A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features.

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Electrocatalysis Boosts the Methanol Thermocatalytic Dehydrogenation for High-Purity H2 and CO Production.

Hydrogen production from methanol represents an energy-sustainable way to produce ethanol, but it normally results in heavy CO2 emissions. The selective conversion of methanol into H2 and valuable chemical feedstocks offers a promising strategy; however, it is limited by the harsh operating conditions and low conversion efficiency. Herein, we realize efficient high-purity H2 and CO production from methanol by coupling the thermocatalytic methanol dehydrogenation with electrocatalytic hydrogen oxidation on a bifunctional Ru/C catalyst. Electrocatalysis enables the acceleration of C-H cleavage and reduces the partial pressure of hydrogen at the anode, which drives the chemical equilibrium and significantly enhances methanol dehydrogenation. Furthermore, a bilayer Ru/C + Pd/C electrode is designed to mitigate CO poisoning and facilitate hydrogen oxidation. As a result, a high yield of H2 (558.54 mmol h-1 g-1) with high purity (99.9%) was achieved by integrating an applied cell voltage of 0.4 V at 200 °C, superior to the conventional thermal and electrocatalytic processes, and CO is the main product at the anode. This work presents a new avenue for efficient H2 production together with valuable chemical synthesis from methanol.

Ultra-low-Potential Methanol Oxidation on Single-Ir-Atom Catalyst.

Methanol oxidation plays a central role to implement sustainable energy economy, which is restricted by the sluggish reaction kinetics due to the multi-electron transfer process accompanied by numerous sequential intermediate. In this study, an efficient cascade methanol oxidation reaction is catalyzed by single-Ir-atom catalyst at ultra-low potential (< 0.1 V) with the promotion of the thermal and electrochemical integration in a high temperature polymer electrolyte membrane electrolyzer. At the elevated temperature, the electron deficient Ir site with higher methanol affinity could spontaneous catalyze the CH3OH dehydrogenation to CO under the voltage, then the generated CO and H2 was electrochemically oxidized to CO2 and proton. However, the methanol cannot thermally decompose with the voltage absence, which confirm the indispensable of the coupling of thermal and electrochemical integration for the methanol oxidation. By assembling the methanol oxidation reaction with hydrogen evolution reaction in the HT-PEME with single-Ir-atom catalysts in the anode chamber, a max hydrogen production rate reaches 18 mol gIr-1 h-1, which is much greater than that of Ir nanoparticles and commercial Pt/C catalyst. This study also demonstrated the electrochemical MOR activity of the single atom catalysts, which broadens the renewable energy devices and the catalyst design by an integration concept.

A Novel Polymer-Encapsulated Multi-Imaging Modality Fiducial Marker with Positive Signal Contrast for Image-Guided Radiation Therapy.

BACKGROUND: Current fiducial markers (FMs) in external-beam radiotherapy (EBRT) for prostate cancer (PCa) cannot be positively visualized on magnetic resonance imaging (MRI) and create dose perturbation and significant imaging artifacts on computed tomography (CT) and MRI. We report our initial experience with clinical imaging of a novel multimodality FM, NOVA. METHODS: We tested Gold Anchor [G-FM], BiomarC [carbon, C-FM], and NOVA FMs in phantoms imaged with kilovoltage (kV) X-rays, transrectal ultrasound (TRUS), CT, and MRI. Artifacts of the FMs on CT were quantified by the relative streak artifacts level (rSAL) metric. Proton dose perturbations (PDPs) were measured with Gafchromic EBT3 film, with FMs oriented either perpendicular to or parallel with the beam axis. We also tested the performance of NOVA-FMs in a patient. RESULTS: NOVA-FMs were positively visualized on all 4 imaging modalities tested. The rSAL on CT was 0.750 ± 0.335 for 2-mm reconstructed slices. In F-tests, PDP was associated with marker type and depth of measurement (p < 10-6); at 5-mm depth, PDP was significantly greater for the G-FM (12.9%, p = 10-6) and C-FM (6.0%, p = 0.011) than NOVA (4.5%). EBRT planning with MRI/CT image co-registration and daily alignments using NOVA-FMs in a patient was feasible and reproducible. CONCLUSIONS: NOVA-FMs were positively visible and produced less PDP than G-FMs or C-FMs. NOVA-FMs facilitated MRI/CT fusion and identification of regions of interest.

A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion.

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu'an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.

A Universal Approach for Sustainable Urea Synthesis via Intermediate Assembly at the Electrode/Electrolyte Interface.

Electrocatalytic C-N coupling process is indeed a sustainable alternative for direct urea synthesis and co-upgrading of carbon dioxide and nitrate wastes. However, the main challenge lies in the unactivated C-N coupling process. Here, we proposed a strategy of intermediate assembly with alkali metal cations to activate C-N coupling at the electrode/electrolyte interface. Urea synthesis activity follows the trend of Li+

Multi-UAV Data Collection and Path Planning Method for Large-Scale Terminal Access.

In the context of the relentless evolution of network and communication technologies, the need for enhanced communication content and quality continues to escalate. Addressing the demands of data collection from the abundance of terminals within Internet of Things (IoT) scenarios, this paper presents an advanced approach to multi-Unmanned Aerial Vehicle (UAV) data collection and path planning tailored for extensive terminal accessibility. This paper focuses on optimizing the complex interplay between task completion time and task volume equilibrium. To this end, a novel strategy is devised that integrates sensor area partitioning and flight trajectory planning for multiple UAVs, forming an optimization framework geared towards minimizing task completion duration. The core idea of this work involves designing an innovative k-means algorithm capable of balancing data quantities within each cluster, thereby achieving balanced sensor node partitioning based on data volume. Then, the UAV flight trajectory paths are discretely modeled, and a grouped, improved genetic algorithm is used to solve the Multiple Traveling Salesman Problem (MTSP). The algorithm introduces a 2-opt optimization operator to improve the computational efficiency of the genetic algorithm. Empirical validation through comprehensive simulations clearly underscores the efficacy of the proposed approach. In particular, the method demonstrates a remarkable capacity to rectify the historical issue of diverse task volumes among multiple UAVs, all the while significantly reducing task completion times. Moreover, its convergence rate substantially outperforms that of the conventional genetic algorithm, attesting to its computational efficiency. This paper contributes an innovative and efficient paradigm to improve the problem of data collection from IoT terminals through the use of multiple UAVs. As a result, it not only augments the efficiency and balance of task distribution but also showcases the potential of tailored algorithm solutions for realizing optimal outcomes in complex engineering scenarios.

Reconfigurable Digital Satellite-Borne Base Station Design and Virtual Function Fast Migration Algorithm.

A breakthrough in the technology for virtualizing satellite-borne networks and computing and storage resources can significantly increase the processing capacity and resource utilization efficiency of satellite-borne base stations in response to the development trend in multi-star and multi-system converged satellite internet iterative systems. The protocol processing function of traditional satellite communication systems is generally placed in the ground station system for processing, with poor flexibility and low efficiency. As a result, a reconfigurable digital satellite-borne base station architecture design is suggested, allowing for separation of the hardware and software of the satellite-borne base station and flexible programming and dynamic loading of the satellite-borne base station's functions by software. Meanwhile, a fast adaptive migration algorithm based on multi-dimensional environment awareness is proposed on top of the reconfigurable digital base station, and migration precomputation and real-time computation are added in order to realize rapid deployment of the digital base station system network. Simulation results demonstrate the effectiveness of the proposed algorithm in enhancing system stability and decreasing real-time calculation costs associated with system network migration under conditions of high dynamic changes for each network element in a star-loaded environment. In conclusion, a digital satellite-borne base station system that effectively addresses the issues of low flexibility and high dynamic changes of nodes in the resource-constrained satellite environment can be created by combining the adaptive migration algorithm and the reconfigurable digitized satellite-borne base station architecture.

Confinement Effects in Carbonized ZIF-Confined Hollow PtCo Nanospheres Enable the Methanol Oxidation Reaction.

Confinement effects in highly porous nanostructures can effectively adjust the selectivity and kinetics of electrochemical reactions, which can boost the methanol oxidation reaction (MOR). In this work, carbonized ZIF-8-confined hollow PtCo nanospheres (PtCo@carbonized ZIF-8) were fabricated using a facile strategy. A monodisperse confined region was successfully prepared, and the dispersion of the PtCo nanoparticles (NPs) could be precisely regulated, allowing for the effective tuning of the confined region. Thus, the precise regulation of the catalytic reaction was achieved. Importantly, hollow PtCo NPs were prepared using a method based on the Kirkendall effect, and their forming mechanism was systematically investigated. Because of the confinement effects of carbonized zeolitic imidazolate framework-8 (ZIF-8), the crystal and electronic structures of the PtCo NPs were able to be effectively tuned. Our electrochemical results show that PtCo@carbonized ZIF-8 composites manifest a higher mass activity (1.4 A mgPt-1) and better stability compared to commercial Pt/C.

Electrodeposition-Assisted Crystal Growth Regulation of PdBi Clusters on Carbon Cloths for Ethanol Oxidation.

Carbon-supported Pd-based clusters are one of the most promising anodic catalysts for ethanol oxidation reaction (EOR) due to their encouraging activity and practical applications. However, unclear growth mechanism of Pd-based clusters on the carbon-based materials has hindered their extensive applications. Herein, we first introduce multi-void spherical PdBi cluster/carbon cloth (PdBi/CC) composites by an electrodeposition routine. The growth mechanism of PdBi clusters on the CC supports has been systemically investigated by evaluating the selected samples and tuning their compositions, which involve the big difference in standard redox potential between Pd2+/Pd and Bi3+/Bi and easy adsorption of Bi3+ on the surface of Pd-rich seeds. Benefitting from the ensembles of many nanocrystal subunits, multi-void spherical PdBi clusters can present collective properties and novel functionalities. In addition, the outstanding characteristics of CC supports enable PdBi clusters with stable nanostructures. Thanks to the unique structure, Pd20Bi/CC catalysts manifest higher EOR activity and better stability compared to Pd/CC. Systematic characterizations and a series of CO poisoning tests further confirm that the dramatically enhanced EOR activity and stability can be attributed to the incorporation of Bi species and the strong coupling of the structure between PdBi clusters and CC supports.

Adipose METTL14-Elicited N6 -Methyladenosine Promotes Obesity, Insulin Resistance, and NAFLD Through Suppressing ß Adrenergic Signaling and Lipolysis.

White adipose tissue (WAT) lipolysis releases free fatty acids as a key energy substance to support metabolism in fasting, cold exposure, and exercise. Atgl, in concert with Cgi-58, catalyzes the first lipolytic reaction. The sympathetic nervous system (SNS) stimulates lipolysis via neurotransmitter norepinephrine that activates adipocyte ß adrenergic receptors (Adrb1-3). In obesity, adipose Adrb signaling and lipolysis are impaired, contributing to pathogenic WAT expansion; however, the underling mechanism remains poorly understood. Recent studies highlight importance of N6 -methyladenosine (m6A)-based RNA modification in health and disease. METTL14 heterodimerizes with METTL3 to form an RNA methyltransferase complex that installs m6A in transcripts. Here, this work shows that adipose Mettl3 and Mettl14 are influenced by fasting, refeeding, and insulin, and are upregulated in high fat diet (HFD) induced obesity. Adipose Adrb2, Adrb3, Atgl, and Cgi-58 transcript m6A contents are elevated in obesity. Mettl14 ablation decreases these transcripts' m6A contents and increases their translations and protein levels in adipocytes, thereby increasing Adrb signaling and lipolysis. Mice with adipocyte-specific deletion of Mettl14 are resistant to HFD-induced obesity, insulin resistance, glucose intolerance, and nonalcoholic fatty liver disease (NAFLD). These results unravel a METTL14/m6A/translation pathway governing Adrb signaling and lipolysis. METTL14/m6A-based epitranscriptomic reprogramming impairs adipose Adrb signaling and lipolysis, promoting obesity, NAFLD, and metabolic disease.

Cellular Network Fault Diagnosis Method Based on a Graph Convolutional Neural Network.

The efficient and accurate diagnosis of faults in cellular networks is crucial for ensuring smooth and uninterrupted communication services. In this paper, we propose an improved 4G/5G network fault diagnosis with a few effective labeled samples. Our solution is a heterogeneous wireless network fault diagnosis algorithm based on Graph Convolutional Neural Network (GCN). First, the common failure types of 4G/5G networks are analyzed, and then the graph structure is constructed with the data in the network parameter, given data sets as nodes and similarities as edges. GCN is used to extract features from the graph data, complete the classification task for nodes, and finally predict the fault types of cells. A large number of experiments are carried out based on the real data set, which is achieved by driving tests. The results show that, compared with a variety of traditional algorithms, the proposed method can effectively improve the performance of network fault diagnosis with a small number of labeled samples.

Peptide YY: A Paneth cell antimicrobial peptide that maintains Candida gut commensalism.

The mammalian gut secretes a family of multifunctional peptides that affect appetite, intestinal secretions, and motility whereas others regulate the microbiota. We have found that peptide YY (PYY1-36), but not endocrine PYY3-36, acts as an antimicrobial peptide (AMP) expressed by gut epithelial paneth cells (PC). PC-PYY is packaged into secretory granules and is secreted into and retained by surface mucus, which optimizes PC-PYY activity. Although PC-PYY shows some antibacterial activity, it displays selective antifungal activity against virulent Candida albicans hyphae-but not the yeast form. PC-PYY is a cationic molecule that interacts with the anionic surfaces of fungal hyphae to cause membrane disruption and transcriptional reprogramming that selects for the yeast phenotype. Hence, PC-PYY is an antifungal AMP that contributes to the maintenance of gut fungal commensalism.

Revealing the origin of activity in phthalocyanine-based dual-metal sites towards electrochemical nitric oxide reduction.

Coordinated ligands play crucial roles in tuning the electrochemical nitrate reduction performance of phthalocyanine (Pc)-based dual atom catalysts. With the assistance of axial O ligands, fast NO to NH3 conversion can be realized on O-Ni2-Pc and O-Cu2-Pc. A 2-N product, N2O, can be synthesized on Co2-Pc, Cr2-Pc, O-Co2-Pc, and O-Fe2-Pc through N-N coupling with high NO coverage. ΔENO can be identified as a valid descriptor to support rational M2-Pc design.

Oxygen-Bridged Copper-Iron Atomic Pair as Dual-Metal Active Sites for Boosting Electrocatalytic NO Reduction.

Electrocatalytic reduction of nitric oxide (NO) to ammonia (NH3 ) is a promising approach to NH3 synthesis. However, due to the lack of efficient electrocatalysts, the performance of electrocatalytic NO reduction reaction (NORR) is far from satisfactory. Herein, it is reported that an atomic copper-iron dual-site electrocatalyst bridged by an axial oxygen atom (OFeN6 Cu) is anchored on nitrogen-doped carbon (CuFe DS/NC) for NORR. The CuFe DS/NC can significantly enhance the electrocatalytic NH3 synthesis performance (Faraday efficiency, 90%; yield rate, 112.52 µmol cm-2  h-1 ) at -0.6 V versus RHE, which is dramatically higher than the corresponding Cu single-atom, Fe single-atom and all NORR single-atom catalysts in the literature so far. Moreover, an assembled proof-of-concept Zn-NO battery using CuFe DS/NC as the cathode outputs a power density of 2.30 mW cm-2 and an NH3 yield of 45.52 µg h-1  mgcat -1 . The theoretical calculation result indicates that bimetallic sites can promote electrocatalytic NORR by changing the rate-determining step and accelerating the protonation process. This work provides a flexible strategy for efficient sustainable NH3 synthesis.

Electrocatalytic Urea Synthesis with 63.5 % Faradaic Efficiency and 100 % N-Selectivity via One-step C-N coupling.

Electrocatalytic urea synthesis via coupling N2 and CO2 provides an effective route to mitigate energy crisis and close carbon footprint. However, the difficulty on breaking N≡N is the main reason that caused low efficiencies for both electrocatalytic NH3 and urea synthesis, which is the bottleneck restricting their industrial applications. Herein, a new mechanism to overcome the inert of the nitrogen molecule was proposed by elongating N≡N instead of breaking N≡N to realize one-step C-N coupling in the process for urea production. We constructed a Zn-Mn diatomic catalyst with axial chloride coordination, Zn-Mn sites display high tolerance to CO poisoning and the Faradaic efficiency can even be increased to 63.5 %, which is the highest value that has ever been reported. More importantly, negligible N≡N bond breakage effectively avoids the generation of ammonia as intermediates, therefore, the N-selectivity in the co-electrocatalytic system reaches100 % for urea synthesis. The previous cognition that electrocatalysts for urea synthesis must possess ammonia synthesis activity has been broken. Isotope-labelled measurements and Operando synchrotron-radiation Fourier transform infrared spectroscopy validate that activation of N-N triple bond and nitrogen fixation activity arise from the one-step C-N coupling process of CO species with adsorbed N2 molecules.

Promoted hydrogen and acetaldehyde production from alcohol dehydrogenation enabled by electrochemical hydrogen pumps.

The dehydrogenation reaction of bioderived ethanol is of particular interest for the synthesis of fuels and value-added chemicals. However, this reaction historically suffered from high energy consumption (>260 °C or >0.8 V) and low efficiency. Herein, the efficient conversion of alcohol to hydrogen and aldehyde is achieved by integrating the thermal dehydrogenation reaction with electrochemical hydrogen transfer at low temperature (120 °C) and low voltage (0.06 V), utilizing a bifunctional catalyst (Ru/C) with both thermal-catalytic and electrocatalytic activities. Specifically, the coupled electrochemical hydrogen separation procedure can serve as electrochemical hydrogen pumps, which effectively promote the equilibrium of ethanol dehydrogenation toward hydrogen and acetaldehyde production and simultaneously purifies hydrogen at the cathode. By utilizing this strategy, we achieved boosted hydrogen and acetaldehyde yields of 1,020 mmol g-1 h-1 and 1,185 mmol g-1 h-1, respectively, which are threefold higher than the exclusive ethanol thermal dehydrogenation. This work opens up a prospective route for the high-efficiency production of hydrogen and acetaldehyde via coupled thermal-electrocatalysis.

TC-Net: A joint learning framework based on CNN and vision transformer for multi-lesion medical images segmentation.

BACKGROUND: With the rapid advancement of medical imaging technology, the demand for accurate segmentation of medical images is increasing. However, most existing methods are unable to capture locality and long-range dependency information in integrated ways for medical images. METHOD: In this paper, we propose an elegant segmentation framework for medical images named TC-Net, which can utilize both the locality-aware and long-range dependencies in the medical images. As for the locality-aware perspective, we employ a CNN-based encoder and decoder structure. The CNN branch uses the locality of convolution operations to dig out local information in medical images. As for the long-range dependencies, we construct a Transformer branch to focus on the global context. Additionally, we proposed a locality-aware and long-range dependency concatenation strategy (LLCS) to aggregate the feature maps obtained from the two subbranches. Finally, we present a dynamic cyclical focal loss (DCFL) to address the class imbalance problem in multi-lesion segmentation. RESULTS: Comprehensive experiments were conducted on lesion segmentation tasks using two fundus image databases and a skin image database. The TC-Net achieves scores of 0.6985 and 0.5171 in the metric of mean pixel accuracy on the IDRiD and DDR databases, respectively. Moreover, on the skin image database, the TC-Net reached mean pixel accuracy of 0.8886. The experiment results demonstrate that the proposed method achieves better performance than other deep learning segmentation schemes. Furthermore, the proposed DCFL achieves higher performance than other loss functions in multi-lesion segmentation. SIGNIFICANCE: The proposed TC-Net is a promising new framework for multi-lesion medical image segmentation and many other challenging image segmentation tasks. © 2001 Elsevier Science. All rights reserved.

Efficient nitrogen removal in a total floc sludge system from domestic wastewater with low C/N: High anammox nitrogen removal contribution driven by endogenous partial denitrification.

Since unsustainable partial nitrification prone to unstable nitrogen removal rates, cultivation and enrichment of AnAOB, further improve autotrophic nitrogen removal contribution have been challenges in the mainstream anammox process. This study proposed a new strategy to enrich AnAOB motivated by endogenous partial denitrification (EPD) in total floc sludge system through the AOA process with sustainable nitrification. The results showed that in the presence of NH4+ and NO3- at the anoxic stage of N-EPDA, Ca. Brocadia was enriched (0.005%→0.92%) in floc sludge via internal carbon source metabolism of EPD. The C/N and temperature of N-EPDA were also optimized to achieve higher activities of EPD and anammox. The N-EPDA was operated at low C/N ratio (3.1) with anammox nitrogen removal contribution of 78% during the anoxic stage, Eff.TIN of 8.3 mg/L and NRE of 83.5% during phase III, achieved efficient autotrophic nitrogen removal and AnAOB enrichment in the absence of partial nitrification.

Determination of partial denitrification kinetic model parameters based on batch tests and metagenomic sequencing.

In this study, a model was developed to investigate the partial denitrification(PD) process. The heterotrophic biomass (XH) proportion in the sludge was determined to be 66.4% based on metagenomic sequencing. The kinetic parameters were first calibrated, then validated using the batch tests results. The results showed rapid decreases in the chemical oxygen demand (COD) and nitrate concentrations and gradual increases in the nitrite concentrations in the first four hours, then remained constant from 4 to 8 h. Anoxic reduction factor (ηNO3 and ηNO2) and half saturation constant (KS1 and KS2) were calibrated at 0.097, 0.13, 89.28 mg COD/L, and 102.29 mg COD/L, respectively. Whereas the simulation results demonstrated that the increase in carbon-to-nitrogen (C/N) ratios and the reduction in XH contributed to the increase in the nitrite transformation rate. This model provides potential strategies for optimizing the PD/A process.