway and locomotion? A detailed questionnaire was applied, posture and gait were evaluated by use of instrumented posturography—and gait analysis with and without slow or rapid horizontal head rotations in the yaw plane with and without sight/visual input. All patients reported some or frequent episodes of dizziness in the range of seconds only elicited by rapid, not by slow head movements. Postural sway in patients was unremarkable in undisturbed conditions without head movements, but specifically increased by rapid but not slow head turns. The latter is best explained by the lack of continuous control of velocity and amplitude of saccadic head movements. Gait analysis revealed a slowed and cautious gait pattern already at undisturbed condition that was even exaggerated during rapid head turns. These observations demonstrate that chronic or recurrent neck pain is associated with episodic experiences of dizziness and above results in both chronic and episodic alterations of stance and gait that resemble those described for patients with phobic postural vertigo/persistent postural perceptual dizziness, a functional gait disorder.

Carbon Nanotube Sensor Platforms for the Detection of Cellular Nitric Oxide Efflux Gradients

Cell communication is the ability to receive, process, and transmit signals from the environment and within cells to ensure functional coordination during physiological events. Cells communicate between themselves using waves of chemical concentration that change in both direction and time. Nitric oxide (NO) is a gaseous, ubiquitous, intra - and inter-cellular signaling molecule. NO’s small size and lipophilic nature allows it to diffuse through cell membranes and reach intracellular compartments of nearby cells. However, NO detection in biological systems represents a challenge due to NO’s short half-life (in the range of seconds) before breaking down into different subproducts. The lack of spatial NO detection, not an upstream or downstream indicator of NO, has hindered understanding of the extracellular NO dynamic signaling during both physiological and pathological events. However, single wall carbon nanotube (SWNT)-based fluorescent sensors allow for the detection of NO, and when immobilized onto a glass substrate, they can be used to quantify extracellular NO in a spatial-temporal manner. We detected changes in extracellular NO efflux from different macrophage cell lines known for their NO production under inflammatory responses, specifically human THP-1 monocytes and murine macrophage RAW 264.7. The cells were seeded on the SWNT platform and treated with Liposaccharides (LPS) 24 h to increase their NO release. The SWNT platform’s sensing response to NO was detected by changes in fluorescence intensity. Fluorescence data was collected on a custom-built hyperspectral microscope to give us near infrared (nIR) signal quantification with spatial resolution. Images were collected before and after the respective NO induction method. To analyze the extracellular NO mappings, bright field images of the cells were used to extract the cell coordinates and translate them to fluorescent images. Hyperspectral image analysis was performed with MATLAB to determine SWNT fluorescence below each cell and its surrounding area. To validate that the change in the fluorescence response was attributed to NO and not from cellular interaction with the SWNT sensor, we added 2-Phenyl-4,4,5,5-tetramethylimidazoline-1-oxyl 3-oxide (PTIO), a NO scavenger, after visualizing the SWNT quenching to recover the initial fluorescence signal, showing that the cells didn’t affect SWNT functionality. Through fluorescence intensity changes with pixel specificity on the image acquisition, we were able to spatially quantify NO released by cells with dynamic diffusion gradients. The newly developed platform provides the means to quantify extracellular NO. In this study, only two cell lines were studied, but the platforms will allow for the quantification of NO for other cell types as well. By comparing extracellular NO concentrations for different types of cells, we will be able to better understand physiological and pathological patterns of NO release, leading to a better understanding of cellular signaling.

Ultralong Blue Organic Room‐Temperature Phosphorescence Promoted by Green Assembly

AbstractIn recent years, there is a growing interest in developing ultralong organic room‐temperature phosphorescence (ORTP) with lifetimes in the range of seconds. As one of the important three primary colors, ultralong blue ORTP is an indispensable core component in RTP regulation and application, however, the large Stokes shift characteristics pose certain challenges in developing ultralong blue ORTP. Here, a new family of blue phosphors are synthesized and ultralong blue ORTP are realized through crystal assembly in water and organic phase. Remarkably, compared to crystal materials obtained in organic phases, assembly materials obtained in water enabled long‐lived blue ORTP with lifetimes up to 2.3 s and phosphorescence quantum yield reached as high as 29.27%. In addition to the efficient and green assembly form, such dark blue ORTP materials possessed high thermal stability and flexible tunability. Moreover, the superiority of the selected assembly components is demonstrated by comparing them with polymer assembly and the proposed blue ORTP materials show great potential in an organic programmable information encryption/anti‐counterfeiting.

Characterization of novel compounds that directly bind to and inactivate the HPV E6 protein for treatment of HPV-associated cervical and oropharyngeal cancers.

e15122 Background: The human papillomavirus (HPV) E6 protein is necessary for viral genome replication and is always expressed in HPV-induced malignancies. We performed in silico screening of chemical libraries for molecules that bind to a pocket on the cancer associated HPV-16 E6 protein. We focused on the protein-protein interaction of E6 with one of its cellular binding partners, the ubiquitin ligase E6AP. In a trimolecular complex with E6, E6AP ubiquitinates p53, leading to its destruction by the proteasome. We designed, synthesized, and characterized >300 compounds that 1) bind to the E6AP interaction surface of the HPV-16 E6 protein; 2) are armed with a ‘warhead’ to make an irreversible bond with a specific cysteine in this HPV-16 E6 binding pocket; 3) and restore levels of p53 and selectively decrease viability of HPV-16 expressing tumor derived cells. Methods: E6 inhibitor optimization was guided by in silico docking strategies, co-crystallization, and medicinal chemistry, followed by biochemical and cellular assays to iteratively design and synthesize molecules with increased E6 inhibitory activities. Results: We used whole protein mass spectrometry to interrogate covalent bonding to E6, showing irreversible binding to the cysteine in the targeted E6 binding pocket without the other 13 cysteines in E6, with Ki in the range of seconds. Biolayer interferometry and fluorescence polarimetry measured blockade of E6 with E6AP, with IC50 in the range of 400-600 nM. To establish in cell inactivation of E6, human cervical cancer derived SiHa cells that express HPV-16 E6 were incubated with compounds, showing dose dependent increase of p53 levels up to 8-fold after 24 hrs, an effect that was not observed in the HPV-16- cell line RPE-1 expressing wild-type p53. Viability of two cervical (SiHa, CaSki) and two oropharyngeal HPV-16+ cancer cell lines (UM-SC-47, UM-SCC-104) was reduced with an IC50 of ~2-3 µM, with an IC50>30µM in RPE-1 cells and telomerase immortalized human keratinocytes. These in vitro and cell-based experiments revealed dependence on the cysteine residue in the E6AP binding pocket of E6. For in vivo proof-of-concept studies, we developed mouse xenograft models, performed by engrafting SiHa, UM-SC-47 and UM-SCC-104 cancer derived cells subcutaneously into the flank of immunodeficient mice. Lead compounds were administered by intraperitoneal injection and demonstrated nearly complete inhibition (> 70% tumor reduction) in HPV-16 tumor growth compared to vehicle. Anti-tumor activity was not observed with the HPV-negative tumor cell line C33A. Conclusions: HPV-16 is identified in about 50% of all cervical cancers and the majority of head and neck malignancies. Inactivation of the HPV E6 protein represents the opportunity for a novel molecular targeted approach for medical treatment of HPV-16 expressing cancers of the cervix and oropharynx.

Federated Secure Computing

Privacy-preserving computation (PPC) enables encrypted computation of private data. While advantageous in theory, the complex technology has steep barriers to entry in practice. Here, we derive design goals and principles for a middleware that encapsulates the demanding cryptography server side and provides a simple-to-use interface to client-side application developers. The resulting architecture, “Federated Secure Computing”, offloads computing-intensive tasks to the server and separates concerns of cryptography and business logic. It provides microservices through an Open API 3.0 definition and hosts multiple protocols through self-discovered plugins. It requires only minimal DevSecOps capabilities and is straightforward and secure. Finally, it is small enough to work in the internet of things (IoT) and in propaedeutic settings on consumer hardware. We provide benchmarks for calculations with a secure multiparty computation (SMPC) protocol, both for vertically and horizontally partitioned data. Runtimes are in the range of seconds on both dedicated workstations and IoT devices such as Raspberry Pi or smartphones. A reference implementation is available as free and open source software under the MIT license.

Wave Farms Integration in a 100% renewable isolated small power system -frequency stability and grid compliance analysis.

In general terms, the variable penetration of RE in power systems has some inherent drawbacks, such as lack of manageability and resource variability [1]. Medium (in the range of minutes) and short term (in the range of seconds) variability has a negative impact on system reliability, causing a deterioration of system frequency quality in both interconnected and, moreover, isolated systems [1-2]. Specifically, the variability of the wave energy resource is medium- and short-term. Therefore, although wave energy could be very suitable to be integrated in islands due to its location, the variable nature of wave energy could negatively impact the stability of the power grid [3].
 The case study of the work focuses on the island of El Hierro (Canary Islands, Spain). It is an isolated electrical system with a very high penetration of renewable energy sources. The generation of the electrical system is composed of a wind farm, a pumped hydroelectric power plant and conventional generation by means of a diesel power plant.
 In a previous analysis [4], the integration of energy storage systems based on flywheels was analyzed. Based on this previous analysis, the manuscript studies the influence of the integration of the wave energy park in the electrical system of El Hierro.
 On the one hand, a wave farm will be proposed to evaluate the generated power and its associated oscillation [5]. The wave energy resource at different locations along the coast of El Hierro will be taken into account. On the other hand, an aggregated inertial dynamic mode of the electrical power system will be used to evaluate the impact of the generated power on the electrical frequency and the aging/degradation effects of the hydropumping elements. The Spanish Grid Code will be taken into account regarding frequency regulation mechanisms in isolated systems.
 The degradation of the hydraulic pumping systems due to additional frequency regulation stresses and electrical frequency deterioration will be calculated and evaluated in relation to the penetration of wave energy into the system, with and without the flywheel energy storage plant. This will allow quantification of certain technical limits to wave energy penetration in isolated systems and to draw conclusions with reference to the size of such a power system.
 [1] R. S. Kaneshiro et al. “Hawaii Island (Big Island) Wind Impacts” Proc. of Workshop on Active Power Control from Wind Power, Broomfield, CO, USA, 2013.
 [2] H. R. Iswadi et al. “Irish power system primary frequency response metrics during different system non synchronous penetration,” IEEE Eindhoven PowerTech 2015, doi: 10.1109/PTC.2015.7232425.
 [3] Isabel Villalba et al. “Wave farms grid code compliance in isolated small power systems,” IET Renewable Power Generation, 2019, doi: 10.1049/iet-rpg.2018.5351.
 [4] Hilel Garcia-Pereira et al. “Comparison and Influence of Flywheels Energy Storage System Control Schemes in the Frequency Regulation of Isolated Power Systems,” IEEE Access, 2022, doi: 10.1109/ACCESS.2022.3163708.
 [5] M. Blanco et al. “Study of the impact of wave energy generation in the frequency of an island electric grid,” Proc. of the 12th European Wave and Tidal Energy Conference (EWTEC). 2017.

A new Markov–Dubins hybrid solver with learned decision trees

In this paper, the applicability of machine learning models and techniques to the Markov–Dubins path planning problem have been explored. Machine learning techniques are already applied to several fields, which range from computer vision, to physics simulation, to item recommendation, to user profiling. This pervasiveness has led to marked improvements in the implementation and support for applying machine learning models, in particular for specialised use cases such as low-power devices, embedded hardware, and real-time applications. On the other hand, the Markov–Dubins path planning problem, which is central in robotic nonholonomic trajectory design, is already covered by established numerical and optimisation techniques. However, the benefits of applying machine learning approaches to this problem remain to be investigated. In particular, there is the need to research potential speed-ups or application domains that would be better solved by a machine learning approach compared to the traditional algorithmic approaches. In this study, we train a state-of-the-art machine learning model in a supervised setting on Markov–Dubins and use it in two different ways: to directly predict the solution, and to filter candidate solutions. Also, a comparison of the quality of these predictions with a state-of-the-art Markov–Dubins solver is made. The results obtained indicate that machine learning approaches are comparable to state-of-the-art solutions: our bare model, directly predicting the solution, appears to be 8.3 times faster than the current standard, sacrificing the accuracy, which amounts to a value close to 92%; the hybrid model that filters the solutions prior to finding the best candidate runs in times that are comparable to the classical solver (58 ms) and has over 98% accuracy. A further comparison with alternative solvers and techniques, such as Optimal Control, NonLinear Programming and Mixed Integer NonLinear Programming has been made, confirming the benefits of the machine learning approach over these, for which the computational times are in the range of seconds. This opens new avenues for interdisciplinary applications of machine learning to more general planning problems (e.g., the same problem in 3D), where the number of possible manoeuvres is large and the computation of each of them requires a considerable computational effort, which makes the brute force trial-and-error infeasible.

Intramolecular electron transfer from biopterin to FeII-O2 complex in nitric oxide synthases occurs at very different rates between bacterial and mammalian enzymes: Direct observation of a catalytically active intermediate

Nitric oxide synthase (NOS) is a cytochrome P450-type mono‑oxygenase that catalyzes the oxidation of L-arginine to nitric oxide. We previously observed that intramolecular electron transfer from biopterin to Fe2+-O2 in Deinococcus radiodurans NOS (DrNOS) using pulse radiolysis. However, the rate of electron transfer in DrNOS (2.2 × 103 s−1) contrasts with a reported corresponding rate (11 s−1) in a mammalian NOS determined using rapid freeze-quench (RFQ) EPR. We applied pulse radiolysis to Bacillus subtilis NOS (bsNOS) and to rat neural NOS oxygenase domain NOS (mNOS). Concurrently, RFQ EPR was used to trap a pterin radical during single-turnover enzyme reactions of the enzymes. By using the pulse radiolysis method, hydrated electrons (eaq−) reduced the heme iron of NOS enzymes. Subsequently, ferrous heme reacted with O2 to form a Fe2+-O2 intermediate. In the presence of pterin, the intermediate of bsNOS was found to convert to other intermediate in the time range of milliseconds. A similar process was determined to have occurred after pulse radiolysis of the pterin-bound mNOS, though the rate was much slower. The intermediates of all of the NOS enzymes further converted to the original ferric form in the time range of seconds. When using the RFQ method, pterin radicals were formed very rapidly in both DrNOS and bsNOS in the time range of milliseconds. In contrast, the pterin radical in mNOS was observed to form slowly, at a rate of ∼20 s−1.

A Stable Chemokine Gradient Controls Directional Persistence of Migrating Dendritic Cells.

Navigation of dendritic cells (DCs) from the site of infection to lymphoid organs is guided by concentration gradients of CCR7 ligands. How cells interpret chemokine gradients and how they couple directional sensing to polarization and persistent chemotaxis has remained largely elusive. Previous experimental systems were limited in the ability to control fast de novo formation of the final gradient slope, long-lasting stability of the gradient and to expose cells to dynamic stimulation. Here, we used a combination of microfluidics and quantitative in vitro live cell imaging to elucidate the chemotactic sensing strategy of DCs. The microfluidic approach allows us to generate soluble gradients with high spatio-temporal precision and to analyze actin dynamics, cell polarization, and persistent directional migration in both static and dynamic environments. We demonstrate that directional persistence of DC migration requires steady-state characteristics of the soluble gradient instead of temporally rising CCL19 concentration, implying that spatial sensing mechanisms control chemotaxis of DCs. Kymograph analysis of actin dynamics revealed that the presence of the CCL19 gradient is essential to stabilize leading edge protrusions in DCs and to determine directionality, since both cytoskeletal polarization and persistent chemotaxis are abrogated in the range of seconds when steady-state gradients are perturbed. In contrast to Dictyostelium amoeba, DCs are unable to decode oscillatory stimulation of soluble chemokine traveling waves into a directional response toward the wave source. These findings are consistent with the notion that DCs do not employ adaptive temporal sensing strategies that discriminate temporally increasing and decreasing chemoattractant concentrations in our setting. Taken together, in our experimental system DCs do not depend on increasing absolute chemokine concentration over time to induce persistent migration and do not integrate oscillatory stimulation. The observed capability of DCs to migrate with high directional persistence in stable gradients but not when subjected to periodic temporal cues, identifies spatial sensing as a key requirement for persistent chemotaxis of DCs.

Investigation of the time shift between wearable photoplethysmography sensors used for continuous heart rate monitoring.

In this study, we investigated the effect of time shift in heartrate measurement by wearables, which might to be used in telehealth applications for patients suffering from heart failure. Six wearables commercially available on the market were tested in a 14-hour measurement. Each wearable was tested three times by five different test persons. A reference sensor was used to test the accuracy of the wearables. We found that different types of time shifts are common in the sensors we tested: time shifts of full days, time shifts of full hours (most probably due to incorrect or unspecified time zones) and time shifts in the range of seconds to minutes (most likely stemming from averaging, data transmission, etc.). We conclude that time shifts of all manufacturers need to be corrected prior comparison of a photoplethysmography signal with other signals. However, even after correction of the time shift, the reliability of the sensors seems to be too low for application in telehealth settings. Clinical relevance- This study shows that signals from state-of-the-art wearable photoplethysmography heart rate measurements show significant time shifts and marked differences even if time shifts were corrected. This limits their utility for clinical applications.

Fine-Grained Power Modeling of Multicore Processors Using FFNNs

To minimize power consumption while maximizing performance, today’s multicore processors rely on fine-grained run-time dynamic power information—both in the time domain, e.g. mu s to ms, and space domain, e.g. core-level. The state-of-the-art for deriving such power information is mainly based on predetermined power models which use linear modeling techniques to determine the core-performance/core-power relationship. However, with multicore processors becoming ever more complex, linear modeling techniques cannot capture all possible core-performance related power states anymore. Although artificial neural networks (ANN) have been proposed for coarse-grained power modeling of servers with time resolutions in the range of seconds, few works have yet investigated fine-grained ANN-based power modeling. In this paper, we explore feed-forward neural networks (FFNNs) for core-level power modeling with estimation rates in the range of 10 kHz. To achieve a high estimation accuracy while minimizing run-time overhead, we propose a multi-objective-optimization of the neural architecture using NSGA-II with the FFNNs being trained on performance counter and power data from a complex-out-of-order processor architecture. We show that relative power estimation error for the highest accuracy FFNN decreases on average by 7.5% compared to a state-of-the-art linear power modeling approach and decreases by 5.5% compared to a multivariate polynomial regression model. For the FFNNs optimized for both accuracy and overhead, the average error decreases between 4.1% and 6.7% compared to linear modeling while offering significantly lower overhead compared to the highest accuracy FFNN. Furthermore, we propose a micro-controller-based and an accelerator-based implementation for run-time inference of the power modeling FFNN and show that the area overhead is negligible.

Accurate classification of Listeria species by MALDI-TOF mass spectrometry incorporating denoising autoencoder and machine learning

Listeria monocytogenes belongs to the category of facultative anaerobic bacteria, and is the pathogen of listeriosis, potentially lethal disease for humans. There are many similarities between L. monocytogenes and other non-pathogenic Listeria species, which causes great difficulties for their correct identification. The level of L. monocytogenes contamination in food remains high according to statistics from the Food and Drug Administration. This situation leads to food recall and destruction, which has caused huge economic losses to the food industry. Therefore, the identification of Listeria species is very important for clinical treatment and food safety. This work aims to explore an efficient classification algorithm which could easily and reliably distinguish Listeria species. We attempted to classify Listeria species by incorporating denoising autoencoder (DAE) and machine learning algorithms in matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). In addition, convolutional neural networks were used to map the high dimensional original mass spectrometry data to low dimensional core features. By analyzing MALDI-TOF MS data via incorporating DAE and support vector machine (SVM), the identification accuracy of Listeria species was 100%. The proposed classification algorithm is fast (range of seconds), easy to handle, and, more importantly, this method also allows for extending the identification scope of bacteria. The DAE model used in our research is an effective tool for the extraction of MALDI-TOF mass spectrometry features. Despite the fact that the MALDI-TOF MS dataset examined in our research had high dimensionality, the DAE + SVM algorithm was still able to exploit the hidden information embedded in the original MALDI-TOF mass spectra. The experimental results in our work demonstrated that MALDI-TOF mass spectrum combined with DAE + SVM could easily and reliably distinguish Listeria species.

Atomic resolution in situ observation on photon-induced reshaping and phase transitions of CsPbBr3 nanocube and quantum dot

Controlling the photon-induced reshaping and phase transitions of low-dimensional halide perovskites is a significant and challenging task. Using in situ Cs-corrected transmission electron microscopy with a nanosecond pulsed laser source, we traced CsPbBr3 nanocubes and quantum dots under different laser irradiation conditions, through a low-dose observation mode. We found that a high laser fluence with a short irradiation time (e.g., 300 mJ/cm2 in the range of seconds) triggers defect growth and a cubic-to-orthorhombic phase transition in perovskite nanocubes; however, a low laser fluence with a long irradiation time (e.g., 30 mJ/cm2 in the range of minutes) is actually like an annealing process that removes defects and gradually reshapes the perovskite samples into a round morphology, with the cubic phase well stabilized by the strong surface tension. Based on the in situ studies, we present a feasible and effective laser engineering approach for low-dimensional halide perovskite materials.

Quantifying the contribution of individual variation in timing to delay-discounting

Delay-discounting studies in neuroscience, psychology, and economics have been mostly focused on concepts of self-control, reward evaluation, and discounting. Another important relationship to consider is the link between intertemporal choice and time perception. We presented 50 college students with timing tasks on the range of seconds to minutes and intertemporal-choice tasks on both the time-scale of seconds and of days. We hypothesized that individual differences in time perception would influence decisions about short experienced delays but not long delays. While we found some evidence that individual differences in internal clock speed account for some unexplained variance between choices across time-horizons, overall our findings suggest a nominal contribution of the altered sense of time in intertemporal choice.

PH sensors based on amino-terminated carbon nanomembrane and single-layer graphene van der Waals heterostructures

The ability of graphene to transduce an adsorption event of ions into a detectable electrical signal has sparked a lot of interest for its use in sensors. However, a low concentration of the chemically active sites for binding analytes on the graphene surface has significantly prevented its applications so far. Here, we report on implementation of the van der Waals heterostructure based on a monolayer graphene and an ∼1-nm-thick molecular carbon nanomembrane (CNM) in a solution-gated field-effect transistor (FET) for pH sensing. The nondestructive functionalization of a graphene FET with the amino-terminated CNM (NH2-CNM) enables the induction of chemically active groups in the vicinity of the graphene sheet, maintaining its charge carrier transport properties. We applied complementary characterization techniques, including Raman spectroscopy, x-ray photoelectron spectroscopy, and optical and atomic force microscopy as well as field-effect and electrical impedance measurements to characterize the engineered NH2-CNM/graphene devices. We demonstrate their high pH resolution with a minimum detectable pH change of ∼0.01 at pH 2 and ∼0.04 at pH 12, with a response time in the range of seconds, and we apply an electrical double-layer model to rationalize the experimentally observed performance theoretically. The developed device concept enables the engineering of microscale pH sensors for applications in biological and environmental sciences.

Drosophila acquires seconds-scale rhythmic behavior.

Detection of the temporal structure of stimuli is crucial for prediction. While perception of interval timing is relevant for immediate behavioral adaptations, it has scarcely been investigated, especially in invertebrates. Here, we examined whether the fruit fly, Drosophila melanogaster, can acquire rhythmic behavior in the range of seconds. To this end, we developed a novel temporal conditioning paradigm utilizing repeated electric shocks. Combined automatic behavioral annotation and time-frequency analysis revealed that behavioral rhythms continued after cessation of the shocks. Furthermore, we found that aging impaired interval timing. This study thus not only demonstrates the ability of insects to acquire behavioral rhythms of a few seconds, but highlights a life-course decline of temporal coordination, which is also common in mammals.

A medium-scale volume dielectric barrier discharge system for short-term treatment of cereal seeds indicates improved germination performance with long-term effects

Atmospheric pressure plasma has the potential of being applied in pre-harvest processes as a sustainable technology for seed and plant treatment. In this study, a medium-scale volume dielectric barrier discharge reactor with a power density of 53.5 mW/cm3 was developed and applied for continuous treatment of larger amount of seeds. Within the argon plasma, reactive oxygen and nitrogen species were observed by optical emission spectroscopy. Short-term plasma treatment in the range of seconds of wheat and barley seeds revealed improved germination performance accompanied by altered seed surface properties. Depending on the exposure time, plasma displayed a positive impact on maximum germination and germination speed in both species. In line with this, the t50 values were significantly decreased for plasma treated seeds compared to untreated seeds for wheat and barley. Cumulative germination was enhanced from 20% up to 53% after 27 h of germination for wheat seeds treated from 10 s to 60 s with plasma. Barley seeds respond to the short plasma treatment times with increased germination of 10%–23% after 24 h observation time. Moreover, the seed surface of both plant species became more hydrophilic after plasma treatment supported by a decrease in the water contact angle and an increase in hydrophilic functional groups being detected by x-ray photoelectron spectroscopy. Water uptake by seeds was moderately increased after 2 h of imbibition for both plant species. The positive effects of plasma treatment on germination performance were still detectable after storage of seeds for one and two months.

Proton-dependent inhibition, inverted voltage activation, and slow gating of CLC-0 Chloride Channel.

CLC-0, a prototype Cl- channel in the CLC family, employs two gating mechanisms that control its ion-permeation pore: fast gating and slow gating. The negatively-charged sidechain of a pore glutamate residue, E166, is known to be the fast gate, and the swinging of this sidechain opens or closes the pore of CLC-0 on the millisecond time scale. The other gating mechanism, slow gating, operates with much slower kinetics in the range of seconds to tens or even hundreds of seconds, and it is thought to involve still-unknown conformational rearrangements. Here, we find that low intracellular pH (pHi) facilitates the closure of the CLC-0’s slow gate, thus generating current inhibition. The rate of low pHi-induced current inhibition increases with intracellular H+ concentration ([H+]i)—the time constants of current inhibition by low pHi = 4.5, 5.5 and 6 are roughly 0.1, 1 and 10 sec, respectively, at room temperature. In comparison, the time constant of the slow gate closure at pHi = 7.4 at room temperature is hundreds of seconds. The inhibition by low pHi is significantly less prominent in mutants favoring the slow-gate open state (such as C212S and Y512A), further supporting the fact that intracellular H+ enhances the slow-gate closure in CLC-0. A fast inhibition by low pHi causes an apparent inverted voltage-dependent activation in the wild-type CLC-0, a behavior similar to those in some channel mutants such as V490W in which only membrane hyperpolarization can open the channel. Interestingly, when V490W mutation is constructed in the background of C212S or Y512A mutation, the inverted voltage-dependent activation disappears. We propose that the slow kinetics of CLC-0’s slow-gate closure may be due to low [H+]i rather than due to the proposed large conformational change of the channel protein. Our results also suggest that the inverted voltage-dependent opening observed in some mutant channels may result from fast closure of the slow gate by the mutations.

FAST AND ACCURATE MULTI-FRAME SUPER-RESOLUTION OF SATELLITE IMAGES

Abstract. Recent constellations of small satellites, such as Planet’s SkySats, offer new acquisition modes where very short videos or bursts of images are acquired instead of a single still image. Compared to sequences of multi-date images, these sequences of consecutive video frames yield a large redundancy of information within the range of seconds. This redundancy enables to increase the spatial resolution using multi-frame super-resolution algorithms. In this paper, we propose a novel super-resolution method based on a high-order spline interpolation model that combines multiple low-resolution frames to produce a high-resolution image. Moreover this method can be implemented efficiently on GPU to process entire images from real satellite acquisitions. Synthetic and real experiments show that the proposed method is able to recover fine details, and measurements of the resulting resolution indicate a gain of 10 cm / pixel with respect to Planet’s SkySat standard imagery products.

Effects of molecular modifications for water splitting enhancement of BiVO4

Combined organic (molecular adsorption) and inorganic (TiO2 passivation) modifications for enhancing water splitting efficiency of porous bismuth vanadate electrodes are tested. The catalytic activity of BiVO4 is increased after adsorption of a newly prepared ruthenium catalyst. TiO2 passivation and sensitization with RuP dye does not show straightforward improvements to the complex photocatalytic behaviour depending on the configuration of the (two- or three-electrode) photoelectrochemical cell, type of the experiment and sample aging. The time constant for electron transport in BiVO4 electrodes (in the range of seconds, revealed by electrochemical impedance measurements) was found to correlate with the stable photocurrent of the cells. The femtosecond transient absorption studies confirm the negligible effects of RuP on the population of the photoexcited carriers in BiVO4. The transient absorption studies also show that the processes responsible for the differences in photocurrents of the modified BiVO4 samples occur on a time scale longer than the first nanoseconds.