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Background Identifying specific residues for protein-DNA interactions are of considerable importance to better recognize the binding mechanism of protein-DNA complexes. Despite the fact that many computational DNA-binding residue prediction approaches have been developed, there is still significant room for improvement concerning overall performance and availability. Results Here, we present an efficient approach termed PDRLGB that uses a light gradient boosting machine (LightGBM) to predict binding residues in protein-DNA complexes. Initially, we extract a wide variety of 913 sequence and structure features with a sliding window of 11. Then, we apply the random forest algorithm to sort the features in descending order of importance and obtain the optimal subset of features using incremental feature selection. Based on the selected feature set, we use a light gradient boosting machine to build the prediction model for DNA-binding residues. Our PDRLGB method shows better overall predictive accuracy and relatively less training time than other widely used machine learning (ML) methods such as random forest (RF), Adaboost and support vector machine (SVM). We further compare PDRLGB with various existing approaches on the independent test datasets and show improvement in results over the existing state-of-the-art approaches. Conclusions PDRLGB is an efficient approach to predict specific residues for protein-DNA interactions.

We explore the forecasting issue in a data set of daily corn cash prices from nearly 500 markets across sixteen states: North Dakota, Iowa, Minnesota, Illinois, Indiana, Ohio, Michigan, Missouri, Nebraska, Arkansas, Kentucky, Wisconsin, South Dakota, Kansas, Oklahoma, and Pennsylvania. We focus on univariate neural network (NN) modeling and bivariate NN modeling with futures prices incorporated. Using simple NNs with twenty hidden neurons and two delays leads to forecasting of high accuracy for the one-day ahead horizon. Including futures prices in the models benefits cash price forecasting. These findings are robust to data splitting ratios for model training, validation, and testing, and to different algorithms employed for model estimates. The forecasting framework here is relatively easy to deploy and has potential to be generalized to other commodities. This study contributes to short-term forecasting users’ information needs in decision making processes.

Lymphoma is a heterogeneous disease with varying clinical manifestations and outcomes. Many subtypes of lymphoma, such as Burkitt′s lymphoma and diffuse large B cell lymphoma, are highly aggressive with dismal prognosis even after conventional chemotherapy and radiotherapy. As such, exploring specific biomarkers for lymphoma is of high clinical significance. Herein, a potential marker, CD38, is investigated for differentiating lymphoma. A CD38‐targeting monoclonal antibody (mAb, daratumumab) is then radiolabeled with Zr‐89 and Lu‐177 for theranostic applications. As the diagnostic component, the Zr‐89‐labeled mAb is highly specific in delineating CD38‐positive lymphoma via positron emission tomography (PET) imaging, while the Lu‐177‐labeled mAb serves well as the therapeutic component to suppress tumor growth after a one‐time administration. These results strongly suggest that CD38 is a lymphoma‐specific marker and prove that 89Zr/177Lu‐labeled daratumumab facilitates immunoPET imaging and radioimmunotherapy of lymphoma in preclinical models. Further clinical evaluation and translation of this CD38‐targeted theranostics may be of significant help in lymphoma patient stratification and management.

Orthorhombic perovskite oxides, ABO3, have been studied extensively for application in advanced functional materials and devices, due to unique and versatile materials properties that arise from stoichiometry engineering. The lattice parameters, a, b, and c, have a critical impact on the structural stability, electronic structure, magnetic ordering, and thus materials performance. In this study, we develop the Gaussian process regression (GPR) model based on the Bayesian optimization to provide statistical relationships among the ionic radii, electronegativities, valence, and lattice constants for orthorhombic perovskite ABO3 compounds with 157 samples. The model shows a high degree of stability and accuracy. After being applied to predict the lattice parameters of doped- and non-synthesized oxides, the GPR model shows promising prediction power as well. The model may be used as an alternative method to obtain lattice parameters as compared to experimental approaches and other modeling methods.

Pervious concrete creates a very porous medium that allows water to penetrate the pavement to underlying soils. It is a promising candidate in permeable pavement systems in urban areas, which could be an efficient solution to sustainable drainage systems. Concrete mixture design usually requires labor-intensive and time-consuming work, which involves a significant amount of “trial batching” approaches. Recently, machine learning methods have demonstrated that a robust model might help reduce the experimental work. Thus, we develop the Gaussian process regression (GPR) model to shed light on the relationship between predictors (nominal coarse aggregate sizes, cement content, water-to-cement ratios, and coarse aggregates content) and each of the different properties (density, compressive strength, tensile strength, and porosity) of pervious concrete. The modeling approach has a high degree of accuracy and stability, contributing to fast, low-cost estimations of multiple properties of pervious concrete.

A photothermal genome-editing strategy is described to improve immune checkpoint blockade (ICB) therapy by CRISPR/Cas9-mediated disruption of PD-L1 and mild-hyperthermia-induced activation of immunogenic cell death (ICD). This strategy relies on a supramolecular cationic gold nanorod that not only serves as a carrier to deliver CRISPR/Cas9 targeting PD-L1, but also harvests the second near-infrared-window (NIR-II) light and converts into mild hyperthermia to induce both ICD and gene expression of Cas9. The genomic disruption of PD-L1 significantly augments ICB therapy by improving the conversion of dendritic cells to T cells, followed by promoting the infiltration of cytotoxic T lymphocytes into tumors, thereby reprogramming immunosuppressive tumor microenvironment into immunoactive one. Such a therapeutic modality greatly inhibits the activity of primary and metastatic tumors and exhibits long-term immune memory effects against both rechallenged and recurrent tumors. The current therapeutic strategy for synergistic PD-L1 disruption and ICD activation represents an appealing way for cancer immunotherapy.

The Youjiang Basin in the western part of the South China Block (SCB), preserves the records of the tectonic history from Tethyan, through Paleo-Pacific to Tibetan domains, with coeval formation of diverse types of giant metallic deposits. The prolonged tectonic evolution and its control on the genesis and spatio-temporal distribution of giant metallic deposits in the Youjiang Basin provide a window for a holistic understanding of the tectono-metalligenesis of SCB. Six tectono-metallogenic stages can be recognized in the geological evolution of the basin and their relationship to the geodynamic evolution of the surrounding tectonic units. Subsequent to the closure of the Proto-Tethyan Ocean and collision between the SCB and the Gondwana continent, a late Ordovician to early Silurian intracontinental orogeny along the southern margin of the basin resulted in the formation of Silurian Cu and W skarn deposits. During Devonian to Carboniferous, during the Paleo-Tethyan spreading, the basin experienced rifting and development of a passive continental margin, when sedimentary Mn carbonate ores were deposited in basin facies sequences in the southern part of the basin. On the northern margin, bauxite karsts formed in carbonate rocks within continental-margin sequences. In the Late Permian, the Paleo-Pacific oceanic plate was subducted below the SCB, which most likely induced migration of the coeval Emeishan mantle plume towards the plate margin. Arc volcanism resulted in voluminous ash falls on the carbonate platform and these were subsequently transformed into bauxite in the southern part of basin. Following this, Triassic intraplate shortening induced thin-skinned crustal deformation with associated formation of orogenic Au deposits throughout the basin. In the Jurassic, the Youjiang Basin experienced slab delamination, during which MVT Pb Zn deposits formed along the northern margin, and clusters of W Sn skarns were generated at the centre of the delamination zone on the eastern margin of the basin. Subduction retreated southwards in the Cretaceous, resulting in the formation of Carlin-type Au deposits in the northern part of the basin, and Sn and Cu skarns and associated carbonate replacement Pb Zn deposits in the central and southern parts. Cenozoic regional denudation and climate change in response to the southeastward propagation of Tibet caused brittle deformation of pre-existing bauxite and Mn orebodies and their further enrichment. The overlap among Paleo-Tethyan, Paleo-Pacific and Tibetan tectonic regimes during the evolution of the Youjiang Basin generated a diverse range of mineral deposit types, including orogenic and Carlin-type Au deposits, both carbonate-replacement and MVT-type Pb Zn deposits, sedimentary Mn‑carbonate deposits and bauxite in both passive and active continental margins.

Spinels can house a large variety of elements into the crystal structure. As a crystallographic parameter, the lattice constant, a, is highly sought in further investigations into materials properties. Experimental approaches to obtain the lattice constant are resource-intensive, and limit the exploration into non-synthesized spinels. Here, we develop the Gaussian process regression model to shed light on the relationship among ionic radii, electronegativities, and lattice constants. 167 samples with lattice constants between 8.044 A and 11.660 A are investigated. The model provides more accurate predictions than previous studies based on linear regressions, and statistical relationships between descriptors and the target.

Using daily prices from 496 corn cash markets for July 2006–February 2011, this study investigates short-run forecast performance of 31 individual and 10 composite models for each market at horizons of 5, 10, and 30 days. Over the performance evaluation period September 2010–February 2011, two composite models are optimal across horizons for different markets based on the mean-squared error. For around half of the markets at the horizon of 5 days and most of them at 10 and 30 days, the mean-squared error of a market's optimal model is significantly different from those of at least other 23 models evaluated for it. Root-mean-squared error reductions through switching from non-optimal models to the optimal are generally around 0.40%, 0.55%, and 0.87% at horizons of 5, 10, and 30 days.

Four highly fluorescent derivatives of bis(phenyl-ethynyl-)-2-naphthyl (BPEN) with push-pull structures were designed and synthesized, of which azetidine was adopted as an electron-donating unit. For the electron withdrawing moiety, it varies from hydrogen, to formyl, then to the 2-ethoxyethyl derivative of dicyanovinyl, and finally to dicyanovinyl itself, and the corresponding fluorophores are denoted as A1, A2, A3, and A4, respectively. To enhance the solubility of the compounds, two n-hexadecyl residues were grafted onto the side positions of BPEN. Interestingly, introduction of azetidine not only improves the fluorescence quantum yield and enlarges the Stoke's shift of the parent compound but also endows them, in particular A2 and A4, exceptional capability to distinguish structurally relevant organic liquids, such as ethylbenzene and its isomers (o-xylenes, m-xylenes, and p-xylenes), monoalkyl-substituted benzene derivatives, gasolines of different grades, and other organic liquids. Theoretical calculation and Lippert-Mataga equation-based tests revealed the intramolecular charge transfer (ICT) nature of the solvatochromic properties of the compounds. Further quantitative analysis of the data obtained from studies of the probes/n-hexane-dioxane systems revealed the big differences in the dipole moments between the excited and ground states of A1, A2, A3, and A4, which are about 23, 29, 43, and 38 D, respectively. Moreover, the four novel fluorophores possess exceptional photochemical stability as demonstrated by the fact that more than 2 h of UV light illumination did not result in detectable reduction in the fluorescence emission of the fluorophores. It is the long wavelength absorption (380, ≈400,410, and430 nm), large molar absorption coefficient (59 000,52 000,39 000, and34 000 cm

Porcine reproductive and respiratory syndrome virus (PRRSV) is one of the leading swine pathogens and causes major economic loss to the global swine industry. In this study, a total of 49 PRRSV isolates were collected from different swine herds in seven provinces in Southeast China from 2014 to 2015. All the ORF5 genes and some Nsp2 genes were sequenced. Phylogenetic analysis showed that all the isolates belonged to the North America genotype. Among them, five isolates formed a new subgenotype IV derived from highly pathogenic PRRSV (HP-PRRSV). Six isolates formed subgenotype III, which were closely related to the NADC30 strain in the US. These isolates formed 13 putative N-linked glycosylation site (NGS) patterns based on N30, 33, 34, 35, 44 and 51. There were fewer NGSs of isolates in subgenotype IV than in subgenotype III. This indicates that the two new subgenotypes of PRRSV strains with different NGS patterns were spreading in those regions of China. The genetic diversity should be considered for the control and prevention of this disease.

This study investigates dynamic relationships among US corn cash prices for the years 2006–2011. Using daily data from 182 markets in seven Midwestern states, an error correction model is estimated and directed acyclic graphs characterize the contemporaneous causal relationships among prices from different states. Empirical results based on the PC algorithm show that three states, Iowa, Ohio, and Minnesota, dominate corn cash prices. Four potential causal paths among the three states also are identified. Given that physical flows of grain are different at different times of the year, the data are divided into storage periods and harvest periods, and a VAR in differences is adopted to model the price relationships. While Iowa and Ohio continue to dominate corn prices during the storage period, the causal flows are mixed during the harvest period. An application of the LiNGAM algorithm refines the results relative to those derived from the PC algorithm and reveals that Iowa, the leading corn-producing state, is the only state that dominates pricing over the crop year.

Huatan Tongluo Fang (HTTLF) is a traditional herbal formula that can resolve phlegm and dredge collaterals. HTTLF has also been used to treat rheumatoid arthritis (RA); however, the mechanism underlying the therapeutic effects of HTTLF on RA has not been clearly elucidated at the molecular level. In the present study, an integrated model of system pharmacology containing chemical space analysis, potential active compound prediction and compound-target-disease network was constructed to investigate the molecular mechanisms of HTTLF. The compounds from HTTLF dispersed well in the chemical space. Most of the compounds from HTTLF had similar chemical spaces to drug/drug-like compounds associated with RA, according to the MDL Drug Data Report. A total of 127 potentially active compounds and 17 targets of RA were identified. Among them, 50 compounds interacted with ≥2 targets, while 77 compounds interacted with only one target. In addition, 17 targets were associated with 82 diseases that belonged to 26 categories. These results indicate that HTTLF has diverse chemical spaces and polypharmacology with regards to the treatment of RA. In addition, HTTLF demonstrated therapeutic potential against diverse diseases other than RA, including osteoarthritis, atherosclerosis and brain cancer. This study provides a novel platform for understanding how HTTLF treats RA; this is beneficial for explaining the diverse functions of HTTLF with regards to RA, and may help develop novel compounds with desirable therapeutic targets to treat RA.

This paper firstly through IBAC, integration of TE and RBAC, the use of compensatory well-formed transaction is proposed, the integrity of the structure can be recovered partial malicious transaction monitoring machine model. In the partial revocation of constitutive affairs, for the operation of data and tracking the affected, with two recovery policies. Conservative recovery policy to stop system the recovery of normal transaction execution, by analyzing log file dependencies list, according to operation performed after first order, cancel each affected operation. Another optimistic recovery policy can be in the normal operation of the system at the same time, the establishment of compensation operation corresponding to the operation to recover, and submitted to the monitoring machine scheduling integrity. This method can recover the system to a secure state in the face of failures and improves the availability of the system. It provides an important exploration for the design and implementation of the trusted recovery mechanisms of high-level secure operating system.

The proinflammatory cytokine interleukin-1β (IL-1β) is involved in the initiation and progression of osteoarthritis (OA) by stimulating the expression of matrix-degrading proteinases, such as a disintegrin metalloproteinase with thrombospondin motifs-5 (ADAMTS-5), a key player in OA pathogenesis. However, how IL-1β induces ADAMTS-5 overexpression is poorly understood. We demonstrate that IL-1β regulates ADAMTS-5 expression by suppressing microRNA-30a (miR-30a). Bioinformatics was performed to predict miRNAs targeting ADAMTS-5. miR-30a inhibited ADAMTS-5 expression by directly targeting its 3'-untranslated region. miR-30a expression was downregulated in OA patients and was negatively correlated with ADAMTS-5 expression and positively correlated with Hospital for Special Surgery (HSS) scores. IL-1β suppressed miR-30a expression by recruiting the activator protein (AP-1) transcription factor c-jun/c-fos to the miR-30a promoter. IL-1β-induced c-jun/c-fos expression regulated ADAMTS-5 expression and cartilage matrix degradation via miR-30a in human chondrocytes. These data indicate that the IL-1β/AP-1/miR-30a/ADAMTS-5 pathway contributes to IL-1β-induced cartilage matrix degradation in human OA chondrocytes. miR-30a may act as a pivotal regulator of cartilage homeostasis and a potential diagnostic and therapeutic target for OA.ADAMTS-5 was identified as a novel direct target of miR-30a. IL-1β suppresses miR-30a expression through activation of AP-1 (c-jun/c-fos). AP-1/miR-30a is essential for IL-1β-induced ADAMTS-5 upregulation in OA. Downregulation of miR-30a in OA is negatively correlated with ADAMTS-5 expression.

P-type transparent conducting films of nanocrystalline (CuS)x:(ZnS)1-x were synthesized by facile and low-cost chemical bath deposition. Wide angle X-ray scattering (WAXS) and high resolution transmission electron microscopy (HRTEM) were used to evaluate the nanocomposite structure, which consists of sub-5 nm crystallites of sphalerite ZnS and covellite CuS. Film transparency can be controlled by tuning the size of the nanocrystallites, which is achieved by adjusting the concentration of the complexing agent during growth; optimal films have optical transmission above 70% in the visible range of the spectrum. The hole conductivity increases with the fraction of the covellite phase and can be as high as 1000 S cm(-1), which is higher than most reported p-type transparent materials and approaches that of n-type transparent materials such as indium tin oxide (ITO) and aluminum doped zinc oxide (AZO) synthesized at a similar temperature. Heterojunction p-(CuS)x:(ZnS)1-x/n-Si solar cells were fabricated with the nanocomposite film serving as a hole-selective contact. Under 1 sun illumination, an open circuit voltage of 535 mV was observed. This value compares favorably to other emerging heterojunction Si solar cells which use a low temperature process to fabricate the contact, such as single-walled carbon nanotube/Si (370-530 mV) and graphene/Si (360-552 mV).

Angiogenesis is essential for tumor growth. Vascular endothelial growth factor (VEGF) is the most important regulator of tumor angiogenesis. However, how transcription factors interact with histone-modifying enzymes to regulate VEGF transcription and tumor angiogenesis remains unclear. Here, we show that transcription factor GATA1 associates with the histone methyltransferase SET7 to promote VEGF transcription and breast tumor angiogenesis. Using chromatin immunoprecipitation assay, we found that GATA1 was required for recruitment of SET7, RNA polymerase II and transcription factor II B to VEGF core promoter. GATA1 enhanced breast cancer cell (MCF7, ZR75-1 and MDA-MB-231)-secreted VEGF via SET7, which promoted vascular endothelial cell (HUVEC) proliferation, migration and tube formation. SET7 was required for GATA1-induced breast tumor angiogenesis and growth in nude mice. Immunohistochemical staining showed that expression of GATA1 and SET7 was upregulated and positively correlated with VEGF expression and microvessel number in 80 breast cancer patients. GATA1 and SET7 are independent poor prognostic factors in breast cancer. Our data provide novel insights into VEGF transcriptional regulation and suggest GATA1/SET7 as cancer therapeutic targets.

Pulsatilla chinensis is one of the 50 famous fundamental herbs used in traditional Chinese medicine. Saponins are the main components of P. chinensis. Although the anti-proliferative function of saponins has been established in plenty types of cancer, the role of saponins on tumor invasion and metastasis has not been reported, and the mechanisms of how saponins exert the anti-tumor functions are still poorly characterized. Here, we demonstrate that, in breast cancer (BC) cells, raddeanoside R13, a component of saponins extracted from P. chinensis, exhibits strong anti-proliferative and anti-metastasis ability, accompanied by cell cycle arrest, apoptosis, autophagy, and reversion of epithelial-mesenchymal transition (EMT). Raddeanoside R13 (R13) inhibits BC cell proliferation via the activation of G1/S checkpoint transitions, concomitant with a marked decrease of the positive cell cycle regulators, including cyclin D1, cyclin A, and cyclin B1. R13 induces BC cell apoptosis accompanied by the increased levels of cleaved PARP and caspase-3. R13 inhibits BC cell migration and invasion and regulates the expression of the markers of EMT, which plays a critical role in cancer cell migration and invasion. Moreover, R13 suppresses BC tumor growth and metastasis in nude mice. These data highlight the important role of R13 in BC cell proliferation and progression and suggest that R13 may be a useful drug for BC therapy.

A new fluorescent conjugate (PNBD) with a structure of D–π–A was designed and synthesized, where the donor (D), the acceptor (A) and the bridge (π) are naphthalyl, dicyanovinyl and phenylethynyl–phenylethynyl, respectively. To improve the solubility of the conjugate, two long alkyl chains were introduced as substituents of the central aromatic ring. Spectroscopic studies demonstrated that PNBD is a strongly solvatochromic probe which is characterized by a large molar absorption coefficient (>32 000 cm−1 M−1), long wavelength absorption (>410 nm), large solvatochromic emission range (470–650 nm), high photochemical stability, and good solubility in common organic solvents. The fluorescent quantum yield of PNBD is limited in some polar solvents due to dual emission, a phenomenon ascribed to radiative decay from a higher excited singlet state. To eliminate dual emission, a covalently bound dimer (BPNBD) of PNBD characterized by weak vibronic coupling, was designed and synthesized. The dimer constituents are linked by a single bond between the naphthalyl moieties of the two PNBD monomers. As expected, BPNBD maintains almost all the strong points of the monomer, exhibits a substantial increase in fluorescence quantum yield, and eliminates dual emission by facilitating efficient internal conversion. Importantly, the use of PNBD and BPNBD in concert provides unprecedented discrimination among solvents of similar structures, such as (CH2Cl2, CHCl3, CCl4), (ethyl ether, THF, dioxane), or (methanol, ethanol, n-propanol, n-butanol, n-pentanol, n-hexanol, n-heptanol, n-octanol, n-decanol), allowing rapid and selective visual identification.

A new model is established to obtain the building energy with a gradual change process of VO2 optical properties. Based on this method, three types of existing VO2 films are employed to simulate the heating and cooling energy consumptions in five typical Chinese cities. The results show that the smart characteristic is not evident in VO2 glasses because these films are almost in the cold state or cold transition state in the entire year. To improve this situation, the concept of an ideal thermochromic intelligent glass is put forward. Unexpectedly, the level of energy consumption increases with the film phase transition temperature decreasing in the heating period. However, the energy-saving effects will be effectively improved both in the cooling and heating periods when the phase transition process interval is shortened. The cooling energy consumptions for the ideal glass decrease by at most 81.7% and 70.5% compared with white glass and Low-E glass, respectively. In the heating period, the level of energy consumption for the ideal glass is better than Low-E glass.

Metal halide perovskites have attracted great attention in the past decade due to unique and tunable optical and electrical properties, which are promising candidates for various applications such as solar cells, light emitting diodes, and laser cooling devices. The lattice constant, a, as the only variable parameter among the six parameters in the crystal structure, has a significant impact on the structural stability, bandgap structure, and thus materials performance. In this work, the Gaussian process regression (GPR) model is developed to elucidate the statistical relationship between ionic radii and lattice constants for cubic perovskite A 2 X Y 6 compounds. A total of 79 samples with lattice constants ranging from 8.109 A to 11.790 A are explored. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants.

As an important thermophysical property, polymers' glass transition temperature, Tg, could sometimes be difficult to determine experimentally. Modeling methods, particularly data-driven approaches, are promising alternatives to predictions of Tg in a fast and robust way. The molecular traceless quadrupole moment and molecule average hexadecapole moment are closely correlated with polymers' Tg. In the current work, these two parameters are used as descriptors in the Gaussian process regression model to predict Tg. We investigate 60 samples with Tg values from 194 K to 440 K. The model provides rapid and low-cost Tg estimations with high accuracy and stability., Materials science; Materials chemistry; Physical chemistry; Glass transition temperature; Polymer; Machine learning; Gaussian process regression

Efficient solid-state refrigeration techniques have drawn increasing attention due to their potential for improving energy efficiency of refrigeration, air-conditioning, and temperature-control systems without using harmful gas as in conventional gas compression techniques. Research on magnetocaloric lanthanum manganites with near-room-temperature Curie temperature, T C , shows promising results for further developments of magnetic refrigeration devices. By incorporating chemical substitutions, oxygen content modifications, and various synthesis methods, these manganites experience lattice distortions from perovskite cubic structures to pseudocubic, orthorhombic, and rhombohedral structures. Further changes in lattice parameters can also be achieved by the introduction of strain due to lattice mismatches, where T C can be tuned more effectively. Empirical results and previous models through thermodynamics and first-principles have shown that changes in lattice parameters correlate with those in T C , but correlations are merely general tendencies and obviously not universal. In this work, the Gaussian process regression model is developed to find statistical correlations between T C and lattice parameters among lanthanum manganites. Nearly 100 lattices, cubic, pseudocubic, orthorhombic, and rhombohedral, with T C ranging from 40 K to 375 K are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of T C , providing guidance on thin film structure design and helping understandings of magnetic phase transformations and magnetocaloric effects in lanthanum manganites.

Tailoring the optical bandgap, E g , of ZnO nanostructured thin films is of great interest to meet increasing demands for diverse practical applications, such as optoelectronics, spintronics, and photonics. Processing parameters, dopant selection, and various combinations of the two have significant effects on the crystal structure and microstructure of fabricated films, and thus optical performance. Empirical results and previous models through the first principle show that changes in lattice parameters and the grain size correlate with those in E g . But correlations are complicated and the models rarely apply to complex situations such as co-doped films. In this work, the Gaussian process regression (GPR) model is developed to elucidate the statistical relationship among the lattice parameters, grain size, and energy bandgap for doped-ZnO films. A total of 65 ZnO films with E g ranging from 2.100 eV to 3.760 eV are explored. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of E g .

Nanofluids, composed of nanoparticles in base liquids, have drawn increasing attention in heat transfer applications due to their anomalously increased thermal conductivity. Pertinent parameters, including the base liquid thermal conductivity, particle thermal conductivity, particle size, particle volume fraction, and temperature, have been shown to have significant but complex effects on thermal performance of nanofluids, which is commonly characterized by the thermal conductivity enhancement, E%. In this work, machine learning is used to develop the Gaussian process regression model to find statistical correlations between E% and aforementioned physical parameters among various types of nanofluids. Nearly 300 nanofluid samples, dispersions of metal and ceramic nanoparticles in water, ethylene glycol, and transformer oil, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of E%.

Magnesium boride, MgB2, has attracted much attention since the discovery of its superconductivity in 2001. The absence of weak-links in grain boundaries, less prominent anisotropy, rather simple powder-in-tube wire fabrication techniques, and much lower prices of raw materials, have made this new superconducting material a promising candidate for high-field magnet applications. Furthermore, it has been demonstrated that various methods, such as chemical doping, irradiations, and different processing parameters, can lead to lattice disorders in the materials and thus alter physical properties. Empirical results have shown that changes in lattice parameters through various methods correlate with changes in Tc but correlations are merely general tendencies and obviously not universal. In this work, the Gaussian process regression model is developed to predict critical temperature based on lattice parameters among disordered MgB2 in various materials systems. This modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost predictions of Tc and understandings of disorders and superconductivity in MgB2 superconductors.

The high-temperature superconductor, YBa 2 Cu 3 O 7 - x (YBCO), is a promising candidate for high field magnet fabrication as it has critical temperature, T c , of over 80 K and an upper critical field over 100 T. In practical applications, the quality and stability of a superconducting magnet depends heavily on T c . Extensive research has been conducted to modify crystal structures of YBCO materials by chemical substitution and doping in order to enhance the superconducting transition temperature. The increase in T c fulfills the needs of practical applications with liquid-helium-free refrigeration and a delay in magnet failure. But the research requires significant manpower for materials synthesis, characterization, and quench detection, as well as costly equipment and facilities. In this work, the Gaussian process regression model is developed to predict YBCO superconducting transition temperature based on lattice parameters. Results here show a high correlation coefficient (99.78%) between the predicted and experimental superconducting transition temperature, a low prediction root mean square error (1.04% of the sample mean) and mean absolute error (0.27% of the sample mean), and stable model performance. This modeling approach contributes to efficient and low-cost estimations of superconducting transition temperature and understandings of the temperature based on lattice parameters.

The Gaussian process regression model is developed as a machine learning tool to find statistical correlations among lattice constants, a0, of half-Heusler compounds, ionic radii, and Pauling electronegativity of their alloying elements. Nearly 140 half-Heusler samples, containing alloying elements of Cr, Mn, Fe, Co, Ni, Rh, Ti, V, Al, Ga, In, Si, Ge, Sn, P, As, and Sb, are explored for this purpose. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of lattice constants of half-Heusler compounds.

The recent Collective Forest Tenure Reform in China has started the process of devolving forest management rights from village collectives to households since 2003. In this paper, we study the impact of the reform on rural energy consumption. Devolving forest tenure improves farmers' access to forest products on their newly acquired forestland, and is therefore expected to increase farmers' fuelwood consumption. The reform also allows farmers to adopt some revenue-enhancing forest technologies which may lead to energy switching in farmer households. Our empirical study finds that the devolution significantly increases household fuelwood consumption for both lower and higher income households; the lower income households benefit more. This is welfare-improving in places where alternative fuels are still too costly. We find limited evidence that higher income households in Yunnan begin to substitute alternative commercial fuels for fuelwood when those are available. Our findings suggest further devolution of forest rights, especially in the poor, forest-rich regions.

Solid-state refrigeration techniques have drawn increasing attention due to their potential for improving the energy efficiency of refrigeration and temperature-control systems without using harmful gas as in conventional gas compression techniques. Research on magnetocaloric lanthanum manganites with near-room-temperature Curie temperature shows promising results for development of magnetic refrigeration devices. Chemical substitutions are one of the most effective methods to tune the magnetocaloric effect, represented by the maximum magnetic entropy change (MMEC), through the incorporation of various lanthanides, rare-earth elements, alkali metals, alkaline-earth metals, transition metals, and other elements. Some theories based on lattice distortions and double-exchange interactions show that ionic radii of the dopants and final compositions correlate with the MMEC, but the correlations are generally limited to A-site substitutions and become less applicable to multi-doped manganites than single-doped ones. In this work, the Gaussian process regression model is developed as a machine learning tool to find statistical correlations between the MMEC and structural parameters among lanthanum manganites. More than 70 lattices, cubic, pseudocubic, orthorhombic, and rhombohedral, with the MMEC ranging from 0.65 J kg−1 K−1 to 8.00 J kg−1 K−1 under a field change of 5 T are explored for this purpose. Structural parameters utilized as descriptors include ionic radii at both A- and B-sites, ⟨Mn–O⟩ bond length, ⟨Mn–O–Mn⟩ bond angle, and compositions consisting of up to six elements. The modeling approach demonstrates a high degree of accuracy and stability, contributing to efficient and low-cost estimations of the magnetocaloric effect.

Binary-single and binary-binary encounters play a pivotal role in the evolution of star clusters, as they may lead to the disruption or hardening of binaries, a novel prediction of the Hills-Heggie law. Based on our recent {\it Chandra} survey of Galactic globular clusters (GCs), we revisit the role of stellar dynamical interactions in GCs, focusing on main-sequence (MS) binary encounters as a potential formation channel of the observed X-ray sources in GCs. We show that the cumulative X-ray luminosity ($L_X$), a proxy of the total number of X-ray-emitting binaries (primarily cataclysmic variables and coronally active binaries) in a given GC, is highly correlated with the MS binary encounter rate ($\Gamma_{b}$), as $L_X \propto \Gamma_{b}^{0.77\pm0.11}$. We further test the Hills-Heggie law against the {\it binary hardness ratio}, defined as the relative number of X-ray-emitting hard binaries to MS binaries and approximated by $L_{X}/(L_{K}f_{b})$, with $L_{K}$ being the GC K-band luminosity and $f_b$ the MS binary fraction. We demonstrate that the binary hardness ratio of most GCs is larger than that of the Solar neighborbood stars, and exhibits a positive correlation with the cluster specific encounter rate ($\gamma$), as $L_{X}/(L_{K}f_{b}) \propto \gamma^{0.65\pm0.12}$. We also find a strong correlation between the binary hardness ratio and cluster velocity dispersion ($\sigma$), with $L_{X}/(L_{K}f_{b}) \propto \sigma^{1.71\pm0.48}$, which is consistent with the Hills-Heggie law. We discuss the role of binary encounters in the context of the Nuclear Star Cluster, arguing that the X-ray-emitting, close binaries detected therein could have been predominatly formed in GCs that later inspiralled to the Galactic center., Comment: 11pages, 2 figures, 1 tables, accepted for publication in ApJ

This study investigates linear and nonlinear causality between the daily Chicago Board of Trade corn futures price series and each of seven regional cash series from Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas for January 2006–March 2011. Empirical results suggest bidirectional linear causality between cash and futures prices under a bivariate vector autoregressive model in differences (Bi-VAR-Diff) which consists of the futures and one of the seven cash series that are not cointegrated, and unidirectional linear causality from futures to cash prices under an octavariate vector error correction model (Octa-VECM) which consists of the futures and all of the seven cash series that are cointegrated. With linear relationships among prices removed using the Bi-VAR-Diff or Octa-VECM filtering, nonlinear causality is tested through a bivariate vector autoregressive model in levels (Bi-VAR-Lev) on residuals associated with the futures and a cash series from the linear models, and is found to be unidirectional from the futures market to Illinois, Indiana, and Ohio in general. Finally, a GARCH-BEKK specification is added to a Bi-VAR-Diff or the Octa-VECM to obtain residuals for the nonlinear causality test using a Bi-VAR-Lev, and the futures market leadership against Illinois, Indiana, and Ohio is still identified.

35 Introduction 35 Materials and Methods 38 Results 41 Discussion 48 Acknowledgments 51 Literature Cited 52

Light-actuating devices that can produce selective motions at small scales are highly desired for on-demand manipulation. For conventional photothermal motors that mostly encounter the homogenous light-induced heat diffusion at the liquid/air interface, it is challenging to effectively control the actuating direction and enhance the actuating speed. To this end, here, we explore aligned thermally conducting one-dimensional nanomaterials to make light-driving motors where the light-induced heat can be transmitted to the water surface along the length direction of the aligned one-dimensional nanomaterials to generate a localized surface tension gradient for high spatial resolution propulsion. When multiwalled carbon nanotubes were studied as a demonstration, the aligned active layer generated sufficient propulsion to drive a centimeter-sized motor that was 10 000 times higher in mass of the actuating layer on water. In addition, the actuating direction had been accurately controlled by varying the illuminated region of the active aligned nanotube layer. The resulting light-driving motors can move as fast as 4.19 cm/s (or 5.2 body length per second), which exceeded the previous motors based on the light activation.

In this paper, we present a novel approach for 3D dental model segmentation via deep Convolutional Neural Networks (CNNs). Traditional geometry-based methods tend to receive undesirable results due to the complex appearance of human teeth (e.g., missing/rotten teeth, feature-less regions, crowding teeth, extra medical attachments, etc.). Furthermore, labeling of individual tooth is hardly enabled in traditional tooth segmentation methods. To address these issues, we propose to learn a generic and robust segmentation model by exploiting deep Neural Networks, namely NNs. The segmentation task is achieved by labeling each mesh face. We extract a set of geometry features as face feature representations. In the training step, the network is fed with those features, and produces a probability vector, of which each element indicates the probability a face belonging to the corresponding model part. To this end, we extensively experiment with various network structures, and eventually arrive at a 2-level hierarchical CNNs structure for tooth segmentation: one for teeth-gingiva labeling and the other for inter-teeth labeling. Further, we propose a novel boundary-aware tooth simplification method to significantly improve efficiency in the stage of feature extraction. After CNNs prediction, we do graph-based label optimization and further refine the boundary with an improved version of fuzzy clustering. The accuracy of our mesh labeling method exceeds that of the state-of-art geometry-based methods, reaching 99.06 percent measured by area which is directly applicable in orthodontic CAD systems. It is also robust to any possible foreign matters on model surface, e.g., air bubbles, dental accessories, and many more.

Background Detection of microRNA (miRNA) aberrations in the peripheral plasma is a new approach for hepatocellular carcinoma (HCC) screening. The aim of this study was to characterise miR-148a in the peripheral plasma as a non-invasive biomarker for diagnosis of HCC. Methods Plasma-based miR148a analysis was performed on 346 plasma samples, including 155 HCCs, 96 liver cirrhosis and 95 healthy controls by using quantitative Real-Time PCR (qRT-PCR). Subsequently, plasma-based miR148a levels were validated in 97 pairs of HCC followed-up after removal of the primary tumour. Finally, Receiver operating characteristics (ROC) curves were generated to confirm predicting the value of plasma-based miR148a in HCC. Results Plasma-based miR-148a expression was significantly lower in 155HCCs compared to 96 liver cirrhosis (p Conclusions Plasma-based miR-148a can be applied as a potential non-invasive biomarker for HCC screening, especially for HCC with negative or less expressing AFP, which can make up AFP deficiency in predicting HCC occurrence. Notes This project was funded by Application Project of Clinical Features in Capital City (Z141107002514507).

Solar radiation, especially ultraviolet (UV) light, is a major hazard for most skin-related cancers. The growing needs for wearable health monitoring systems call for a high-performance real-time UV sensor to prevent skin diseases caused by excess UV exposure. To this end, here a novel self-powered p-CuZnS/n-TiO

PURPOSE: CD38 is considered a potential biomarker for multiple myeloma (MM) and has shown a strong link with chronic lymphocytic leukemia due to high and uniform expression on plasma cells. In vivo evaluation of CD38 expression may provide useful information about lesion detection and prognosis of treatment in MM. In this study, immunoPET imaging with (89)Zr-labeled daratumumab was used for differentiation of CD38 expression in murine lymphoma models to provide a potential noninvasive method for monitoring CD38 in the clinic. METHODS: Daratumumab was radiolabeled with (89)Zr (t(1/2) = 78.4 h) via conjugation with desferrioxamine (Df). After Western blot (WB) was used to screen CD38 expression in five lymphoma cell lines, flow cytometry and cellular binding assays were performed to test the binding ability of labeled or conjugated daratumumab with CD38 in vitro. PET imaging and biodistribution studies were performed to evaluate CD38 expression after injection of (89)Zr-Df-daratumumab. (89)Zr-Df-IgG was also evaluated as a non-specific control group in the Ramos model. Finally, CD38 expression in tumor tissues was verified by histological analysis. RESULTS: Using WB screening, the Ramos cell line was found to express the highest level of CD38 while the HBL-1 cell line had the lowest expression. Df-conjugated and (89)Zr-labeled daratumumab displayed similar high binding affinities with Ramos cells. PET imaging of (89)Zr-Df-daratumumab showed a high tumor uptake of up to 26.6 ± 8.0 %ID/g for Ramos at 120 h post-injection, and only up to 6.6 ± 2.9 %ID/g for HBL-1 (n = 4). Additionally, (89)Zr-Df-IgG demonstrated a low tumor uptake in the Ramos model (only 4.3 ± 0.8 %ID/g at 120 h post-injection). Ex vivo biodistribution studies showed similar trends with imaging results. Immunofluorescent staining of tumor tissues verified higher CD38 expression of Ramos than that of HBL-1. CONCLUSIONS: The role of (89)Zr-Df-daratumumab was investigated for evaluating CD38 expression in lymphoma models noninvasively and was found to be to a promising imaging agent of CD38 positive hematological diseases such as MM in future clinical applications.

Soybean meal-induced enteritis (SBMIE) is a well-described condition in the distal intestine (DI) of several cultured fish species, but the exact cause is still unclear. The work on Atlantic salmon and zebrafish suggested soya-saponins, as heat-stable anti-nutritional factors in soybean meal, are the major causal agents. However, this conclusion was not supported by the research on some other fish, such as gilthead sea bream and European sea bass. Our previous work proved that soybean could induce SBMIE on turbot and the present work aimed to investigate whether soya-saponins alone could cause SBMIE and the effects of soya-saponins on the intestinal barrier function in juvenile turbot. Turbots with initial weight 11.4 ± 0.02 g were fed one of four fishmeal-based diets containing graded levels of soya-saponins (0, 2.5, 7.5, 15 g kg-1) for 8 weeks. At the end of the trial, all fish were weighed and plasma was obtained for diamine oxidase (DAO) activity and d-lactate level analysis and DI was sampled for histological evaluation and quantification of antioxidant parameters and inflammatory marker genes. The activities of superoxide dismutase, catalase, glutathione peroxidase, glutathione reductase and intestinal glutathione level were selected to evaluated intestinal antioxidant system. The distal intestinal epithelial cell (IEC) proliferation and apoptosis were investigated by proliferating cell nuclear antigen (PCNA) labelling and TdT-mediated dUTP nick end labeling (TUNEL), respectively. The results showed that soya-saponins caused significantly dose-dependent decrease in the growth performance and nutrient utilization (p < 0.05). Enteritis developed in DI of the fish fed diet containing soya-saponins. Significantly dose-dependent increases in severity of the inflammation concomitant with up-regulated expression of il-1β, il-8, and tnf-α, increased IEC proliferation and apoptosis, and decreases in selected antioxidant parameters were detected (p < 0.05). The epithelial permeability (evaluated by the plasma DAO activity and d-lactate level) was significantly increased with the increasing of dietary level of soya-saponins (p < 0.05), which was concomitant with the destroyed the intracellular junctions. In conclusion, the present work proved that soya-saponins induced enteritis and compromised the intestinal barrier functions. Based on the present work, strategies focus on regulation of cell apoptosis, epithelial permeability, intracellular junctions and redox homeostasis worth further investigating to develop new and efficient ways for SBMIE alleviation.

We examine the short-run forecasting problem in a data set of daily prices from 134 corn buying locations from seven states – Iowa, Illinois, Indiana, Ohio, Minnesota, Nebraska, and Kansas. We ask the question: is there useful forecasting information in the cash bids from nearby markets? We use several criteria, including a Granger causality criterion, to specify forecast models that rely on the recent history of a market, the recent histories of nearby markets, and the recent histories of futures prices. For about 65% of the markets studied, the model consisting of futures prices, a market’s own history, and the history of nearby markets forecasts better than a model only incorporating futures prices and the market’s own history. That is, nearby markets have predictive content. But the magnitude varies with the forecast horizon. For short-run forecasts, the forecast accuracy improvement from including nearby markets is modest. As the forecast horizon increases, however, including nearby prices tends to significantly improve forecasts. We also examine the role played by physical market density in determining the value of incorporating nearby prices into a forecast model.

The mean white dwarf (WD) mass in the Galactic bulge cataclysmic variables (CVs) was measured by applying the the shock temperature-WD mass correlation of magnetic cataclysmic variables (mCVs) to the Galactic Bulge X-ray Emission (GBXE) spectra. However, the resulting mean WD mass is lower than that of the local CVs. This discrepancy could be explained by the dominating sources in the GBXE are non-mCVs instead of mCVs. In this work, we conduct an thorough investigation on the X-ray spectra of local DNe from \suzaku\ archives, and derive semi-empirical correlations between the shock temperature $T_{\rm max}$, the flux ratio of \fec\ to \feb\ lines, and WD mass for quiescent, non-magnetic CVs. By applying these correlations to the GBXE, we derive the average WD mass of CVs in the Galactic bulge to be $0.81\pm 0.07M_\odot$. This value is consistent with previous optical measurements of WD mass in local CVs., Comment: 11 pages, 4 figures, accepted for publication in The Astrophysical Journal

Objectives: Psoriasis vulgaris is a chronic inflammatory and immune-mediated skin disease. 44 metabonomics biomarkers were identified by high-throughput liquid chromatography coupled to mass spectrometry in our previous work, but the roles of metabonomics biomarkers in the pathogenesis of psoriasis is unclear. Methods: The metabonomics biomarker-enzyme network was constructed. The key metabonomics biomarkers and enzymes were screened out by network analysis. The binding affinity between each metabonomics biomarker and target was calculated by molecular docking. A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways. Results: Long-chain fatty acids, phospholipids, Estradiol and NADH were the most important metabonomics biomarkers. Most key enzymes belonged hydrolase, thioesterase and acyltransferase. Six proteins (TNF-alpha, MAPK3, iNOS, eNOS, COX2 and mTOR) were extensively involved in inflammatory reaction, immune response and cell proliferation, and might be drug targets for psoriasis. PI3K-Akt signaling pathway and five other pathways had close correlation with the pathogenesis of psoriasis and could deserve further research. Conclusions: The inflammatory reaction, immune response and cell proliferation are mainly involved in psoriasis. Network pharmacology provide a new insight into the relationships between metabonomics biomarkers and the pathogenesis of psoriasis.KEY MESSAGES • Network pharmacology was adopted to identify key metabonomics biomarkers and enzymes. • Six proteins were screened out as important drug targets for psoriasis. • A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways. • Network pharmacology was adopted to identify key metabonomics biomarkers and enzymes. • Six proteins were screened out as important drug targets for psoriasis. • A binding energy-weighted polypharmacological index was introduced to evaluate the importance of target-related pathways.

蛋白质分子链上接上某种化学基团, 从而改变其执行生命复杂的调控和信息传递的功能, 这一过程称为“蛋白质修饰”. 常见的蛋白质翻译后修饰过程有磷酸化、乙酰化、泛素化、糖基化和甲基化等, 它们使蛋白质的结构更为复杂, 功能更为完善, 作用更为专一, 调控更为精细. 蛋白质修饰在生命体中具有十分重要的作用. 在肿瘤代谢中, 肿瘤细胞即使在氧气充足的情况下仍进行糖酵解, 称为Warburg效应. 目前越来越多的研究表明, 蛋白质翻译后修饰与肿瘤糖代谢密切相关. 本文将就常见的蛋白质修饰方式对肿瘤糖代谢的影响方面的研究进展进行综述.

Estrogen exerts its physiological and pathological functions through two estrogen receptors (ERs), ERα and ERβ, which act as transcription factors. Coregulators, including coactivators and corepressors, have been shown to be crucial for regulation of ER transcriptional activity. Although many coregulators have been identified to regulate activities of ERs, novel coregulators are still needed to be investigated. Here, we show that human methyltransferase-like 17 (METTL17), whose function is unknown, physically interacts with ERα and ERβ, and functionally acts as a coactivator for ERs. METTL17 interacts with ER in vitro and in yeast and mammalian cells. Activation function-1 (AF1) and AF2 domains of ERs are responsible for the interaction between METTL17 and ERs. Knockdown of METTL17 reduces transcriptional activities of ERα and ERβ in breast cancer cells, whereas METTL17 overexpression increases ERα and ERβ transcriptional activities. Inhibition of METTL17 expression decreases mRNA and protein levels of ER target genes, including PR, cathepsin D, and pS2. Moreover, METTL17 knockdown reduces breast cancer cell growth. These results indicate that METTL17 is a novel coactivator of ERs and may play a role in breast tumorigenesis.

Hematopoietic pre-B cell leukemia transcription factor interacting protein (HPIP) has been shown to play an important role in the development and progression of some cancers. However, the role of HPIP in gastric cancer (GC) is unclear. Here, we show that HPIP is upregulated in most GC patients and promotes GC cell proliferation, migration, and invasion. In GC patients, HPIP positively associates with tumor size and nodal metastasis, and negatively associates with tumor differentiation. Hematopoietic pre-B cell leukemia transcription factor interacting protein increases GC cell proliferation through activation of G1 /S and G2 /M cell cycle transitions, accompanied by a marked increase of the positive cell cycle regulators, including cyclin D1, cyclin A, and cyclin B1. Hematopoietic pre-B cell leukemia transcription factor interacting protein enhances GC cell migration and invasion, and modulates epithelial-mesenchymal transition, which plays a key role in cancer cell migration and invasion. These data underscore the critical role of HPIP in GC cell proliferation and progression and suggest that HPIP inhibition may be a useful therapeutic strategy for GC treatment.