Distribution and off–shelf transport of dissolved manganese in the East China Sea

To gain a better understanding of the geochemical behavior of dissolved manganese (Mn) in the marginal seas with respect to distribution and exchange fluxes, more than 200 water samples were collected in the East China Sea (ECS) in May, August, and October of 2013. The concentration of dissolved Mn in the ECS ranged from 1.1 to 81.5 nM, with a gradual decrease with distance from the shore. Seasonal distribution of dissolved Mn varies significantly in the Changjiang estuary, mainly regulated by freshwater input from the Changjiang (Yangtze River) and redox variations. The ECS continental shelf is an important source of Mn for adjacent waters, and the export of Mn–rich coastal waters had an important effect on its re-distribution and internal cycling. The dynamic variation fluxes of water and dissolved Mn across the 100– and 200–m isobaths in the ECS were calculated with an aid of the Finite−Volume Coastal Ocean Model (FVCOM). The ECS continental shelf exported (5.69 ± 1.14) × 108 mol/yr of Mn into the East/Japan Sea from the Tsushima Strait. The Kuroshio surface waters receive an additional (1.02 ± 3.12) × 108 mol/yr of Mn from the ECS continental shelf through a cross–shelf exchange process, which could potentially affect dissolved Mn in the Northwest Pacific. Our data suggest that off-shelf transport from the ECS continental shelf is essential for understanding the biogeochemical cycles of trace metals in the Northwest Pacific Ocean and the East/Japan Sea

The Anti-Sigma Factor MucA of Pseudomonas aeruginosa: Dramatic Differences of a mucA22 vs. a ΔmucA Mutant in Anaerobic Acidified Nitrite Sensitivity of Planktonic and Biofilm Bacteria in vitro and During Chronic Murine Lung Infection

Mucoid mucA22 Pseudomonas aeruginosa (PA) is an opportunistic lung pathogen of cystic fibrosis (CF) and chronic obstructive pulmonary disease (COPD) patients that is highly sensitive to acidified nitrite (A-NO2-). In this study, we first screened PA mutant strains for sensitivity or resistance to 20 mM A-NO2- under anaerobic conditions that represent the chronic stages of the aforementioned diseases. Mutants found to be sensitive to A-NO2- included PA0964 (pmpR, PQS biosynthesis), PA4455 (probable ABC transporter permease), katA (major catalase, KatA) and rhlR (quorum sensing regulator). In contrast, mutants lacking PA0450 (a putative phosphate transporter) and PA1505 (moaA2) were A-NO2- resistant. However, we were puzzled when we discovered that mucA22 mutant bacteria, a frequently isolated mucA allele in CF and to a lesser extent COPD, were more sensitive to A-NO2- than a truncated ΔmucA deletion (Δ157–194) mutant in planktonic and biofilm culture, as well as during a chronic murine lung infection. Subsequent transcriptional profiling of anaerobic, A-NO2--treated bacteria revealed restoration of near wild-type transcript levels of protective NO2- and nitric oxide (NO) reductase (nirS and norCB, respectively) in the ΔmucA mutant in contrast to extremely low levels in the A-NO2--sensitive mucA22 mutant. Proteins that were S-nitrosylated by NO derived from A-NO2- reduction in the sensitive mucA22 strain were those involved in anaerobic respiration (NirQ, NirS), pyruvate fermentation (UspK), global gene regulation (Vfr), the TCA cycle (succinate dehydrogenase, SdhB) and several double mutants were even more sensitive to A-NO2-. Bioinformatic-based data point to future studies designed to elucidate potential cellular binding partners for MucA and MucA22. Given that A-NO2- is a potentially viable treatment strategy to combat PA and other infections, this study offers novel developments as to how clinicians might better treat problematic PA infections in COPD and CF airway diseases

A fast framework construction and visualization method for particle-based fluid

© 2017, The Author(s). Fast and vivid fluid simulation and visualization is a challenge topic of study in recent years. Particle-based simulation method has been widely used in the art animation modeling and multimedia field. However, the requirements of huge numerical calculation and high quality of visualization usually result in a poor computing efficiency. In this work, in order to improve those issues, we present a fast framework for 3D fluid fast constructing and visualization which parallelizes the fluid algorithm based on the GPU computing framework and designs a direct surface visualization method for particle-based fluid data such as WCSPH, IISPH, and PCISPH. Considering on conventional polygonization or adaptive mesh methods may incur high computing costs and detail losses, an improved particle-based method is provided for real-time fluid surface rendering with the screen-space technology and the utilities of the modern graphics hardware to achieve the high performance rendering; meanwhile, it effectively protects fluid details. Furthermore, to realize the fast construction of scenes, an optimized design of parallel framework and interface is also discussed in our paper. Our method is convenient to enforce, and the results demonstrate a significant improvement in the performance and efficiency by being compared with several examples

Uncertainty-based Traffic Accident Anticipation with Spatio-Temporal Relational Learning

Traffic accident anticipation aims to predict accidents from dashcam videos as early as possible, which is critical to safety-guaranteed self-driving systems. With cluttered traffic scenes and limited visual cues, it is of great challenge to predict how long there will be an accident from early observed frames. Most existing approaches are developed to learn features of accident-relevant agents for accident anticipation, while ignoring the features of their spatial and temporal relations. Besides, current deterministic deep neural networks could be overconfident in false predictions, leading to high risk of traffic accidents caused by self-driving systems. In this paper, we propose an uncertainty-based accident anticipation model with spatio-temporal relational learning. It sequentially predicts the probability of traffic accident occurrence with dashcam videos. Specifically, we propose to take advantage of graph convolution and recurrent networks for relational feature learning, and leverage Bayesian neural networks to address the intrinsic variability of latent relational representations. The derived uncertainty-based ranking loss is found to significantly boost model performance by improving the quality of relational features. In addition, we collect a new Car Crash Dataset (CCD) for traffic accident anticipation which contains environmental attributes and accident reasons annotations. Experimental results on both public and the newly-compiled datasets show state-of-the-art performance of our model. Our code and CCD dataset are available at https://github.com/Cogito2012/UString.Comment: Accepted by ACM MM 202

Research status and prospect of plate elements in absolute nodal coordinate formulation

The Absolute Nodal Coordinate Formulation (ANCF) is a milestone in the study of flexible multibody dynamics and is of great significance for the study of the dynamics of multi-flexible systems, of which the plate element is an important part. In this article, the construction and principles of this type of element are systematically traced, the types of elements that have been studied are summarized, and the research history of the element locking problem and extended applications in different fields are briefly described. Through the systematic summary, the shortcomings in the current research and application of the element are identified, and some suggestions for future theoretical research on the plate element are given. The functional expansion of the plate element under the conditions of constraints, materials and physical fields as well as practical engineering applications are discussed

A Large-Scale Multibody Manipulator Soft Sensor Model and Experiment Validation

Stress signal is difficult to obtain in the health monitoring of multibody manipulator. In order to solve this problem, a soft sensor method is presented. In the method, stress signal is considered as dominant variable and angle signal is regarded as auxiliary variable. By establishing the mathematical relationship between them, a soft sensor model is proposed. In the model, the stress information can be deduced by angle information which can be easily measured for such structures by experiments. Finally, test of ground and wall working conditions is done on a multibody manipulator test rig. The results show that the stress calculated by the proposed method is closed to the test one. Thus, the stress signal is easier to get than the traditional method. All of these prove that the model is correct and the method is feasible

Effect of L-thyroxine treatment versus a placebo on serum lipid levels in patients with sub-clinical hypothyroidism

U radu je obrađena tema „Traktorski priključak za strojno posipanje soli“. Tema je obrađena od ideje do tehničke dokumentacije konstrukcijskog rješenja zadanog problema. U sklopu rada prvo je proveden uvid u zakonsku regulativu, odnosno smjernice za konstrukcijski razvoj opreme za zimsko održavanje prometnica, nakon čega slijedi analiza tržišta već postojećih rješenja. S obzirom da je ovaj proizvod prvenstveno namijenjen održavanju površina u nadležnosti samooformljenih zimskih službi pojedinih gospodarstvenih subjekata (tvornica, pojedinih ustanova, robnih terminala, lokaliteta ugostiteljsko-turističke i sportsko-rekreacijske namjene, poljoprivrednih gospodarstva i sl.), analizom tržišta uočene se mogućnosti pojednostavljenja složene i skupe profesionalne opreme za zimsko održavanje prometnica. U fazi generiranja koncepata usvojena su pojedina pojednostavljenja, a vrednovanjem koncepata odabran je koncept na kojem je provedena detaljna konstrukcijska razrada s provedenim odgovarajućim proračunima prema važećim normama. Rezultat konstrukcijske razrade je oblikovan 3D model s odgovarajućom tehničkom dokumentacijom u 3D CAD (Solidworks) softveru

Analysis of the Invasion of Acetes into the Water Intake of the Daya Bay Nuclear Power Base

The invasions of marine organisms into the intake of nuclear power plants threaten the normal operation of such plants. Most published numerical models assumed that marine organisms passively follow the current, but such models neglected their biological swimming ability. In this work, adopting a hydrodynamic mathematical model to replicate the flow around the Daya Bay nuclear power base, the invasion characteristics of Acetes were explored by considering the behavior of biological movement. A concept of biological residual current was introduced to describe biological movements that were dominated by both tidal current and biological swimming ability. The biological residual currents near the nuclear power plant were obtained for cases with different nocturnal migration periods (12 h, 13 h, 14 h, 15 h, and 16 h). Using the Lagrangian particle-tracking method, the primary invasion paths of Acetes were obtained, as well as the travel time of Acetes to the intake, based on the biological residual current along monitoring points. The results showed that the invading time for Acetes reaching the water intake of the nuclear power base was significantly decreased when biological migration behavior was considered. When the nocturnal active period was over 13 h, it took only 10 days for Acetes to enter the western waters of Daya Bay from the southwest of Da Lajia Island and then continue migrating to the water intake in the nuclear power base. When the nocturnal active period was less than 13 h, it took more than 20 days for Acetes to travel the same distance. The present work provides a new methodology for the simulation and prediction of the migration of marine organisms

Analysis of the Invasion of Acetes into the Water Intake of the Daya Bay Nuclear Power Base

The invasions of marine organisms into the intake of nuclear power plants threaten the normal operation of such plants. Most published numerical models assumed that marine organisms passively follow the current, but such models neglected their biological swimming ability. In this work, adopting a hydrodynamic mathematical model to replicate the flow around the Daya Bay nuclear power base, the invasion characteristics of Acetes were explored by considering the behavior of biological movement. A concept of biological residual current was introduced to describe biological movements that were dominated by both tidal current and biological swimming ability. The biological residual currents near the nuclear power plant were obtained for cases with different nocturnal migration periods (12 h, 13 h, 14 h, 15 h, and 16 h). Using the Lagrangian particle-tracking method, the primary invasion paths of Acetes were obtained, as well as the travel time of Acetes to the intake, based on the biological residual current along monitoring points. The results showed that the invading time for Acetes reaching the water intake of the nuclear power base was significantly decreased when biological migration behavior was considered. When the nocturnal active period was over 13 h, it took only 10 days for Acetes to enter the western waters of Daya Bay from the southwest of Da Lajia Island and then continue migrating to the water intake in the nuclear power base. When the nocturnal active period was less than 13 h, it took more than 20 days for Acetes to travel the same distance. The present work provides a new methodology for the simulation and prediction of the migration of marine organisms