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Psoriasis is an immune-mediated chronic inflammatory skin disease that affects 2% to 3% of the world's population. It is widely assumed that immune cells and cytokines acting together play a crucial part in the pathophysiology of psoriasis by promoting the excessive proliferation of skin keratinocytes and inflammatory infiltration. Interleukins (ILs), as a critical component of cytokines, have been closely associated with the pathogenesis and progression of psoriasis. This review summarizes the current contribution of ILs to psoriasis and describes the role each IL performs in psoriasis. Furthermore, the paper presents the therapeutic effects and application prospects of biologics developed for ILs in clinical treatment and experiments. The study aims to further the research on ILs in the treatment of psoriasis. [ABSTRACT FROM AUTHOR]

C/SiC aerogels with both ultra-low thermal conductivity and extremely high strength were fabricated by freeze casting. SiC junctions originated from pyrolysis of polycarbosilane (PCS) were formed between carbon nanofibers (C f) to enhance the strength of aerogels. The effects of PCS content and total solid content on the phase composition, pore structure, thermal conductivity and compressive property were studied. The fabricated aerogels possess hierarchical pore structure. In the micro-scale, it contains circular pores with size of about 15 µm, while it is mesoporous and macroporous in the nano-scale. Both thermal conductivity and compressive strength increase with the increase in PCS content. Through tailoring PCS content and total solid content, C f /SiC aerogels with porosity of 99.5%, thermal conductivity of 33 mW·m−1·K−1 and compressive strength of 7.14 MPa can be obtained. The specific strength of the fabricated C f /SiC aerogels is up to 467.6 MPa/(g/cm3), which is the highest value for ceramic aerogels. [ABSTRACT FROM AUTHOR]

Background: Stroke is not only a major cause of disability but also the third leading cause of death, following heart disease and cancer. It has been established that stroke causes permanent disability in 80% of survivors. However, current treatment options for this patient population are limited. Inflammation and immune response are major features that are well‐recognized to occur after a stroke. The gastrointestinal tract hosts complex microbial communities, the largest pool of immune cells, and forms a bidirectional regulation brain‐gut axis with the brain. Recent experimental and clinical studies have highlighted the importance of the relationship between the intestinal microenvironment and stroke. Over the years, the influence of the intestine on stroke has emerged as an important and dynamic research direction in biology and medicine. Aims: In this review, we describe the structure and function of the intestinal microenvironment and highlight its cross‐talk relationship with stroke. In addition, we discuss potential strategies aiming to target the intestinal microenvironment during stroke treatment. Conclusion: The structure and function of the intestinal environment can influence neurological function and cerebral ischemic outcome. Improving the intestinal microenvironment by targeting the gut microbiota may be a new direction in treating stroke. [ABSTRACT FROM AUTHOR]

Due to the influence of complex factors such as atmospheric dynamic processes, physical processes and local topography and geomorphology, the prediction of near-surface meteorological elements in the numerical weather model often has deviation. The deep learning neural networks are more flexible but with high variance. Here, we proposed a stacking ensemble model named FLT, which consists of a fully connected neural network with embedded layers (ED-FCNN), a long short-term memory (LSTM) network and a temporal convolutional network (TCN) to overcome the high variance of a single neural network and to improve prediction of maximum air temperature. The case study of daily maximum temperature forecast evaluated with observation of almost 2400 weather stations shows substantial improvement over that of single neural network model, ECMWF-IFS and statistical post-processing model. The FLT model can more effectively improve the forecast bias of the ECMWF-IFS model than that of any of the above single neural network model, with the RMSE reduced by 52.36% and the accuracy of temperature forecast increased by 43.12% compared with the ECMWF-IFS model. The average RMSEs of the FLT model decreases by 8.39%, 1.50%, 2.96% and 16.03%, respectively, compared with ED-FCNN, LSTM, TCN and the decaying average method. [ABSTRACT FROM AUTHOR]

The repetitive unclamped inductive switching (UIS) avalanche stress is conducted to investigate the degradation and breakdown behaviors of conventional shield gate trench MOSFET (C-SGT) and P-ring SGT MOSFETs (P-SGT). It is found that the static and dynamic parameters of both devices show different degrees of degradation. Combining experimental and simulation results, the hot holes trapped into the Si/SiO2 interface and the increase of crystal lattice temperature should be responsible for the degradation and breakdown behaviors. Moreover, under repetitive UIS avalanche stress, the reliability of P-SGT overcomes that of C-SGT, benefitting from the decreasing of the impact ionization rate at bottom of field oxide caused by the existence of P-ring. [ABSTRACT FROM AUTHOR]

• A new method of entity alignment in Chinese medical records is proposed. • Bert is introduced into the siamese network structure. • Large scale public synonym data sets are used. Entity alignment aims at associating semantically similar entities in knowledge graphs from different sources. It is widely used in the integration and construction of professional medical knowledge. The existing deep learning methods lack term-level embedding representation, which limits the performance of entity alignment and causes a massive computational overhead. To address these problems, we propose a Siamese-based BERT (SiBERT) for Chinese medical entities alignment. SiBERT generates term-level embedding based on word embedding sequences to enhance the features of entities in similarity calculation. The process of entity alignment contains three steps. Specifically, the SiBERT is firstly pre-trained with synonym dictionary in the public domain, and transferred to the task of medical entity alignment. Secondly, four different categories of entities (disease, symptom, treatment, and examination) are labeled based on the standard terms selected from standard terms dataset. The entities and their standard terms form term pairs to train SiBERT. Finally, combined with the entity alignment algorithm, the most similar standard term is selected as the final result. To evaluate the effectiveness of our method, we conduct extensive experiments on real-world datasets. The experimental results illustrate that SiBERT network is superior to other compared algorithms both in alignment accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Early cataract screening is essential to help patients prevent irreversible vision loss. Existing data-driven methods can automatically extract deep fundus features for automatic cataract classification. However, due to the lack of medical domain knowledge, these deep features may contain large pathological irrelevant information, thus reducing the classification accuracy of data-driven methods. To fully take the advantages of domain knowledge to enhance the discriminative representation and avoid the irrelevant impact, we comprehensively investigate how to incorporate it into data-driven models. Considering the vessel information and multi-granularity representation are two key points of domain knowledge for cataract diagnosis, we propose a vessel attention guided multi-granularity network (VAM-Net), which contains three main components. Specifically, the vessel-level segmentation subnet is firstly designed by leveraging U-shape segmentation network. The refined vessel information obtained in this step can be regarded as visual attention to enable deep network extract discriminative features. With the guidance of vessel attention, the global-local classification subnet is further proposed to eliminate the interference of irrelevant information and enhance the multi-granularity feature learning (including global structural features and local subtle features). Finally, the multiple levels predictions are integrated into the final cataract diagnosis decision via constructing multi-granularity ensemble network. In this manner, both domain knowledge and deep networks are organically combined for pursuing better diagnosis performance. By setting extensive comparative and ablation experiments, we evaluate the effectiveness of the proposed VAM-Net on the real-world cataract dataset (92.78% detection accuracy and 87.68% grading accuracy). • A attention guided multi-granularity network is proposed for cataract diagnosis. • We aim to incorporate medical domain information into data-driven method. • Extensive experiments are conducted to prove the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Stroke, a lethal neurological disease, accounts for a grave economic burden on society. Despite extensive basic and clinical studies on stroke prevention, a precise effective treatment approach for stroke at this stage remains unavailable. The majority of our body's gut microbiota plays a vital role in food digestion, immune regulation, and nervous system development, which is highly associated with the development of some diseases. Multiple clinical studies have documented variation in the composition of gut microbiota between stroke patients and healthy counterparts. Moreover, the intervention of intestinal symbiotic microorganisms via several mechanisms plays an active role in stroke prognosis. In the prevention and treatment of stroke, the gut microbiota gives off a seductive glow, this is a promising therapeutic target. This paper summarizes the current knowledge of stroke and gut microbiota, and systematically describes the possible mechanisms of interaction between stroke and gut microbiota, the relationship between stroke-related risk factors and gut microbiota, and the treatment of gut flora using microorganisms. Thus, it could valuably elucidate the correlation of gut microbiota with stroke incidence, providing stroke researchers with a new strategy for stroke prevention and treatment by regulating gut microbiota. [ABSTRACT FROM AUTHOR]

Ischemic stroke (IS) is a significant global public health issue with a high incidence, disability, and mortality rate. A robust inflammatory cascade with complex and wide-ranging mechanisms occurs following ischemic brain injury. Inflammasomes are multiprotein complexes in the cytoplasm that modulate the inflammatory response by releasing pro-inflammatory cytokines and inducing cellular pyroptosis. Among these inflammasomes, the Absent in Melanoma 2 (AIM2) inflammasome shows the ability to detect a wide range of pathogen DNAs, thereby triggering an inflammatory response. Recent studies have indicated that the aberrant expression of AIM2 inflammasome in various cells is closely associated with the pathological processes of ischemic brain injury. This paper summarizes the expression and regulatory role of AIM2 in CNS and peripheral immune cells and discusses current therapeutic approaches targeting AIM2 inflammasome. These findings aim to serve as a reference for future research in this field. • The AIM2 inflammasomes are regarded as the key participants in the inflammatory response following ischemic stroke. • The pathological progression of ischemic brain injury involves aberrant expression of AIM2 inflammasomes in various cells. • Specific inhibitors directed towards AIM2 inflammasomes have the potential to serve as therapeutic agents for the management of neurological damage following an ischemic stroke [ABSTRACT FROM AUTHOR]

Xiangsha-Liujunzi-Tang (XSLJZT) is a classical formula for treating the diseases of digestive system, which can effectively and significantly improve the symptoms of functional dyspepsia (FD) patients. The main function of XSLJZT is to benefit Qi and spleen, and harmonize stomach. The purpose of this study was to investigate the intervention effect of XSLJZT on duodenal mucosal injury in FD rats and the response mechanism of MC/Tryptase/PAR-2 signal pathway. Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was employed to qualitatively and quantitatively identify the chemical component of XSLJZT. A comprehensive modeling method (iodoacetamide infusion + irregular diet + swimming exhaustion) was used to construct the FD rat model. XSLJZT decoction was given to intervene FD rats for 2 weeks. The indicators of digestive function including body mass, 3-h food intake, visceral sensitivity, gastric emptying rate and intestinal propulsion rate were routinely measured for FD rats. The pathological changes of duodenum and microstructure of intestinal epithelial cells were observed by HE staining and transmission electron microscopy respectively. The inflammatory factors (VCAM-1, IL-6, TNF-α, and ICAM-1) and histamine content were evaluated by enzyme-linked immunosorbent assay (ELISA). The expression levels of Tryptase, PAR-2, ZO-1, β-catenin, p–NF–κBp65 and p-ERK1/2 in duodenal tissues were measured by Western blot (WB) and immunofluorescence colony-staining (IFC). XSLJZT administration significantly improved the survival of FD rats, increased body mass and 3-h food intake, improved visceral sensitivity, and restored gastric emptying rate and intestinal propulsion rate. HE staining showed that XSLJZT recovered the structure of duodenal mucosal and reduced inflammatory infiltration. ELISA revealed that XSLJZT reduced the content of inflammatory factors (VCAM-1, IL-6, TNF-α, and ICAM-1) and histamine. In addition, WB and IFC uncovered that the protein levels of ZO-1 and β-catenin were up-regulated and MC/Tryptase/PAR-2 signaling pathway was inhibited by XSLJZT. XSLJZT significantly improved the integrity of duodenal mucosa and decreased the inflammation in FD rats through the inhibition of MC/Tryptase/PAR-2 signaling pathway response. [Display omitted] • The intervention effect of Xiangsha Liujunzi Tang on FD model was confirmed. • The "duodenum-stomach" feedback mechanism was confirmed in FD rats. • The response of MC/Tryptase/PAR-2 signal pathway in duodenal tissue of FD rats was inhibited by XSLJZT. [ABSTRACT FROM AUTHOR]

Porous ceramics with porosity up to 92.5 % have been successfully fabricated by freeze casting of polycarbosilane (PCS) solution. The effect of PCS concentration and thermal oxidation curing on the pore structure and compressive properties was investigated. Curing mechanism and thermodynamics were illuminated through analyzing the molecular structure, curing activation energy, and curing degree. Porous ceramics, mainly composed of SiC and a small amount of SiO 2 , have dendritic pore structure which well replicates the solidification morphology of camphene solvent. Results of FT-IR and Gaussian computation of PCS electron density show that Si–H and Si–CH 3 bonds play a dominant role in thermal oxidation curing reaction. Both curing degree and ceramic yield increase with the increase in curing temperature and time. The curing degree of Si–H bond is close to 52 % and the corresponding ceramic yield is about 83 % when the porous PCS was cured at 200 °C for 90 min. Both polymer concentration and curing time have influences on the compressive strength of porous ceramics. [ABSTRACT FROM AUTHOR]

Methicillin-resistant Staphylococcus aureus (MRSA) isolates that carry mecA but are oxacillin susceptible (OS-MRSA; oxacillin minimum inhibitory concentration [MIC] ≤2 μg/mL) are increasingly reported worldwide in the past decade. In this study, an OS-MRSA isolate FC1102921 from an imported meat sample was characterized and the oxacillin resistance mutation frequency and mutant prevention concentration (MPC) to oxacillin was determined. Isolate FC1102921 was pvl negative and contained SCCmec V (5C2&5), the sequence type and spa type were identified as ST398-t034. The MPCs of isolate FC1102921 to oxacillin was 16 μg/mL. The resistance mutation frequency of isolate FC1102921 to oxacillin was 0.036%. The expression of mecA in isolate FC1102921 was detectable, but the mecA expression in the mutant isolate FC1102921m increased 13.8-folds. Our data indicated that S. aureus FC1102921 was not a hypermutator, and the high-resistance mutation frequency of S. aureus FC1102921 was specific to oxacillin and penicillin. SCCmec V region of isolate FC1102921 and FC1102921m was identical, functional blaI and blaR1 were identified in SCCmec region of both isolates, and no plasmid loss was identified in mutant isolate FC1102921m. To the best of our knowledge, this is the first report of the resistance mutation frequency and MPC study of an OS-MRSA ST398-t034 isolate. The high oxacillin MPC value and high oxacillin resistance mutation frequency was not related to a curable plasmid as previously reported, which further highlighted that an in-depth study should be carried out to clarify the mechanism and role of the OS-MRSA isolates in the emergence and transmission of MRSA ST398-t034. [ABSTRACT FROM AUTHOR]

The murine model serves as an important experimental system in biomedical science because of its high degree of similarities at the sequence level with human. Recent studies have compared the transcriptional landscapes between human and mouse, but the general co-expression landscapes have not been characterized. Here, we calculated the general co-expression coefficients and constructed the general co-expression maps for human and mouse. The differences and similarities of the general co-expression maps between the two species were compared in detail. The results showed low similarities in the human and mouse, with only about 36.54% of the co-expression relationships conserved between the two species. These results indicate that researchers should pay attention to these differences when performing research using the expression data of human and mouse. To facilitate use of this information, we also developed the human–mouse general co-expression difference database (coexpressMAP) to search differences in co-expression between human and mouse. This database is freely available at http://www.bioapp.org/coexpressMAP. [ABSTRACT FROM AUTHOR]

At present, understanding of DNA methylation at the population level is still limited. Here, we first extended the classical framework of population genetics, such as single nucleotide polymorphism allele frequency, linkage disequilibrium(LD), LD block and haplotype, to epigenetics. Then, as an example, we compared the DNA methylation disequilibrium (MD)maps between HapMap CEU (Caucasian residents of European ancestry from Utah) population and YRI (Yoruba people from Ibadan) population (lymphoblastoid cell lines). We analyzed the differences and similarities between CEU and YRI from the following aspects: SMP (single methylation polymorphism) allele frequency, SMP allele association, MD, MD block andmethylation haplotype (meplotype) frequency. The results showed that CEU and YRI had similar distribution of SMP allele frequency, and sharedmany MD block region. We believe that the framework of population genetics can be used in the population epigenetics. The population epigenetic framework also has potential prospects in the study of complex diseases, such as epigenome-wide association study. [ABSTRACT FROM AUTHOR]

Urbanization and industrialization in the recent 30 years have caused frequent heavy haze pollution in the Guanzhong basin, China during wintertime. Based on the categorized large-scale synoptic situations, the local circulation and its impact on the air pollution in the basin have been investigated using observational data analyses and model simulations with the WRF-CHEM model. The simulated mountain-valley breeze circulations are well established under most of the large-scale synoptic situations in the south-north direction. The downward mountain-breeze not only causes the convergence zone in the basin but also tends to bring the pollutants back from the mountain areas to the basin and enhance pollutants concentrations in the evening. The intensity of the mountain-valley breeze circulations may be different under different synoptic situations, but in general, aside emissions, every pollution event is the synthetic result of the synoptic situation at the large scale and the local circulation at the small scale in the Guanzhong basin. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate high performance GaN-based Schottky-barrier ultraviolet (UV) photodetectors with graded doping prepared on patterned sapphire substrates. The fabricated devices exhibit an extremely low dark current density of ∼1.3 × 10 −8 A/cm 2 under −5 V bias, a large UV-to-visible light rejection ratio of ∼4.2 × 10 3 , and a peak external quantum efficiency of ∼50.7% at zero bias. Even in the deeper 250–360 nm range, the average external quantum efficiency still remains ∼40%. From the transient response characteristics, the average rising and falling time constants are estimated ∼115 μs and 120 μs, respectively, showing a good electrical and thermal reliability. The specific detectivities D ∗ , limited by the thermal equilibrium noise and the low-frequency 1/ f noise, are derived ∼5.5 × 10 13 cm Hz 1/2 /W (at 0 V) and ∼2.68 × 10 10 cm Hz 1/2 W −1 (at −5 V), respectively. [ABSTRACT FROM AUTHOR]

In this paper, a comprehensive study on the forward and reverse current degradation mechanisms in 1.2 kV planar SiC MOSFETs are investigated. With the help of Sentaurus TCAD simulations, numerical fitting method, step-bias stress, current-voltage (I - V) and capacitance-voltage (C V) experiments, we proposed defect-related transport models to describe the progression of gate leakage currents under forward and reverse high biases. It is found that, (1) trap-assisted tunneling (TAT) and Fowler-Nordheim (FN) tunneling dominate the forward low and high current transport process respectively. Moreover, the holes trapping is mainly responsible for the degradation of gate oxide and premature breakdown of SiC MOSFETs; (2) the reverse leakage current under high electric field is mainly carried by FN tunneling electrons, which lead to an increase of the intermediate localized states distributed in the SiC/SiO 2 interface. • Degradation mechanisms of gate current under accelerated stress are investigated. • TAT and FN tunneling dominate current behavior at low and high bias, respectively. • Holes and electrons trapping are responsible for the degradation of SiC MOSFETs. [ABSTRACT FROM AUTHOR]

Background: The effect of high doses preservatives in the leave‐on cosmetic products to the skin microbiota is not clear. Studies have shown that the preservatives might alter the balance of the skin microbiota. Aim: In this study, we aimed to evaluate antimicrobial effect of nine cosmetic chemical preservatives. Material & Method: A total of 77 Staphylococcus epidermidis isolates from 46 healthy zygomatic skin samples were characterized by multilocus sequence typing (MLST). Nine preservatives used in leave‐on cosmetics were analyzed by testing the minimal inhibitory concentrations (MICs) against S. epidermidis isolates. We also determined the mutant prevention concentration (MPC) and bactericidal kinetics on selected isolates. Results: More than 17 sequence types were recognized among 77 S. epidermidis isolates. Our data demonstrated that the maximum permitted doses of 2‐bromo‐2‐nitro‐1,3‐propanediol, ethyl 4‐hydroxybenzoate, hexadecyltrimethylammonium bromide, and imidazolidinyl urea were significantly higher than both their MICs and MPCs. We showed that, at the maximum permitted doses, two preservatives could completely kill 107 CFU/mL S. epidermidis in less than 1 h in MH broth. Conclusion: Our data demonstrated that certain preservatives of the leave‐on cosmetics might inhibit or kill S. epidermidis cells and perturb the skin microbiota balance. The determination of the maximum permitted doses of the preservatives should not only be based on the toxicological data, but also antimicrobial susceptibility analysis. This comprehensive evaluation would ensure a balanced and healthy skin microbiota. [ABSTRACT FROM AUTHOR]

Separation of arteries and veins in the cerebral cortex is of significant importance in the studies of cortical hemodynamics, such as the changes of cerebral blood flow, perfusion or oxygen concentration in arteries and veins under different pathological and physiological conditions. Yet the cerebral vessel segmentation and vessel-type separation are challenging due to the complexity of cortical vessel characteristics and low spatial signal-to-noise ratio. In this work, we presented an effective full-field method to differentiate arteries and veins in cerebral cortex using dual-modal optical imaging technology including laser speckle imaging (LSI) and optical intrinsic signals (OIS) imaging. The raw contrast images were acquired by LSI and processed with enhanced laser speckle contrast analysis (eLASCA) algorithm. The vascular pattern was extracted and segmented using region growing algorithm from the eLASCA-based LSI. Meanwhile, OIS images were acquired alternatively with 630 and 870 nm to obtain an oxyhemoglobin concentration map over cerebral cortex. Then the separation of arteries and veins was accomplished by Otsu threshold segmentation algorithm based on the OIS information and segmentation of LSI. Finally, the segmentation and separation performances were assessed using area overlap measure (AOM). The segmentation and separation of cerebral vessels in cortical optical imaging have great potential applications in full-field cerebral hemodynamics monitoring and pathological study of cerebral vascular diseases, as well as in clinical intraoperative monitoring. [ABSTRACT FROM AUTHOR]

Low temperature is a major environmental stress for plants. Many important cultivated crops have limited capacity to survive below freezing/subfreezing temperatures. Low inorganic phosphate (Pi) is reportedly important in triggering cold acclimatization. SPX (SYG1/Pho81/XPR1: SYG1, suppressor of yeast gpal; Pho81, CDK inhibitor in yeast PHO pathway; XPR1, xenotropic and polytropic retrovirus receptor) domain proteins have been shown to be involved in the phosphate-related signal transduction and regulation pathways. Recently, Arabidopsis AtSPX family genes have been found to possess diverse functions in plant tolerance to phosphorus starvation, and OsSPX1 is involved in phosphate homeostasis in rice and optimizes growth under phosphate-limited conditions through a negative feedback loop. In this study, our phylogenetic and gene expression profiling approaches identified six rice OsSPX genes up-regulated during cold stress. Transgenic tobacco plants with constitutive expression of OsSPX1 were more tolerant to cold stress than were wild-type plants, and showed better seedling survival and reduced cellular electrolyte leakage. In addition, there was decreased total leaf Pi content and accumulation of free proline and sucrose in transgenic tobacco plants during cold stress. To further establish a cause-and-effect relationship between intracellular Pi level and cold acclimatization in transgenic plants, we generated transgenic Arabidopsis plants with constitutive expression of OsSPX1. Cold stress resulted in reduced leaf Pi levels in Arabidopsis transgenic relative to wild-type plants. From real-time reverse transcriptase-polymerase chain reaction analysis, several Pi starvation-related genes, such as AtSPX1 (orthologue of OsSPX1) , PHO2, PLDZ2 and ATSIZ1, showed differential expression between wild-type and transgenic plants during cold stress. Our results indicate that OsSPX1 may play an important role in linking cold stress and Pi starvation signal transduction pathways. [ABSTRACT FROM AUTHOR]

We isolated a clone, named Si69, from a foxtail millet immature seed cDNA library. The protein encoded by Si69 contains a conserved Wali7 (wheat aluminum induced protein 7) domain and shares high-level homology with aluminum-induced proteins from other species including rice and Arabidopsis. The Si69 gene presents as a single locus In foxtail millet genome and is globally expressed in all tissues examined. Its expression is up-regulated by aluminum. The sequence feature and expression pattern suggest that the Si69 gene is involved in aluminum tolerance or detoxification. To confirm its biological functions, Si69 controlled by the CaMV35S promoter was introduced into Arabidopsis. Transgenic plants did not show any visible morphological changes compared to wild-type plants under normal growth conditions. However, when treated with 20 or 50 μmol/L Aluminum (Al), the root apices of wild-type plants were heavily stained by hematoxylin, whereas those of Si69 transgenic plants were not stained when treated with 20 μmol/L Al and slightly stained when treated with 50 μmoI/L Al. Scanning electron microscopy (SEM) results further demonstrated that the damage of the root apices was severer in wild-type plants than in transgenic plants. Inhibition of root growth and accumulation of malondialdehyde (MDA), an indicator of lipid peroxidation, were lower in transgenic plants than in wild-type plants. The results show that overexpression of Si69 may increase Al tolerance in transgenic plants, indicating that a series of Wali7-containing genes may play similar roles in Al tolerance/detoxification. [ABSTRACT FROM AUTHOR]

Abstract: An integrated dust storm modeling system is developed for the prediction of dust storms. The system couples a wind erosion scheme, a dust transportation model and the Penn State/UCAR modeling system (MM5) with a geographic information database. The system can be used for the prediction of dust emission rate and dust concentration associated with individual dust storm events. Two severe dust storm events occurred in spring 2002, one on the 19th–22nd of March and the other on the 6th–9th of April. The integrated modeling system is used to simulate the two events. The numerical results are compared with surface weather records and satellite images and good agreement is found between the model results and observation in dust concentration distribution and evolutions. The Gobi Desert in southern Mongolia and the Badain Jaran Desert, Tengger Desert and Hunshandake sandy land in Inner Mongolia (China) are identified to be the dust sources for the two events. The dominant modes of dust particles over western Inner Mongolia and Mongolia are from 2 to 11 μm in size, and 2 to 22 μm over Beijing and its surrounding area. The emission of particles in the 2–11 μm size range is found to be most important for Northeast Asian dust storms. [Copyright &y& Elsevier]

Abstract: Mongolian cyclones are important to the outbreaks of severe dust storms in Northeast Asia. In this paper, we conduct a diagnostic study and a numerical simulation of a rapidly developing Mongolian cyclone that produced the April 5 to 7, 2000 severe dust storm in East Asia. The surface pressure of the cyclone decreased rapidly on April 5 to 6 causing strong surface winds. The diagnosis shows that the cyclone was a “dry cyclone”, as the convergence of moisture flux was weak and the baroclinic forcing was strong. The analysis of the Q vectors also reveals that ageostrophic wind was significant in the middle and upper part of the cyclone, resulting in strong vertical motion in the lower part. The three-dimensional structure of the cyclone is characterized by an ascending southern warm current and a descending northern cold current. This structure is favorable to the release of available potential energy and the intensification of the cyclone. In the second part of this study, we develop a coupled system of a dust emission scheme and a mesoscale numerical model (MM5V3) and applied the system to the simulation of the Mongolian cyclone. The main features of the cyclone identified from the diagnostic analysis are successfully reproduced. The predicted dust storm-affected area is found to be consistent with the meteorological observations and satellite remote sensing. It is shown that the coupled system is capable of predicting the spatial and temporal variations of the dust storm. [Copyright &y& Elsevier]

Motivation With the development of biotechnology, DNA methylation data showed exponential growth. Epigenome-wide association study (EWAS) provide a systematic approach to uncovering epigenetic variants underlying common diseases/phenotypes. But the EWAS software has lagged behind compared with genome-wide association study (GWAS). To meet the requirements of users, we developed a convenient and useful software, EWAS2.0. Results EWAS2.0 can analyze EWAS data and identify the association between epigenetic variations and disease/phenotype. On the basis of EWAS1.0, we have added more distinctive features. EWAS2.0 software was developed based on our ‘population epigenetic framework’ and can perform: (i) epigenome-wide single marker association study; (ii) epigenome-wide methylation haplotype (meplotype) association study and (iii) epigenome-wide association meta-analysis. Users can use EWAS2.0 to execute chi-square test, t-test, linear regression analysis, logistic regression analysis, identify the association between epi-alleles, identify the methylation disequilibrium (MD) blocks, calculate the MD coefficient, the frequency of meplotype and Pearson's correlation coefficients and carry out meta-analysis and so on. Finally, we expect EWAS2.0 to become a popular software and be widely used in epigenome-wide associated studies in the future. Availability and implementation The EWAS software is freely available at http://www.ewas.org.cn or http://www.bioapp.org/ewas. [ABSTRACT FROM AUTHOR]

Disease diagnosis represents a critical and arduous endeavor within the medical field. Artificial intelligence (AI) techniques, spanning from machine learning and deep learning to large model paradigms, stand poised to significantly augment physicians in rendering more evidence-based decisions, thus presenting a pioneering solution for clinical practice. Traditionally, the amalgamation of diverse medical data modalities (e.g., image, text, speech, genetic data, physiological signals) is imperative to facilitate a comprehensive disease analysis, a topic of burgeoning interest among both researchers and clinicians in recent times. Hence, there exists a pressing need to synthesize the latest strides in multi-modal data and AI technologies in the realm of medical diagnosis. In this paper, we narrow our focus to five specific disorders (Alzheimer's disease, breast cancer, depression, heart disease, epilepsy), elucidating advanced endeavors in their diagnosis and treatment through the lens of artificial intelligence. Our survey not only delineates detailed diagnostic methodologies across varying modalities but also underscores commonly utilized public datasets, the intricacies of feature engineering, prevalent classification models, and envisaged challenges for future endeavors. In essence, our research endeavors to contribute to the advancement of diagnostic methodologies, furnishing invaluable insights for clinical decision making. [ABSTRACT FROM AUTHOR]

In this study, the (Fe,Cr)7C3/Fe dense ceramic layer was produced by in situ synthesis method with subsequent heat treatment. According to the results of thermal analysis, the as-cast specimen was subjected to heat treatment at 1186, 1208, 1230 °C for 1–5 h in argon atmosphere respectively. The microstructures of the compound region were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM), and the kinetics of the (Fe,Cr)7C3 dense ceramic layer were analyzed. The results show that a-Fe and (Fe,Cr)7C3 are the predominant crystalline phases in the composite obtained. Depending on the layer process time and temperature, the thickness of the reaction layer formed on the substrate ranged from 12.3 to 32.6 mm. The kinetics of dense ceramic layer by the in situ synthesis method was also studied and the activation energy for the diffusion process was estimated to be 292 kJ/mol. Moreover, an attempt was made to investigate the possibility of predicting the iso-thickness of chromium carbide layer variation and to establish some empirical relationships between process parameters and dense ceramic layer thickness. [ABSTRACT FROM AUTHOR]

A very important cause of the frustration with drug therapy for central nervous system (CNS) diseases is the failure of drug delivery. The blood–brain barrier (BBB) prevents most therapeutic molecules from entering the brain while maintaining CNS homeostasis. Scientists are keen to develop new brain drug delivery systems to solve this dilemma. Extracellular vesicles (EVs), as a class of naturally derived nanoscale vesicles, have been extensively studied in drug delivery due to their superior properties. This review will briefly present current brain drug delivery strategies, including invasive and non-invasive techniques that target the brain, and the application of nanocarriers developed for brain drug delivery in recent years, especially EVs. The cellular origin of EVs affects the surface protein, size, yield, luminal composition, and other properties of EVs, which are also crucial in determining whether EVs are useful as drug carriers. Stem cell-derived EVs, which inherit the properties of parental cells and avoid the drawbacks of cell therapy, have always been favored by researchers. Thus, in this review, we will focus on the application of stem cell-derived EVs for drug delivery in the CNS. Various nucleic acids, proteins, and small-molecule drugs are loaded into EVs with or without modification and undergo targeted delivery to the brain to achieve their therapeutic effects. In addition, the challenges facing the clinical application of EVs as drug carriers will also be discussed. The directions of future efforts may be to improve drug loading efficiency and precise targeting. [ABSTRACT FROM AUTHOR]

Existing pollen identification methods heavily rely on the scale and quality of pollen images. However, there are many impurities in real-world SEM images that should be considered. This paper proposes a collaborative learning method to jointly improve the performance of pollen segmentation and classification in a weakly supervised manner. It first locates pollen regions from the raw images based on the detection model. To improve the classification performance, we segmented the pollen grains through a pre-trained U-Net using unsupervised pollen contour features. The segmented pollen regions were fed into a deep convolutional neural network to obtain the activation maps, which were used to further refine the segmentation masks. In this way, both segmentation and classification models can be collaboratively trained, supervised by just pollen contour features and class-specific information. Extensive experiments on real-world datasets were conducted, and the results prove that our method effectively avoids impurity interference and improves pollen identification accuracy (86.6%) under the limited supervision (around 1000 images with image-level labels). [ABSTRACT FROM AUTHOR]

In this paper, we perform a comprehensive study on the reverse current degradation mechanisms in Au/Ni/n-GaN Schottky diodes based on an in-depth understanding on the defect-related current transport mechanisms. Instead of traditional Poole–Frenkel (PF) emission model, an extended bulk-limited PF transport process, including the compensation effect, is adopted to explain the variation of the PF current slope as a function of the stress time, which majorly takes place inside the depletion region near the neutral semiconductor side. Based on the electrostatic analysis, we develop a shallow donor-like defects model to address the current degradation kinetics, which states that the energetic electrons produced by Fowler–Nordheim tunneling can induce significant Joule heating effect during the subsequent drift move of field, and give rise to the formation of the donor-like defects, and in turn enhance the surface electrical field to cause a significant increase of the tunneling component, in good agreement with the emission microscope observations. [ABSTRACT FROM PUBLISHER]

Airborne pollen identification is crucial to help patients prevent pollinosis symptoms. Existing data-driven methods rely on large-scale pollen images with simple backgrounds. In real scenarios, the background is complex and the data scale is small. Therefore, these methods suffer from two challenges: (1) Irrelevant information interference; (2) Incomplete feature attention. To overcome these challenges, we propose a prior knowledge-guided deep feature learning (PK-DFL) for real-world optical microscope image classification. Its main steps are as follows: Pollen location is designed to locate pollen grains based on color features, aiming to boost the accuracy of shape and texture prior feature extraction. Shape-texture awareness helps to extract the shape and texture of pollen grains via predefined feature extractors (i.e., a set of shape descriptors and an improved SFTA). These features are used to construct two types of prior knowledge, namely shape-texture attention maps (STA maps) and shape-texture feature vectors (STF vectors). Pollen classification uses a deep network (CNN) to classify pollen via imitating the pollen identification procedure of palynologists. It uses STA maps to weight pollen images and convolutional feature maps for instructing the CNN to focus on critical areas of pollen images (for the first challenge). STF vectors are employed to obtain the inter-class similarity of pollen via template matching. This information is further converted to soft targets that are used to supervise the CNN attending to comprehensive key features (for the second challenge). Extensive experiments on real-world datasets demonstrate the effectiveness of our PK-DFL (with accuracy and F1-score over 88%). • A deep learning model for identifying allergic pollen like a palynologist. • Pollen location and predefined feature extractors for shape-texture extraction. • Attention instruction manner for guiding CNNs to focus on critical areas. • Soft target supervision manner for guiding CNNs to attend to key features. • Extensive experiments for performance evaluation (accuracy over 88%). [ABSTRACT FROM AUTHOR]

Bone marrow mesenchymal stem cells (BMSCs) have therapeutic potential in the subacute/chronic phase of acute ischemic stroke (AIS), but the underlying mechanisms are not yet fully elucidated. There is a knowledge gap in understanding the metabolic mechanisms of BMSCs in stroke therapy. In this study, we administered BMSCs intravenously 24 h after reperfusion in rats with transient cerebral artery occlusion (MCAO). The treatment with BMSCs for 21 days significantly reduced the modified neurological severity score of MCAO rats (P < 0.01) and increased the number of surviving neurons in both the striatum and hippocampal dentate gyrus region (P < 0.01, respectively). Moreover, BMSCs treatment resulted in significant enhancements in various structural parameters of dendrites in layer V pyramidal neurons in the injured hemispheric motor cortex, including total length (P < 0.05), number of branches (P < 0.05), number of intersections (P < 0.01), and spine density (P < 0.05). Then, we performed plasma untargeted metabolomics analysis to study the metabolic changes of BMSCs on AIS. There were 65 differential metabolites identified in the BMSCs treatment group. Metabolic profiling analysis revealed that BMSCs modulate abnormal sphingolipid metabolism and glycerophospholipid metabolism, particularly affecting core members such as sphingomyelin (SM), ceramide (Cer) and sphingosine-1-phosphate (S1P). The metabolic network analysis and pathway-based compound-reaction-enzyme-gene network analysis showed that BMSCs inhibited the Cer-induced apoptotic pathway and promoted the S1P signaling pathway. These findings suggest that the enhanced effects of BMSCs on neuronal survival and synaptic plasticity after stroke may be mediated through these pathways. In conclusion, our study provides novel insight into the potential mechanisms of BMSCs treatment in stroke and sheds light on the possible clinical translation of BMSCs. • BMSCs treatment promotes neurological recovery and neuronal survival in MCAO rats. • BMSCs treatment improves dendritic plasticity in the ischemic penumbra. • BMSCs modulates abnormal sphingolipid metabolism and glycerophospholipid metabolism. • BMSCs inhibited the Cer-induced apoptotic pathway and promoted S1P signaling pathway. [ABSTRACT FROM AUTHOR]

Automatic pollen detection based on light microscope (LM) images is helpful for pollinosis symptoms prevention. Recently, many deep learning methods have been proposed to identify pollen grains based on multi-scale feature fusion mechanism. However, in real scenarios, there are two main challenges that need to be considered: (1) Complex pollen characteristics; (2) Irrelevant objects interference. It means that the pollen detection requires not only learning the relationship among multi-scale features but also refining the feature representation. To this end, this paper proposes an attention-based multi-scale feature fusion network (AMFF-Net) for automatic pollen detection on real-world LM images. The proposed AMFF-Net includes three modules: The feature extraction module utilizes the series-connection attention to capture the spatial and channel dependencies of different level feature maps (for solving the challenge 1). In the feature fusion module, a parallel connection attention is able to learn more discriminative feature representation based on bidirectional pathway guidance (for solving the challenge 2). Both are jointly adopted to enhance the representational capacity of the final results in the pollen prediction module. Extensive experiments are conducted on the real-world RPD dataset, and our AMFF-Net achieves the best performance (83.9% of mean average precision) comparing with other state-of-the-art methods. We believe that this work can serve as an important reference for the development of pollen monitoring system in a real scenario. • An attention-based feature fusion network is proposed for pollen detection. • Series connection attention aims to learn multiple dependencies of feature maps. • Parallel connection attention is used to extract discriminative features. • Both are jointly adopted to enhance the representational capacity. • Extensive Experiments are conducted to prove the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Charge transport mechanisms of forward and reverse leakage currents in vertical GaN Schottky barrier diodes are investigated by measuring the temperature-dependent current–voltage characteristics. The results show that the leakage current is primarily governed by dislocation-associated thermionic field emission (TFE). The primary transport path is the reduced, localized conduction band around the dislocation core rather than the continuum defect states. A refined phenomenological physical model is developed for conductive dislocations in GaN, emphasizing that: 1) surface donors, surrounding the core of dislocations, can significantly shrink the barrier region after ionization, causing severe TFE leakage; 2) the ON donors likely to be responsible for TFE have a typical density of ~ 1 × 1018 cm−3 at 300 K and activation energy of 78 meV; and 3) the barrier height at donor sites is ~0.65 eV at 300 K, which is reduced by ~0.4 eV with respect to the dislocation-free region. [ABSTRACT FROM AUTHOR]

A variety of bacteria have been used as agents and vectors for antineoplastic therapy. A series of mechanisms, including native bacterial toxicity, sensitization of the immune system and competition for nutrients, may contribute to antitumor effects. However, the antitumor effects of Proteus species have been minimally studied, and it is not clear if bacteria can alter tumor hypoxia as a component of their antineoplastic effect. In the present study, Proteus mirabilis bacteria were evaluated for the ability to proliferate and accumulate in murine tumors after intravenous injection. To further investigate the efficacy and safety of bacterial injection, mice bearing 4T1 tumors were treated with an intravenous dose of 5×107 CFU Proteus mirabilis bacteria via the tail vein weekly for three treatments. Histopathology, immunohistochemistry (IHC) and western analysis were then performed on excised tumors. The results suggested Proteus mirabilis localized preferentially to tumor tissues and remarkably suppressed the growth of primary breast cancer and pulmonary metastasis in murine 4T1 models. Results showed that the expression of NKp46 and CD11c was significantly increased after bacteria treatment. Furthermore, tumor expression of carbonic anhydrase IX (CA IX) and hypoxia inducible factor-1a (HIF-1a), surrogates for hypoxia, was significantly lower in the treated group than the control group mice as assessed by IHC and western analysis. These findings demonstrated that Proteus mirabilis may a promising bacterial strain for used against primary tumor growth and pulmonary metastasis, and the immune system and reduction of tumor hypoxia may contribute to the antineoplastic and antimetastatic effects observed. [ABSTRACT FROM AUTHOR]

Similar to the SNP (single nucleotide polymorphism) data, there is non-random association of the DNA methylation level (we call it methylation disequilibrium, MD) between neighboring methylation loci. For the case-control study of complex diseases, it is important to identify the association between methylation levels combination types (we call it methylecomtype) and diseases/phenotypes. We extended the classical framework of SNP haplotype-based association study in population genetics to DNA methylation level data, and developed a software EWAS to identify the disease-related methylecomtypes. EWAS can provide the following basic functions: (1) calculating the DNA methylation disequilibrium coefficient between two CpG loci; (2) identifying the MD blocks across the whole genome; (3) carrying out case-control association study of methylecomtypes and identifying the disease-related methylecomtypes. For a DNA methylation level data set including 689 samples (354 cases and 335 controls) and 473864 CpG loci, it takes only about 25 min to complete the full scan. EWAS v1.0 can rapidly identify the association between combinations of methylation levels (methylecomtypes) and diseases. EWAS v1.0 is freely available at: http://www.ewas.org.cn or http://www.bioapp.org/ewas. [ABSTRACT FROM AUTHOR]

• The entity-type alignment is first proposed for coarse-grained relation extraction. • The relation embeddings are learned and used as knowledge-enhanced features. • A novel word attention mechanism derived from the relation translation is given. • A bootstrap learning process is presented for fine-grained relation disambiguation. The goal of biomedical relation extraction is to obtain structured information from electronic medical records by identifying relations among clinical entities. By integrating the advantages of unsupervised and semi-supervised learning, the distant supervision approach has achieved significant success for a relation extraction task without a large amount of labeled corpora. However, in many cases, the recognized entities from the Chinese clinical text are not defined in semantic knowledge base, which limits the application of distant supervision for biomedical relation extraction. This work proposes a Knowledge Guided Distance Supervision (KGDS) model for handling the biomedical relation extraction task in Chinese electronic medical records. To handle the unknown entities, entity-type alignment (instead of entity alignment in traditional distant supervision) is employed for extracting coarse-grained relations. Then, by learning the relation embeddings both from semantic knowledge base and electronic medical record dataset as knowledge-enhanced features, this work presents a knowledge-enhanced bootstrapping learning process for fine-grained relation disambiguation. The empirical experiments on the real-world dataset of electronic medical records illustrate that our KGDS model achieves the best performance comparing to other state-of-the-art models, thereby advancing the field of biomedical relation extraction from Chinese electronic medical records. [ABSTRACT FROM AUTHOR]

The aim of this study was to evaluate prevalence and characteristics of mcr-9 -positive Salmonella isolated from retail food in China. For this purpose, 2486 Salmonella isolated from retail foods from 2007 to 2016 in China were investigated. Based on PCR methods, 5.6% (140/2486) of the isolates were confirmed carrying mcr-9. Those mcr-9 -positive isolates distributed in 9 of 10 provinces and municipalities and in 8 of 12 food types including poultry-related foods (5.9%, 136/2314) and non-poultry-related foods (2.3%, 4/172). Among 138 Salmonella serotypes, isolates of 8 serotypes were detected carrying mcr-9. Interestingly, S. Thompson was the most dominant serotype (90.7%, 127/140). Most mcr-9 -positive isolates were susceptible to colistin, but 91.4% of which were multi-drug resistant. Among 140 mcr-9- positive isolates, 116 (82.9%) exhibited extended spectrum β-lactamase (ESBL) producing phenotype, 87.1% of them co-carried bla CTX-M-3 and bla TEM-1. Significant difference was found between mcr-9- positive isolates recovered before/including 2010 and after 2010, as well as S. Thompson and non- S. Thompson, in terms of detection rate and antibiotic resistance. PFGE revealed close genetic relatedness of mcr-9 -positive ESBL-producing S. Thompson. Our findings indicated that the specificity of transmission of mcr-9 -positive Salmonella in retail foods in China, providing valuable baseline data for food safety risk assessment. • mcr-9 -positive Salmonella were prevalent in poultry-related foods in China. • S. Thompson was the most dominant serotype in mcr-9 -positive Salmonella. • Most mcr-9 -positive S. Thompson were susceptible to colistin and polymyxin B, but showed MDR and ESBL phenotype. • Close genetic relatedness of mcr-9 -positive S. Thompson was found based on PFGE analysis. [ABSTRACT FROM AUTHOR]

Obesity and associated metabolic associated fatty liver diseases (MAFLD) are strongly associated with dysfunction of glucose and lipid metabolism. AMPKα and PPARα are key regulators in the lipid and glucose homeostasis, indicating that novel agents to activate them are promising therapeutic approaches for metabolic syndrome. Noticeably, as a natural anthraquinone derivative extracted from rhubarb , danthron can activate AMPKα in vitro. However, the protective effect of danthron on obesity and associated MAFLD in vivo, as well as the underlying mechanism remains unknown. In this study, obesity and associated MAFLD was induced in C57BL/6J mice by high fat diet (HFD), which were subjected to evaluations on the parameters of systematic metabolism. Simultaneously, the molecular mechanism of danthron on lipid metabolism was investigated in 3T3-L1-derived adipocytes and HepG2 cells in vitro. In vivo , danthron significantly attenuated the obesity and MAFLD by enhancing hepatic fatty acid oxidation, decreasing lipid synthesis, and promoting mitochondrial homeostasis. Mechanistically, danthron significantly promoted combination of RXRα and PPARα, enhanced the binding of RXRα/PPARα heterodimer to the promoter of adiponectin receptor 2 (AdipoR2), by which activating the AMPKα and PPARα pathway. Moreover, PPARα and AdipoR2 can interplay in a loop style. Collectively, this study demonstrates that danthron can substantially ameliorate obesity and associated hepatic steatosis via AdipoR2-mediated dual PPARα/AMPKα activation, which suggests that danthron might be a novel therapeutic approach for inhibition of obesity and hepatic steatosis. [Display omitted] • Danthron can attenuate the obesity and MAFLD by regulating lipid metabolism. • Danthron can activate the interplay between RXRα/PPARα heterodimer and adiponectin receptor 2 (AdipoR2). • PPARα and AdipoR2 can interplay in a loop style. • Danthron might be a therapeutic approach for inhibition of obesity and MAFLD. [ABSTRACT FROM AUTHOR]

• Synoptic patterns affecting BTH during winter of 2013–2017 are categorized into six types. • Unfavorable synoptic conditions correspond to high or increasing PM 2.5 concentrations. • Unfavorable synoptic patterns induce occurrence of wintertime haze episodes in BTH. Meteorological conditions play a key role in formation of air pollution, determining dispersion or accumulation of air pollutants. Aggressive emission mitigation measures have been taken recently in the Beijing-Tianjin-Hebei region (BTH), China, but pervasive and persistent haze still frequently engulfs this region during wintertime. Occurrence frequency of unfavorable meteorological conditions in winter is anticipated to constitute a significantly important factor in driving the heavy haze formation in BTH. Large scale synoptic patterns influencing BTH during the wintertime from 2013 to 2017 are categorized into six types, including "north-low", "southwest-trough", "southeast-high", "southeast-trough", "transition", and "inland-high" using the NCEP reanalysis data. "Southwest-trough" and "southeast-high" are defined as favorable synoptic patterns and the remaining four categories are unfavorable ones based on FLEXPART simulations. Compared to measurements of fine particulate matter (PM 2.5) in BTH, favorable synoptic conditions generally correspond to the low level or decreasing trend of PM 2.5 concentrations while under unfavorable conditions PM 2.5 concentrations are high or increasing. Occurrence of wintertime haze episodes in BTH correlates well with the evolution trend of unfavorable synoptic patterns from 2013 to 2017 although the anthropogenic emissions have substantially decreased. PM 2.5 concentrations also exhibit correlations with local meteorological elements, including winds, temperature, and relative humidity, which are ultimately steered by large scale synoptic situations. The WRF-Chem model simulations further reveal the critical role of large-scale synoptic patterns in the heavy haze formation. Overall, under unfavorable synoptic situations, emission mitigation is the best choice to improve the air quality in BTH. [ABSTRACT FROM AUTHOR]

Homogeneous PEGylated lipid bilayer coated highly ordered MSNs (PLMSNs) which were systematically optimized and characterized to co-encapsulate paclitaxel (Tax) and curcumin (Cur) were verified to manifest prolonged and enhanced cytotoxic effect against canine breast cancer cells in our previous study. In this article, we took further study of the pharmacokinetic property, cellular uptake, subcellular localization, in vivo distribution and tumor accumulation ability, and treatment efficacy of the drug delivery system. The results revealed that the delivery system could significantly increase the AUC of two drugs, and the anti-tumor effect showed that both intravenous and intratumoral administration group better controlled the tumor weight than that of other groups (P <.05), and the anti-tumor rates were 58.4% and 58.3% respectively. Cell uptake and localization study showed that PLMSNs could effectively carry drugs into cancer cells with sustained release characteristics. The subcellular localization of PLMSNs was mainly in lysosomes and mitochondria. In vivo fluorescence tracing results showed that PLMSNs could be effectively accumulated in the tumor site. The results revealed that the delivery system could effectively reduce the clinical dosage of drugs and reduce its toxic side effects, effectively carry drugs into cancer cells, and exhibit good targeting characteristics for breast cancer. Unlabelled Image [ABSTRACT FROM AUTHOR]

Rice SPX domain gene, OsSPX1, plays an important role in the phosphate (Pi) signaling network. Our previous work showed that constitutive overexpression of OsSPX1 in tobacco and Arabidopsis plants improved cold tolerance while also decreasing total leaf Pi. In the present study, we generated rice antisense and sense transgenic lines of OsSPX1 and found that down-regulation of OsSPX1 caused high sensitivity to cold and oxidative stresses in rice seedlings. Compared to wild-type and OsSPX1-sense transgenic lines, more hydrogen peroxide accumulated in seedling leaves of OsSPX1-antisense transgenic lines for controls, cold and methyl viologen (MV) treatments. Glutathione as a ROS scavenger could protect the antisense transgenic lines from cold and MV stress. Rice whole genome GeneChip analysis showed that some oxidative-stress marker genes (e.g. glutathione S-transferase and P450s) and Pi-signaling pathway related genes (e.g. OsPHO2) were significantly down-regulated by the antisense of OsSPX1. The microarray results were validated by real-time RT-PCR. Our study indicated that OsSPX1 may be involved in cross-talks between oxidative stress, cold stress and phosphate homeostasis in rice seedling leaves. [ABSTRACT FROM AUTHOR]