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Floating bodies are widely used in the field of offshore engineering. Existing studies show that the motion responses of a floating body in waves will change with the internal water in the cabins, and it is essential to analyze its hydrodynamic performance under various potential operating conditions. However, most of the research only considers the interaction between the floating body and the internal water in the upright position, and there has been little research on the inclined floating body caused by water partially filled in the broadside. In this study, a floating body with a plurality of longitudinal and transverse cabins was designed. The regular wave model test was carried out in a wave basin, and the numerical results were compared with the experimental results, which verified the accuracy of the model. The effects of wave direction, wave frequency, water-filling depth, and cabin division on the motion responses of the floating body are analyzed. The results show that the water inside the cabins has a significant impact on the roll motion. With the increase of the water-filling depth, the natural frequency of the roll motion decreases. Special attention should be paid to the impact on the wave direction and cabin division on the partially water-filled inclined floating body. [ABSTRACT FROM AUTHOR]

The unclear mechanism that ischemia–reperfusion injury (IRI) contributes to the development of primary graft dysfunction (PGD) and chronic lung allograft dysfunction (CLAD) remains a major issue in lung transplantation. Differentially expressed PGD-related genes and CLAD-related genes during IRI (IRI-PGD common genes and IRI-CLAD common genes) were identified using GEO datasets (GSE127003, GSE8021, GSE9102) and GeneCards datasets. Enrichment analysis and four network analyses, namely, protein-protein interaction, microRNA (miRNA)-gene, transcription factor (TF)-gene, and drug-gene networks, were then performed. Moreover, GSE161520 was analyzed to identify the differentially expressed core miRNAs during IRI in rats. Finally, Pearson correlation analysis and ROC analysis were performed. Eight IRI-PGD common genes (IL6, TNF, IL1A, IL1B, CSF3, CXCL8, SERPINE1, and PADI4) and 10 IRI-CLAD common genes (IL1A, ICAM1, CCL20, CCL2, IL1B, TNF, PADI4, CXCL8, GZMB, and IL6) were identified. Enrichment analysis showed that both IRI-PGD and IRI-CLAD common genes were significantly enriched in “AGE-RAGE signaling pathway in diabetic complication” and “IL-17 signaling pathway”. Among the core miRNAs, miR-1-3p and miR-335 were differentially expressed in IRI rats. Among core TFs, CEBPB expression had a significant negative correlation with P/F ratio (r = −0.33, P = 0.021). In the reperfused lung allografts, the strongest positive correlation was exhibited between PADI4 expression and neutrophil proportion (r = 0.76, P < 0.001), and the strongest negative correlation was between PADI4 expression and M2 macrophage proportion (r = −0.74, P < 0.001). In lung allografts of PGD recipients, IL6 expression correlated with activated dendritic cells proportion (r = 0.86, P < 0.01), and IL1B expression correlated with the neutrophils proportion(r = 0.84, P < 0.01). In whole blood of CLAD recipients, GZMB expression correlated with activated CD4+ memory T cells proportion (r = 0.76, P < 0.001).Our study provides the novel insights into the molecular mechanisms by which IRI contributes to PGD and CLAD and potential targets for therapeutic intervention. [ABSTRACT FROM AUTHOR]

How to present an intelligent model based on known diagnostic knowledge to assist medical diagnosis and display the reasoning process is an interesting issue worth exploring. This study developed a novel intelligent model for visualized inference of medical diagnosis with a case of Traditional Chinese Medicine (TCM). Four classes of TCM's diagnosis composed of Yin deficiency, Liver Yin deficiency, Kidney Yin deficiency, and Liver-Kidney Yin deficiency were selected as research examples. According to the knowledge of diagnostic points in "Diagnostics of TCM", a total of 2000 samples for training and testing were randomly generated for the four classes of TCM's diagnosis. In addition, a total of 60 clinical samples were collected from hospital clinical cases. Training samples were sent to the pre-training language model of Chinese Bert for training to generate intelligent diagnostic module. Simultaneously, a mathematical algorithm was developed to generate inferential digraphs. In order to evaluate the performance of the model, the values of accuracy, F1 score, Mse, Loss and other indicators were calculated for model training and testing. And the confusion matrices and ROC curves were plotted to estimate the predictive ability of the model. The novel model was also compared with RF and XGBOOST. And some instances of inferential digraphs with the model were displayed and analyzed. It may be a new attempt to solve the problem of interpretable and inferential intelligent models in the field of artificial intelligence on medical diagnosis of TCM. • Developing a novel intelligent model that can show the inference process of medical diagnosis with visualized digraphs. • Developing a novel method to train model with known and priori knowledge instead of clinical samples (cases). • Developing a novel model that can assist in medical diagnosis. [ABSTRACT FROM AUTHOR]

Soil heterotrophic respiration (Rh) represents an important component of the terrestrial carbon cycle that affects whether ecosystems function as carbon sources or sinks. Due to the complex interactions between biological and physical factors controlling microbial growth, Rh is uncertain and difficult to predict, limiting our ability to anticipate future climate trajectories. Here we analyze the global FLUXNET 2015 database aided by a probabilistic model of microbial growth to examine the ecosystem-scale dynamics of Rh and identify primary predictors of its variability. We find that the temporal variability in Rh is consistently distributed according to a Gamma distribution, with shape and scale parameters controlled only by rainfall characteristics and vegetation productivity. This distribution originates from the propagation of fast hydrologic fluctuations on the slower biological dynamics of microbial growth and is independent of biome, soil type, and microbial physiology. This finding allows us to readily provide accurate estimates of the mean Rh and its variance, as confirmed by a comparison with an independent global dataset. Our results suggest that future changes in rainfall regime and net primary productivity will significantly alter the dynamics of Rh and the global carbon budget. In regions that are becoming wetter, Rh may increase faster than net primary productivity, thereby reducing the carbon storage capacity of terrestrial ecosystems. [ABSTRACT FROM AUTHOR]

This study aimed to elucidate the effect of depression on the healing of acute wounds in rats. We hypothesized that depression would have negative effects on inflammation and wound healing and that antidepressant therapy would reverse these effects. This study included 100 rats randomly allocated into five groups: control group (CG), depression group (DG), pre‐depression group (PDG), antidepressant group (AG), and pre‐antidepressant group (PAG). Acute wounds were created on the rats' backs. The groups were subjected to no interventions (CG), aversive stimuli before (PDG) and after (DG) wound creation, and antidepressant treatment before (PAG) and after (AG) wound creation. On the day of wound creation and on days 3, 6, 9, and 12 after wound creation, observations of the wound area and degree of depression (evaluated using the sucrose preference test, open‐field test, and weight change) were recorded. On days 6 and 12 after wound creation, venous serum and wound tissues were collected. Tumor necrosis factor alpha (TNF‐α), interleukin (IL)‐1β, IL‐6, and IL‐10 levels were measured using the enzyme‐linked immunosorbent assay. Results showed an initial increase followed by a decrease in the degree of depression in all groups except DG (continuous decline). The wound‐healing rate was significantly lower in PDG and DG than in CG; it was higher in AG and PAG than in CG. DG and PDG had higher concentrations of inflammatory cytokines than CG, and AG and PAG had lower concentrations than CG. This indicates that the onset of depression delays the healing of acute wounds and aggravates the inflammatory response in rats. Antidepressant treatment counteracts both of these negative effects. [ABSTRACT FROM AUTHOR]

This study assumes that the infiltrated depth of border irrigation follows a normal distribution, and the soil infiltration variability represented by the variability of infiltration coefficient (k) and final infiltration rate (f0) are the Kostiakov-Lewis equation, which can be scaled in the same proportion. Then, a combination of variance method, validated from field-experiment data, is proposed to evaluate the closed-end border irrigation that accounts for soil infiltration variability. The results show that the normalization equation for estimating the infiltrated water depth is reliable. Accuracy of simulated advance and recession trajectories can be improved with soil infiltration variations being considered. The irrigation performance indications estimated using the proposed method are in agreement with the measured values and simulated values under the variable soil. Meanwhile, in terms of infiltration parameters variation, Christiansen's uniformity (CU) is the most significant factor, followed by the application efficiency (Ea) and the storage efficiency (Es), and resulting in 37.01, 15.22, and 15.24% decreases of CU, Ea, and Es when the variation coefficient of the normalization factor (CVF) increased from 0.0 to 0.5. Therefore, the variability of soil infiltration must be considered when evaluating closed-end border irrigation. [ABSTRACT FROM AUTHOR]

Background: The pro-inflammatory cytokine, tumor necrosis factor-α (TNF-α), is expressed in ischemic tissue and is known to modulate angiogenesis; however, the role of the two distinct TNF-α receptors, TNFR1 and TNFR2, in mediating angiogenic signaling after cerebral ischemic stroke is relatively unknown.Methods: C57BL6 mice were subject to 90 min of ischemia by temporary occlusion of the middle cerebral artery (MCAO) and given daily intra-cerebroventricular injections of antibodies against TNFR1, TNFR2 or control IgG (doses of 10, 50, and 100 ng/day) for 4 days following 90 min MCAO. Vascular remodeling and α5β1 and αVβ3 integrin expression were then examined in the brains of these mice after 4, 7, and 14 days post-ischemia. In parallel in vitro studies, flow cytometry was used to determine the influence of TNF-α on proliferation and integrin expression of human brain microvascular endothelial cells (HBMECs).Results: The post-ischemic cerebral angiogenic response was inhibited by antibodies against TNFR1 but not TNFR2, and this correlated with reduced endothelial proliferation and decreased α5β1 and αVβ3 integrin expression after 4 and 7 days post-ischemia. Consistent with these findings, in vitro studies showed that TNF-α induced endothelial proliferation and upregulation of α5β1 and αVβ3 integrins was abrogated by anti-TNFR1 but not anti-TNFR2 antibodies in cultured HBMECs. In addition, blocking antibodies to α5β1 and αVβ3 integrins significantly inhibited TNF-α-induced HBMEC proliferation.Conclusions: Our results suggest that TNFR1-mediated signaling plays a critical role in triggering angiogenic integrins and subsequent angiogenic responses following cerebral ischemia. These novel findings could form a platform for future therapeutic strategies aimed at stimulating angiogenesis following cerebral ischemia. [ABSTRACT FROM AUTHOR]

Background: Recently, structured sparse canonical correlation analysis (SCCA) has received increased attention in brain imaging genetics studies. It can identify bi-multivariate imaging genetic associations as well as select relevant features with desired structure information. These SCCA methods either use the fused lasso regularizer to induce the smoothness between ordered features, or use the signed pairwise difference which is dependent on the estimated sign of sample correlation. Besides, several other structured SCCA models use the group lasso or graph fused lasso to encourage group structure, but they require the structure/group information provided in advance which sometimes is not available. Results: We propose a new structured SCCA model, which employs the graph OSCAR (GOSCAR) regularizer to encourage those highly correlated features to have similar or equal canonical weights. Our GOSCAR based SCCA has two advantages: 1) It does not require to pre-define the sign of the sample correlation, and thus could reduce the estimation bias. 2) It could pull those highly correlated features together no matter whether they are positively or negatively correlated. We evaluate our method using both synthetic data and real data. Using the 191 ROI measurements of amyloid imaging data, and 58 genetic markers within the APOE gene, our method identifies a strong association between APOE SNP rs429358 and the amyloid burden measure in the frontal region. In addition, the estimated canonical weights present a clear pattern which is preferable for further investigation. Conclusions: Our proposed method shows better or comparable performance on the synthetic data in terms of the estimated correlations and canonical loadings. It has successfully identified an important association between an Alzheimer's disease risk SNP rs429358 and the amyloid burden measure in the frontal region. [ABSTRACT FROM AUTHOR]

Motivation: Structured sparse canonical correlation analysis (SCCA) models have been used to identify imaging genetic associations. These models either use group lasso or graph-guided fused lasso to conduct feature selection and feature grouping simultaneously. The group lasso based methods require prior knowledge to define the groups, which limits the capability when prior knowledge is incomplete or unavailable. The graph-guided methods overcome this drawback by using the sample correlation to define the constraint. However, they are sensitive to the sign of the sample correlation, which could introduce undesirable bias if the sign is wrongly estimated. Results: We introduce a novel SCCA model with a new penalty, and develop an efficient optimization algorithm. Our method has a strong upper bound for the grouping effect for both positively and negatively correlated features. We show that our method performs better than or equally to three competing SCCA models on both synthetic and real data. In particular, our method identifies stronger canonical correlations and better canonical loading patterns, showing its promise for revealing interesting imaging genetic associations. [ABSTRACT FROM AUTHOR]

In this paper, we explore the influencing factors as well as inherent mechanism related to the formation of Chinese agricultural brand value. On the basis of empirical survey on Chinese agricultural brand, we establish a scientific and operable evaluation indicator system to measure Chinese agricultural brand value, and this system consists of four parts ( brand quality and safety control force, brand market power, brand innovation force, brand vitality). [ABSTRACT FROM AUTHOR]

Newton methods can be applied in many supervised learning approaches. However, for large-scale data, the use of the whole Hessian matrix can be time-consuming. Recently, subsampled Newton methods have been proposed to reduce the computational time by using only a subset of data for calculating an approximation of the Hessian matrix. Unfortunately, we find that in some situations, the running speed is worse than the standard Newton method because cheaper but less accurate search directions are used. In this work, we propose some novel techniques to improve the existing subsampled Hessian Newton method. The main idea is to solve a two-dimensional subproblem per iteration to adjust the search direction to better minimize the second-order approximation of the function value. We prove the theoretical convergence of the proposed method. Experiments on logistic regression, linear SVM, maximum entropy, and deep networks indicate that our techniques significantly reduce the running time of the subsampled Hessian Newton method. The resulting algorithm becomes a compelling alternative to the standard Newton method for large-scale data classification. [ABSTRACT FROM AUTHOR]

A two-dimensional (2D) simplified model of the very large floating structure (VLFS) is formulated based on Huston's interpretation of the Kane methodology. In this proposed model, the VLFS is considered as a series of discrete floating bodies connected by elastic hinges. The rotation stiffness of elastic hinges has a great influence on the dynamic responses of VLFS and its value is determined based on the vertical displacements equivalent between the simply supported beam model and the elastically hinged multiple bodies model with the same boundary conditions on a concentrated load. Reduced Kane equations are used in the actual dynamic analysis, once initial conditions and mechanical analysis of the system have been formulated. Validation of the Kane-based method and the reliability of the corresponding program developed are established by several comparative studies on a continuous structure and a hinged structure with three parts. The predictions based on the proposed method are essentially identical to the model test data and calculation results provided by related literature. [ABSTRACT FROM AUTHOR]

The equilibrium network signal control problem is represented as a Stackelberg game. Due to the characteristics of a Stackelberg game, solving the upper-level problem and lower-level problem iteratively cannot be expected to converge to the solution. The reaction function of the lower-level problem is the key information to solve a Stackelberg game. Usually, the reaction function is approximated by the network sensitivity information. This paper firstly presents the general form of the second-order sensitivity formula for equilibrium network flows. The second-order sensitivity information can be applied to the second-order reaction function to solve the network signal control problem efficiently. Finally, this paper also demonstrates two numerical examples that show the computation of second-order sensitivity and the speed of convergence of the nonlinear approximation algorithm. [ABSTRACT FROM AUTHOR]

Smad7 is an intracellular inhibitory protein that antagonizes the signaling of TGF-β family members. Deletion of Smad7 in the mouse leads to an abnormality in heart development. However, whether Smad7 has a functional role in the development of other organs has been elusive. Here we present evidence that Smad7 imparts a role to eye development in the mouse. Smad7 is expressed in both the lens and retina in the developing embryonic eye. Depletion of Smad7 caused various degrees of coloboma and microphthalmia with alterations in cell apoptosis and proliferation in eyes. Smad7 was implicated in lens differentiation but was not required for the induction of the lens placode. The development of the periocular mesenchyme was retarded with the down-regulation of Bmp7 and Pitx2 in mutant mice. Retinal spatial patterning was affected by Smad7 deletion and was accompanied by altered bone morphogenetic protein (BMP) signaling. At late gestation stages, TGF-β signaling was up-regulated in the differentiating retina. Smad7 mutant mice displayed an expanded optic disc with increasing of sonic hedgehog (SHH) signaling. Furthermore, loss of Smad7 led to a temporal change in retinal neurogenesis. In conclusion, our study suggests that Smad7 is essential for eye development. In addition, our data indicate that alterations in the signaling of BMP, TGF-β and SHH likely underlie the defects in eye development caused by Smad7 deletion. [ABSTRACT FROM AUTHOR]

The molecular basis of the lymphatic development remains largely unknown. Using zebrafish as a model, we discovered a novel role for the Ras guanine-releasing protein 1 (RasGRP1), a protein involved in Ras activation in lymphangiogenesis. Secondary lymphatic sprouts from the posterior cardinal vein give rise to thoracic duct which is the first lymphatic vessel in zebrafish. Knockdown of rasgrp1 by injecting morpholino in zebrafish embryos impaired formation of thoracic duct accompanied by pericardial and truck edema, whereas blood vessel development of the embryos was largely unaffected. In rasgrp1-knockdown embryos, the number of sprouts producing the string of parachordal lymphangioblast cells was reduced. Meanwhile the total number of the secondary sprouts was not changed. As a result, the number of venous intersegmental vessels was increased, whereas the number of lymphatic vessel was reduced at a later stage. The lymphatic developmental defects caused by rasgrp1 knockdown could be rescued by ectopic expression of a constitutively active HRas. Further analysis revealed that RasGRP1 knockdown could synergize with flt4/vegfr3 knockdown to induce defects in lymphangiogenesis. Taken together, this finding demonstrates a critical role for RasGRP1 in lymphatic development in zebrafish. [ABSTRACT FROM AUTHOR]

The recently described transcriptomic switch to a mitosis program in castration-resistant prostate cancer (CRPC) suggests that mitotic proteins may be rationally targeted at this lethal stage of the disease. In this study, we showed upregulation of the mitosis-phase at the protein level in our cohort of 51 clinical CRPC cases and found centrosomal aberrations to also occur preferentially in CRPC compared with untreated, high Gleason-grade hormone-sensitive prostate cancer (P<0.0001). Expression profiling of chemotherapy-resistant CRPC samples (n = 25) was performed, and the results were compared with data from primary chemotherapy-naïve CRPC (n = 10) and hormone-sensitive prostate cancer cases (n = 108). Our results showed enrichment of mitosis-phase genes and pathways, with progression to both castration-resistant and chemotherapyresistant disease. The mitotic centromere-associated kinesin (MCAK) was identified as a novel mitosis-phase target in prostate cancer that was overexpressed in multiple CRPC gene-expression datasets. We found concordant gene expression of MCAK between our parent and murine CRPC xenograft pairs and increased MCAK protein expression with clinical progression of prostate cancer to a castration-resistant disease stage. Knockdown of MCAK arrested the growth of prostate cancer cells suggesting its utility as a potential therapeutic target. [ABSTRACT FROM AUTHOR]

Although streptavidin's high affinity for biotin has made it a widely used and studied binding protein and labeling tool, its tetrameric structure may interfere with some assays. A streptavidin mutant with a simpler quaternary structure would demonstrate a molecular-level understanding of its structural organization and lead to the development of a novel molecular reagent. However, modulating the tetrameric structure without disrupting biotin binding has been extremely difficult. In this study, we describe the design of a stable monomer that binds biotin both in vitro and in vivo. To this end, we constructed and characterized monomers containing rationally designed mutations. The mutations improved the stability of the monomer (increase in Tm from 31 to 47 °C) as well as its affinity (increase in Kd from 123 to 38 nM). We also used the stability-improved monomer to construct a dimer consisting of two streptavidin subunits that interact across the dimer-dimer interface, which we call the A/D dimer. The biotin binding pocket is conserved between the tetramer and the A/D dimer, and therefore, the dimer is expected to have a significantly higher affinity than the monomer. The affinity of the dimer (Kd = 17 nM) is higher than that of the monomer but is still many orders of magnitude lower than that of the wild-type tetramer, which suggests there are other factors important for high-affinity biotin binding. We show that the engineered streptavidin monomer and dimer can selectively bind biotinylated targets in vivo by labeling the cells displaying biotinylated receptors. Therefore, the designed mutants may be useful in novel applications as well as in future studies in elucidating the role of oligomerization in streptavidin function. [ABSTRACT FROM AUTHOR]

Recently, optical stimulation has begun to unravel the neuronal processing that controls certain animal behaviours. However, optical approaches are limited by the inability of visible light to penetrate deep into tissues. Here, we show an approach based on radio-frequency magnetic-field heating of nanoparticles to remotely activate temperature-sensitive cation channels in cells. Superparamagnetic ferrite nanoparticles were targeted to specific proteins on the plasma membrane of cells expressing TRPV1, and heated by a radio-frequency magnetic field. Using fluorophores as molecular thermometers, we show that the induced temperature increase is highly localized. Thermal activation of the channels triggers action potentials in cultured neurons without observable toxic effects. This approach can be adapted to stimulate other cell types and, moreover, may be used to remotely manipulate other cellular machinery for novel therapeutics. [ABSTRACT FROM AUTHOR]

Background: Array-based comparative genomic hybridization (array CGH) is a highly efficient technique, allowing the simultaneous measurement of genomic DNA copy number at hundreds or thousands of loci and the reliable detection of local one-copy-level variations. Characterization of these DNA copy number changes is important for both the basic understanding of cancer and its diagnosis. In order to develop effective methods to identify aberration regions from array CGH data, many recent research work focus on both smoothing-based and segmentation-based data processing. In this paper, we propose stationary packet wavelet transform based approach to smooth array CGH data. Our purpose is to remove CGH noise in whole frequency while keeping true signal by using bivariate model. Results: In both synthetic and real CGH data, Stationary Wavelet Packet Transform (SWPT) is the best wavelet transform to analyze CGH signal in whole frequency. We also introduce a new bivariate shrinkage model which shows the relationship of CGH noisy coefficients of two scales in SWPT. Before smoothing, the symmetric extension is considered as a preprocessing step to save information at the border. Conclusion: We have designed the SWTP and the SWPT-Bi which are using the stationary wavelet packet transform with the hard thresholding and the new bivariate shrinkage estimator respectively to smooth the array CGH data. We demonstrate the effectiveness of our approach through theoretical and experimental exploration of a set of array CGH data, including both synthetic data and real data. The comparison results show that our method outperforms the previous approaches. [ABSTRACT FROM AUTHOR]

We report organically modified silica nanoparticles co-encapsulating two-photon fluorescent dye aggregates and a red-fluorescent energy acceptor, where the latter is indirectly excited through intraparticle energy transfer following two-photon excitation of the former. In vitro two-photon microscopy has demonstrated that these nanoprobes are avidly internalized into cells with bright acceptor fluorescence and that cellular optical highlighting is effected with red-to-green photoconversion by selective acceptor bleaching, while keeping the donor and the nanostructure of particles intact. [ABSTRACT FROM AUTHOR]

Computer models of the heart have become useful tools in improving understanding of cardiac electrical activation at the cellular level, and in analyzing mechanisms of arrhythmias and antiarrhythmic therapies. In this paper, we propose to use spherical harmonic (SPHARM) surface reconstruction and alignment methods to generate curvilinear meshes for finite element models of ventricular anatomy which incorporate detailed structural information. The endocardial and epicardial surfaces are defined by SPHARM, and the volume between these surfaces is divided into nested shells. Consequently, the resulting mesh comprises hexahedral elements. Using this novel SPHARM-based meshing method, the transmembrane potential propagation is simulated, based on FitzHugh—Nagumo reaction—diffusion equations. The dynamic electrical activation propagations are simulated on realistic cardiac data during a heart cycle. The obtained results clearly demonstrate an accurate resolution of the cardiac potential during the excitation. Our novel curvilinear mesh models have a great potential to be used in an improved simulation of cardiovascular pathologies for testing therapy strategies and planning interventions. [ABSTRACT FROM AUTHOR]

Introduces an idea in layout design that decomposes a layout into a network of 'layout modules.' Unique material-flow pattern of each module; Mathematical formulation of the problem of designing a facility layout as a network of layout modules; Heuristic solution approach for solving the problem.

A two-dimensional model to estimate the hydrodynamic response of hinged multiple floating body systems in the time domain is established based on the Kane method. The reduced Kane equations applicable to the dynamic response of multi-floating body system with hinges are first deduced. The issue of hinge constraint in the system is addressed by defining the corresponding generalized speeds as zeros, while the wave actions are considered based on the potential flow theory. Then the corresponding calculation program is developed prior to undertaking the model test. Verification of the Kane-based model and the veracity of the program developed is performed through a series of contrastive analyses on a hinged floating bridge in various cases including regular waves, moving loads, and their combinations. The predictions obtained by the proposed model show satisfactory agreements with the model test measurements. The related results indicate that the motion responses of the first pontoon are greatest in a hinged floating bridge, and its motion amplitudes descend nonlinearly with the increment of wave frequency. The time-history motion responses of hinged multi-floating bodies in the middle present saddle shapes with some fluctuations as a whole under the combined effect of wave and moving loads. The Kane-based model is convenient to analyze the dynamic characteristics of a hinged multi-floating body system in regular waves, and it could be further extended to consider the effects of irregular waves, inhomogeneous sea conditions, as well as the nonlinear connections on the system. [ABSTRACT FROM AUTHOR]

Suggests that exogenous and endogenous melanocortins acting centrally exert divergent influences on different aspects of the acute phase response of anorexia and hypoactivity in rats. Effects of the hormone on lipopolysaccharide-induced anorexia; Monitoring of food intake, locomotor activity and body temperature of rats.

In the title compound, [Co(C10H7NO4)(H2O)3]·H2O, the CoII atom is N, O, O′-chelated by the dianionic Schiff base ligand and is also coordinated by three aqua ligands, showing a slightly deformed octa­hedral coordination geometry. In the crystal structure, complex mol­ecules are linked together with solvent water mol­ecules via O—H⋯O hydrogen bonds, forming a three-dimensional network. [ABSTRACT FROM AUTHOR]

In the title controsymmetric dimeric copper(II) compound, [Cu2(C10H7NO4)2(H2O)2], the CuII ion is five-coordinated in a square-pyramidal configuration, with one imine N atom, one phenolate O atom, one carboxylate O atom of the Schiff base and one O atom of a coordinated water molecule defining the basal plane, and the phenolate O atom of another Schiff base occupying the apical position. The CuII atoms are bridged by two phenolic O atoms, with a Cu…Cu distance of 3.250 (4) Å. In the crystal structure, the molecules are linked through intermolecular O—H…O hydrogen bonds, forming chains running along the b axis. [ABSTRACT FROM AUTHOR]

Background: Bioimage classification is a fundamental problem for many important biological studies that require accurate cell phenotype recognition, subcellular localization, and histopathological classification. In this paper, we present a new bioimage classification method that can be generally applicable to a wide variety of classification problems. We propose to use a high-dimensional multi-modal descriptor that combines multiple texture features. We also design a novel subcategory discriminant transform (SDT) algorithm to further enhance the discriminative power of descriptors by learning convolution kernels to reduce the within-class variation and increase the between-class difference. Results: We evaluate our method on eight different bioimage classification tasks using the publicly available IICBU 2008 database. Each task comprises a separate dataset, and the collection represents typical subcellular, cellular, and tissue level classification problems. Our method demonstrates improved classification accuracy (0.9 to 9%) on six tasks when compared to state-of-the-art approaches. We also find that SDT outperforms the well-known dimension reduction techniques, with for example 0.2 to 13% improvement over linear discriminant analysis. Conclusions: We present a general bioimage classification method, which comprises a highly descriptive visual feature representation and a learning-based discriminative feature transformation algorithm. Our evaluation on the IICBU 2008 database demonstrates improved performance over the state-of-the-art for six different classification tasks. [ABSTRACT FROM AUTHOR]

The in vivo mechanisms underlying dominant syndromes caused by mutations in SRY-Box Transcription Factor 9 (SOX9) and SOX10 (SOXE) transcription factors, when they either are expressed alone or are coexpressed, are ill-defined. We created a mouse model for the campomelic dysplasia SOX9Y440X mutation, which truncates the transactivation domain but leaves DNA binding and dimerization intact. Here, we find that SOX9Y440X causes deafness via distinct mechanisms in the endolymphatic sac (ES)/duct and cochlea. By contrast, conditional heterozygous SoxS-null mice are normal. During the ES development of Sox9Y440X/+ heterozygotes, Sox10 and genes important for ionic homeostasis are down-regulated, and there is developmental persistence of progenitors, resulting in fewer mature cells. Sox10 heterozygous null mutants also display persistence of ES/duct progenitors. By contrast, SOX10 retains its expression in the early Sox9Y440X/+ mutant cochlea. Later, in the postnatal stria vascularis, dominant interference by SOX9Y440X is implicated in impairing the normal cooperation of SOX9 and SOX10 in repressing the expression of the water channel Aquaporin 3, thereby contributing to endolymphatic hydrops. Our study shows that for a functioning endolymphatic system in the inner ear, SOX9 regulates Sox10, and depending on the cell type and target gene, it works either independently of or cooperatively with SOX10. SOX9Y440X can interfere with the activity of both SOXE factors, exerting effects that can be classified as haploinsufficient/hypomorphic or dominant negative depending on the cell/gene context. This model of disruption of transcription factor partnerships may be applicable to congenital deafness, which affects ~0.3% of newborns, and other syndromic disorders. [ABSTRACT FROM AUTHOR]

Motivation: Peaks are the key information in mass spectrometry (MS) which has been increasingly used to discover diseases-related proteomic patterns. Peak detection is an essential step for MS-based proteomic data analysis. Recently, several peak detection algorithms have been proposed. However, in these algorithms, there are three major deficiencies: (i) because the noise is often removed, the true signal could also be removed; (ii) baseline removal step may get rid of true peaks and create new false peaks; (iii) in peak quantification step, a threshold of signal-to-noise ratio (SNR) is usually used to remove false peaks; however, noise estimations in SNR calculation are often inaccurate in either time or wavelet domain. In this article, we propose new algorithms to solve these problems. First, we use bivariate shrinkage estimator in stationary wavelet domain to avoid removing true peaks in denoising step. Second, without baseline removal, zero-crossing lines in multi-scale of derivative Gaussian wavelets are investigated with mixture of Gaussian to estimate discriminative parameters of peaks. Third, in quantification step, the frequency, SD, height and rank of peaks are used to detect both high and small energy peaks with robustness to noise. Results: We propose a novel Gaussian Derivative Wavelet (GDWavelet) method to more accurately detect true peaks with a lower false discovery rate than existing methods. The proposed GDWavelet method has been performed on the real Surface- Enhanced Laser Desorption/Ionization Time-Of-Flight (SELDI-TOF) spectrum with known polypeptide positions and on two synthetic data with Gaussian and real noise. All experimental results demonstrate that our method outperforms other commonly used methods. The standard receiver operating characteristic (ROC) curves are used to evaluate the experimental results. [ABSTRACT FROM AUTHOR]

On analyzing the results of cell-based assays, we have previously shown that perilla (Perilla frutescens) leaf extract (PLE), a food supplement and orally deliverable traditional Chinese medicine approved by the Taiwan Food and Drug Administration, effectively inhibits SARS-CoV-2 by directly targeting virions. PLE was also found to modulate virusinduced cytokine expression levels. In this study, we explored the anti-SARS-CoV-2 activity of PLE in a hamster model by examining viral loads and virus-induced immunopathology in lung tissues. Experimental animals were intranasally challenged with different SARS-CoV-2 doses. Jugular blood samples and lung tissue specimens were obtained in the acute disease stage (3-4 post-infection days). As expected, SARS-CoV-2 induced lung inflammation and hemorrhagic effusions in the alveoli and perivascular areas; additionally, it increased the expression of several immune markers of lung injury -- including lung Ki67-positive cells, Iba-1-positive macrophages, and myeloperoxidase-positive neutrophils. Virus-induced lung alterations were significantly attenuated by orally administered PLE. In addition, pretreatment of hamsters with PLE significantly reduced viral loads and immune marker expression. A purified active fraction of PLE was found to confer higher antiviral protection. Notably, PLE prevented SARS-CoV-2-induced increase in serum markers of liver and kidney function as well as the decrease in serum high-density lipoprotein and total cholesterol levels in a dose-dependent fashion. Differently from lung pathology, monitoring of serum biomarkers in Syrian hamsters may allow a more humane assessment of the novel drugs with potential anti-SARS-CoV-2 activity. Our results expand prior research by confirming that PLE may exert an in vivo therapeutic activity against SARS-CoV-2 by attenuating viral loads and lung tissue inflammation, which may pave the way for future clinical applications. [ABSTRACT FROM AUTHOR]

Background: Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual processes. However, automated segmentation is challenging due to large intensity inhomogeneities in the cell nuclei and the background. Results: We present a new method for automated progressive localization of cell nuclei using data-adaptive models that can better handle the inhomogeneity problem. We perform localization in a three-stage approach: first identify all interest regions with contrast-enhanced salient region detection, then process the clusters to identify true cell nuclei with probability estimation via feature-distance profiles of reference regions, and finally refine the contours of detected regions with regional contrast-based graphical model. The proposed region-based progressive localization (RPL) method is evaluated on three different datasets, with the first two containing grayscale images, and the third one comprising of color images with cytoplasm in addition to cell nuclei. We demonstrate performance improvement over the state-of-the-art. For example, compared to the second best approach, on the first dataset, our method achieves 2.8 and 3.7 reduction in Hausdorff distance and false negatives; on the second dataset that has larger intensity inhomogeneity, our method achieves 5% increase in Dice coefficient and Rand index; on the third dataset, our method achieves 4% increase in object-level accuracy. Conclusions: To tackle the intensity inhomogeneities in cell nuclei and background, a region-based progressive localization method is proposed for cell nuclei localization in fluorescence microscopy images. The RPL method is demonstrated highly effective on three different public datasets, with on average 3.5% and 7% improvement of region- and contour-based segmentation performance over the state-of-the-art. [ABSTRACT FROM AUTHOR]

In the paper, we propose a class of accelerated zeroth-order and first-order momentum methods for both nonconvex mini-optimization and minimax-optimization. Specifically, we propose a new accelerated zeroth-order momentum (Acc-ZOM) method for black-box mini-optimization where only function values can be obtained. Moreover, we prove that our Acc-ZOM method achieves a lower query complexity of ~O(d3=4ffl3) for finding an -stationary point, which improves the best known result by a factor of O(d1=4) where d denotes the variable dimension. In particular, our Acc-ZOM does not need large batches required in the existing zeroth-order stochastic algorithms. Meanwhile, we propose an accelerated zeroth-order momentum descent ascent (Acc-ZOMDA) method for black-box minimax optimization, where only function values can be obtained. Our Acc-ZOMDA obtains a low query complexity of ~O((d1 + d2)3=4≤4:5 y ffl3) without requiring large batches for finding an ffl-stationary point, where d1 and d2 denote variable dimensions and ≤y is condition number. Moreover, we propose an accelerated first-order momentum descent ascent (Acc-MDA) method for minimax optimization, whose explicit gradients are accessible. Our Acc-MDA achieves a low gradient complexity of ~O(≤4:5 y ffl3) without requiring large batches for finding an ffl-stationary point. In particular, our Acc-MDA can obtain a lower gradient complexity of O (≤2:5 y ffl3) with a batch size O(≤4 y), which improves the best known result by a factor of O(≤1=2 y). Extensive experimental results on black-box adversarial attack to deep neural networks and poisoning attack to logistic regression demonstrate efficiency of our algorithms. [ABSTRACT FROM AUTHOR]

Objective: Longitudinal neuroimaging data have been widely used to predict clinical scores for automatic diagnosis of Alzheimer’s Disease (AD) in recent years. However, incomplete temporal neuroimaging records of the patients pose a major challenge to use these data for accurately diagnosing AD. In this paper, we propose a novel method to learn an enriched representation for imaging biomarkers, which simultaneously captures the information conveyed by both the baseline neuroimaging records of all the participants in a studied cohort and the progressive variations of the available follow-up records of every individual participant. Methods: Taking into account that different participants usually take different numbers of medical records at different time points, we develop a robust learning objective that minimizes the summations of a number of not-squared $\ell _2$ -norm distances, which, though, is difficult to efficiently solve in general. Thus we derive a new efficient iterative algorithm with rigorously proved convergence. Results: We have conducted extensive experiments using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. Clear performance gains have been achieved when we predict different cognitive scores using the enriched biomarker representations learned by our new method. We further observe that the top selected biomarkers by our proposed method are in perfect accordance with the known knowledge in existing clinical AD studies. Conclusion: All these promising experimental results have demonstrated the effectiveness of our new method. Significance: We anticipate that our new method is of interest to biomedical engineering communities beyond AD research and have open-sourced the code of our method online. 1 The code package of this paper have been made publicly available online at https://github.com/lyujian/Improved-Prediction-of-Cognitive-Outcomes [ABSTRACT FROM AUTHOR]

Facility layout and flexible automation are two approaches for reduction of material handling costs and space requirements in a machining facility that have always been implemented independently of each other. This paper describes an integrated approach to compaction of existing machining facilities using machine grouping algorithms and multifunction machining centers, also referred to as Flexible Machining Modules (FMMs). First, we decompose a facility into a network of layout modules to reduce product travel distances and simplify material flow control. Then, subject to design feasibility, we identify those sets of machines in each module that could be replaced by multi-function FMMs that could be linked into a Flexible Machining System (FMS). The proposed approach uses a combination of pattern recognition and graph theory algorithms utilized for facility layout. The paper concludes with a description of a validation study conducted in an aerospace machining jobshop. [ABSTRACT FROM AUTHOR]

Zeroth-order (ZO) optimization has been the key technique for various machine learning applications especially for black-box adversarial attack, where models need to be learned in a gradient-free manner. Although many ZO algorithms have been proposed, the high function query complexities hinder their applications seriously. To address this challenging problem, we propose two stagewise black-box reduction frameworks for ZO algorithms under convex and non-convex settings respectively, which lower down the function query complexities of ZO algorithms. Moreover, our frameworks can directly derive the convergence results of ZO algorithms under convex and non-convex settings without extra analyses, as long as convergence results under strongly convex setting are given. To illustrate the advantages, we further study ZO-SVRG, ZO-SAGA and ZO-Varag under strongly-convex setting and use our frameworks to directly derive the convergence results under convex and non-convex settings. The function query complexities of these algorithms derived by our frameworks are lower than that of their vanilla counterparts without frameworks, or even lower than that of state-of-the-art algorithms. Finally we conduct numerical experiments to illustrate the superiority of our frameworks. [ABSTRACT FROM AUTHOR]

Asynchronous stochastic algorithms with various variance reduction techniques (such as SVRG, S2GD, SAGA and q-SAGA) are popular in solving large scale learning problems. Recently, Reddi et al. (2015) proposed an unified variance reduction framework (i.e., HSAG) to analyze the asynchronous stochastic gradient optimization. However, the HSAG framework cannot incorporate the S2GD technique, the analysis of the HSAG framework is limited to the SVRG and SAGA techniques on the smooth convex optimization. They did not analyze other important various variance techniques (e.g., S2GD and q-SAGA) and other important optimization problems (e.g., convex optimization with non-smooth regularization and non-convex optimization with cardinality constraint). In this paper, we bridge this gap by using an unified q-memorization framework for various variance reduction techniques (including SVRG, S2GD, SAGA, q-SAGA) to analyze asynchronous stochastic algorithms for three important optimization problems. Specifically, based on the q-memorization framework, 1) we propose an asynchronous stochastic gradient hard thresholding algorithm with q-memorization (AsySGHT-qM) for the non-convex optimization with cardinality constraint, and prove that the convergence rate of AsySGHT-qM before reaching the inherent error induced by gradient hard thresholding methods is geometric. 2) We propose an asynchronous stochastic proximal gradient algorithm (AsySPG-qM) for the convex optimization with non-smooth regularization, and prove that AsySPG-qM can achieve a linear convergence rate. 3) We propose an asynchronous stochastic gradient descent algorithm (AsySGD-qM) for the general non-convex optimization problem, and prove that AsySGD-qM can achieve a sublinear convergence rate to stationary points. The experimental results on various large-scale datasets confirm the fast convergence of our AsySGHT-qM, AsySPG-qM and AsySGD-qM through concrete realizations of SVRG and SAGA. [ABSTRACT FROM AUTHOR]

In the polymeric title compound, {[Cu(C10H7NO5)(C5H5N)]·H2O} n, the Cu atom adopts square-based pyramidal coordination involving the tridentate N, O, O′-chelated dianionic ligand, a pyridine N atom and an apical, bridging, carboxyl­ate O atom from an adjacent ligand. This results in helical chains propagating along the b axis of the monoclinic unit cell. [ABSTRACT FROM AUTHOR]

(FePt)1 - x Cux nanoparticles were prepared by the simultaneous polyol reduction of platinum acetylacetonate and copper acetylacetonate, and the thermal decomposition of Fe carbonyl. The addition of copper reduces the transition temperature of disordered fcc structure to ordered L10 by about 50 °C-100 °C. Together with the X-ray powder diffraction (XRD) and X-ray absorption spectroscopy (XAS) results, the structure of FePtCu ternary alloy was verified and the Fe site was replaced by Cu. [ABSTRACT FROM AUTHOR]

Spectral clustering is a key research topic in the field of machine learning and data mining. Most of the existing spectral clustering algorithms are built on gaussian Laplacian matrices, which is sensitive to parameters. We propose a novel parameter-free distance-consistent locally linear embedding. The proposed distance-consistent LLE can promise that edges between closer data points are heavier. We also propose a novel improved spectral clustering via embedded label propagation. Our algorithm is built on two advancements of the state of the art. First is label propagation, which propagates a node's labels to neighboring nodes according to their proximity. We perform standard spectral clustering on original data and assign each cluster with k-nearest data points and then we propagate labels through dense unlabeled data regions. Second is manifold learning, which has been widely used for its capacity to leverage the manifold structure of data points. Extensive experiments on various data sets validate the superiority of the proposed algorithm compared to state-of-theart spectral algorithms. [ABSTRACT FROM AUTHOR]

Purpose: We previously demonstrated that 7 days post-ischemia, angiogenic vessels in the ischemic penumbra show strong upregulation of fibronectin (Fn) and its receptors, α5β1 and αvβ3 integrins. The aim of the current study was to precisely define the time-course of angiogenic responses and glial activation following experimental ischemia in the mouse. Methods: Male C57Bl/6 mice were subject to 90 minutes of ischemia by temporary occlusion of the middle cerebral artery followed by reperfusion. Vascular remodeling and glial activation were then examined in the brains of these mice after 0, 1, 2, 4, 7 and 14 days post-ischemia. Results: Immunofluorescent studies demonstrated that in the ischemic penumbra, blood vessel density increased up to day 14. In contrast, within the ischemic core, vessel density declined, reaching a low point at day 4, but then started to increase. In the penumbra, expression of Fn and the α5 and β3 integrins peaked at day 7, and this coincided exactly with maximal endothelial proliferation. Conclusions: Our results suggest that upregulation of the Fn-α5β1 and αvβ3 integrin axis on blood vessels stimulates BEC proliferation at an early stage of angiogenesis post-ischemia. This could form the basis of novel therapeutic strategies aimed at promoting angiogenesis following cerebral ischemia. [ABSTRACT FROM AUTHOR]

Simultaneous and accurate measurements of whole-plant instantaneous carbon-use efficiency (ICUE) and annual total carbon-use efficiency (TCUE) are difficult to make, especially for trees. One usually estimates ICUE based on the net photosynthetic rate or the assumed proportional relationship between growth efficiency and ICUE. However, thus far, protocols for easily estimating annual TCUE remain problematic. Here, we present a theoretical framework (based on the metabolic scaling theory) to predict whole-plant annual TCUE by directly measuring instantaneous net photosynthetic and respiratory rates. This framework makes four predictions, which were evaluated empirically using seedlings of nine Picea taxa: (i) the flux rates of CO2 and energy will scale isometrically as a function of plant size, (ii) whole-plant net and gross photosynthetic rates and the net primary productivity will scale isometrically with respect to total leaf mass, (iii) these scaling relationships will be independent of ambient temperature and humidity fluctuations (as measured within an experimental chamber) regardless of the instantaneous net photosynthetic rate or dark respiratory rate, or overall growth rate and (iv) TCUE will scale isometrically with respect to instantaneous efficiency of carbon use (i.e., the latter can be used to predict the former) across diverse species. These predictions were experimentally verified. We also found that the ranking of the nine taxa based on net photosynthetic rates differed from ranking based on either ICUE or TCUE. In addition, the absolute values of ICUE and TCUE significantly differed among the nine taxa, with both ICUE and temperature-corrected ICUE being highest for Picea abies and lowest for Picea schrenkiana. Nevertheless, the data are consistent with the predictions of our general theoretical framework, which can be used to access annual carbon-use efficiency of different species at the level of an individual plant based on simple, direct measurements. Moreover, we believe that our approach provides a way to cope with the complexities of different ecosystems, provided that sufficient measurements are taken to calibrate our approach to that of the system being studied. [ABSTRACT FROM AUTHOR]

Motivation: Solenoid proteins are emerging as a protein class with properties intermediate between structured and intrinsically unstructured proteins. Containing repeating structural units, solenoid proteins are expected to share sequence similarities. However, in many cases, the sequence similarities are weak and non-detectable. Moreover, solenoids can be degenerated and widely vary in the number of units. So that it is difficult to detect them. Recently, several solenoid repeats detection methods have been proposed, such as self-alignment of the sequence, spectral analysis and discrete Fourier transform of sequence. Although these methods have shown good performance on certain data sets, they often fail to detect repeats with weak similarities. In this article, we propose a new approach to recognize solenoid repeats and non-solenoid proteins using stationary wavelet packet transform (SWPT). Our method associates with three advantages: (i) naturally representing five main factors of protein structure and properties by wavelet analysis technique; (ii) extracting novel wavelet features that can capture hidden components from solenoid sequence similarities and distinguish them from global proteins; (iii) obtaining statistics features that capture repeating motifs of solenoid proteins. Results: Our method analyzes the characteristics of amino acid sequence in both spectral and temporal domains using SWPT. Both global and local information of proteins are captured by SWPT coefficients. We obtain and integrate wavelet-based features and statistics-based features of amino acid sequence to improve the classification task. Our proposed method is evaluated by comparing to state-of-the-art methods such as HHrepID and REPETITA. The experimental results show that our algorithm consistently outperforms them in areas under ROC curve. At the same false positive rate, the sensitivity of our WAVELET method is higher than other methods. [ABSTRACT FROM AUTHOR]

Dysregulation of the complement system has been linked to pathogenesis of hypertension. However, whether genetic changes of complement factor H (CFH) and its related genes are associated with hypertension is unknown. We genotyped three SNPs in the CFH gene cluster that are closely linked to age-related macular degeneration, namely rs1061170 (Y402H), rs2274700 (A473A) and rs7542235 (CFHR1-3Δ), and tested for their associations with blood pressure and hypertension risk in a population-based cohort including 3,210 unrelated Chinese Hans (50-70 years of age) from Beijing and Shanghai. We found that rs2274700 (A473A) and rs7542235 (CFHR1-3Δ) were both significantly associated with diastolic blood pressure (DBP) (β = 0.632-1.431, P⩽0.038) and systolic blood pressure (SBP) (β= 1.567-4.445, P⩽0.008), and rs2274700 (A473A) was associated with hypertension risk (OR [95%CI]: 1.175 [1.005-1.373], P = 0.048). Notably, the associations of rs2274700 (A473A) with DBP (P = 2.1610-3), SBP (P=8610-5) and hypertension risk (P = 7.9610-3) were significant only in the individuals with low CRP levels (<2.0 mg/l), but not in those with RP levels ⩾2.0 mg/l (P⩾0.0807) (P for interaction ⩽0.0467). However, no significant association between rs1061170 (Y402H) and blood pressure or hypertension risk was observed (P⩾0.259). In conclusion, our results suggest that genetic variations in CFH and its related genes may contribute to hypertension risk in Chinese Hans. [ABSTRACT FROM AUTHOR]

Interleukin 22 (IL-22) is a T-cell secreted cytokine that modulates inflammatory response in nonhematopoietic tissues such as epithelium and liver. The function of IL-22 in adipose tissue is currently unknown. We generated a transgenic mouse model with overexpression of IL-22 specifically in adipose tissue. The IL-22 transgenic mice had no apparent changes in obesity and insulin resistance after feeding with high fat diet (HFD). Unexpectedly, all the IL-22 transgenic mice fed with HFD for four months developed spontaneous tumors in epididymal adipose tissue. Histological analysis indicated that the tumors were well-differentiated liposarcomas with infiltration of inflammatory cells. IL-22 overexpression promotes production of inflammatory cytokines such as IL-1&bgr; and IL-10 and stimulates ERK phosphorylation in adipose tissue. Furthermore, IL-22 treatment in differentiated 3T3-L1 adipocytes could induce IL-1&bgr; and IL-10 expression, together with stimulation of ERK phosphorylation. Taken together, our study not only established a novel mouse model with spontaneous liposarcoma, but also revealed that IL-22 overexpression may collaborate with diet-induced obesity to impact on tumor development in mouse. [ABSTRACT FROM AUTHOR]

PB1-F2 is a viral protein that is encoded by the PB1 gene of influenza A virus by alternative translation. It varies in length and sequence context among different strains. The present study examines the functions of PB1-F2 proteins derived from various human and avian viruses. While H1N1 PB1-F2 was found to target mitochondria and enhance apoptosis, H5N1 PB1-F2, surprisingly, did not localize specifically to mitochondria and displayed no ability to enhance apoptosis. Introducing Leu into positions 69 (Q69L) and 75 (H75L) in the C terminus of H5N1 PB1-F2 drove 40.7% of the protein to localize to mitochondria compared with the level of mitochondrial localization of wild-type H5N1 PB1-F2, suggesting that a Leu-rich sequence in the C terminus is important for targeting of mitochondria. However, H5N1 PB1-F2 contributes to viral RNP activity, which is responsible for viral RNA replication. Lastly, although the swine-origin influenza virus (S-OIV) contained a truncated form of PB1-F2 (12 amino acids [aa]), potential mutation in the future may enable it to contain a full-length product. Therefore, the functions of this putative S-OIV PB1-F2 (87 aa) were also investigated. Although this PB1-F2 from the mutated S-OIV shares only 54% amino acid sequence identity with that of seasonal H1N1 virus, it also increased viral RNP activity. The plaque size and growth curve of the viruses with and without S-OIV PB1-F2 differed greatly. The PB1-F2 protein has various lengths, amino acid sequences, cellular localizations, and functions in different strains, which result in strain-specific pathogenicity. Such genetic and functional diversities make it flexible and adaptable in maintaining the optimal replication efficiency and virulence for various strains of influenza A virus. [ABSTRACT FROM AUTHOR]

With the recent commissioning of a gas-filled recoil separator at Institute of Modern Physics (IMP) in Lanzhou, the decay properties of 271Ds (Z = 110) were studied via the 208Pb(64Ni, n) reaction at a beam energy of 313.3MeV. Based on the separator coupled with a position sensitive silicon strip detector, we carried out the energy-position-time correlation measurements for the implanted nucleus and its subsequent decay ??'s. One ??- decay chain for 271Ds was established. The α energy and decay time of the 271Ds nucleus were measured to be 10.644MeV and 96.8 ms, which are consistent with the values reported in the literature. [ABSTRACT FROM AUTHOR]

Grating-based x-ray phase contrast imaging has attracted increasing interest in recent decades as multimodal and laboratory source usable method. Specific efforts have been focused on establishing a new extraction method to perform practical applications. In this work, noise properties of multi-combination information of newly established information extraction method, so-called angular signal radiography method, are investigated to provide guidelines for targeted and specific applications. The results show that how multi-combination of images can be used in targeted practical applications to obtain a high-quality image in terms of signal-to-noise ratio. Our conclusions can also hold true for upcoming targeted practical applications such as biomedical imaging, non-destructive imaging, and materials science. [ABSTRACT FROM AUTHOR]

In the title complex, [Ni(C10H7NO4)(H2O)3]·H2O, a mononuclear nickel(II) compound, the NiII atom is six-coordinated in an octahedral geometry by the imine N, the phenolate O and a carboxyl O atom from the tridentate Schiff base ligand, and by three O atoms from three coordinated water molecules. In the crystal structure, the molecules are linked through intermolecular O—H…O hydrogen bonds, forming a three-dimensional framework. [ABSTRACT FROM AUTHOR]