Karadaly6110

The distribution of aliphatic hydrocarbons in three sediment cores from Brunei Bay was investigated in order to understand their sources and the biogeochemical processes of these hydrocarbons. The total concentrations of C15 to C37n-alkanes ranged from 0.70 to 16.5 μg g-1. Traces of hopanes with C29-C31 carbon homologs were detected in the study area. The carbon preference index (CPI15-37) ranged from 1.23 to 3.42 coupled with the natural n-alkane ratio (NAR19-32) ratios (1.52 to 5.34), and the presence of unresolved complex mixtures and hopanes, suggested slight contamination by anthropogenic hydrocarbons, presumably derived from activities along the coasts. The presence of C27 trisnorhopene and diploptene, as well as their association with long-chain and short-chain n-alkanes, revealed a depositional environment of organic matter in the sediment cores.Despite a progressive reduction of oil spills caused by the activity of maritime transportation, the latent sources of pollution still exist. Although the harmful impact of heavy fuel oil (HFO) on the marine environment is widely known, many shipwrecks cause contamination of the surrounding areas. In this paper, an approach to monitor the area of the HFO spill around a shipwreck is made using a bottom backscattering strength (BBS) obtained by a multibeam echosounder (MBES). As a case study, the s/s Stuttgart wreck located in the Gulf of Gdansk (Poland) is verified. Two different measurement campaigns have been carried out in shallow waters using low (190 kHz) and high (420 kHz) MBES frequency. The results indicate that the polluted area around s/s Stuttgart was estimated at 49.1 ha, which is around 18.3% more in comparison to the geological surveys made four years earlier.A unique feature of seagrass among other ecosystem services is to have high phytoremediation potential that is a cost-effective plant-based approach and environmentally friendly solution for metal contamination in coastal areas. The goal of this study was to assess the phytoremediation prospective of seagrass for Cu, Fe, Mn and Zn in Fiji Islands. Heavy metal content was measured in sediments and tissues of the seagrasses Halophila ovalis, Halodule pinifolia and Halodule uninervis to test for local-scale differences. The local study shows that metal concentration in sediment and seagrass tissue was significantly variable, regardless of species and sediment type. Sedimentary concentration of Cu, Fe, Mn and Zn obtained in the present study seemed to be lower than that of previous studies. The results support that H. ovalis is a good bioindicator species since it accumulated up to 5-fold more of these metals compared to the Halodule species.Surgical workflow recognition is a fundamental task in computer-assisted surgery and a key component of various applications in operating rooms. Existing deep learning models have achieved promising results for surgical workflow recognition, heavily relying on a large amount of annotated videos. However, obtaining annotation is time-consuming and requires the domain knowledge of surgeons. In this paper, we propose a novel two-stage Semi-Supervised Learning method for label-efficient Surgical workflow recognition, named as SurgSSL. Our proposed SurgSSL progressively leverages the inherent knowledge held in the unlabeled data to a larger extent from implicit unlabeled data excavation via motion knowledge excavation, to explicit unlabeled data excavation via pre-knowledge pseudo labeling. Specifically, we first propose a novel intra-sequence Visual and Temporal Dynamic Consistency (VTDC) scheme for implicit excavation. It enforces prediction consistency of the same data under perturbations in both spatial and temporal spaces, encouraging model to capture rich motion knowledge. We further perform explicit excavation by optimizing the model towards our pre-knowledge pseudo label. It is naturally generated by the VTDC regularized model with prior knowledge of unlabeled data encoded, and demonstrates superior reliability for model supervision compared with the label generated by existing methods. We extensively evaluate our method on two public surgical datasets of Cholec80 and M2CAI challenge dataset. Our method surpasses the state-of-the-art semi-supervised methods by a large margin, e.g., improving 10.5% Accuracy under the severest annotation regime of M2CAI dataset. Using only 50% labeled videos on Cholec80, our approach achieves competitive performance compared with full-data training method.White matter hyperintensities (WMHs) have been associated with various cerebrovascular and neurodegenerative diseases. Reliable quantification of WMHs is essential for understanding their clinical impact in normal and pathological populations. Automated segmentation of WMHs is highly challenging due to heterogeneity in WMH characteristics between deep and periventricular white matter, presence of artefacts and differences in the pathology and demographics of populations. In this work, we propose an ensemble triplanar network that combines the predictions from three different planes of brain MR images to provide an accurate WMH segmentation. In the loss functions the network uses anatomical information regarding WMH spatial distribution in loss functions, to improve the efficiency of segmentation and to overcome the contrast variations between deep and periventricular WMHs. We evaluated our method on 5 datasets, of which 3 are part of a publicly available dataset (training data for MICCAI WMH Segmentation Challenge 2017 - MWSC 2017) consisting of subjects from three different cohorts, and we also submitted our method to MWSC 2017 to be evaluated on the unseen test datasets. On evaluating our method separately in deep and periventricular regions, we observed robust and comparable performance in both regions. Our method performed better than most of the existing methods, including FSL BIANCA, and on par with the top ranking deep learning methods of MWSC 2017.Uranium (U) pollution is an environmental hazard caused by the development of the nuclear industry. GX15-070 Microbial reduction of hexavalent uranium (U(VI)) to tetravalent uranium (U(IV)) reduces U solubility and mobility and has been proposed as an effective method to remediate uranium contamination. In this review, U(VI) remediation with respect to U(VI)-reducing bacteria, mechanisms, influencing factors, products, and reoxidation are systematically summarized. Reportedly, some metal- and sulfate-reducing bacteria possess excellent U(VI) reduction capability through mechanisms involving c-type cytochromes, extracellular pili, electron shuttle, or thioredoxin reduction. In situ remediation has been demonstrated as an ideal strategy for large-scale degradation of uranium contaminants than ex situ. However, U(VI) reduction efficiency can be affected by various factors, including pH, temperature, bicarbonate, electron donors, and coexisting metal ions. Furthermore, it is noteworthy that the reduction products could be reoxidized when exposed to oxygen and nitrate, inevitably compromising the remediation effects, especially for non-crystalline U(IV) with weak stability.Rainwater chemistry of extreme rain events is not well characterized. This is despite an increasing trend in intensity and frequency of extreme events and the potential excess loading of elements to ecosystems that can rival annual loading. Thus, an assessment of the loading imposed by hurricane/tropical storm (H/TS) can be valuable for future resiliency strategies. Here the chemical characteristics of H/TS and normal rain (NR) in the US from 2008 to 2019 were determined from available National Atmospheric Deposition Program (NADP) data by correlating NOAA storm tracks with NADP rain collection locations. It found the average pH of H/TS (5.37) was slightly higher (p less then 0.05) than that of NR (5.12). On average, H/TS events deposited 14% of rain volume during hurricane season (May to October) at affected collection sites with a maximum contribution reaching 47%. H/TS events contributed a mean of 12% of Ca2+, 22% of Mg2+, 18% of K+, 25% of Na+, 7% of NH4+, 6% of NO3-, 25% of Cl- and 11% of SO42- during hurricane season with max loading of 77%, 62%, 94%, 65%, 39%, 34%, 64% and 60%, respectively, which can lead to ecosystems exceeding ion-specific critical loads. Four potential sources (i.e., marine, soil dust, agriculture and industry/fossil fuel) were indicated by PCA. The positive matrix factorization (PMF) suggested Mg2+, Na+ and Cl- were primarily marine-originated in both event types, while 36% more sea-salt Ca2+ and 33% more sea-salt SO42- were deposited during H/TS. Agriculture and industry/fossil fuel were the main sources of NH4+ and NO3-, respectively, in both rain event types. However the NH4+ contribution from industry/fossil fuel increased by 13% during H/TS indicating a potential vehicle source associated with emergency evacuations. This work provides a comprehensive assessment of the rainwater chemistry of H/TS and insight to expected ecosystem loading for future extreme events.Following decades of riparian buffer zone (RBZ) studies there remains a need to look across individual site data for collective evidence on the site-specific pollution mitigation and river water quality. We explored primary study evidence on runoff, sediment, P, N, coliforms and pesticides using complimentary styles of metadata interpretation. A quantitative assessment of pollution retention (75 studies, 474 data rows) derived relationships for retention versus width, including significant covariates of clay particle size and buffer slope for sediment, total and dissolved P. Total N and coliforms related to texture and slope but were independent of width. Other factors across pollutants were inconsistently reported. With limitations on quantitative studies a second approach examined factor significance (formal testing versus inferred; 93 studies) across source pressure, transport/physical, vegetation and soil biogeochemical factors on pollution effectiveness. The RBZ evidence showed considerable disagreement ement to extend timeframes, wider study of belowground (soil biogeochemical and transport) processes and studies should document site contexts across source pressures, riparian hydrological, soil and vegetation factors.Sepiolite is an efficient mineral for the immobilization of Cd in contaminated soils. Here, we conducted a 3-year field experiment to investigate the effect of sepiolite on soil aggregation and porosity, Cd availability, and organic carbon content in the bulk and aggregate soils and Cd accumulation by leafy vegetables. The sepiolite-treated soils showed a 15.4%-53.4% and 5.5%-63.0% reduction in available Cd content in the bulk soil and different particle-size aggregates, respectively. Moreover, the Cd concentrations in the edible parts of Brassica campestris, Lactuca sativa L., and Lactuca sativa var. ramosa Hort. decreased by 5.9%-26.2%, 22.8%-30.1%, and 14.4%-19.1%, respectively, compared with those of the control groups. Treatments with 0.5%-1.5% sepiolite resulted in a significant increase (P less then 0.05) in the proportion of 0.25-5.0 mm aggregates, and the increase in the mean weight diameter and geometric mean weight of the soil aggregates indicated that sepiolite treatments enhanced soil aggregate stability.