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In this work, hierarchical NiS@ZnIn2S4 heterostructure was developed by constructing ultra-thin ZnIn2S4 (ZIS) nanosheets on hollow NiS nanospheres for hydrogen production from photocatalytic water splitting. The NiS@ZIS displayed a strong optical absorption ability in the visible region and a high specific surface area of 33.14 m2/g. The Type-I band alignment in NiS@ZIS heterostructure was determined by the combination of UV–vis absorption spectroscopy and Mott-Schottky curves. The photocatalytic hydrogen production of NiS@ZIS (1.24 mmol g−1 h−1) was nearly 5.6 times higher than that of ZIS under visible light, in the absence of any co-catalyst and sacrificial agent. The separation and migration of charge in NiS@ZIS were characterized by a series of spectroscopy and photo/electrochemical tests, which verified the efficient charge transfer from ZIS to NiS.

Developing photocatalyst with effective charge separation and fast surface reaction kinetics is crucial to high-efficiency photocatalytic H2 evolution. Herein we spatially separate the reductive and oxidative reaction sites of ZnIn2S4 nanosheet and accelerate the hole transfer process by simply depositing cobalt phosphate (CoHxPOy, noted as Co-Pi), a non-precious co-catalyst, on its basal plane. Theoretical calculations combined with comprehensive characterizations reveal that Zn vacancies induced deep local energy levels serve as hole trap states to pin the photogenerated holes on (0 0 1) surface of ZnIn2S4. Upon Co-Pi deposition, Co atoms preferentially bond with S atoms nearby the Zn vacancies of ZnIn2S4. Taking advantage of the formation of Co-S bonds, Co-Pi acts as a hole receptor to extract the trapped holes effectively. As the photocatalytic activity of ZnIn2S4 is caused by active S atoms on its edge side, deposition of Co-Pi on planar side of nanosheets helps electron-hole migration to different facets. As a result of inert facet activation and spatial carrier separation, Co-Pi/ZnIn2S4 exhibits a superior photocatalytic H2 evolution rate of 10.19 mmol g-1h−1 under visible light, which is superior to most of reported ZnIn2S4-based photocatalysts. This work will provide novel insights into the activation of inert basal planes of two-dimensional photocatalysts.

Graph Neural Networks (GNN) has been used to exploit global features in text representation learning for natural language processing (NLP) tasks, including text classification, sequence tagging, neural machine translation and relational reasoning. However, GNN based models usually build a graph for the entire corpus, they have high memory consumption, ignoring the order of words and containing test documents in the training graph. Thus, these models are inherently transductive and have difficulties in inductive learning. In order to solve the above problems, we propose a Graph Transformer Networks based Text representation (GTNT) model. It first constructs a degree-centric text graph, which generates a text graph for each document in the corpus. Then it adopts a graph transformer network to model the graph to obtain node embeddings. When we apply our proposed GTNT model to citation recommendation and text classification tasks, the experimental results show that our model outperforms other state-of-the-art models.

Cotton encounters long-term drought stress problems resulting in major yield losses. Transcription factors (TFs) plays an important role in response to biotic and abiotic stresses. The coexpression patterns of gene networks associated with drought stress tolerance were investigated using transcriptome profiles. Applying a weighted gene coexpression network analysis, we discovered a salmon module with 144 genes strongly linked to drought stress tolerance. Based on coexpression and RT-qPCR analysis GH_D01G0514 was selected as the candidate gene, as it was also identified as a hub gene in both roots and leaves with a consistent expression in response to drought stress in both tissues. For validation of GH_D01G0514, Virus Induced Gene Silencing was performed and VIGS plants showed significantly higher excised leaf water loss and ion leakage, while lower relative water and chlorophyll contents as compared to WT (Wild type) and positive control plants. Furthermore, the WT and positive control seedlings showed higher CAT and SOD activities, and lower activities of hydrogen peroxide and MDA enzymes as compared to the VIGS plants. Gh_D01G0514 (GhNAC072) was localized in the nucleus and cytoplasm. Y2H assay demonstrates that Gh_D01G0514 has a potential of auto activation. It was observed that the Gh_D01G0514 was highly upregulated in both tissues based on RNA Seq and RT-qPCR analysis. Thus, we inferred that, this candidate gene might be responsible for drought stress tolerance in cotton. This finding adds significantly to the existing knowledge of drought stress tolerance in cotton and deep molecular analysis are required to understand the molecular mechanisms underlying drought stress tolerance in cotton.

In this investigation, whole-genome identification and functional characterization of the cotton dehydrin genes was carried out. A total of 16, 7, and 7 dehydrin proteins were identified in G. hirsutum, G. arboreum and G. raimondii, respectively. Through RNA sequence data and RT-qPCR validation, Gh_A05G1554 (GhDHN_03) and Gh_D05G1729 (GhDHN_04) were highly upregulated, and knockdown of the two genes, significantly reduced the ability of the plants to tolerate the effects of osmotic and salt stress. The VIGS-plants recorded significantly higher concentration levels of oxidants, hydrogen peroxide (H2O2) and malondialdehyde (MDA), furthermore, the four stress responsive genes GhLEA2, Gh_D12G2017 (CDKF4), Gh_A07G0747 (GPCR) and a transcription factor, trihelix, Gh_A05G2067, were significantly downregulated in VIGS-plants, but upregulated in wild types under osmotic and salt stress condition. The result indicated that dehydrin proteins are vital for plants and can be exploited in developing a more osmotic and salt stress-resilient germplasm to boost and improve cotton production.

Drought, salinity and cold stresses have a major impact on cotton production, thus identification and utilization of plant genes vital for plant improvement Whole-genome identification and functional characterizations of the IQ67-domain (IQD) protein family was carried out in which 148, 77, and 79 IQD genes were identified in Gossypium hirsutum, G. raimondii, and G. arboreum. The entire IQD proteins had varied physiochemical properties, however; their grand hydropathy values were negative, which demonstrated that the proteins were hydrophilic, a property common among the proteins encoded by various stresses responsive genes, such as the late embryogenesis abundant (LEA) proteins. The IQD proteins were predicted to be majorly sublocalized in the nucleus; moreover, various cis-regulatory elements with higher role in enhancing abiotic stress tolerance were detected. RNA-seq and RT-qPCR analysis revealed two key genes, Gh_D06G0014 and Gh_A09G1608 with significantly higher upregulation across the various tissues under drought, salt and cold stress. Knockdown of the two genes negatively affected the ability of G. hirsutum to tolerate the effects of the three stress factors, being all the antioxidant assayed were significantly low concentrations compared to the oxidizing enzymes in VIGS plants under stress, furthermore, morphological and physiological traits were all negatively affected in VIGS plants. Expression levels of GhLEA2, GhCDK_F4, GPCR (TOM1) and Gh_A05G2067 (TH), the stress responsive genes were all downregulated in the VIGS plants, but significantly upregulated in WT and positively controlled plants. The results demonstrated that the IQD genes could be responsible for enhancing drought, salt and cold stress tolerance in cotton.

Wide-bandgap semiconductor photocatalysts such as La2Ti2O7 (LTO) are stable but can only work under ultraviolet (UV) light, while narrow-bandgap semiconductor photocatalysts such as CdS and black phosphorus (BP) with broad-wavelength range absorption generally have low stability and high electron-hole pairs recombination rate. In this work, ternary heterostructure composing of BP quantum dots (QDs), CdS nanoparticles (NPs) and LTO nanosteps (NSP) is developed for the first time to make full use of their respective advantages, which offers a promising approach to achieving desired stability and solar energy harvesting. As an effective noble metal-free photocatalyst, BP-CdS-LTO composite generates H2 from Na2S/Na2SO3 aqueous solution with a rate of 0.96 mmol g−1 h−1 under solar light irradiation, and 0.26 mmol g−1 h−1 in the near-infrared (NIR) range. In this system, BP serves as a NIR photosensitizer; CdS not only contributes to visible light absorption but also bonds with BP to promote the transmission of photogenerated charge carriers; LTO absorbs UV light as well as provides reaction sites for photoinduced electrons. Femtosecond transient absorption spectroscopy is used to elucidate the kinetics of the injection of photogenerated electrons from BP QDs to CdS NPs and finally to LTO. This work provides deep insight into charge transfer between semiconductors with different band alignments, which can open a new avenue for more rationally designing heterostuctured photocatalysts for H2 production.

Large-size ultrathin g-C3N4 nanosheets have attracted extensive attention for their peculiar properties and promising practical application. Temporarily, g-C3N4 nanosheets are mostly obtained through thermal oxidation etching of bulk g-C3N4 which generally introduces defects including oxygen doping impurities. Here, we construct high-quality g-C3N4 nanosheets with thickness of about 2 nm and large lateral size up to 4 µm by hydrogen atom etching of bulk g-C3N4. The as constructed large-size ultrathin g-C3N4 nanosheets exhibit an enhanced photoluminescence with a quantum yield about two times that of the bulk g-C3N4. This work employs a facile method to prepare large-size g-C3N4, providing great guidance to the synthesis of other materials with similar structure.

Graphical abstract COVIDSum (COVID-19 scientific paper Summarization) consists of four major modules: (1) Dataset Preprocessing, (2) Heuristic Sentence Extraction, (3) Word Cooccurrence Graph Construction, and (4) Linguistically Enriched Abstractive Summarization. The Data Preprocessing module retrieves abstract and textual content of each paper and removes papers which have missed abstracts or are not written in English language. Sentence Extraction module applies three heuristic methods to extract sentences of each paper. Word Co-occurrence Relationship Graph Construction module extracts word co-occurrence relationship to construct an un-weighted directed word co-occurrence graph. Linguistically Enriched Abstractive Summarization module proposes a hybrid summarization approach, which utilizes SciBERT and a GATbased graph encoder to encode the word sequences and word co-occurrence graphs respectively, adopts highway networks to fuse the above two encodings for obtaining context vectors of sentences, and applies Transformer decoder to generate summaries., The coronavirus disease (COVID-19) has claimed the lives of over 350,000 people and infected more than 173 million people worldwide, it triggers researchers from diverse fields are accelerating their research to help diagnostics, therapies, and vaccines. Researchers also publish their recent research progress through scientific papers. However, manually writing the abstract of a paper is time-consuming, and it increases the writing burden of the researchers. Abstractive summarization technique which automatically provides researchers reliable draft abstracts, can alleviate this problem. In this work, we propose a linguistically enriched SciBERT-based summarization model for COVID-19 scientific papers, named COVIDSum. Specifically, we first extract salient sentences from source papers and construct word co-occurrence graphs. Then, we adopt a SciBERT-based sequence encoder and a Graph Attention Networks-based graph encoder to encode sentences and word co-occurrence graphs, respectively. Finally, we fuse the above two encodings and generate an abstractive summary of each scientific paper. When evaluated on the publicly available COVID-19 open research dataset, the performance of our proposed model achieves significant improvement compared with other document summarization models.

Drug repurposing refers to discovery of new medical instructions for existing chemical drugs, which has great pharmaceutical significance. Recently, large-scale biological datasets are increasingly available, and many graph neural network (GNN) based methods for drug repurposing have been developed. These methods often deem drug repurposing as a link prediction problem, which mines features of biological data to identify drug-disease associations (i.e., drug-disease links). Due to heterogeneity of data, we need to deeply explore heterogeneous information of biological network for drug repurposing. In this paper, we propose a Relation-aware Heterogeneous Graph Transformer (RHGT) model to capture heterogeneous information for drug repurposing. We first construct a drug-gene-disease interactive network-based on biological data, and then propose a three-level network embedding model, which learns network embeddings at fine-grained subtype-level, node-level and coarse-grained edge-level, respectively. The output of subtype-level is the input of node-level and edge-level, and the output of node-level is the input of edge level. We get edge embeddings at edge-level, which integrates edge type embeddings and node embeddings. We deem that in this way, characteristics of drug-gene-disease interactive network can be captured more comprehensively. Finally, we identify drug-disease associations (i.e., drug-disease links) based on the relationship between drug-gene edge embeddings and gene-disease edge embeddings. Experimental results show that our model performs better than other state-of-the-art graph neural network methods, which validates effectiveness of the proposed model.

With the rapid development of electronic medical records (EMRs), most existing medicine recommendation systems based on EMRs explore knowledge from the diagnosis history to help doctors prescribe medication correctly. However, due to the limitations of the EMRs' content, recommendation systems cannot explicitly reflect relevant medical data, such as drug interactions. In recent years, medicine recommendation approaches based on medical knowledge graphs and graph neural networks have been proposed, and the methods based on the Transformer model have been widely used in medicine recommendation systems. Transformer-based medicine recommendation approaches are readily applicable to inductive problems. Unfortunately, traditional Transformer-based medicine recommendation approaches require complex computing power and suffer information loss among the multi-heads in Transformer model, which causes poor performance. At the same time, these approaches have rarely considered the side effects of drug interaction in traditional medical recommendation approaches. To overcome the drawbacks of the current medicine recommendation approaches, we propose a Star Interactive Enhanced-based Transformer (SIET) model. It first constructs a high-quality heterogeneous graph by bridging EMR (MIMIC-III) and a medical knowledge graph (ICD-9 ontology and DrugBank). Then, based on the constructed heterogeneous graph, it extracts a disease homogeneous graph, a medicine homogeneous graph, and a negative factors homogeneous graph to get auxiliary information of disease or drug (named enhanced neighbors). These are fed into the SIET model in conjunction with the relevant information in the EMRs to obtain representations of diseases and drugs. It finally generates the recommended drug list by calculating the cosine similarity between disease combination representations and drug combination representations. Extensive experiments on the MIMIC-III, DrugBank, and ICD-9 ontology datasets demonstrate the outstanding performance of our proposed model. Meanwhile, we show that our SIET model outperforms strong baselines on an inductive medicine recommendation task.

Objective The associations between statins use and incidence or recurrence of venous thromboembolism (VTE) are controversial. We aimed to conduct a meta-analysis to reconcile the conflicting results. Methods We searched PubMed, Embase and Cochrane Library for studies published from database inception until May 31, 2021. Cohort studies and Randomized Controlled Trials that reported incidence or recurrence of VTE using statins compared with placebo or non-statins were included for meta-analysis. Results A total of 43 studies comprising over 8.6 million participants were included for analysis. The median follow-up duration was 38.1 months. Compared with no statins treatment, statins appeared to have a protective effect in primary prevention of VTE (RR 0.78, 95% CI 0.72–0.85), but significant heterogeneity was found among included studies (I2 = 81%). Statins was also associated with a 26% reduced risk of recurrent VTE (RR 0.74, 95% CI 0.70–0.78), even in patients receiving anticoagulant therapy (RR 0.77, 95% CI 0.65–0.92). In patients with a history of VTE, statins was associated with a reduced risk of bleeding and all cause mortality. The NNT of statins to prevent one case of VTE in the cancer population, and one case of recurrent VTE in patients with a history of VTE was 103.1 and 90.7 person-years respectively. Conclusion In high-risk patients, statins treatment may reduce the incidence of VTE. Statins can also reduce the risk of recurrent VTE and all-cause mortality in patients with a history of VTE.

In photocatalytic systems, TiO2 based particles in suspensions tend to aggregate spontaneously, resulting in low efficiency for light utilization and photocatalytic activity. In this study, various TiO2 nanoparticles with different shapes were synthesized and characterized via various analysis. In atmosphere and aqueous environment, the nanoparticles demonstrated different properties in the nature of the agglomerations. Photocatalytic performances of the as-prepared samples were confirmed to be strongly influenced by the dispersion stabilities with TiO2 nanotube exhibited higher colloidal dispersion stability and photocatalytic activity. Mixing binary TiO2 photocatalysts with different shapes as a simple approach was firstly proposed for enhancing photoreforming hydrogen production from bio-glycerol aqueous solution. At a specific mixing ratio, the mixed suspension of TiO2 nanosphere and nanosheet retained excellent colloidal dispersion stability, and photocatalytic hydrogen production was significantly promoted with its maximum H2 production amount as 2.1–2.9 times high as that of TNP and TNS, respectively. This strategy of enhancing colloidal dispersion stability may provide new ideas for the design of efficient and energy-saving photocatalytic system.

In this study, the CuBi2O4 films were prepared by a facile drop-casting method for visible-light-driven water splitting, while the effect of film thickness and annealing temperature on the actual photoelectrochemical (PEC) performance were investigated. The annealing temperature was optimized as 550 °C. With increasing the layer number, the PEC performance was firstly increased and then decreased, reaching a maximum photocurrent density of 1.1 mA/cm2 at a layer number of 3 under a bias of 0 V vs. reverse hydrogen electrode (RHE). Besides, both the electrochemical impedance and photoluminescence (PL) spectra were collected to reveal the mechanism underlying the performance changes. It was found that the enhanced PEC performance was attributed to high specific surface area and broad spectral response of CuBi2O4 photocathodes. Moreover, a tandem PEC cell was constructed by combining CuBi2O4 with a ZnIn2S4 photoanode for unbiased solar water splitting. The photocurrent density at the joint point was 0.13 mA/cm2, corresponding to an unbiased solar water splitting efficiency of 0.056%. This work shows that CuBi2O4 has a good potential as a photocathode for solar-driven overall water splitting.

The exploitation of low-cost, high-performance and visible-light-activated photocatalysts is of great significance for hydrogen economy. Herein, we report the rational design of a ZnIn2S4-based noble-metal-free photocatalyst for direct splitting of water into H2 and O2. Semi-metallic NiCo2S4 with low reduction barrier is selected as H2 evolving co-catalyst under the guidance of density functional theory (DFT) computation. Controllable preparation of hierarchical heterostructure is achieved by fine-tuning NiCo2S4 yolk-shell hollow spheres from metal-organic framework (MOF) precursors, followed by tight growth of ZnIn2S4 nanosheets on its outer surface and decoration of O2 evolving co-catalyst, Co3O4 nanoparticles, on the outermost layers. Locating reduction and oxidation sites on interior and exterior surfaces of ZnIn2S4 effectively harness the charge recombination as well as undesired back reaction. As a result of directional charge separation, ZnIn2S4 modified with separated dual co-catalysts exhibits promising photocatalytic activity, which is superior to most of analogous sulfide-based photocatalysts.

A photocatalyst which consists of zero-dimension (0D) ZnFe2O4 (ZFO) and two-dimension (2D) Fe-doped g-C3N4 (Fe-CN) has been prepared successfully through a facile solvothermal method. The Fe doping into g-C3N4 lattice has been identified by both the infrared (IR) and X-ray photoelectron spectra (XPS), respectively. Introducing Fe into CN extends the spectral response to 700 nm while ZFO serves as a near infrared (NIR) photosensitizer of the Fe-CN component. The formation of Fe-CN heterostructure not only contributes to visible light absorption but also promotes the separation of photo-generated electron-hole pairs. Furthermore, the density functional theory (DFT) calculation indicated that the N2 molecules prefer to be adsorbed onto the Fe sites with a reduced potential barrier for N2 fixation. More importantly, the Fe atoms doped into the CN lattice acted as the reaction sites for accelerating photocatalytic N2 fixation reaction, which was also demonstrated by DFT. These factors lead to a high photocatalytic NH4+ generation rate of 25.3 mg g−1 h−1 under a full-spectrum irradiation, which is much higher than the value (4.4 mg g−1 h−1) of pure CN catalysts. This research might provide a novel approach for rationally designing full-spectrum-response N2 fixation photocatalyst.

Gossypium hirsutum races are believed to be potential reservoirs of desirable traits, which can play crucial roles to overcome the existing narrow genetic base of modern Upland cotton cultivars. However, prior to utilizing the races in cotton improvement programs, understanding their genetic constitutions is needed. Thus, this study used molecular and morphological techniques to characterize 110 G. hirsutum germplasm including 109 semi-wild accessions and one Upland cotton cultivar, CRI12. In the study, 104 SSR markers detected 795 alleles, with an average of 7.64 alleles per marker, ranging from 3 to 14, and average polymorphism information content (PIC) value of 0.71. And 96 of the markers were found to be highly informative, with PIC value≥0.50. Pairwise genetic similarity coefficient across the accessions ranged from 0.19 to 1.00, with an average value of 0.46. Morphological characterization was done using fiber length, fiber strength, micronaire, fiber uniformity index, and fiber elongation. Pairwise taxonomic distance within the accessions ranged from 0.17 to 3.41, with a mean of 1.33. The SSR and fiber quality traits data set based unweighted pair group method of arithmetic mean (UPGMA) analysis grouped the accessions into 7 and 12 distinct clusters, respectively, that corresponds well with the results of principal component analysis (PCA). Our study revealed the existence of vast molecular and morphological diversities within the accessions and provided valuable information on each semi-wild accession for quick and better informed germplasm utilization in cotton breeding programs.

In this paper, two dimensional (2D) perovskite La 2 Ti 2 O 7 (LTO) nanosheet was synthesized and used as support for the decoration of Pt nanoparticles. Ultrasmall Pt nanoparticles with a diameter of 4.5 nm were well dispersed on the surface of LTO nanosheets. By using as-prepared Pt-LTO as working electrode, great improvement of photoelectrocatalytic activity for methanol oxidation reaction (MOR) was achieved under light irradiation. The Pt-LTO electrode showed 10.6 times enhanced catalytic activities of MOR under light illumination compared to dark condition. The unique of 2D structures as well as synergistic effect of electrocatalytic and photocatalytic result in the improvement of catalytic performance. The mechanism of above improved photoelectrocatalytic performance is also proposed.

Ni based oxygen transfer materials (OTMs) and Li2ZrO3 based sorbent have been synthesized for their potential use in a sorption enhanced chemical looping steam reforming (SE-CLSR) process. Ni based OTMs were firstly tested under chemical looping steam reforming (CLSR) process and the gaseous products with temperature variety during this process were measured and analyzed. Experimental results indicated that both of the OTMs exhibited good activities and stability during multiple glycerol reduction-air oxidation cycles towards H2 production. The desired maximum hydrogen selectivities of 87.9% and 84.7% were obtained for NiO/NiAl2O4 and NiO/ZrO2 for steam to carbon ratio of 3 at 550 °C. The fuel feed step of reduction process could be divided into two periods with the OTMs possessing a dual function of oxygen carrier and catalyst. The decrease of conversions and selectivity for both OTMs was mainly due to the coke deposition, which reduced the catalytic effect in the reforming reaction as well as the oxygen transfer effect in the reduction reaction. Sorption enhanced chemical looping steam reforming (SE-CLSR) process for H2 production with in-situ CO2 removal was carried out with OTM/sorbent ratio of 1 and multi-cyclic reaction/regeneration processes under 550 °C/850 °C with a steam/carbon ratio of 3 was also achieved. H2 purity were both enhanced by the two OTMs and sorbent in SE-CLSR. Therefore, it is possible to utilize oxygen from NiO and steam to oxidize glycerol, while enhancing the water-gas-shift reaction by capturing CO2 at the same time.

Visible-light-driven (oxy)nitride photoanode materials have received extensive attention for photoelectrocatalytic (PEC) water splitting due to the unique photoelectric properties. However, developing efficient and earth-abundant (oxy)nitrides with favorable band edge alignments and high charge carrier mobility remains a great challenge. In this study, a practical strategy is proposed to fine-tune the energy band structure of a newly designed porous oxynitride by forming a series of solid solutions between two perovskite-type materials. Taking advantage of the Na/La and O/N compensated substitution in LaTiO2N, the Na0.5xLa1-0.5xTiO2+xN1−x (x = 0–0.3) solid solutions exhibited a constant small band gap of ca. 2.0 eV (corresponding to a ca. 620 nm absorption edge), while the positively shifted valence band potential improved the water oxidation activity. Among various oxynitride photoanodes, Co3O4/Na0.1La0.9TiO2.2N0.8 photoanode exhibited the largest photocurrent density of 4.02 mA/cm2 at 1.23 V vs. RHE under AM 1.5 G solar irradiation (100 mW/cm2). First-principle calculations and photoelectrochemical measurements separately provided theoretical and experimental evidences for the efficient charge transfer from the bulk of the solid solution photoanodes to the surface, resulting from the enhanced charge carrier mobility.

In plants, aldehyde dehydrogenases (ALDHs) function as 'aldehyde scavengers' by removing reactive aldehydes and thus play important roles in stress responses. To date, 30 ALDHs have been identified in Gossypium raimondii, whereas ALDHs have not been studied in Gossypium arboreum or in tetraploid cotton. In this study, we identified 30, 59 and 59 aldehyde dehydrogenase (ALDH) genes from G. arboreum, G. hirsutum and G. barbadense, respectively. Gene structure analysis revealed that members of the same family exhibit similar exon-intron structures and structural domains, and all members of the ALDH18 family possess a distinct AA-kinase domain. Synteny analysis showed that segmental and tandem duplications have played an important role in the expansion and evolution of ALDHs in cotton. Phylogenetic and synteny analysis between G. arboreum and G. raimondii demonstrated that all GaALDHs and GrALDHs are orthologous and that most GaALDHs are located in syntenic blocks corresponding to those of G. raimondii, implying that these genes appeared before the divergence of G. arboreum and G. raimondii and that no expansion of the ALDH superfamily has occurred in these two cotton species. Quantitative real-time PCR analysis revealed that the majority of GaALDHs and GhALDHs are up-regulated under conditions of high salinity and drought, indicating that these genes may be stress responsive. The findings of this study, based on genome-wide identification of ALDHs in Gossypium and analysis of their evolution and expression, provide a foundation for further analysis of ALDHs and suggest potential target genes for improving stress resistance in cotton.

Photoreforming hydrogen production from glycerol aqueous solutions has been investigated over Ag2O TiO2 catalysts with variable compositions (0.72–6.75 wt.% of Ag2O) synthesized by a sol–gel method. The structural and morphologic characteristics were examined by means of X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), high resolution transmission electron microscopy (HRTEM) and Brunauer–Emmett–Teller (BET) method etc. By comparing the results of FESEM and DLS, aggregations of all samples were found to occur in both dry powder and water suspension states. The TiO2 based catalyst with 2.82 wt.% Ag2O calcined under 500 °C showed less average particle size differences between in air and in aqueous state, suggesting better dispersion stability which could lead to a more efficient of using light. It was found that mixing certain amounts (0.72–2.82 wt.%) of Ag2O to TiO2 could reduce anatase grain size and increased the specific surface area of the catalysts. The hydrogen production results indicated that the photocatalytic activity and the reaction rate of TiO2 were significantly improved by mixing Ag2O. The optimal Ag2O mixing amount and calcination temperature for H2 production was found. With an appropriate catalyst composition, the heterostructures could be well generated to enhance the separation of electrons and holes and it was also found that the desirable heterostructure was likely to disappear after the calcination temperature of 700 °C.

Aiming at improving photocatalytic hydrogen production with TiO2, a series of TiO2 coupled with metal oxide nanoparticles have been prepared by a sol-gel method with a fixed molar ratio in various compositions. The photocatalysts were characterized by surface and bulk techniques (XRD, FESEM, HRTEM etc.) and UV–vis DRS method was employed to understand the optical properties. The as-prepared TiO2 coupled with metal oxide was mainly present as anatase, and the anatase grain sizes were reduced comparing to that in bare TiO2, as evidenced by XRD analysis. By analyzing FESEM and DLS results, aggregations of all samples occurred in both dry powder and water suspension. H2 TPR analysis revealed H2 reduction properties of the samples. Among those, Ag2O-TiO2 presented better dispersion stability in different environmental phases. Improved adsorption of the TiO2 for UV–vis light was confirmed. Photoreforming H2 production was improved by the addition of those metal oxides mainly due to the promotion of heterostructure formation where the generated heterostructures could enhance the separation of electrons and holes under UV–vis light. In particular, Ag2O-TiO2 among the samples exhibited the best hydrogen production activities with the maximum quantum efficiency and light-to-hydrogen energy conversion efficiency of 3.02% and 1.15‰, respectively. Considering the effect of glycerol concentration for photoreforming over Ag2O-TiO2, the obtained hydrogen evolution rate fitted a typical kind of Langmuirian catalytic behavior. Langmuir-Hinshelwood-type kinetic model was employed to calculate the corresponding reaction rate constant and glycerol adsorption constant.

Hydrogen production from photocatalytic reforming of glycerol aqueous solutions has been investigated over Ag 2 O-TiO 2 nanocomposite mixed oxide catalysts with variable compositions (0.75-6.75 wt.%Ag 2 O) synthesized by a sol-gel method. Their structural and morphologic characteristics were investigated by means of X-ray diffraction (XRD), field-emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM) and Brunauer-Emmett-Teller (BET) method etc. It was found that a certain amount (ca.0.72-2.82 wt.%) of Ag 2 O mixing could reduce TiO 2 anatase grain size and increases the specific surface area of the catalysts. From UV-vis spectroscopy analysis, all these synthesized catalysts have certain degrees of red-shift compared with bare TiO 2 sample, which indicates a great potential in improving the photocatalytic activities. The results of photocatalytic performance showed that H 2 -production activity and the reaction rate of TiO 2 were significantly enhanced by mixing Ag 2 O, and the effect of Ag 2 O mixing amount for H 2 production was discussed. The reaction rate could be increased by increasing the initial glycerol concentration which fitted a typical Langmuirian catalytic behavior.

Perovskite-type La 2 Ti 2 O 7 (LTO), having a layered structure and the separated H 2 and O 2 evolution sites, is attractive as an efficient photocatalyst. However, the photocatalytic activity is often limited by the poor electron mobility. This problem can be conquered by hybridization with materials having efficient properties for the visible light absorption and charge carrier transport. Here, we report a two-dimensional (2D) layered composite hybridized by approximately 2 nm thick graphitic C 3 N 4 nanosheets (g-C 3 N 4 ) and 7 nm thick nitrogen doped LTO nanosheets (NLTO) (g-C 3 N 4 /NLTO), in which g-C 3 N 4 and NLTO act as hole receptor and electron conductor, respectively. g-C 3 N 4 /NLTO exhibited high photocatalytic activities for H 2 production via water splitting and dye degradation under the UV and visible light irradiation, due to the interfacial charge transfer between g-C 3 N 4 and NLTO. The electrochemical measurement showed the type II band alignment with favorable charge transfer from g-C 3 N 4 to NLTO. The 2D architecture with a maximized interfacial area allows efficient charge separation with a short interfacial distance. This efficient interfacial charge transfer is further elucidated from monitoring of charge separation and trapping processes using the femtosecond time-resolved diffused reflectance (TDR) measurement.

Heterojunction composite plays an important role in enhancing photocatalysis activity through inhibit electron-hole recombination. In this paper, biphasic heterojunction BiVO4 and HoVO4 is insitu-grown and characterized through XRD, TEM, Raman, SEM, EDS, and XPS. These two structures form n type staggered band configuration using Mott-Schottky and DRS. Rhodamine B (RhB) photodegradation efficiency of the optimal sample is 90.7% after 90 min, higher than single BiVO4 54% and HoVO4 34.5%. The main reason is ascribed to fast photogenerated charge separation via O O bond connection according to photoluminescence, photocurrent response, and theory calculation. The RhB degradation path is revealed from intermediate products identification. Therefore this study unveils photodegradation dyes application of BiVO4/HoVO4 samples.

We present a NMF-based method for PU Learning of networked text data.Our algorithm integrates feature and network information via a consensus principle.Our method deals with networked data with extremely limited positive examples.We demonstrate the effectiveness of our algorithm. The rapid growth in the number of networked applications that naturally generate complex text data, which contains not only inner features but also inter-dependent relations, has created the demand of efficiently classifying such data. Many classification algorithms have been proposed, but they usually require as input fully labeled text examples. In many networked applications, however, the cost to label a text data may be expensive and hence a large amount of text may be unlabeled. In this paper we study the problem of classifying networked text data with only positive and unlabeled examples available. We present a non-negative matrix factorization-based approach to networked text classification by factorizing content matrix of the nodes and topological network structures, and by incorporating supervised information into the learning of objective function via a consensus principle. We propose a novel learning algorithm, namely puNet (positive and unlabeled learning algorithm for Networked text data), for efficiently classifying networked text, even if training datasets contain only a small amount of positive examples and a large amount of unlabeled ones. We conduct a series of experiments on benchmark networked datasets and illustrate the effectiveness of our algorithm.

The cultivated allotetraploid species Gossypium hirsutum, accounts for 90% of the world cotton production, has narrow genetic basis that's why its yield, quality or stress resistance breeding is stagnant. It is therefore, essential to explore desirable genes from Gossypium darwinii which has enviable traits such as high fiber fineness, drought tolerance, fusarium and verticillium resistance. We used G. darwinii as primary plant materials in this study not only to enrich the genetic diversity of exiting germplasm but also to better understand its genome structure. An interspecific high density linkage map of allotetraploid cotton was constructed using F2 population (G. hirsutum×G. darwinii). The map was based entirely on genome-wide simple sequence repeat (SSR) markers. A total of 2763 markers were mapped in 26 linkage groups (chromosomes) covering a genome length of 4176.7cM with an average inter-locus distance of 1.5cM. The length of the chromosomes ranged from 84.7 to 238.5cM with an average length of 160.6cM. At subgenome length was 2160.7cM with an average distance of 1.6cM, where as Dt genome length was 2016cM with an average distance of 1.4cM. There were 601 distorted SSR loci. Less number of segregation distortion loci were located in At subgenome than in Dt subgenome. Two post-polyploidization reciprocal translocations of "A2/A3 and A4/A5" were suggested by 44 pairs of duplicate loci.

Rod-like ZnO with the different morphologies were grown on polycrystalline Zn substrate by a simple hydrothermal process in a NaOH or NH 4 OH solution at the hydrothermal temperature range from 80 to 150 °C for different reaction time. Variations preparation in the different alkali solution concentration, hydrothermal temperature, and reaction times were explored to shed light on the morphology of the rod-like nanostructures. The thorough structural characterization including X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), electron diffraction, and X-ray photoelectron spectrum (XPS) were employed to examine the morphology and the microstructure of the final products. It was found that alkali solution concentration, hydrothermal temperature and time have important influence on the morphology of the rod-like nanostructures. The dye removal efficiency of ZnO nanorods was explored by the decoloration of azo dye Congo red (CR). In order to obtain the optimum removal conditions of Congo red, the performance of removing CR with various initial concentrations by ZnO nanorods on Zn substrates with different morphologies was tested under various ambient conditions (visible light illumination and darkness). All prepared samples showed an excellent dye removal efficiency for organic pollutants CR from wastewater, making them promising candidates for the wastewater treatment.

Sentence clustering has been successfully applied in document summarization to discover the topics conveyed in a collection of documents. However, existing clustering-based summarization approaches are seldom targeted for both diversity and coverage of summaries, which are believed to be the two key issues to determine the quality of summaries. The focus of this work is to explore a systematic approach that allows diversity and coverage to be tackled within an integrated clustering-based summarization framework. Given the fact that normally each topic can be described by a set of keywords and the choice of the keywords among the topics is topic-dependent, we take the advantage of the newly emerged subspace clustering to enable the flexibility of keyword selection and the improved quality of sentence clustering. On this basis, we develop two clustering-based optimization strategies, namely local optimization and global optimization to pursue our targets. Experimental results on the DUC datasets demonstrate effectiveness and robustness of the proposed approach.

The coral-like ZnO–CdO composites with the 3D hierarchical porous structure were prepared in a facile flash from self-sustained decomposition of a metal–organic complex. The phases and the morphologies of the resulting materials were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray photoelectron spectrum (XPS), respectively. The results showed that the ZnCd composites have a coral-like shape with a size of tens of micrometers and exhibit the hierarchically porous morphology, in which the walls of the macropores contain smaller mesopores. The 3D hierarchical architecture consisted of nanoparticles. The gas-sensing characteristics of the resulted porous ZnO–CdO composites for isopropanol were investigated. Indirect-heating sensors using porous ZnO–CdO composites as sensitive materials were fabricated on an alumina tube with Au electrodes and platinum wires. All measurements were performed at several operating temperatures from 175 to 315 °C. Good gas-sensing performances to isopropanol have been found for the prepared sample of porous ZnO–CdO composite with a content of 10 at% CdO (ZnCd10). It is shown that the sensor exhibits the highest gas response and selectivity at the operating temperature of 248 °C, making them promising candidates as practical detectors for isopropanol.

Sentence clustering plays a pivotal role in theme-based summarization, which discovers topic themes defined as the clusters of highly related sentences in order to avoid redundancy and cover more diverse information. As the length of sentences is short and the content it contains is limited, the bag-of-words cosine similarity traditionally used for document clustering is no longer reasonably suitable. Special treatment for measuring sentence similarity is necessary. In this paper, we propose a ranking-based clustering framework that utilizes ranking distribution of documents and terms to help generate high quality sentence clusters. The effectiveness of the proposed framework is demonstrated by both the cluster quality analysis and the summarization evaluation conducted on the DUC 2004 and DUC2007 datasets. Refereed/Peer-reviewed

A new visible-light sensitive photocatalyst, Ni(OH)2 nanosheets/ZnO nanorods composites with different Ni(OH)2 contents, was synthesized via a facile water bath route. X-ray diffraction (XRD), transmission electron microscopy (TEM) and UV–vis spectroscopy were used to examine the morphology and microstructure of the composites. XRD analysis revealed that the two phases were composed of well crystallized ZnO and poorly crystallized Ni(OH)2·0.75H2O. Ni(OH)2/ZnO composites showed a red shift in band edge absorption peak in the UV–vis absorbance spectrum, which enhanced their photocatalytic activity. The composites were used for the photodecolourization of water solution of representative organic azo dyes (rhodamine B (RB), Congo red (CR), methylene blue (MB) and methyl orange (MO)) under visible light. All synthesized composites showed better photodegradation performance compared to either pure ZnO or Ni(OH)2. It was noted that composites removal rate strongly depended on the Ni(OH)2 content. When Ni(OH)2 fraction was 10 wt% composites exhibited maximum photodegradation performance for all organic azo dyes. Finally, the possible formation mechanism of the Ni(OH)2/ZnO composites and their photocatalytic decomposition mechanism were also proposed and discussed.

Background Studies of the association of prehypertension with the incidence of end-stage renal disease (ESRD) after adjusting for other cardiovascular risk factors have shown controversial results. Study Design Systematic review and meta-analysis of prospective cohort studies. Setting & Population Adults with prehypertension. Selection Criteria for Studies Studies evaluating the association of prehypertension with the incidence of ESRD identified by searches in PubMed, EMBASE, and Cochrane Library databases and conference proceedings, without language restriction. Predictor Prehypertension. Outcomes The relative risks (RRs) of ESRD were calculated and reported with 95% CIs. Subgroup analyses were conducted according to blood pressure (BP), age, sex, ethnicity, and study characteristics. Results Data from 1,003,793 participants were derived from 6 prospective cohort studies. Compared with optimal BP, prehypertension significantly increased the risk of ESRD (RR, 1.59; 95% CI, 1.39-1.91). In subgroup analyses, prehypertension significantly predicted higher ESRD risk across age, sex, ethnicity, and study characteristics. Even low-range (BP, 120-129/80-84mm Hg) prehypertension increased the risk of ESRD compared with optimal BP (RR, 1.44; 95% CI, 1.19-1.74), and the risk increased further with high-range (BP, 130-139/85-89mm Hg) prehypertension (RR, 2.02; 95% CI, 1.70-2.40). The RR was significantly higher in the high-range compared with the low-range prehypertensive population ( P =0.01). Limitations No access to individual patient-level data. Conclusions Prehypertension is associated with incident ESRD. The increased risk is driven largely by high-range prehypertension.

The study by Lopes et al. [(1)][1] showed that in patients with atrial fibrillation (AF) taking digoxin, the risk of death was increased when serum digoxin concentration (SDC) was ≥1.2 ng/ml. We wish to discuss some views about the reference laboratory values for digoxin. Digoxin was

Graphical abstract: The nickel hydroxide and nickel oxide nanosheets prepared using CTAB at room temperature exhibit a high specific capacitance, prompt charge/discharge rate. - Highlights: • The nickel hydroxide nanosheets were prepared using CTAB at room temperature. • Ni(OH){sub 2} nanosheet can be successfully converted to NiO nanosheet via calcination. • The NiO nanosheet has a specific capacitance of 388 F g{sup −1} at 5 A g{sup −1} in KOH solution. • Anneal temperature impacts capacitive properties as electrode. - Abstract: The single crystalline Ni(OH){sub 2} nanosheets were synthesized by a simple chemical precipitation method using nickel chloride as precursors and ammonia as precipitating agent. The Ni(OH){sub 2} nanosheets were successfully converted to NiO nanosheets via calcination under appropriate conditions. Analytical methods such as X-ray diffraction (XRD) spectra, scanning electron microscopy (SEM), transmission electron microscopy (TEM), high-resolution TEM (HRTEM), and Fourier transformed infrared (FTIR) spectra were employed to characterize the morphology and microstructure of the final products. The experimental results revealed that Ni(OH){sub 2} nanosheets were shape-preserved transformed to NiO nanosheets at 250 °C for 24 h. Ni(OH){sub 2} and NiO nanosheets were directly functionalized as supercapacitor electrodes for potential energy storage applications, whose charge–discharge properties, electrochemical impedance spectra, cyclicmore » voltammetry, and cycle performance were examined. The experimental results show that the single-crystalline NiO nanosheets are a promising candidate for the supercapacitor electrode. They exhibit a high specific capacitance, prompt charge/discharge rate.« less

The one-dimensional (1D) Ni-doped ZnO powders with variable dopant contents were synthesized at a low temperature (90 °C) using ZnCl 2 , NiCl 2 and NaOH solution as reaction precursors by a simple water bath method. The morphology and the microstructure of the as-prepared undoped and Ni-doped ZnO samples have been characterized by X-ray powder diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), Raman spectra, X-ray Photoelectron Spectroscopy (XPS), and UV–vis spectroscopy. The results revealed that the samples were one-dimensional nanorods. Ni-doped ZnO nanorods were crystalline hexagonal wurtzite ZnO crystal structure, and the Ni ion was in a 2+ charge state in the crystal lattice of ZnO. The absorption spectra presented the existence of special two-absorption-region (strong UV-light and weak visible-light at 550–800 nm). The performance of Ni-doped ZnO powders as efficient photocatalyst was further demonstrated in the degradation of Rhodamine B (RB) under UV-light irradiation. The Ni-doped ZnO powders show high photocatalytic activity during the degradation of RB under UV-light. It was found that an appropriate amount of Ni dopant can greatly increase photocatalytic activity and the sample with 10% Ni doping exhibits the highest photocatalytic efficiency.

A new and facial (low temperature and low cost) method for a direct synthesis of the stable and crystalline phase pure Ni(OH) 2 nanosheets was developed using cationic surfactant cetyltrimethylammonium bromide (CTAB). X-ray diffraction (XRD) analysis, Fourier transformed infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and high-resolution TEM (HRTEM) were used to examine the morphology and the microstructure of the obtained compound. The morphological study clearly revealed that obtained compounds appear in a flake-like nanosheet form with a typical length in the range of several hundreds of nanometers and a width in the range of several tens of nanometers. A possible formation mechanism is preliminary proposed for the formation of the nanostructure. The adsorption properties of Ni(OH) 2 nanosheets were tested by the adsorption of azo dye Congo red (CR). The results show an excellent removal capacity for organic pollutant CR from the wastewater, making them a promising candidate for wastewater treatment.