Search

Measuring neuronal electrical activity, such as action potential propagation in cells, requires the sensitive detection of the weak electrical signal with high spatial and temporal resolution. None of the existing tools can fulfill this need. Recently, plasmonic-based electrochemical impedance microscopy (P-EIM) was demonstrated for the label-free mapping of the ignition and propagation of action potentials in neuron cells with subcellular resolution. However, limited by the signal-to-noise ratio in the high-speed P-EIM video, action potential mapping was achieved by averaging 90 cycles of signals. Such extensive averaging is not desired and may not always be feasible due to factors such as neuronal desensitization. In this study, we utilized advanced signal processing techniques to detect action potentials in P-EIM extracted signals with fewer averaged cycles. Matched filtering successfully detected action potential signals with as few as averaging five cycles of signals. Long short-term memory (LSTM) recurrent neural network achieved the best performance and was able to detect single-cycle stimulated action potential successfully [satisfactory area under the receiver operating characteristic curve (AUC) equal to 0.855]. Therefore, we show that deep learning-based signal processing can dramatically improve the usability of P-EIM mapping of neuronal electrical signals.

Here, we explored a new manufacturing strategy that uses the mask laser interference additive manufacturing (MLIAM) technique, which combines the respective strengths of laser interference lithography and mask lithography to efficiently fabricate across-scales three-dimensional bionic shark skin structures with superhydrophobicity and adhesive reduction. The phenomena and mechanisms of the MLIAM curing process were revealed and analyzed, showing the feasibility and flexibility. In terms of structural performance, the adhesive force on the surface can be tuned based on the growth direction of the bionic shark skin structures, where the maximum rate of the adhesive reduction reaches about 65%. Furthermore, the evolution of the directional diffusion for the water droplet, which is based on the change of the contact angle, was clearly observed, and the mechanism was also discussed by the models. Moreover, no-loss transportations were achieved successfully using the gradient adhesive force and superhydrophobicity on the surface by tuning the growth direction and modifying by fluorinated silane. Finally, this work gives a strategy for fabricating across-scale structures on micro- and nanometers, which have potential application in bioengineering, diversional targeting, and condenser surface.

A three-component cascade reaction involving cyclohexanones, anilines, and diaryl diselenides under metal-free conditions is reported. The ortho-selenation of cyclohexanones with diaryl diselenides, followed by sequential dehydroaromatization with anilines, enables the preparation of a variety of o-selanyl anilines in moderate to excellent yields. This innovative transformation is notable for its excellent tolerance of functional groups and is suitable for the late-stage modification of complex pharmaceuticals.

L-type amino acid transporter 1 (LAT1, SLC7A5) is upregulated in various cancers and associated with disease progression. Nanvuranlat (Nanv; JPH203, KYT-0353), a selective LAT1 inhibitor, suppresses the uptake of large neutral amino acids required for rapid growth and proliferation of cancer cells. Previous studies have suggested that the inhibition of LAT1 by Nanv induces the cell cycle arrest at G0/G1 phase, although the underlying mechanisms remain unclear. Using pancreatic cancer cells arrested at the restriction check point (R) by serum deprivation, we found that the Nanv drastically suppresses the G0/G1-S transition after release. This blockade of the cell cycle progression was accompanied by a sustained activation of p38 mitogen-activated protein kinase (MAPK) and subsequent phosphorylation-dependent proteasomal degradation of cyclin D1. Isoform-specific knockdown of p38 MAPK revealed the predominant contribution of p38α. Proteasome inhibitors restored the cyclin D1 amount and released the cell cycle arrest caused by Nanv. The increased phosphorylation of p38 MAPK and the decrease of cyclin D1 were recapitulated in xenograft tumor models treated with Nanv. This study contributes to delineating the pharmacological activities of LAT1 inhibitors as anti-cancer agents and provides significant insights into the molecular basis of the amino acid-dependent cell cycle checkpoint at G0/G1 phase.

This article introduces a broadband outphasing power amplifier (OPA) design in virtue of a reconfigurable output combiner. Two T-type matching structures loaded with varactors are applied to replace the transmission lines in the conventional architecture. To have broadband performance, the bias voltage applied to the varactors is adjusted when the frequency changes, ensuring proper phase shifting amounts required for outphasing operation. This technique compensates for the frequency dispersion effects inherent in the conventional combiner. Besides, to provide the subamplifiers with correct reactance compensation and impedance in a large frequency band, a postmatching network (PMN) is further employed. These design strategies help to improve the bandwidth and drain efficiency (DE) of the whole OPA. For demonstration, a prototype circuit is successfully implemented using two 10-W GaN HEMT transistors. At 6-dB back-off (BO) power, over 49.3% DE is achieved from 2.4 to 2.8 GHz, accounting for 15.4% fractional bandwidth. In addition, a minimum of 63.6% DE is maintained at saturation in the same frequency band.

Road segmentation from remote sensing images is a challenging task in capturing weak, long, and irregular road features due to the limited connectivity-preserving modeling capability. In this work, we proposed a U-shaped Fourier-deformable convolution network (FDNet) for road segmentation, which integrates the merits of deformable convolutions (DCs) and Fourier convolutions compactly. Specifically, a saliency-aware DC (SD-Conv) layer is proposed for tracing salient road features based on an iterative dynamic offset learning mechanism to grasp extremely tender and weak road objects. Meanwhile, a lightweight global feature extracting module based on spectral convolutions, namely, the adaptive Fourier convolution (AF-Conv) layer, is adopted to learn long-range dependency to extract long and continuous road structures. The proposed SD-Conv layer worked in parallel with the AF-Conv layer to construct a basic and compact block to build the U-shaped FDNet model for road segmentation. Furthermore, to maintain the continuity of road objects in complex road conditions, we introduced a topology-oriented loss function based on the Hausdorff distance (HD) on the persistence diagram (PD) of segmented results, and further combined with softDice loss components for fully supervised training. Our FDNet has been trained and evaluated on two benchmarks, and experimental results show that FDNet achieved state-of-the-art (SOTA) performance. Specifically, it achieved 80.34% on accuracy, 88.42% on precision, and 84.70% on mean intersection over union (mIoU), respectively, on the Massachusetts dataset, and achieved 99.05% on accuracy, 89.21% on precision, 88.61% on recall, and 81.37% on mIoU, respectively, on the DeepGlobe dataset, outperforming most previous methods on both datasets. Codes are available at: https://github.com/zhoucharming/FDNet.

Antidepressants are among the most extensively prescribed psychotropic drugs worldwide. Discontinuation induced withdrawal symptoms have been reported for almost all antidepressants. The incidence of antidepressant withdrawal syndrome (AWS) and other characteristics remain unknown. We searched the PubMed, Embase, PsycINFO, MEDLINE, CINAHL, and Cochrane Central Register of Controlled Trials databases from inception to December 31, 2023. Randomized double-blinded trials, longitudinal or cross-sectional studies that reported the incidence and other characteristics of antidepressant withdrawal symptoms were included. The pooled incidence of AWS was calculated by a random effects model. We included 35 studies, of which 2 studies just provided incidence of specific withdrawal symptoms, and 4 studies only described other characteristics. The pooled incidence of AWS from all available studies was 42.9%, from 11 RCTs was 44.4%, in studies in which the treatment duration was mostly 8-12 weeks, which usually appear within 2 weeks, and were generally measured for <4 weeks. The incidence in selective serotonin-norepinephrine reuptake inhibitors was the lowest (29.7%), followed by selective serotonin reuptake inhibitors (45.6%) and tricyclic antidepressants (59.7%), without significant differences (p= 0.221). Treatment duration showed a dose-response to the incidence of AWS (6–12 W: 35.1%, 12–24 W: 42.7%, >24 W: 51.4%). The half-life did not show such a simple dose-dependent relationship. The pooled estimate was robust regardless whether withdrawal symptoms were measured in RCTs or observational studies (including face-to-face and online survey studies). Tapering the dose reduced the incidence of AWS compared with abrupt stoppage (34.5% vs 42.5%), without a significant difference (p= 0.484). Risk factors for withdrawal symptoms included being female, younger, experiencing adverse effects early in treatment, taking higher doses or longer duration of medication, abrupt cessation of drugs, and those with a lower clearance of drugs or with serotonin 1A receptor gene variation. The findings suggest the incidence of AWS are common and some clinical characteristics and risk factors which can help clinicians identify who is at greater risk of experiencing AWS. Discontinuation studies on long-term antidepressant users with long follow-up periods are required in the future.

In recent years, Artificial Bee Colony (ABC) algorithm has been criticised for its solution search equation, which makes the search capability bias to exploration at the expense of sacrificing exploitation. To solve the defect, many improved ABC variants have been proposed aiming to utilise the elite individuals. Although these related works have been shown to be effective, they rarely take the factor of search direction into account. In fact, the search direction has an important role in determining the performance of ABC. Thus, in this work, we are motivated to investigate how to combine the idea of utilising the elite individuals with the search direction, and a new ABC variant, called DDABC, is designed. In the DDABC, the Depth-First Search (DFS) mechanism and Direction Information Learning (DIL) mechanism are introduced, and the former mechanism is to allocate more computation resources to the elite individuals, while the latter mechanism aims to adapt the search to the promising directions. To verify the effectiveness of the DDABC, experiments are carried out on 22 classic test functions and three relative ABC variants are included as the competitors. The comparison results show the competitive performance of our approach.

The integration of Augmented Reality (AR) with game-based learning helps to create highly immersive, interactive, and stimulating mobile learning environments and has been shown to be effective in practical teaching and learning, but the moderating role of collaborative and competitive strategies in gamified augmented reality learning has not been studied in detail. To investigate the effectiveness of augmented reality-based gamified teaching and learning and the differences in effectiveness under different competitive strategies, this study developed a gamified learning course and the accompanying AR software, and conducted a systematic random sampling of 175 first-year university students to differentiate between different experimental groups, with those who used a team competitive strategy as the control group and those who used an individual competitive strategy as the experimental group, and used the Cultural Literacy, Computational Thinking, Cognitive Load and Game Experience questionnaire instruments to measure learning outcomes. Results showed that the use of augmented reality-based gamification learning strategies significantly improved students' cultural literacy and algorithmic thinking in computational thinking. The collaboration group had a significantly higher sense of positive game experience and cultural literacy than the individual group, while the individual group had significantly higher mental effort and cognitive load than the collaboration group. These finding shed light on the effect of AR–GBL promoting learning engagement, knowledge presentation and teamwork. It also verify potential impact of integrating AR technology and the use of motivational strategies of inter-group competition can be considered in sensory experience-based activities.

This article presents theoretical analysis and design methodology of a dual-mode three-way Doherty power amplifier (DPA) with an extended high-efficiency range and robust load mismatch adaptability. A nonlinear current model for active devices is established and employed to analyze the impact of load mismatches in the three-way DPA and a novel output combiner is proposed to minimize the mismatch impact by reconfiguring the gate bias conditions and setting distinct loads at the control port of the combiner. To validate the proposed DPA architecture and design approach, a prototype using commercial Gallium Nitride (GaN) transistors operated at the center frequency of 2.0 GHz was designed and fabricated. Under the ideal condition with a matched $50~\Omega $ load, the fabricated DPA attains higher than 60% efficiency at 9 dB back-off. Through mode re-configuration, the proposed DPA presents 47.8%–56.7% back-off efficiency with less than 1.5 dB power fluctuation at 2:1 voltage standing wave ratio (VSWR) over 360° phase span. When driven by a 80 MHz signal with 8 dB peak to average power ratio (PAPR), the DPA achieves 45.7%–52.2% average efficiency and better than −45 dBc adjacent channel power ratio (ACPR) with digital pre-distortion (DPD) under the load mismatch conditions.

We present an ultra-broadband and low-modal-crosstalk mode multiplexer based on cascaded vertical directional couplers formed by adiabatic-tapered waveguides without mode conversion. The input and output ports of the mode multiplexer are designed in the same planar layer to meet the standard package requirement. We design and fabricate a three-mode mode multiplexer, which serves to spatially combine and separate the E11, E21 and E12 modes in the few-mode-core outputs without mode conversion with polymer material, where adiabatic tapers are utilized in the design of directional coupler to extend the operation wavelength and two pairs of directional couplers serve to suppress the modal crosstalks. We fabricate a typical device to demonstrate the idea. The fabricated device has a length of 29 mm, which provides coupling ratios higher than 97% and modal crosstalks lower than −19.6 dB over the C+L band and beyond with weak polarization dependence. The proposed mode multiplexer is fiber capable and allows to connect with the few-mode fibers directly by the use of fan-in and fan-out technology. The proposed mode multiplexer is simple, scalable, and effective to extend the operation wavelength as well as suppressing the modal crosstalks, which provides a powerful device to manipulate the modes in a mode division multiplexing system and an effective method to realize an all-optical chip for simplifying the Multi-Input Multi-Output signal processing with low power consumption.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, and prognosis assessment is crucial for guiding treatment decisions. In this study, we aimed to develop a personalized prognostic model for HCC based on RNA editing. RNA editing is a post-transcriptional process that can affect gene expression and, in some cases, play a role in cancer development. By analyzing RNA editing sites in HCC, we sought to identify a set of sites associated with patient prognosis and use them to create a prognostic model. We gathered RNA editing data from the Synapse database, comprising 9990 RNA editing sites and 250 HCC samples. Additionally, we collected clinical data for 377 HCC patients from the Cancer Genome Atlas (TCGA) database. We employed a multi-step approach to identify prognosis-related RNA editing sites (PR-RNA-ESs). We assessed how patients in the high-risk and low-risk groups, as defined by the model, fared in terms of survival. A nomogram was developed to predict the precise survival prognosis of HCC patients and assessed the prognostic model’s utility through a receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Our analysis identified 33 prognosis-related RNA editing sites (PR-RNA-ESs) associated with HCC patient prognosis. Using a combination of LASSO regression and cross-validation, we constructed a prognostic model based on 13 PR-RNA-ESs. Survival analysis demonstrated significant differences in the survival outcomes of patients in the high-risk and low-risk groups defined by this model. Additionally, the differential expression of the 13 PR-RNA-ESs played a role in shaping patient survival. Risk-prognostic investigations further distinguished patients based on their risk levels. The nomogram enabled precise survival prognosis prediction. Our study has successfully developed a highly personalized and accurate prognostic model for individuals with HCC, leveraging RNA editing data. This model has the potential to revolutionize clinical evaluation and medical management by providing individualized prognostic information. The identification of specific RNA editing sites associated with HCC prognosis and their incorporation into a predictive model holds promise for improving the precision of treatment strategies and ultimately enhancing patient outcomes in HCC.

Neuromorphic cameras are emerging imaging technology that has advantages over conventional imaging sensors in several aspects including dynamic range, sensing latency, and power consumption. However, the signal-to-noise level and the spatial resolution still fall behind the state of conventional imaging sensors. In this article, we address the denoising and super-resolution problem for modern neuromorphic cameras. We employ 3D U-Net as the backbone neural architecture for such a task. The networks are trained and tested on two types of neuromorphic cameras: a dynamic vision sensor and a spike camera. Their pixels generate signals asynchronously, the former is based on perceived light changes and the latter is based on accumulated light intensity. To collect the datasets for training such networks, we design a display-camera system to record high frame-rate videos at multiple resolutions, providing supervision for denoising and super-resolution. The networks are trained in a noise-to-noise fashion, where the two ends of the network are unfiltered noisy data. The output of the networks has been tested for downstream applications including event-based visual object tracking and image reconstruction. Experimental results demonstrate the effectiveness of improving the quality of neuromorphic events and spikes, and the corresponding improvement to downstream applications with state-of-the-art performance.

Study interaction between ligands and protein receptors is a key step for biomarker research and drug discovery. In situ measurement of cell surface membrane protein binding on whole cells eliminates the cost and pitfalls associated with membrane protein purification. Ligand binding to membrane protein was recently found to induce nanometer-scale cell membrane deformations, which can be monitored with real-time optical imaging to quantify ligand/protein binding kinetics. However, the insight into this phenomenon has still not been fully understood. We hypothesize that ligand binding can change membrane stiffness, which induces membrane deformation. To investigate this, cell height and membrane stiffness changes upon ligand binding are measured using atomic force microscopy (AFM). Wheat germ agglutinin (WGA) is used as a model ligand that binds to the cell surface glycoprotein. The changes in cell membrane stiffness and cell height upon ligand bindings are determined for three different cell lines (human A431, HeLa, and rat RBL-2H3) on two different substrates. AFM results show that cells become stiffer with increased height after WGA modification for all cases studied. The increase in cell membrane stiffness is further confirmed by plasmonic scattering microscopy, which shows an increased cell spring constant upon WGA binding. Therefore, this study provides direct experimental evidence that the membrane stiffness changes are directly correlated with ligand binding-induced cell membrane deformation.

Making full use of temporal and spatial information is critical to cope with the appearance changes of objects in visual object tracking. However, existing methods in the tracking field, which employ a memory network at frame level to learn this information, bring redundancy and cannot build long-term relationships among historical frames due to the limited memory size. In this paper, we propose a novel memory network, Pixel-level Spatio-Temporal Memory (PSTM), which organizes object features in an efficient way to leverage temporal and spatial context information. Specifically, PSTM is constructed and updated by a memory writer, which includes a pixel-level updating strategy to maintain the temporal consistency and dynamically memorize the noteworthy variations. Furthermore, in order to exploit relationships between the object and search region and precisely estimate the state of the object, we propose a memory reader, Pixel-wise Matching and Refinement module (PMR), and model spatial context without a complex manual-designed mechanism. Comprehensive experiments and comparisons on challenging large-scale benchmarks, including GOT-10k, TrackingNet, LaSOT, OTB2015, VOT2020, and NfS, have demonstrated the effectiveness of our proposed method, which performs favorably against state-of-the-art trackers.

This study investigated differences in eye movement indicators among forklift operators with different situation awareness (SA) and the relationship between eye movement indicators and the SA of forklift operators to verify the effectiveness of eye movement tracking in assessing SA and the factors affecting operators’ SA for improving forklift operation safety. An eye movement tracking system was used to collect eye movement data from 15 forklift operators while they performed a series of forklift tasks. The SA global assessment technique (SAGAT) was used to determine the SA score of each operator. The results indicated that the fixation duration percentage (FDP), fixation count percentage (FCP) and average fixation duration (AFD) all had a positive relationship with SA in the work-related area of interest (AOI). These findings support eye movement tracking as an effective technique to quantitatively evaluate forklift operators’ SA and provide insights into how forklift operators’ SA could be improved.

High-performance phototransistors based on sputtered amorphous ultrathin 15-nm-Ga2O3/9-nm-IGZO heterojunction were fabricated and achieved excellent solar-blind detection performances under 254-nm light illumination with the short rise detection time of 1.18 s, the responsivity of $8.35\times 10^{{3}}\text{A}\cdot \text{W}^{-{1}}$ , detectivity of $2.56\times 10^{{17}}$ Jones, and photo-to-dark current ratio of $5.21\times 10^{{6}}$ . Compared with those of the reference single layer amorphous Ga2O3 and IGZO phototransistors, the Ga2O3/IGZO phototransistors show much higher performances, and this is attributed to the synergistic effect of the effective photocarrier generation in the wide bandgap Ga2O3 layer, the effective separation of the photocarriers at the heterojunction, and the enhanced electron transport in the IGZO channel layer. Furthermore, a high contrast solar-blind imaging of an “N” pattern was realized with the $10\times10$ array based on these Ga2O3/IGZO phototransistors. The superior solar-blind detection performances, the large area and mass produced sputtering method, and the low maximum preparation temperature of 170°C of these amorphous Ga2O3/IGZO phototransistors, indicate their great application potential in flexible solar-blind imaging and photoelectronic integrated circuits.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia seen in clinical settings, which has been associated with substantial rates of mortality and morbidity. However, clinically available drugs have limited efficacy and adverse effects. We aimed to investigate the mechanisms of action of andrographolide (Andr) with respect to AF. We used network pharmacology approaches to investigate the possible therapeutic effect of Andr. To define the role of Andr in AF, HL-1 cells were pro-treated with Andr for 1 h before rapid electronic stimulation (RES) and rabbits were pro-treated for 1 d before rapid atrial pacing (RAP). Apoptosis, myofibril degradation, oxidative stress, and inflammation were determined. RNA sequencing (RNA-seq) was performed to investigate the relevant mechanism. Andr treatment attenuated RAP-induced atrial electrophysiological changes, inflammation, oxidative damage, and apoptosis both in vivo and in vitro. RNA-seq indicated that oxidative phosphorylation played an important role. Transmission electron microscopy and adenosine triphosphate (ATP) content assay respectively validated the morphological and functional changes in mitochondria. The translocation of nuclear factor erythroid 2-related factor 2 (Nrf2) to the nucleus and the molecular docking suggested that Andr might exert a therapeutic effect by influencing the Keap1-Nrf2 complex. In conclusions, this study revealed that Andr is a potential preventive therapeutic drug toward AF via activating the translocation of Nrf2 to the nucleus and the upregulation of heme oxygenase-1 (HO-1) to promote mitochondrial bioenergetics.

Detection of infrared dim and small targets with diverse and cluttered background plays a significant role in many applications. In this article, we propose a deep low-rank and sparse patch-image network, termed as Deep-LSP-Net, to effectively detect small targets in a single infrared image. Specifically, by using the local patch construction scheme, we first transform the original infrared image into a patch image, which can be decomposed as a superposition of the low-rank background component and the sparse target component. The target detection is thus formulated as an optimization problem with low-rank and sparse regularizations, which can be solved by the alternating direction method of multipliers (ADMMs). We unroll the iterative algorithm into deep neural networks, where a generalized sparsifying transform and a singular value thresholding (SVT) operator are learned by the convolutional neural networks (CNNs) to avoid tedious parameter tuning and improve the interpretability of the neural networks. We conduct comprehensive experiments on two public datasets. Both qualitative and quantitative experimental results demonstrate that the proposed algorithm can obtain improved performance in small infrared target detection compared with state-of-the-art algorithms.

A design strategy to enhance the efficiency of the dual-mode Doherty power amplifier (DPA) is proposed. The continuous multiple harmonic regulation approach is adopted to realize high efficiency of dual-mode DPA in a wide operating frequency band. Multi-harmonic control and dual-mode impedance matching are performed by a single load modulation network. The DPA’s operating frequency band and harmonic control type can be simultaneously switched through reciprocal gate bias. A DPA using GaN devices with continuous Class $F$ for Mode I (1.5–2.4 GHz) and Class $J$ /continuous inverse Class $F$ for Mode II (1.3–1.4 GHz/2.5–2.7 GHz) is designed and implemented. The fabricated DPA achieves a measured 10.5–14.3-dB gain and 39.2–41.8-dBm saturated power. Due to the proposed strategies, 39.1%–58.2% and 55.6%-71.9% drain efficiencies are obtained respectively at 6-dB output power back-off and saturation output power for the designed DPA. For the down-link long-term evolution (LTE) signal with a channel bandwidth of 20 MHz and a peak-to-average power ratio (PAPR) of 7.5 dB, the average adjacent channel power ratio of the fabricated DPA is better than 40 dBc after digital predistortion at 1.3, 2.0 and 2.6 GHz with average output power back-off (OBO) efficiency of 37.8%, 39.3% and 43.3%.

Nowadays, the presence of highly toxic and persistent residues of pesticides in water and food around the world is becoming a serious problem, and so their rapid and sensitive detection is critical to human health. In this work, a 3D composite nanoparticle of porous PDA (polydopamine) microspheres and Au NPs (PPDA@Au NPs) was proposed as a SERS substrate to detect pesticides. Porous PDA as a substrate was first synthesized with F127 (Pluronic F127), dopamine hydrochloride, and 1,3,5-TMB (1,3,5-trimethylbenzene) under weakly alkaline conditions by a one-step method. Then, HAuCl4was in situreduced in the pores of PPDA spheres and grew sequentially for effecting the reducibility of PPDA. As a result, uniform 3D PPDA@Au NPs with “hot spots” were successfully synthesized as SERS substrates, which could effectively avoid the agglomeration of gold nanoparticles to greatly improve the sensitivity and uniformity of the SERS platform. At the same time, methyl parathion, 4-chlorophenol, and 2,4-D as representatives of pesticides were detected with the proposed PPDA@Au NP-based SERS platform, with detection limits lower than 7.26, 7.52, and 11 ng mL−1, separately. The current work presents a simple preparation method to prepare sensitive and uniform SERS platform PPDA@Au NPs, which have potential for applications in actual pesticide and drug testing.

Long-wave infrared (LWIR) bands in multispectral datasets are extremely useful in many applications. However, the LWIR bands usually suffer from undesirable stripe noise, which impedes their further application. Compared with emission-dominated LWIR, the mid-wave infrared (MWIR) bands containing both emitted and reflected radiation usually exhibit higher image quality. In this article, we propose a novel two-stage MWIR energy separation and image guidance (MES-IG) algorithm to destripe the LWIR images with the assistance of the MWIR bands. In the first stage, we decompose the MWIR image into the emitted and reflected components by solving a constrained optimization problem. Specifically, we impose the low-rank penalty to enforce the similarities between MWIR and LWIR, and we use the total variation (TV) regularization to exploit the similarities between MWIR and visible and near-infrared (VNIR) images. In the second stage, the obtained emitted component of MWIR is considered as the guidance image to remove the stripes in the LWIR images by adopting the 1-D guided filter algorithm. Numerical experiments on the Chinese Gaofen-5 satellite and the Moderate Resolution Imaging Spectroradiometer (MODIS) data demonstrate the utility of the proposed method in providing improved LWIR image destriping performance over the state-of-the-art algorithms.

•Pancreatic tumors grew more aggressively in MG53 knockout mice compared to wild type mice in animal models.•MG53 localizes to the nucleus of pancreatic cancer cells, exerting inhibitory effects on colony formation and proliferation.•Decreased MG53 and increased KIF11 expression were found in pancreatic cancer.•MG53 can bind to KIF11 promoter to transcriptionally suppress KIF11 expression, leading to cell cycle arrest in pancreatic cancer cells.

In this paper, two types of balanced-to-unbalanced quadrature couplers with six hybrid ports and arbitrary power division ratio are proposed. For Type 1, the quadrature coupler can realize two power division functions, including balanced/unbalanced to balanced-unbalanced-hybrid, and for Type 2, the quadrature coupler contains balanced -to-unbalanced and unbalanced-to-balanced power division functions. To reduce the circuit size, double-sided parallel-strip line (DSPSL) swap structure is used to replace the half-wavelength transmission line, and compact circuit size can be easily achieved. To validate the performance of the quadrature couplers, two types of the balanced-to-unbalanced quadrature couplers centered at 1 GHz with power division ratios 1:1 and 3:1 are fabricated and measured, respectively. The common mode suppression higher than 10 dB (bandwidths over than 176%) can be realized, the isolation and in-band matching level can be also improved.

This article presents a modeling approach and its implementation to study the impact of the top-side diamond integration on the ${f}_{\text {T}}$ and ${f}_{\text {max}}$ performance of a millimeter-wave (mm-wave) N-polar gallium nitride (GaN) high-electron-mobility transistor (HEMT). This approach uses a co-simulation model formed by an equivalent small-signal circuit model of the device implemented in PathWave advanced design system (ADS) and full-wave simulations of 3-D modeling diamond passivation from Ansys high frequency simulation software (HFSS). Thin-film diamond as a passivation layer and a heat spreader on top of the device channel is explored as a function of the diamond’s dielectric constant and its thickness to understand how it affects the device’s ${f}_{\text {T}}$ and ${f}_{\text {max}}$ . The simulation results serve as a guide to the optimization of the radio frequency (RF) performance of mm-wave HEMT devices, aiding the device design of the diamond passivation. The designed methodology was applied to other passivation, such as benzocyclobutene (BCB) for benchmarking. This method allowed us to estimate the tradeoff in electrical performance for anticipated thermal benefits. A maximum reduction of 23.6% in ${f}_{\text {T}}$ and 21.8% in ${f}_{\text {max}}$ was obtained when the diamond passivation thickness is $2.7 \mu \text{m}$ with a dielectric constant of 4.

Administration of recombinant human MG53 (rhMG53) intravenously following a traumatic brain injury in swine led to decreased brain lesion size with localization to the injured region. rhMG53 has potential as a future therapy in treatment of TBI. #braininjury #TBI

Single-molecule detection can push beyond ensemble averages and reveal the statistical distributions of molecular properties. Measuring the binding kinetics of single proteins also represents one of the critical and challenging tasks in protein analysis. Here, we report total internal reflection-based evanescent scattering microscopy with label-free single-protein detection capability. Total internal reflection is employed to excite the evanescent field to enhance light–analyte interaction and reduce environmental noise. As a result, the system provides wide-field imaging capability and allows excitation and observation using one objective. In addition, this system quantifies protein binding kinetics by simultaneously counting the binding of individual molecules and recording their binding sites with nanometer precision, providing a digital method to measure binding kinetics with high spatiotemporal resolution. This approach does not employ specially designed microspheres or nanomaterials and may pave a way for label-free single-protein analysis in conventional microscopy.

In nature, the foraging behaviour of bee colony is always guided by some elite honeybees with the aim of maximising the overall nectar amount. Being inspired by this phenomenon, we propose an improved artificial bee colony (ABC) variant by using elite information. In our approach, as the main way of generating new offspring, two novel solution search equations are developed based on utilising elite information, which has the advantages of accelerating convergence rate. Moreover, to preserve the search experience of the scout bee phase, a new reinitialisation method is proposed based on using elite information. Extensive experiments are conducted on the CEC 2013 and CEC 2015 test suites, and other four relevant ABC variants are included in the comparison. The results show that our approach has better performance in terms of convergence speed and result accuracy.