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The orthogonal frequency-division multiplexing (OFDM) mode with a linear frequency modulation (LFM) signal as the baseband waveform has been widely studied and applied in multiple-input multiple-output (MIMO) radar systems. However, its high sidelobe levels after pulse compression affect the target detection of radar systems. For this paper, theoretical analysis was performed, to investigate the causes of high sidelobe levels in OFDM-LFM waveforms, and a novel waveform optimization design method based on deep neural networks is proposed. This method utilizes the classic ResNeXt network to construct dual-channel neural networks, and a new loss function is employed to design the phase and bandwidth of the OFDM-LFM waveforms. Meanwhile, the optimization factor is exploited, to address the optimization problem of the peak sidelobe levels (PSLs) and integral sidelobe levels (ISLs). Our numerical results verified the correctness of the theoretical analysis and the effectiveness of the proposed method. The designed OFDM-LFM waveforms exhibited outstanding performance in pulse compression and improved the detection performance of the radar.

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This paper proposes an adaptive image enhancement method that aims to effectively restore the brightness, detail, and natural color of various low-illumination images. To be specific, the method first constructs the initial dual-channel illumination map of the image. Next, the optimal illumination correction coefficient is calculated by the prior information entropy of the initial illumination map, which helps to correct potentially erroneous illumination estimates. To restore the illumination, gamma correction is used with the optimal illumination correction coefficient. Finally, an improved perfect reflection constraint model is used to restore the color of the image. Both visual analysis and quantitative comparison with state-of-the-art methods demonstrate the effectiveness of the method in terms of brightness adjustment, detail recovery, and color restoration.

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Predicting regional carbon dioxide (CO2) emissions is essential for advancing toward global carbon neutrality. This study introduces a novel CO2 emissions prediction model tailored to the unique environmental, economic, and energy consumption of Shanghai Chongming. Utilizing an innovative hybrid approach, the study first applies grey relational analysis to evaluate the influence of economic activity, natural conditions, and energy consumption on CO2 emissions. This is followed by the implementation of a dual-channel pooled convolutional neural network (DCNN) that captures both local and global features of the data, enhanced through feature stacking. Gated recurrent unit (GRU) network then assesses the temporal aspects of these features, culminating in precise CO2 emission predictions for the region. The results indicate: (1) The proposed hybrid model achieves accurate predictions based on accounting data, with high precision, low error, and good stability. (2) The study found an overall increase in Chongming's carbon emissions from 2000 to 2022, with the prediction results being generally consistent with existing research findings. (3) The proposed method, based on Chongming's CO2 emission predictions, addresses issues such as the scarcity of effective accounting data and inaccuracies in traditional calculation methods. The results can provide effective technical support for local government policies on carbon reduction and promote sustainable development.

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Cell type identification is an indispensable analytical step in single-cell data analyses. To address the high noise stemming from gene expression data, existing computational methods often overlook the biologically meaningful relationships between genes, opting to reduce all genes to a unified data space. We assume that such relationships can aid in characterizing cell type features and improving cell type recognition accuracy. To this end, we introduce scPriorGraph, a dual-channel graph neural network that integrates multi-level gene biosemantics. Experimental results demonstrate that scPriorGraph effectively aggregates feature values of similar cells using high-quality graphs, achieving state-of-the-art performance in cell type identification.

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BACKGROUND: With the continuous progress of surgical technology and improvements in medical standards, the treatment of gastric cancer surgery is also evolving. Proximal gastrectomy is a common treatment, but double-channel anastomosis and tubular gastroesophageal anastomosis have attracted much attention in terms of surgical options. Each of these two surgical methods has advantages and disadvantages, so it is particularly important to compare and analyze their clinical efficacy and safety. AIM: To compare the surgical safety, clinical efficacy, and safety of double-channel anastomosis and tubular gastroesophageal anastomosis in proximal gastrectomy. METHODS: The clinical and follow-up data of 99 patients with proximal gastric cancer who underwent proximal gastrectomy and were admitted to our hospital between January 2018 and September 2023 were included in this retrospective cohort study. According to the different anastomosis methods used, the patients were divided into a double-channel anastomosis group (50 patients) and a tubular gastroesophageal anastomosis group (49 patients). In the double-channel anastomosis, Roux-en-Y anastomosis of the esophagus and jejunum was performed after proximal gastric dissection, and then side-to-side anastomosis was performed between the residual stomach and jejunum to establish an antireflux barrier and reduce postoperative gastroesophageal reflux. In the tubular gastroesophageal anastomosis group, after the proximal end of the stomach was cut, tubular gastroplasty was performed on the distal stump of the stomach and a linear stapler was used to anastomose the posterior wall of the esophagus and the anterior wall of the stomach tube. The main outcome measure was quality of life 1 year after surgery in both groups, and the evaluation criteria were based on the postgastrectomy syndrome assessment scale. The greater the changes in body mass, food intake per meal, meal quality subscale score, and total measures of physical and mental health score, the better the condition; the greater the other indicators, the worse the condition. The secondary outcome measures were intraoperative and postoperative conditions, the incidence of postoperative long-term complications, and changes in nutritional status at 1, 3, 6, and 12 months after surgery. RESULTS: In the double-channel anastomosis cohort, there were 35 males (70%) and 15 females (30%), 33 (66.0%) were under 65 years of age, and 37 (74.0%) had a body mass index ranging from 18 to 25 kg/m2. In the group undergoing tubular gastroesophageal anastomosis, there were eight females (16.3%), 21 (42.9%) individuals were under the age of 65 years, and 34 (69.4%) had a body mass index ranging from 18 to 25 kg/m2. The baseline data did not significantly differ between the two groups (P > 0.05 for all), with the exception of age (P = 0.021). The duration of hospitalization, number of lymph nodes dissected, intraoperative blood loss, and perioperative complication rate did not differ significantly between the two groups (P > 0.05 for all). Patients in the dual-channel anastomosis group scored better on quality of life measures than did those in the tubular gastroesophageal anastomosis group. Specifically, they had lower scores for esophageal reflux [2.8 (2.3, 4.0) vs 4.8 (3.8, 5.0), Z = 3.489, P < 0.001], eating discomfort [2.7 (1.7, 3.0) vs 3.3 (2.7, 4.0), Z = 3.393, P = 0.001], total symptoms [2.3 (1.7, 2.7) vs 2.5 (2.2, 2.9), Z = 2.243, P = 0.025], and other aspects of quality of life. The postoperative symptoms [2.0 (1.0, 3.0) vs 2.0 (2.0, 3.0), Z = 2.127, P = 0.033], meals [2.0 (1.0, 2.0) vs 2.0 (2.0, 3.0), Z = 3.976, P < 0.001], work [1.0 (1.0, 2.0) vs 2.0 (1.0, 2.0), Z = 2.279, P = 0.023], and daily life [1.7 (1.3, 2.0) vs 2.0 (2.0, 2.3), Z = 3.950, P < 0.001] were all better than those of the tubular gastroesophageal anastomosis group. The group that underwent tubular gastroesophageal anastomosis had a superior anal exhaust score [3.0 (2.0, 4.0) vs 3.5 (2.0, 5.0) (Z = 2.345, P = 0.019] compared to the dual-channel anastomosis group. Hemoglobin, serum albumin, total serum protein, and the rate at which body mass decreased one year following surgery did not differ significantly between the two groups (P > 0.05 for all). CONCLUSION: The safety of double-channel anastomosis in proximal gastric cancer surgery is equivalent to that of tubular gastric surgery. Compared with tubular gastric surgery, double-channel anastomosis is a preferred surgical technique for proximal gastric cancer. It offers advantages such as less esophageal reflux and improved quality of life.

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Multi-focus image fusion (MFIF) is an important technique that aims to combine the focused regions of multiple source images into a fully clear image. Decision-map methods are widely used in MFIF to maximize the preservation of information from the source images. While many decision-map methods have been proposed, they often struggle with difficulties in determining focus and non-focus boundaries, further affecting the quality of the fused images. Dynamic threshold neural P (DTNP) systems are computational models inspired by biological spiking neurons, featuring dynamic threshold and spiking mechanisms to better distinguish focused and unfocused regions for decision map generation. However, original DTNP systems require manual parameter configuration and have only one stimulus. Therefore, they are not suitable to be used directly for generating high-precision decision maps. To overcome these limitations, we propose a variant called parameter adaptive dual channel DTNP (PADCDTNP) systems. Inspired by the spiking mechanisms of PADCDTNP systems, we further develop a new MFIF method. As a new neural model, PADCDTNP systems adaptively estimate parameters according to multiple external inputs to produce decision maps with robust boundaries, resulting in high-quality fusion results. Comprehensive experiments on the Lytro/MFFW/MFI-WHU dataset show that our method achieves advanced performance and yields comparable results to the fourteen representative MFIF methods. In addition, compared to the standard DTNP systems, PADCDTNP systems improve the fusion performance and fusion efficiency on the three datasets by 5.69% and 86.03%, respectively. The codes for both the proposed method and the comparison methods are released at https://github.com/MorvanLi/MFIF-PADCDTNP.

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Hypochlorous acid (HClO) serves as a critical biomarker in inflammatory diseases such as rheumatoid arthritis (RA), and its real-time imaging is essential for understanding its biological functions. In this study, we designed and synthesized a novel probe, RHMB, which ingeniously integrates rhodamine B and methylene blue fluorophores with HClO-specific responsive moieties into a single molecular framework. Upon exposure to HClO, RHMB exhibited significant dual-channel fluorescence enhancement characterized by high sensitivity (LODs of 2.55 nM and 14.08 nM), excellent selectivity, and rapid response time (within 5 s). Notably, RHMB enabled reliable imaging of both exogenous and endogenous HClO in living cells and in zebrafish, employing a unique duplex-imaging turn-on approach that highlighted its adaptability across various biological contexts. Furthermore, RHMB effectively monitored HClO fluctuations in an RA mouse model and assessed the therapeutic efficacy of diclofenac (Dic) in alleviating RA symptoms. These findings underscore the potential of RHMB as an invaluable tool for elucidating the biological roles of HClO in various diseases.

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Endoscopic mucosal resection (EMR) is the recommended technique for colon polypectomy for nonpedunculated lesions that are >20 mm in size not requiring excision. Dual-channel EMR (DC-EMR) uses an endoscope with two working channels to facilitate easier submucosal injection, snare resection, and clip closure of polypectomy defects. There is also promising early literature indicating that this endoscopic modality can reduce the overall learning curve present for single-channel colonoscopy EMR. This chapter will describe the steps and techniques required to perform DC-EMR, potential complications, recommended postprocedure surveillance, and future directions.

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Although research on photodynamic therapy (PDT) of malignant tumor has made considerable progress in recent years, it is a remaining challenge to extend PDT to the second near-infrared window (NIR-II) along with real-time and accurate NIR-II fluorescence imaging to determine drug enrichment status and achieve high treatment efficacy. In this work, lanthanide nanoparticles (Ln NPs)-based nanoplatform (LCR) equipped with photosensitizer Chlorin e6 (Ce6) and targeting molecular NH2-PEG1000-cRGDfK are developed, which can achieve NIR-II photodynamic therapy (PDT) and NIR-II fluorescence imaging by dual channel excitation. Under 808 nm excitation, Nd3+ in the outer layer can absorb the energy and transfer inward to emit strong NIR-II emissions (1064 and 1525 nm). Due to the low background noise of NIR-II light and the targeting effect of NH2-PEG1000-cRGDfK, LCR can recognize tiny tumor tissue (≈3 mm) and monitor drug distribution in vivo. Under 1530 nm excitation, internal Er3+ can be self-sensitized, generating intense upconversion emission (662 nm) that can effectively activate Ce6 for in vivo PDT due to the deep tissue penetration of NIR-II light. This study provides a paradigm of theranostic nanoplatform for both real-time fluorescence imaging and PDT of orthotopic breast tumor in NIR-II window.

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Benzoyl peroxide (BPO), as a widely used organic peroxide, has attracted widespread attention from all sectors of society for its environmental hazards and potential risks to human health. Herein, we employed a Förster resonance energy transfer (FRET) strategy to construct a novel ratiometric fluorescent probe CY-DCI for BPO detection in food, zebrafish, and mice. Specifically, a hemicyanine fluorophore and a dicyanoisophorone fluorophore were connected with a piperazine group as donor and acceptor, respectively, and an olefinic unsaturated bond as the reaction site. CY-DCI has favorable selectivity and an excellent detection limit as low as 58.1 nM, and the recovery rates for real-sample detection ranged from 95.8 % to 104 %, with relative standard deviations (RSD) less than 2.58 %. To further improve its practicality, silica gel plates and test strips containing CY-DCI (0-50 µM) were developed for naked-eye detection of BPO with satisfactory results. Additionally, this novel probe was then applied for ratiometric imaging of living zebrafish and mice and showed high ratiometric imaging resolution in the green and red channels, thus demonstrating its practical application for BPO detection and toxicity early warning in food and biosystems.

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Methamphetamine (METH) is the second abused drug which affects abusers' health and induces social crimes, developing novel methods with high sensitivity and selectivity for METH detecting is still challenging. In this paper, a colorimetric and fluorescent dual-channel sensor for METH has been constructed. We combine the enzyme-mimic catalytic activity of gold nanoparticles (GNPs) with high target specificity of METH aptamer to create a nanosensor (Apt-GNP), in the presence of METH, the absorption of 3,3',5,5'-tetramethylbenzidine (OxTMB) at 650 nm enhanced with METH concentration increasing, while the absorption characteristic peak of GNPs at 530 nm remained almost unchanged. The ratio of A650nm/A530nm and METH concentration had a good linear relationship when METH concentration was in the range of 5-50 µM, and the corresponding linear equation is A650nm/A530nm = 0.00727CMETH (µM) + 0.783 with R2 = 0.997 and LOD = 0.40 µM (LOD = 3σ/s, n = 11). Interestingly, the fluorescence emission of Rhodamine B (RB) overlaps with the absorption spectrum of OxTMB which represents the content of METH and the fluorescence signal of RB can be quenched through internal filtering effect (IEF). Hence, when RB was doped to the detection system, the decay of RB fluorescence can reflect the concentrations change of METH. Accordingly, the linear equation is F/FR = -0.00751CMETH (µM) + 0.895 with R2 = 0.993 and LOD = 0.40 µM, where F was the fluorescence of the analytical solution at 580 nm with METH and FR was fluorescence of RB control solution. The dual-channel sensor can measure METH in serum and artificial urine successfully which is potential to be applied in drug-using crime sites and provide direct evidence to law enforcement officials.

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Understanding charge transport in metal ion-mediated glutathione-stabilized gold nanoclusters (GSH-Au NCs) has proved difficult due to the presence of various competitive mechanisms, such as electron transfer (ET) and aggregation induction effect (AIE). In this paper, we present a dual-channel fluorescence (FL) and second-order Rayleigh scattering (SRS) sensing method for high-throughput classification of metal ions, relying on the competition between ET and AIE using GSH-Au NCs. The SRS signals show significant enhancement when Pb2+, Ag+, Al3+, Cu2+, Fe3+, and Hg2+ are present, as a result of the aggregation of GSH-Au NCs. Notably, the fluorescence signal exhibits the opposite trend. The FL intensities of GSH-Au NCs are enhanced by Pb2+, Ag+, and Al3+ through the AIE mechanism, while they are quenched by Cu2+, Fe3+, and Hg2+, which is dominated by the ET mechanism. By employing principal component analysis and hierarchical cluster analysis, these signals are transformed into unique fingerprints and Euclidean distances, respectively, enabling successful distinction of six metal ions and their mixtures with a low detection limit of 30 nM. This new strategy has successfully addressed interference from impurities in the testing of real water samples, demonstrating its strong ability to detect multiple metal ions. Impressively, we have achieved molecular cryptosteganography, which involves encoding, storing, and concealing information by transforming the selective response of GSH-Au NCs to binary strings. This research is anticipated to advance utilization of nanomaterials in logic sensing and information safety, bridging the gap between molecular sensors and information systems.

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Mercury (Hg) researchers have made progress in understanding atmospheric Hg, especially with respect to oxidized Hg (HgII) that can represent 2 to 20% of Hg in the atmosphere. Knowledge developed over the past ∼10 years has pointed to existing challenges with current methods for measuring atmospheric Hg concentrations and the chemical composition of HgII compounds. Because of these challenges, atmospheric Hg experts met to discuss limitations of current methods and paths to overcome them considering ongoing research. Major conclusions included that current methods to measure gaseous oxidized and particulate-bound Hg have limitations, and new methods need to be developed to make these measurements more accurate. Developing analytical methods for measurement of HgII chemistry is challenging. While the ultimate goal is the development of ultrasensitive methods for online detection of HgII directly from ambient air, in the meantime, new surfaces are needed on which HgII can be quantitatively collected and from which it can be reversibly desorbed to determine HgII chemistry. Discussion and identification of current limitations, described here, provide a basis for paths forward. Since the atmosphere is the means by which Hg is globally distributed, accurately calibrated measurements are critical to understanding the Hg biogeochemical cycle.

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Tetracycline (TC) and copper ion (Cu2+), as important additives in animal feed, play a crucial role in disease prevention and growth regulation. However, the abuse leads to concentration accumulation, which seriously threatens human health and the ecological environment. There is an urgent need to develop a detection method to achieve fast and synchronous detection of these pollutants without cross-interference. Here, a carbon dots-doped lanthanide-based fluorescent nanosensor (CDs@Tb-MOFs@SiO2-NH2-Eu) was synthesized, which can detect TC in the 380 nm channel by "antenna effect" and internal filtering effects (IFE), and identify Cu2+ in the 320 nm channel. The sensor was highly sensitive to TC within 0-4 µM with a detection limit as low as 3.64 nM, and Cu2+ could be detected within 0-40 µM with a detection limit of 38 nM. A portable dual-channel visual fluorescence sensor was obtained by loading the probes onto test paper and cotton swabs in food samples, which indicates the practicability of this sensing strategy.

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Biothiols, including cysteine (Cys), homocysteine (Hcy), and glutathione (GSH), play distinct yet crucial roles in various mitochondrial physiological activities. However, due to their similar chemical structures, distinguishing and detecting Cys/Hcy/GSH poses a considerable challenge. In this study, we developed a dual-channel, mitochondrial-targeted fluorescent probe termed QX-NBD, designed specifically for discriminating Cys/Hcy from GSH. The incorporation of a quinolinium group endowed the probe with excellent mitochondrial targeting capabilities. This functionality arose from the positively charged group's ability to selectively bind to negatively charged mitochondrial membranes through electrostatic interactions. Additionally, the ether bond between 4-chloro-7-nitro-1,2,3-benzoxadiazole and the near-infrared fluorophore QX-OH rendered the probe susceptible to nucleophilic attack by biothiols. Upon the introduction of Cys/Hcy, the probe exhibited dual fluorescence emissions in red and green. Conversely, the presence of GSH resulted in only red fluorescence emission. The detection limits of the probe for Cys and Hcy at 542 nm in buffer solution were determined to be 0.044 µM and 0.042 µM, respectively. Similarly, the detection limit for all these biothiols was 0.028 µM at 678 nm. Furthermore, the response times for Cys/Hcy/GSH were recorded as 4.0 min, 5.5 min, and 9.5 min, respectively. Moreover, the probe was employed to monitor fluctuations in biothiol levels during oxidative stress in both HeLa cells and zebrafish, demonstrating its applicability and utility in biological contexts.

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The nicotinamide phosphoribosyltransferase (NAMPT)-catalyzed substitution reaction plays a pivotal role in the biosynthesis of nucleotide compounds. However, industrial applications are hindered by the low activity of NAMPTs. In this study, a novel dual-channel protein engineering strategy was developed to increase NAMPT activity by enhancing substrate accessibility. The best mutant (CpNAMPTY13G+Y15S+F76P) with a remarkable 5-fold increase in enzyme activity was obtained. By utilizing CpNAMPTY13G+Y15S+F76P as a biocatalyst, the accumulation of ß-nicotinamide mononucleotide reached as high as 19.94 g L-1 within 3 h with an impressive substrate conversion rate of 99.8%. Further analysis revealed that the newly generated substrate channel, formed through crack propagation, facilitated substrate binding and enhanced byproduct tolerance. In addition, three NAMPTs from different sources were designed based on the dual-channel protein engineering strategy, and the corresponding dual-channel mutants with improved enzyme activity were obtained, which proved the effectiveness and practicability of the approach.

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A multichannel speech enhancement system usually consists of spatial filters such as adaptive beamformers followed by postfilters, which suppress remaining noise. Accurate estimation of the power spectral density (PSD) of the residual noise is crucial for successful noise reduction in the postfilters. In this paper, we propose a postfilter utilizing proposed a posteriori speech presence probability (SPP) and noise PSD estimators, which are based on both the coherence and the statistical models. We model the coherence-based a posteriori SPP as a simple function of the magnitude of coherence between two microphone signals and combine it with a single-channel SPP based on statistical models. The coherence-based estimator for the PSD of the noise remaining in the beamformer output in the presence of speech is derived using the pseudo-coherence considering the effect of the beamformers, which is used to construct the coherence-based noise PSD estimator. Then, the final noise PSD estimator is obtained by combining the coherence-based and statistical model-based noise PSD estimators with the proposed SPP. The spectral gain function is also modified, incorporating the proposed SPP. Experimental results demonstrate that the proposed method led to more accurate noise PSD estimation and perceptual evaluation of speech quality scores in various diffuse noise environments, and did not degrade the speech quality under the presence of directional interference, although the proposed method utilizes the coherence information.

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Noncoding RNA (NcRNA)-protein interactions (NPIs) play fundamentally important roles in carrying out cellular activities. Although various predictors based on molecular features and graphs have been published to boost the identification of NPIs, most of them often ignore the information between known NPIs or exhibit insufficient learning ability from graphs, posing a significant challenge in effectively identifying NPIs. To develop a more reliable and accurate predictor for NPIs, in this article, we propose NPI-DCGNN, an end-to-end NPI predictor based on a dual-channel graph neural network (DCGNN). NPI-DCGNN initially treats the known NPIs as an ncRNA-protein bipartite graph. Subsequently, for each ncRNA-protein pair, NPI-DCGNN extracts two local subgraphs centered around the ncRNA and protein, respectively, from the bipartite graph. After that, it utilizes a dual-channel graph representation learning layer based on GNN to generate high-level feature representations for the ncRNA-protein pair. Finally, it employs a fully connected network and output layer to predict whether an interaction exists between the pair of ncRNA and protein. Experimental results on four experimentally validated datasets demonstrate that NPI-DCGNN outperforms several state-of-the-art NPI predictors. Our case studies on the NPInter database further demonstrate the prediction power of NPI-DCGNN in predicting NPIs. With the availability of the source codes (https://github.com/zhangxin11111/NPI-DCGNN), we anticipate that NPI-DCGNN could facilitate the studies of ncRNA interactome by providing highly reliable NPI candidates for further experimental validation.

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A simple benzopyran-based fluorescence probe DCA-Apa detection of volatile amine has been synthesized. DCA-Apa can recognize volatile amines by dual channel mode (changing from blue to light yellow in sunlight, and from weak pink to orange under 365 nm) in pure water system. DCA-Apa has the advantages of ultra-fast response (∼6 s), NIR emission (655 nm), and a good fluorescence response for many amines. The sensing label or gel loaded with DCA-Apa was prepared by the dipping or mixing method using filter paper or gelatin as solid carriers, which can identify volatile amine vapor and monitor the freshness of salmon by colorimetric and fluorescent dual channels. When the color of the label changes to light yellow-green or the fluorescence of the label becomes orange fluorescence (365 nm UV lamp), it indicates that the fish has rotted. The two-channel method makes up for the deficiency of the single colorimetric method, and establishes a theoretical foundation for more precise assessment of fish freshness.

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