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The orientation of deep-seated orebodies was observed shifted from the northwest to north at the Xincheng deposit in the Jiaoxibei gold field during the mining operations. To make informed decisions, understanding the geological factors influencing this directional change is crucial for mining engineers. Prior research suggested that the ore-controlling fault morphology might correlate with the orebody positions and the thickness of alteration zones. Hence, the morphology of fault structures emerges as a key point in this work. First, we constructed a three-dimensional (3D) fault surface model. A large fault framework was interpreted using gravity–magnetic–electrical geophysical datasets, and then, the local details of the fault surfaces were characterized using an interpolation method based on the drillhole fault gouge data. Subsequently, we statistically assessed the spatial association between fault morphology and orebody positioning using the standardized C-values method and demarcated the ore-controlling structures on fault surfaces using quantitative indicators. The indicators of fault dips, fault azimuths, and the second-order trend residuals of fault surfaces represented the fault morphology. The drillhole grade data (Σsampling grade * sampling length) were used to describe the scale of mineralization zones, and the cumulative thickness of the pyrite–sericite altered rocks indicated the intensity of alteration zones. Finally, a geostatistical structural analysis was used to depict the mineralization orientation at different locations. Through these methods, we aim to propose a potential pattern of how ore-controlling fault morphology spatially influences the positions and orientations of orebodies. This comprehensive approach offers valuable insights into exploring deep-seated mineralization at the Xincheng deposit, aiding in a better understanding of the deposit’s geological dynamics for future mining endeavors.

The waters near the Antarctic Peninsula are characterized with unique oceanographic conditions and rich krill resources. Based on samples collected around the South Shetland Islands (SSI) in austral summer of 2019/2020, the net- phytoplankton community structure and relevant major biotic and abiotic influencing factors were investigated. Eighty-one taxa were identified by light microscope, and diatoms were the most abundant group. The most abundant species were Chaetoceros atlanticus, C. criophilus, C. dichaeta, Fragilariopsis kerguelensis and Pseudo−nitzschia lineola. The abundance and Shannon-Weaver index of net-phytoplankton ranged from 100 to 2.64×107 cells/m3 and 0.0747 to 4.0176 respectively, with significantly low values detected in the Bransfield Strait (BS) and high values in the west of the SSI. The dissimilarity was mainly caused by the differences in abundance of diatoms (including Thalassiothrix antarctica and the species in genus Rhizosolenia, Chaetoceros, Fragilariophsis). These diatoms and Dictyocha speculum were found in higher abundance in the west of the SSI, while Corethron pennatum and cryptophytes were found in higher abundance in the BS. Combined with acoustic density of krill and environmental data (Sea Surface Temperature and Sea Ice Concentration). The multivariate analysis suggested that phytoplankton community was positively affected by the SST, and the acoustic- derived krill density would be associated with the spatial distribution of pennate diatoms. This study enhances the knowledge about the selective feeding for krill and provides ecological implications for the Antarctic marine ecosystem.

The pathology of depression involves various factors including the interaction between genes and the environment. The deficiency of n-3 polyunsaturated fatty acids (n-3 PUFAs) in the brain and depressive symptoms are closely related. Krill oil contains abundant amounts of n-3 PUFAs incorporated in phosphatidylcholine. However, the effect of krill oil treatment on depression-like behaviors induced by chronic stress and its molecular mechanism in the brain remain poorly understood. Here, we used a chronic unpredictable mild stress (CUMS) model to evaluate the effect of krill oil on depression-like behaviors and explored its molecular mechanism through lipid metabolomics and mRNA profiles in the whole brain. We observed that CUMS-induced depression-like behaviors were ameliorated by krill oil supplementation in mice. The metabolism of glycerophospholipids and sphingolipids was disrupted by CUMS treatment, which were ameliorated after krill oil supplementation. Further analysis found that differently expressed genes after krill oil supplementation were mainly enriched in the membrane structures and neuroactive ligand–receptor interaction pathway, which may be responsible for the amelioration of CUMS-induced depression-like behaviors. Altogether, our results uncovered the relationship between lipid metabolism and CUMS, and provided new strategies for the prevention and treatment of depression.

With the development of acoustic data processing technology, it is possible to make full use of the “chaotic” acoustic data obtained by fishing vessels. The purpose of this study is to explore a feasible statistical approach to assess the Antarctic krill density rationally and scientifically based on the acoustic data collected during routine fishing operations. The acoustic data used in this work were collected from the surveys conducted by the Chinese krill fishing vessel F/V Fu Rong Hai since the 2015/16 fishing season in the Bransfield Strait. We first processed acoustic data into small units of 0.1 nm, then selected the location of the central fishing ground for grid processing. Because of many zero and low values, we established a Regional Gridding and Extended Delta-distribution (RGED) model to evaluate the acoustic density of the krill. We defined the selection coefficient of grid size by using the coefficient of variation (CV) of the mean density and the weight of the effective covered area of the grids. Through the comparison of selection indexes, cells of 5′S × 10′W were selected as a computational grid and applied to the hotspot in the Bransfield Strait. Acoustic data reveal the distribution of krill density to be spatially heterogeneous. The CV of the mean density for 4 months converges at ~15% for cells of 5′S × 10′W. Simulations estimate krill resource densities in February to be ~1990 m2 nm−2 and to increase to ~8760 m2 nm−2 in May (4.4 times higher). We deem the RGED model to be useful to explore dynamic changes in krill resources in the hotspot. It is not only of great significance for guiding krill fishery, but it also provides krill density data for studying the formation mechanism of the resource hotspots.

The objective of this research is to explore the relationships among various multidimensional factor groups and the density of fishery resources of ecosystems in offshore waters and to expand the application of deep machine learning algorithm in this field. Based on XGBoost and random forest algorithms, we first conducted regulatory importance ranking analysis on the time factor, space factor, acoustic technology factor, abiotic factor, and acoustic density of offshore fishery resources in the South China Sea. Based on these analyses, data slicing is carried out for multiple factors and acoustic density, and the relationship between multidimensional factor group and the density of marine living resources in the ecosystem of offshore waters is elaborately compared and analyzed. Importance ranking shows that the concentration of active silicate at 20 m depth, water depth, moon phase perfection, and the number of pulses per unit distance (Ping) are the first-order factors with a cumulative contribution rate of 50%. The comparative analysis shows that there are some complex relationships between the multidimensional factor group and the density of marine biological resources. Within a certain range, one factor strengthens the influence of another factor. When Si20 is in the range of 0–0.1, and the moon-phase perfection is in the range of 0.3–1, both Si20 and moon-phase perfection strengthened the positive influence of water depth on the density of fishery biological resources.

Zhao, X., Wang, Y., and Dai, F. 2008. Depth-dependent target strength of anchovy (Engraulis japonicus) measured in situ. – ICES Journal of Marine Science, 65: 882–888.Three sets of target strength (TS) data were collected in the southern part of the Yellow Sea using a calibrated, 38 kHz, Simrad EK500 split-beam echosounder. Midwater trawl sampling showed that >97% of the catch by number was anchovy (Engraulis japonicus), with total lengths ranging from 6 to 15 cm, and that the arithmetic-mean length and root-mean-square length were 10.6 and 10.8 cm, respectively. The mean TS of anchovy in the 10–45-m layer was estimated to be –50.9 dB, with a 95% confidence interval of (–51.0, –50.8) dB. The TS data showed, however, a clear depth-dependence that was very close to and not significantly different from what might be expected according to Boyle’s law. The TS model was estimated to be TS = 20 log L − 71.6 for the conventional relationship between TS and length, but TS = 20 log L− (20/3) log (1+z/10) − 67.6 when the depth (z, m) effect was included according to Boyle’s law. These results may have a significant influence on abundance estimates of anchovy derived from acoustic surveys, both in the Yellow Sea and in other parts of the world. [ABSTRACT FROM PUBLISHER]