Naringin ameliorates obesity via stimulating adipose thermogenesis and browning, and modulating gut microbiota in diet-induced obese mice.

Naringin, a natural flavanone primarily found in citrus fruits, has garnered increased attention due to its recognized antioxidative, anti-inflammatory, and cardioprotective attributes. However, the functions of naringin in regulating energy expenditure are poorly understood. In the present study, we observed that twelve weeks of naringin supplementation substantially reshaped the metabolic profile of high-fat diet (HFD)-fed mice, by inhibiting body weight gain, reducing liver weight, and altering body compositions. Notably, naringin exhibited a remarkable capacity to augment whole-body energy expenditure of the tested mice by enhancing the thermogenic activity of brown adipose tissue (BAT) and stimulating browning of inguinal white adipose tissue (iWAT). Furthermore, our results showed naringin supplementation modified gut microbiota composition, specifically increasing the abundance of Bifidobacterium and Lachnospiraceae_bacterium_28-4, while reducing the abundance of Lachnospiraceae_bacterium_DW59 and Dubosiella_newyorkensis. Subsequently, we also found naringin supplementation altered fecal metabolite profile, by significantly promoting the production of taurine, tyrosol, and thymol, which act as potent activators of thermoregulation. Interestingly, the metabolic effects of naringin were abolished upon gut microbiota depletion through antibiotic intervention, concurrently leading the disappearance of naringin-induced thermogenesis and protective actions on diet-induced obesity. This discovery revealed a novel food-driven cross-sectional communication between gut bacteria and adipose tissues. Collectively, our data indicate that naringin supplementation stimulates BAT thermogenesis, alters fat distribution, promotes the browning process, and consequently inhibits body weight gain; importantly these metabolic effects require the participation of gut bacteria.

The First High-quality Reference Genome of Sika Deer Provides Insights into High-tannin Adaptation.

Sika deer are known to prefer oak leaves, which are rich in tannins and toxic to most mammals; however, the genetic mechanisms underlying their unique ability to adapt to living in the jungle are still unclear. In identifying the mechanism responsible for the tolerance of a highly toxic diet, we have made a major advancement by explaining the genome of sika deer. We generated the first high-quality, chromosome-level genome assembly of sika deer and measured the correlation between tannin intake and RNA expression in 15 tissues through 180 experiments. Comparative genome analyses showed that the UGT and CYP gene families are functionally involved in the adaptation of sika deer to high-tannin food, especially the expansion of the UGT family 2 subfamily B of UGT genes. The first chromosome-level assembly and genetic characterization of the tolerance to a highly toxic diet suggest that the sika deer genome may serve as an essential resource for understanding evolutionary events and tannin adaptation. Our study provides a paradigm of comparative expressive genomics that can be applied to the study of unique biological features in non-model animals.

Biomass-Derived Anion-Anchoring Nano-CaCO3 Coating for Regulating Ion Transport on Li Metal Surface.

The free transport of anions in a Li metal battery can cause multiple issues, including a high anion transference number, space charge, and concentration polarization, eventually leading to uncontrolled dendrite formation and decreased performance. Herein, we report an anion-anchoring nano-CaCO3 (NC) coating derived from eggshell biowaste for stabilizing Li metal anodes. As the adsorption of local TFSI- anions onto the NC adsorbent can undermine the anion concentration gradient and promote rapid Li-ion diffusion, it can effectively inhibit the proliferation of Li dendrites assisted by the NC coating. Consequently, Li/Cu cells with NC@Cu electrode can achieve a high Coulombic efficiency of ∼98.4% for more than 420 cycles and can even reach ∼99.1% at an ultrahigh areal capacity of 20 mAh cm-2. In particular, full cells with NC/Li@Cu electrodes show a stable lifespan of over 240 cycles with an average efficiency of ∼99.8% at a low N/P ratio of ∼3.3.

Trace Doping of Multiple Elements Enables Stable Cycling of High Areal Capacity LiNi0.5 Mn1.5 O4 Cathode.

High-voltage spinel cobalt-free LiNi0.5 Mn1.5 O4 (LNMO) is one of the most promising cathode candidates for next-generation lithium-ion batteries (LIBs) due to its high specific capacity, high operating voltage, and low cost. However, inferior electronic conductivity, transition metal dissolution, and fast capacity degradation of LNMO, especially in high mass loading for high areal capacity, are the critical material challenges for its practical application. Herein, trace multiple Cr-Fe-Cu elements doping of LiNi0.45 Cr0.0167 Fe0.0167 Cu0.0167 Mn1.5 O4 (CFC0.5-LNMO) cathode is achieved by a blow-spinning strategy to exhibit very stable cycling at a practical level of areal capacity up to 3 mAh cm-2 . It is demonstrated that the Cu, Fe, and Cr doping into the LNMO lattice can suspend the Mn dissolution and improve the Li ion diffusivity and electronic conductivity of the LNMO host. As a result, the obtained CFC0.5-LNMO cathode exhibits an excellent rate performance (1.75 mAh cm-2 at 1C) and long cycling stability under an areal capacity of 3 mAh cm-2 (78% capacity retention over 300 cycles at 0.5C).

MDADP: A Webserver Integrating Database and Prediction Tools for Microbe-Disease Associations.

More and more evidence has demonstrated that microbiota play important roles in the life processes of the human body. In recent years, various computational methods have been proposed for identifying potentially disease-associated microbes to save costs in traditional biological experiments. However, prediction performances of these methods are generally limited by outdated and incomplete datasets. And moreover, until now, there are limited studies that can provide visual predictive tools for inferring possible microbe-disease associations (MDAs) as well. Hence, in this manuscript, a novel webserver called MDADP will be proposed to identify latent MDAs, in which, a new MDA database together with interactive prediction tools for MDAs studies will be designed simultaneously. Especially, in the newly constructed MDA database, 2019 known MDAs between 58 diseases and 703 microbes have been manually collected first. And then, through adopting the average ranking method and the co-confidence method respectively, eight representative computational models have been integrated together to identify potential disease-related microbes. As a result, MDADP can provide not only interactive features for users to access and capture MDAs entities, but alsoeffective tools for users to identify candidate microbes for different diseases. To our knowledge, MDADP is the first online platform that incorporates a new MDA database with comprehensive MDA prediction tools. Therefore, we believe that it will be a valuable source of information for researches in microbiology and disease-related fields. MDADP can be accessed at http://mdadp.leelab2997.cn.

Identification of Essential Proteins Based on Local Random Walk and Adaptive Multi-View Multi-Label Learning.

Accumulating evidences have indicated that essential proteins play vital roles in human physiological process. In recent years, although researches on prediction of essential proteins have been developing rapidly, there are as well various limitations such as unsatisfactory data suitability, low accuracy of predictive results and so on. In this manuscript, a novel method called RWAMVL was proposed to predict essential proteins based on the Random Walk and the Adaptive Multi-View multi-label Learning. In RWAMVL, considering that the inherent noise is ubiquitous in existing datasets of known protein-protein interactions (PPIs), a variety of different features including biological features of proteins and topological features of PPI networks were obtained by adopting adaptive multi-view multi-label learning first. And then, an improved random walk method was designed to detect essential proteins based on these different features. Finally, in order to verify the predictive performance of RWAMVL, intensive experiments were done to compare it with multiple state-of-the-art predictive methods under different expeditionary frameworks. And as a result, RWAMVL was proven that it can achieve better prediction accuracy than all those competitive methods, which demonstrated as well that RWAMVL may be a potential tool for prediction of key proteins in the future.

An iteration model for identifying essential proteins by combining comprehensive PPI network with biological information.

BACKGROUND: Essential proteins have great impacts on cell survival and development, and played important roles in disease analysis and new drug design. However, since it is inefficient and costly to identify essential proteins by using biological experiments, then there is an urgent need for automated and accurate detection methods. In recent years, the recognition of essential proteins in protein interaction networks (PPI) has become a research hotspot, and many computational models for predicting essential proteins have been proposed successively. RESULTS: In order to achieve higher prediction performance, in this paper, a new prediction model called TGSO is proposed. In TGSO, a protein aggregation degree network is constructed first by adopting the node density measurement method for complex networks. And simultaneously, a protein co-expression interactive network is constructed by combining the gene expression information with the network connectivity, and a protein co-localization interaction network is constructed based on the subcellular localization data. And then, through integrating these three kinds of newly constructed networks, a comprehensive protein-protein interaction network will be obtained. Finally, based on the homology information, scores can be calculated out iteratively for different proteins, which can be utilized to estimate the importance of proteins effectively. Moreover, in order to evaluate the identification performance of TGSO, we have compared TGSO with 13 different latest competitive methods based on three kinds of yeast databases. And experimental results show that TGSO can achieve identification accuracies of 94%, 82% and 72% out of the top 1%, 5% and 10% candidate proteins respectively, which are to some degree superior to these state-of-the-art competitive models. CONCLUSIONS: We constructed a comprehensive interactive network based on multi-source data to reduce the noise and errors in the initial PPI, and combined with iterative methods to improve the accuracy of necessary protein prediction, and means that TGSO may be conducive to the future development of essential protein recognition as well.

Lithium Fluoride in Electrolyte for Stable and Safe Lithium-Metal Batteries.

Electrolyte engineering via fluorinated additives is promising to improve cycling stability and safety of high-energy Li-metal batteries. Here, an electrolyte is reported in a porous lithium fluoride (LiF) strategy to enable efficient carbonate electrolyte engineering for stable and safe Li-metal batteries. Unlike traditionally engineered electrolytes, the prepared electrolyte in the porous LiF nanobox exhibits nonflammability and high electrochemical performance owing to strong interactions between the electrolyte solvent molecules and numerous exposed active LiF (111) crystal planes. Via cryogenic transmission electron microscopy and X-ray photoelectron spectroscopy depth analysis, it is revealed that the electrolyte in active porous LiF nanobox involves the formation of a high-fluorine-content (>30%) solid electrolyte interphase layer, which enables very stable Li-metal anode cycling over one thousand cycles under high current density (4 mA cm-2 ). More importantly, employing the porous LiF nanobox engineered electrolyte, a Li || LiNi0.8 Co0.1 Mn0.1 O2 pouch cell is achieved with a specific energy of 380 Wh kg-1 for stable cycling over 80 cycles, representing the excellent performance of the Li-metal pouch cell using practical carbonate electrolyte. This study provides a new electrolyte engineering strategy for stable and safe Li-metal batteries.

A Novel Model for Identifying Essential Proteins Based on Key Target Convergence Sets.

In recent years, many computational models have been designed to detect essential proteins based on protein-protein interaction (PPI) networks. However, due to the incompleteness of PPI networks, the prediction accuracy of these models is still not satisfactory. In this manuscript, a novel key target convergence sets based prediction model (KTCSPM) is proposed to identify essential proteins. In KTCSPM, a weighted PPI network and a weighted (Domain-Domain Interaction) network are constructed first based on known PPIs and PDIs downloaded from benchmark databases. And then, by integrating these two kinds of networks, a novel weighted PDI network is built. Next, through assigning a unique key target convergence set (KTCS) for each node in the weighted PDI network, an improved method based on the random walk with restart is designed to identify essential proteins. Finally, in order to evaluate the predictive effects of KTCSPM, it is compared with 12 competitive state-of-the-art models, and experimental results show that KTCSPM can achieve better prediction accuracy. Considering the satisfactory predictive performance achieved by KTCSPM, it indicates that KTCSPM might be a good supplement to the future research on prediction of essential proteins.

Method for Identifying Essential Proteins by Key Features of Proteins in a Novel Protein-Domain Network.

In recent years, due to low accuracy and high costs of traditional biological experiments, more and more computational models have been proposed successively to infer potential essential proteins. In this paper, a novel prediction method called KFPM is proposed, in which, a novel protein-domain heterogeneous network is established first by combining known protein-protein interactions with known associations between proteins and domains. Next, based on key topological characteristics extracted from the newly constructed protein-domain network and functional characteristics extracted from multiple biological information of proteins, a new computational method is designed to effectively integrate multiple biological features to infer potential essential proteins based on an improved PageRank algorithm. Finally, in order to evaluate the performance of KFPM, we compared it with 13 state-of-the-art prediction methods, experimental results show that, among the top 1, 5, and 10% of candidate proteins predicted by KFPM, the prediction accuracy can achieve 96.08, 83.14, and 70.59%, respectively, which significantly outperform all these 13 competitive methods. It means that KFPM may be a meaningful tool for prediction of potential essential proteins in the future.

Method for Essential Protein Prediction Based on a Novel Weighted Protein-Domain Interaction Network.

In recent years a number of calculative models based on protein-protein interaction (PPI) networks have been proposed successively. However, due to false positives, false negatives, and the incompleteness of PPI networks, there are still many challenges affecting the design of computational models with satisfactory predictive accuracy when inferring key proteins. This study proposes a prediction model called WPDINM for detecting key proteins based on a novel weighted protein-domain interaction (PDI) network. In WPDINM, a weighted PPI network is constructed first by combining the gene expression data of proteins with topological information extracted from the original PPI network. Simultaneously, a weighted domain-domain interaction (DDI) network is constructed based on the original PDI network. Next, through integrating the newly obtained weighted PPI network and weighted DDI network with the original PDI network, a weighted PDI network is further constructed. Then, based on topological features and biological information, including the subcellular localization and orthologous information of proteins, a novel PageRank-based iterative algorithm is designed and implemented on the newly constructed weighted PDI network to estimate the criticality of proteins. Finally, to assess the prediction performance of WPDINM, we compared it with 12 kinds of competitive measures. Experimental results show that WPDINM can achieve a predictive accuracy rate of 90.19, 81.96, 70.72, 62.04, 55.83, and 51.13% in the top 1%, top 5%, top 10%, top 15%, top 20%, and top 25% separately, which exceeds the prediction accuracy achieved by traditional state-of-the-art competing measures. Owing to the satisfactory identification effect, the WPDINM measure may contribute to the further development of key protein identification.

ICLRBBN: a tool for accurate prediction of potential lncRNA disease associations.

Growing evidence has elucidated that long non-coding RNAs (lncRNAs) are involved in a variety of complex diseases in human bodies. In recent years, it has become a hot topic to develop effective computational models to identify potential lncRNA-disease associations. In this article, a novel method called ICLRBBN (Internal Confidence-Based Local Radial Basis Biological Network) is proposed to detect potential lncRNA-disease associations by adopting an internal confidence-based radial basis biological network. In ICLRBBN, a novel internal confidence-based collaborative filtering recommendation algorithm was designed first to mine hidden features between lncRNAs and diseases, which guarantees that ICLRBBN can be more effectively applied to predict new diseases. Then, a unique three-layer local radial basis function network consisting of diseases and lncRNAs was constructed, based on which the association probability between diseases and lncRNAs was calculated by combining different characteristics of lncRNAs with local information of diseases. Finally, we compared ICLRBBN with 6 state-of-the-art methods based on two different validation frameworks. Simulation results showed that area under the receiver operating characteristic curve (AUC) values achieved by ICLRBBN outperformed all competing methods. Furthermore, case studies illustrated that ICLRBBN has a promising future as a powerful tool in the practical application of lncRNA-disease association prediction. A web service for prediction of potential lncRNA-disease associations is available at http://leelab2997.cn/.

An Iterative Method for Predicting Essential Proteins Based on Multifeature Fusion and Linear Neighborhood Similarity.

Growing evidence have demonstrated that many biological processes are inseparable from the participation of key proteins. In this paper, a novel iterative method called linear neighborhood similarity-based protein multifeatures fusion (LNSPF) is proposed to identify potential key proteins based on multifeature fusion. In LNSPF, an original protein-protein interaction (PPI) network will be constructed first based on known protein-protein interaction data downloaded from benchmark databases, based on which, topological features will be further extracted. Next, gene expression data of proteins will be adopted to transfer the original PPI network to a weighted PPI network based on the linear neighborhood similarity. After that, subcellular localization and homologous information of proteins will be integrated to extract functional features for proteins, and based on both functional and topological features obtained above. And then, an iterative method will be designed and carried out to predict potential key proteins. At last, for evaluating the predictive performance of LNSPF, extensive experiments have been done, and compare results between LNPSF and 15 state-of-the-art competitive methods have demonstrated that LNSPF can achieve satisfactory recognition accuracy, which is markedly better than that achieved by each competing method.

Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model.

Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human diseases. However, traditional biological experiments are time-consuming and expensive, so it has become a research topic in bioinformatics to predict potential microbe-disease associations by adopting computational methods. In this study, a novel calculative method called BPNNHMDA is proposed to identify potential microbe-disease associations. In BPNNHMDA, a novel neural network model is first designed to infer potential microbe-disease associations, its input signal is a matrix of known microbe-disease associations, and its output signal is matrix of potential microbe-disease associations probabilities. And moreover, in the novel neural network model, a new activation function is designed to activate the hidden layer and the output layer based on the hyperbolic tangent function, and its initial connection weights are optimized by adopting Gaussian Interaction Profile kernel (GIP) similarity for microbes, which can improve the training speed of BPNNHMDA efficiently. Finally, in order to verify the performance of our prediction model, different frameworks such as the Leave-One-Out Cross Validation (LOOCV) and k-Fold Cross Validation ( k-Fold CV) are implemented on BPNNHMDA respectively. Simulation results illustrate that BPNNHMDA can achieve reliable AUCs of 0.9242, 0.9127 ± 0.0009 and 0.8955 ± 0.0018 in LOOCV, 5-Fold CV and 2-Fold CV separately, which are superior to previous state-of-the-art methods. Furthermore, case studies of inflammatory bowel disease (IBD), asthma and obesity demonstrate that BPNNHMDA has excellent prediction ability in practical applications as well.

High Capacity and Energy Density of Zn-Ni-Co-P Nanowire Arrays as an Advanced Electrode for Aqueous Asymmetric Supercapacitor.

Developing multicomponent transition-metal phosphides has become an efficient way to improve the capacitive performance of single-component transition-metal phosphides. However, reports on quaternary phosphides for supercapacitor applications are still scarce. Here, we report high capacity and energy density of Zn-Ni-Co-P quaternary phosphide nanowire arrays on nickel foam (ZNCP-NF) composed of highly conductive metal-rich phosphides as an advanced binder-free electrode in aqueous asymmetric supercapacitors. In a three-electrode system using the new electrode, a high specific capacity of 1111 C g-1 was obtained at a current density of 10 A g-1. Analysis of this aqueous asymmetric supercapacitor with ZNCP-NF as the positive electrode and commercial activated carbon as the negative electrode reveals a high energy density (37.59 Wh kg-1 at a power density of 856.52 W kg-1) and an outstanding cycling performance (capacity retention of 92.68% after 10 000 cycles at 2 A g-1). Our results open a path for a new design of advanced electrode material for supercapacitors.

Blow-Spinning Enabled Precise Doping and Coating for Improving High-Voltage Lithium Cobalt Oxide Cathode Performance.

Lithium cobalt oxide (LiCoO2) possesses an attractive theoretical specific capacity (274 mAh g-1) and high discharge voltage (∼4.2 V vs Li+/Li). However, only a half of the theoretical capacity of LiCoO2 is available in commercialized lithium ion batteries because of the intrinsic structural instability and detrimental interface of LiCoO2 at the charging voltage over 4.2 V. Here, a facile blow-spinning synthetic method is developed to realize precise doping and simultaneous self-assembly coating of LiCoO2 particles, achieving a record performance among present LiCoO2 cathodes. Owing to the spatial confinement effect of microfibers fabricated by blow-spinning, homogeneously Mn and La doped in the LiCoO2 host and uniformly Li-Ti-O segregated at the LiCoO2 surface can be realized in every batch of samples. It is demonstrated that the Mn and La codoping can suspend the intrinsic instability and increase the Li+ diffusivity of the LiCoO2 host, and the Ti-based coating can stabilize the interface of LiCoO2 particles at the charging voltage up to 4.5 V. As a result, the obtained comodified LiCoO2 cathode shows the best rate performance (1.85 mAh cm-2 at 2C) and longest cycling stability under an areal capacity of 2.04 mAh cm-2 (83% capacity retention over 300 cycles at 0.3C), in comparison to previously reported LiCoO2 cathodes.

A Nacre-Inspired Separator Coating for Impact-Tolerant Lithium Batteries.

The commercial ceramic nanoparticle coated microporous polyolefin separators used in lithium batteries are still vulnerable under external impact, which may cause short circuits and consequently severe safety threats, because the protective ceramic nanoparticle coating layers on the separators are intrinsically brittle. Here, a nacre-inspired coating on the separator to improve the impact tolerance of lithium batteries is reported. Instead of a random structured ceramic nanoparticle layer, ion-conductive porous multilayers consisting of highly oriented aragonite platelets are coated on the separator. The nacre-inspired coating can sustain external impact by turning the violent localized stress into lower and more uniform stress due to the platelet sliding. A lithium-metal pouch cell using the aragonite platelet coated separator exhibits good cycling stability under external shock, which is in sharp contrast to the fast short circuit of a lithium-metal pouch cell using a commercial ceramic nanoparticle coated separator.

A fluorinated alloy-type interfacial layer enabled by metal fluoride nanoparticle modification for stabilizing Li metal anodes.

Using highly dispersed metal fluoride nanoparticles to construct a uniform fluorinated alloy type interfacial layer on the surface of Li metal anodes is realized by an ex situ solution chemical modification method. The fluorinated alloy-type interfacial layer can effectively inhibit the growth of undesirable Li dendrites while enhancing the performance of Li metal anodes.

High Voltage Magnesium-ion Battery Enabled by Nanocluster Mg3Bi2 Alloy Anode in Noncorrosive Electrolyte.

Currently, developing high voltage (beyond 2 V) rechargeable Mg-ion batteries still remains a great challenge owing to the limit of corrosive electrolyte and low compatibility of anode material. Here we report a facile one step solid state alloying route to synthesize nanoclustered Mg3Bi2 alloy as a high-performance anode to build up a 2 V Mg-ion battery using noncorrosive electrolyte. The fabricated nanoclustered Mg3Bi2 anode delivers a high reversible specific capacity (360 mAh g-1) with excellent stability (90.7% capacity retention over 200 cycles) and high Coulombic efficiency (average 98%) at 0.1 A g-1. The good performance is attributed to the stable nanostructures, which effectively accommodate the reversible Mg2+ ion insertion/deinsertion without losing electric contact among clusters. Significantly, the nanoclustered Mg3Bi2 anode can be coupled with high voltage cathode Prussian Blue to assemble a full cell using noncorrosive electrolyte, showing a stable cycling (88% capacity retention over 200 cycles at 0.2 A g-1) and good rate capability (103 mAh g-1 at 0.1 A g-1 and 58 mAh g-1 at 2 A g-1). The energy and power density of the as-fabricated full cell can reach up to 81 Wh kg-1 and 2850 W kg-1, respectively, which are both the highest values among the reported Mg-ion batteries using noncorrosive electrolytes. This study demonstrates a cost-effective route to fabricate stable and high voltage rechargeable Mg-ion battery potentially for grid-scale energy storage.

A novel method for creating working space during endoscopic thyroidectomy via bilateral areolar approach.

BACKGROUND: Endoscopic thyroidectomy (ET) can be performed through the bilateral areolar approach (BAA). A working space (WS) is typically created on the surface of the pectoral fascia in the chest wall and in the subplatysmal space in the neck. There are several limitations of using this WS. The aim of this study was to establish a new WS for ET. SUBJECTS AND METHODS: A retrospective review was performed on 85 patients with benign thyroid nodules who had undergone ET through a BAA. A WS was created between the anterior and poster layers of the superficial pectoral fascia (SPF) in the chest and underneath the deep layer of the investing layer (IL) in the neck. RESULTS: The time for creating the WS was 7.2 ± 2.1 (range, 5-12) minutes. No hemorrhage occurred during the procedure. Fat liquefaction occurred in 2 patients. Edema of the neck skin flap presented as lack of a suprasternal notch. No skin numbness occurred. No patient required postoperative pain medication. All patients were extremely satisfied with the cosmetic results. CONCLUSIONS: This new method of establishing a WS between the two layers of the SPF and underneath the IL is simple and fast, provides good exposure, yields less postoperative pain, and has a lower risk of skin burn.