GPro: generative AI-empowered toolkit for promoter design.

MOTIVATION: Promoters with desirable properties are crucial in biotechnological applications. Generative AI (GenAI) has demonstrated potential in creating novel synthetic promoters with significantly enhanced functionality. However, these methods' reliance on various programming frameworks and specific task-oriented contexts limits their flexibilities. Overcoming these limitations is essential for researchers to fully leverage the power of GenAI to design promoters for their tasks. RESULTS: Here, we introduce GPro (Generative AI-empowered toolkit for promoter design), a user-friendly toolkit that integrates a collection of cutting-edge GenAI-empowered approaches for promoter design. This toolkit provides a standardized pipeline covering essential promoter design processes, including training, optimization, and evaluation. Several detailed demos are provided to reproduce state-of-the-art promoter design pipelines. GPro's user-friendly interface makes it accessible to a wide range of users including non-AI experts. It also offers a variety of optional algorithms for each design process, and gives users the flexibility to compare methods and create customized pipelines. AVAILABILITY AND IMPLEMENTATION: GPro is released as an open-source software under the MIT license. The source code for GPro is available on GitHub for Linux, macOS, and Windows: https://github.com/WangLabTHU/GPro, and is available for download via Zenodo repository at https://zenodo.org/doi/10.5281/zenodo.10681733.

Bismuth-Based Electrocatalysts for Identical Value-Added Formic Acid Through Coupling CO2 Reduction and Methanol Oxidation.

It is an effective way to reduce atmospheric CO2 via electrochemical CO2 reduction reaction (CO2 RR), while the slow oxygen evolution reaction (OER) occurs at the anode with huge energy consumption. Herein, methanol oxidation reaction (MOR) is used to replace OER, coupling CO2 RR to achieve co-production of formate. Through enhancing OCHO* adsorption by oxygen vacancies engineering and synergistic effect by heteroatom doping, Bi/Bi2 O3 and Ni─Bi(OH)3 are synthesized for efficient production of formate via simultaneous CO2 RR and methanol oxidation reaction (MOR), achieving that the coupling of CO2 RR//MOR only required 7.26 kWh gformate -1 power input, much lower than that of CO2 RR//OER (13.67 kWh gformate -1 ). Bi/Bi2 O3 exhibits excellent electrocatalytic CO2 RR performance, achieving FEformate >80% in a wide potential range from -0.7 to -1.2 V (vs RHE). For MOR, Ni─Bi(OH)3 exhibits efficient MOR catalytic performance with the FEformate >98% in the potential range of 1.35-1.6 V (vs RHE). Not only demonstrates the two-electrode systems exceptional stability, working continuously for over 250 h under a cell voltage of 3.0 V, but the cathode and anode can maintain a FE of over 80%. DFT calculation results reveal that the oxygen vacancies of Bi/Bi2 O3 enhance the adsorption of OCHO* intermediate, and Ni─Bi(OH)3 reduce the energy barrier for the rate determining step, leading to high catalytic activity.

A Portable Integrated Electrochemical Sensing System for On-Site Nitrite Detection in Food.

Ensuring an appropriate nitrite level in food is essential to keep the body healthy. However, it still remains a huge challenge to offer a portable and low-cost on-site food nitrite analysis without any expensive equipment. Herein, a portable integrated electrochemical sensing system (IESS) is developed to achieve rapid on-site nitrite detection in food, which is composed of a low-cost disposable microfluidic electrochemical patch for few-shot nitrite detection, and a reusable smartphone-assisted electronic device based on self-designed circuit board for signal processing and wireless transmission. The electrochemical patch based on MXene-Ti3 C2 Tx /multiwalled carbon nanotubes-cyanocobalamin (MXene/MWCNTs-VB12 )-modified working electrode achieves high sensitivity of 10.533 µA mm-1 and low nitrite detection limit of 4.22 µm owing to strong electron transfer ability of hybrid MXene/MWCNTs conductive matrix and high nitrite selectivity of VB12 bionic enzyme-based ion-selective layer. Moreover, the portable IESS can rapidly collect pending testing samples through a microfluidic electrochemical patch within 1.0 s to conduct immediate nitrite analysis, and then wirelessly transmit data from a signal-processing electronic device to a smartphone via Bluetooth module. Consequently, this proposed portable IESS demonstrates rapid on-site nitrite analysis and wireless data transmission within one palm-sized electronic device, which would pave a new avenue in food safety and personal bespoke therapy.

High-Performance Thermoelectric Flexible Ag2Se-Based Films with Wave-Shaped Buckling via a Thermal Diffusion Method.

Herein, an n-type Ag2Se thermoelectric flexible thin film has been fabricated on a polyimide (PI) substrate via a novel thermal diffusion method, and the thermoelectric performance is well-optimized by adjusting the pressure and temperature of thermal diffusion. All of the Ag2Se films are beneficial to grow (013) preferred orientations, which is conducive to performing a high Seebeck coefficient. By increasing the thermal diffusion temperature, the electrical conductivity can be rationally regulated while maintaining the independence of the Seebeck coefficient, which is mainly attributed to the increased electric mobility. As a result, the fabricated Ag2Se thin film achieves a high power factor of 18.25 µW cm-1 K-2 at room temperature and a maximum value of 21.7 µW cm-1 K-2 at 393 K. Additionally, the thermal diffusion method has resulted in a wave-shaped buckling, which is further verified as a promising structure to realize a larger temperature difference by the simulation results of finite element analysis (FEA). Additionally, this unique surface morphology of the Ag2Se thin film also exhibits outstanding mechanical properties, for which the elasticity modulus is only 0.42 GPa. Finally, a flexible round-shaped module assembled with Sb2Te3 has demonstrated an output power of 166 nW at a temperature difference of 50 K. This work not only introduces a new method of preparing Ag2Se thin films but also offers a convincing strategy of optimizing the microstructure to enhance low-grade heat utilization efficiency.

Deep nonlocal low-rank regularization for complex-domain pixel super-resolution.

Pixel super-resolution (PSR) has emerged as a promising technique to break the sampling limit for phase imaging systems. However, due to the inherent nonconvexity of phase retrieval problem and super-resolution process, PSR algorithms are sensitive to noise, leading to reconstruction quality inevitably deteriorating. Following the plug-and-play framework, we introduce the nonlocal low-rank (NLR) regularization for accurate and robust PSR, achieving a state-of-the-art performance. Inspired by the NLR prior, we further develop the complex-domain nonlocal low-rank network (CNLNet) regularization to perform nonlocal similarity matching and low-rank approximation in the deep feature domain rather than the spatial domain of conventional NLR. Through visual and quantitative comparisons, CNLNet-based reconstruction shows an average 1.4 dB PSNR improvement over conventional NLR, outperforming existing algorithms under various scenarios.

Deep flanking sequence engineering for efficient promoter design using DeepSEED.

Designing promoters with desirable properties is essential in synthetic biology. Human experts are skilled at identifying strong explicit patterns in small samples, while deep learning models excel at detecting implicit weak patterns in large datasets. Biologists have described the sequence patterns of promoters via transcription factor binding sites (TFBSs). However, the flanking sequences of cis-regulatory elements, have long been overlooked and often arbitrarily decided in promoter design. To address this limitation, we introduce DeepSEED, an AI-aided framework that efficiently designs synthetic promoters by combining expert knowledge with deep learning techniques. DeepSEED has demonstrated success in improving the properties of Escherichia coli constitutive, IPTG-inducible, and mammalian cell doxycycline (Dox)-inducible promoters. Furthermore, our results show that DeepSEED captures the implicit features in flanking sequences, such as k-mer frequencies and DNA shape features, which are crucial for determining promoter properties.

Metal-organic frameworks-derived oxalate ligand modified NiCo hydroxides for enhanced electrochemical glycerol oxidation reaction.

Glycerol oxidation reaction can be substituted for oxygen evolution reaction for more efficient hydrogen production due to its lower thermodynamic potential. Herein, a series of NiCo hydroxide nanosheets containing abundant Ni3+ species and surface ligands were synthesized by in-situ structural transformation of bimetallic organic frameworks in alkaline media for efficient glycerol oxidation reaction. It is found that the incorporation of Co ions increases the content of the Ni3+ species, and that the Ni/Co ratio of 1.0 lead to the optimal catalytic performance. The oxalate-modified nickel-cobalt hydroxide with the optimized Ni/Co ratio can deliver a current density of 10 mA cm-2 at 1.26 V vs. RHE (reversible hydrogen electrode), and reaches its maximum selectivity and Faradaic efficiency at 1.30 V vs. RHE. A high selectivity of 82.9% and a Faradaic efficiency of 91.0% are achieved. The high catalytic activity can be mainly attributed to the abundant Ni3+ species and surface carboxyl groups.

Normal-weight visceral obesity promotes a higher 10-year atherosclerotic cardiovascular disease risk in patients with type 2 diabetes mellitus-a multicenter study in China.

BACKGROUND: Visceral obesity is associated with high cardiovascular events risk in type 2 diabetes mellitus (T2DM). Whether normal-weight visceral obesity will pose a higher atherosclerotic cardiovascular disease (ASCVD) risk than body mass index (BMI)-defined overweight or obese counterparts with or without visceral obesity remains unclear. We aimed to explore the relationship between general obesity and visceral obesity and 10-year ASCVD risk in patients with T2DM. METHODS: Patients with T2DM (6997) who satisfied the requirements for inclusion were enrolled. Patients were considered to have normal weight when 18.5 kg/m2 ≤ BMI < 24 kg/m2; overweight when 24 kg/m2 ≤ BMI < 28 kg/m2; and obesity when BMI ≥ 28 kg/m2. Visceral obesity was defined as a visceral fat area (VFA) ≥ 100 cm2. Patients were separated into six groups based on BMI and VFA. The odd ratios (OR) for a high 10-year ASCVD risk for different combinations of BMI and VFA were analysed using stepwise logistic regression. Receiver operating characteristic (ROC) curves for diagnosing the high 10-year ASCVD risk were constructed, and areas under the ROC curves were estimated. Potential non-linear relationships between VFA levels and high 10-year ASCVD risk were examined using restricted cubic splines (knot = 4). Multilinear regression was used to identify factors affecting VFA in patients with T2DM. RESULTS: In patients with T2DM, subjects with normal-weight visceral obesity had the highest 10-year ASCVD risk among the six groups, which had more than a 2-fold or 3-fold higher OR than those who were overweight or obese according to BMI but did not have visceral obesity (all P < 0.05). The VFA threshold for high 10-year ASCVD risk was 90 cm2. Multilinear regression showed significant differences in the effect of age, hypertension, drinking, fasting serum insulin, fasting plasma glucose, 2 h postprandial C-peptide, triglyceride, total cholesterol, high-density lipoprotein cholesterol, and low-density lipoprotein cholesterol on VFA in patients with T2DM (all P < 0.05). CONCLUSIONS: T2DM patients with normal-weight visceral obesity had a higher 10-year ASCVD risk than BMI-defined overweight or obese counterparts with or without visceral obesity, which should initiate standardised management for ASCVD primary prevention.

Gemini: memory-efficient integration of hundreds of gene networks with high-order pooling.

MOTIVATION: The exponential growth of genomic sequencing data has created ever-expanding repositories of gene networks. Unsupervised network integration methods are critical to learn informative representations for each gene, which are later used as features for downstream applications. However, these network integration methods must be scalable to account for the increasing number of networks and robust to an uneven distribution of network types within hundreds of gene networks. RESULTS: To address these needs, we present Gemini, a novel network integration method that uses memory-efficient high-order pooling to represent and weight each network according to its uniqueness. Gemini then mitigates the uneven network distribution through mixing up existing networks to create many new networks. We find that Gemini leads to more than a 10% improvement in F1 score, 15% improvement in micro-AUPRC, and 63% improvement in macro-AUPRC for human protein function prediction by integrating hundreds of networks from BioGRID, and that Gemini's performance significantly improves when more networks are added to the input network collection, while Mashup and BIONIC embeddings' performance deteriorates. Gemini thereby enables memory-efficient and informative network integration for large gene networks and can be used to massively integrate and analyze networks in other domains. AVAILABILITY AND IMPLEMENTATION: Gemini can be accessed at: https://github.com/MinxZ/Gemini.

Development and validation of a multivariable risk prediction model for identifying ketosis-prone type 2 diabetes.

BACKGROUND: To develop and validate a multivariable risk prediction model for ketosis-prone type 2 diabetes mellitus (T2DM) based on clinical characteristics. METHODS: A total of 964 participants newly diagnosed with T2DM were enrolled in the modeling and validation cohort. Baseline clinical data were collected and analyzed. Multivariable logistic regression analysis was performed to select independent risk factors, develop the prediction model, and construct the nomogram. The model's reliability and validity were checked using the receiver operating characteristic curve and the calibration curve. RESULTS: A high morbidity of ketosis-prone T2DM was observed (20.2%), who presented as lower age and fasting C-peptide, and higher free fatty acids, glycated hemoglobin A1c and urinary protein. Based on these five independent influence factors, we developed a risk prediction model for ketosis-prone T2DM and constructed the nomogram. Areas under the curve of the modeling and validation cohorts were 0.806 (95% confidence interval [CI]: 0.760-0.851) and 0.856 (95% CI: 0.803-0.908). The calibration curves that were both internally and externally checked indicated that the projected results were reasonably close to the actual values. CONCLUSIONS: Our study provided an effective clinical risk prediction model for ketosis-prone T2DM, which could help for precise classification and management.

Dual-Atom Catalysts Derived from a Preorganized Covalent Organic Framework for Enhanced Electrochemical Oxygen Reduction.

Dual-atom catalysts (DAC) are deemed as promising electrocatalysts due to the abundant active sites and adjustable electronic structure, but the fabrication of well-defined DAC is still full of challenges. Herein, bonded Fe dual-atom catalysts (Fe2 DAC) with Fe2 N6 C8 O2 configuration were developed through one-step carbonization of a preorganized covalent organic framework with bimetallic Fe chelation sites (Fe2 COF). The transition from Fe2 COF to Fe2 DAC involved the dissociation of the nanoparticles and the capture of atoms by carbon defects. Benefitting from the optimized d-band center and enhanced adsorption of OOH* intermediates, Fe2 DAC exhibited outstanding oxygen reduction activity with a half-wave potential of 0.898 V vs. RHE. This work will guide more fabrication of dual-atom and even cluster catalysts from preorganized COF in the future.

Multilingual translation for zero-shot biomedical classification using BioTranslator.

Existing annotation paradigms rely on controlled vocabularies, where each data instance is classified into one term from a predefined set of controlled vocabularies. This paradigm restricts the analysis to concepts that are known and well-characterized. Here, we present the novel multilingual translation method BioTranslator to address this problem. BioTranslator takes a user-written textual description of a new concept and then translates this description to a non-text biological data instance. The key idea of BioTranslator is to develop a multilingual translation framework, where multiple modalities of biological data are all translated to text. We demonstrate how BioTranslator enables the identification of novel cell types using only a textual description and how BioTranslator can be further generalized to protein function prediction and drug target identification. Our tool frees scientists from limiting their analyses within predefined controlled vocabularies, enabling them to interact with biological data using free text.

Urban stormwater management at the meso-level: A review of trends, challenges and approaches.

Cities worldwide are facing a significant threat of stormwater hazards caused by the increase in extreme downpours and urbanization. Meso-level urban stormwater management focuses on alleviating the detrimental impacts of urban flooding and enhancing water resource utilization at the block or community scale, typically through 1) specific policies and management rules; 2) catchment-scale scenario simulation, optimization and evaluation; 3) the group of stormwater control measures implementation. It may effectively coordinate macro-level urban stormwater management planning and micro-level distributed stormwater control facilities. This study conducts a review of Urban Stormwater Management at Meso-level (USM-M) with a view to research publication trends, citation analysis, geographic spread and subject category, as well as content analysis, including temporal progression and research gaps. The Web of Science database and CiteSpace are used for the bibliometric analysis of 66 articles from 2006 to 2021. The results show that the number of USM-M topic articles generally has an upward trend over the years. Whilst the United States and China are leading research on this topic, the European countries have diverse local research and dense cooperation. Research foci have generally shifted from theoretical frameworks to multi-element subdivided topics and specific technical roadmaps. Moreover, the spatial layout optimization and multi-functional integration are, or will be, potential research directions in terms of enhancing stormwater utilization and co-benefits of USM-M. This systematic review concludes trends, challenges and potential approaches of USM-M, and aims to provide recommendations for researchers and policymakers on the development of a more advanced and comprehensive USM-M.

Effects of Si Substrates with Variable Initial Orientations on the Growth and Thermoelectric Properties of Bi-Sb-Te Thin Films.

For thermoelectric thin film, the substrate plays an important role during the growing process and produces effects on its thermoelectric properties. Some special kinds of substrates provide an optimal combination of influences on both the structure and thermoelectric properties. In this work, Bi-Sb-Te films are deposited on Si substrates with different initial orientations by magnetron sputtering in two ways: with and without a pre-coating process. The preferred orientations of the Bi-Sb-Te films are greatly affected by the substrates, in which the thin film tends to deposit on Si substrate with (100) initial orientation and high (015)-texture, while the (00l)-textured Bi-Sb-Te film easily deposits on Si substrate with (110) initial orientation. The experimental and theoretical calculation results indicate that Bi-Sb-Te film with (00l)-texture presents good electrical conductivity and a higher power factor than that of film with (015)-texture.

Surface metal-EDTA coordination layer activates NixFe3-xO4 spinel as an outstanding electrocatalyst for oxygen evolution reaction.

Nickel-iron oxides are competitive electrocatalysts for oxygen evolution reaction, but their practical applications are restricted by the less-than-desirable intrinsic activity and working stability. To tackle the challenge, surface coordination chemistry is applied to the nickel-iron oxides through a complex-assisted in-situ crystal growth strategy. The ethylenediaminetetraacetate (EDTA) coordinated NixFe3-xO4 (NixFe3-xO4-EDTA) is prepared by a simple one-pot hydrothermal process. The coordinated EDTA molecules can deeply alter the surface coordination structure of the NixFe3-xO4. The NixFe3-xO4-EDTA demonstrates outstanding intrinsic activity towards oxygen evolution reaction, requiring only a small overpotential of 180 mV to reach 10 mA cm-2 in 1.0 M KOH. Moreover, the NixFe3-xO4-EDTA exhibits extremely stable long-term working stability. Density functional theory calculations show that the highly enhanced intrinsic activity is attributed to the surface coordinated EDTA-induced favorable electronic structure and coordination environment, which tunes the adsorption strength of the intermediates and optimizes the energetics of the elementary steps, while the high stability is ascribed to the strong coordination ability of EDTA.

Significance of the great protection of the Yangtze River: Riverine input contributes primarily to the presence of PAHs and HMs in its estuary and the adjacent sea.

The Yangtze River protection strategies are expected to improve the water quality and ecological function of the Yangtze River Estuary (YRE). The concentrations of 16 polycyclic aromatic hydrocarbons (PAHs) and 6 heavy metals (HMs) in the YRE were measured and the riverine fluxes were calculated subsequently. In particular, the concentrations of low molecular weight PAHs (LMW-PAHs), arsenic (As) and mercury (Hg) in seawater decreased over time, while those of other studied pollutants did not change a lot. In sediments, the concentration changes for all the pollutants were insignificant. For the present pollutants, the river input is the dominant source, and the flux decreased after the protection. The contribution of the discharge from wastewater treatment plants (WWTPs) was quantified. Its influence cannot be ignored. The seafood quality remained stable and the risk via diet was insignificant. Long-term monitoring is necessary, and the positive impact of the Protection Strategy is gradually emerging.

Amorphous chromium oxide confined Ni/NiO nanoparticles-assembled nanosheets for highly efficient and stable overall urea splitting.

Applications of urea oxidation reaction (UOR) in various sustainable energy-conversion systems are greatly hindered by its slow kinetics. Herein, we demonstrate an in-situ confined synthesis method that produces amorphous chromium oxide confined Ni/NiO nanoparticles-assembled nanosheets (Ni/NiO@CrOx) with fast reaction kinetics towards UOR. The confinement effect of the in-situ generated CrOx overlay contributes to ultrafine Ni/NiO nanoparticles, bringing about rich Ni/NiO and NiO/CrOx interfaces. In-situ Raman and electrochemical characterization show that both CrOx and metallic Ni can promote the formation of the NiOOH species and the electron transfer, leading to high intrinsic activity and fast reaction kinetics. At 1.40 V vs. reversible hydrogen electrode, the Ni/NiO@CrOx delivers a current density of 275 mA cm-2, which is about 2.6 and 6.1 times as large as those of the NiO@CrOx and NiO, respectively. In addition, the protective effect of the CrOx overlay leads to robust working stability towards UOR. Further, the Ni/NiO@CrOx nanosheets are used as bifunctional catalysts for overall urea splitting, and a small electrolysis cell voltage of 1.44 V is needed to reach the benchmark current density of 10 mA cm-2.

Metal organic framework-assisted in-situ synthesis of ß-NiMnOOH nanosheets with abundant NiOOH active sites for efficient electro-oxidation of urea.

NiOOH has been considered as the active center for urea oxidation reaction (UOR), but it remains challenging to synthesize high-performance NiOOH-based catalysts. Herein, we realize the synthesis of a high-performance NiOOH-based catalyst through in-situ transformation from the NiMn-based metal-organic framework to NiMnOOH. X-ray photoelectron spectroscopy characterization shows that the Ni3+/Ni2+ ratio in the NiMnOOH is 3.9 times as big as that in the Ni(OH)2, and in-situ Raman characterization further consolidates the presence of the NiOOH species in the NiMnOOH and as well unveils the faciliated Ni2+/Ni3+ redox reaction. The abundant NiOOH species, the markedly facilitated Ni2+/Ni3+ redox reaction and the Ni-Mn synergy contribute to the high intrinsic activity of the NiMnOOH towards UOR. The NiMnOOH exhibits an impressively low onset potential of 1.305 V vs reversible hydrogen electrode (RHE) and requires only a small potential of 1.34 V vs RHE to deliver a current density of 100 mA cm-2 in 1.0 M KOH + 0.33 M urea. In addition, the NiMnOOH catalyst possesses good long-term working stability.

A Hexagonal Nut-Like Metal-Organic Framework and Its Conformal Transformation.

Hollow structured metal-organic frameworks (MOFs) and their derivatives are desired in catalysis, energy storage, etc. However, fabrication of novel hollow MOFs and revelation of their formation mechanisms remain challenging. Herein, open hollow 2D MOFs in the form of hexagonal nut are prepared through self-template method, which can be readily scaled up at gram scale in a one-pot preparation. The evolution from the initial superstructure to the final stable MOFs is tracked by wide-angle X-ray scattering, transforming from solid hexagon to open hollow hexagon. More importantly, this protocol can be extended to synthesizing a series of open hollow structured MOFs with sizes ranging from ≈120 to ≈1200 nm. Further, open hollow structured cobalt/N-doped porous carbon composites are realized through conformal transformation of the as-prepared MOFs, which demonstrates promising applications in sustainable energy conversion technologies. This study sheds light on the kinetically controlled synthesis of novel 2D MOFs for their extended utilizations.

Revealing the surface structure-performance relationship of interface-engineered NiFe alloys for oxygen evolution reaction.

NiFe alloys are among the most promising electrocatalysts for oxygen evolution reaction (OER). However, a comprehensive study is yet to be done to reveal the surface structure-performance relationship of NiFe alloys. In particular, the role of the ultrathin surface oxide layer, which is unavoidable for pure NiFe alloys, is always neglected. Herein, a series of NiFe alloys with different Ni/Fe ratios are fabricated. It is found that different Ni/Fe ratios lead to significant differences in surface composition and structure of the NiFe alloys, and thus affect their catalytic performance. Then, the oxide/metal interface of the Ni4Fe1 alloy is tailored by adjusting the hydrogenation temperature to further understand the surface structure-activity relationship, and the optimal OER performance is achieved at the oxide/metal interfaces that have suitable surface Fe/Ni ratio and an appropriate amount of oxygen vacancies. In-situ Raman characterization shows that the Ni4Fe1 alloy with well-tailored oxide/metal interface facilitates the formation of active species. Density functional theory calculations demonstrate that the ultrathin surface oxide layers are responsible for the high catalytic activity of the NiFe alloys, and that the quantity of oxygen vacancies in the surface oxides affects the adsorption energy of O* and thus to a great extent determines the catalytic activity.