This paper aims to describe the spatial-temporal variability in catch of the main fishery resources of the Amazon River and floodplain lakes of the Lower Amazon, as well as relating the Catch per Unit of Effort with anomalies of some of the Amazon River, atmosphere and Atlantic Ocean system variables, determining the influence of the environment on the Amazonian fishery resources. Finfish landings data from the towns and villages of the Lower Amazon for the fisheries of three sites (Óbidos, Santarém and Monte Alegre), were obtained for the period between January 1993 and December 2004. Analysis of variance, detrended correspondence analysis, redundancy analysis and multiple regression techniques were used for the statistical analysis of the distinct time series. Fisheries production in the Lower Amazon presents differences between the Amazon River and the floodplain lakes. Production in the Amazon River is approximately half of the one of the floodplain lakes. This variability occurs both along the Lower Amazon River region (longitudinal gradient) and laterally (latitudinal gradient) for every fishing ground studied here. The distinct environmental variables alone or in association act differently on the fishery stocks and the success of catches in each fishery group studied here. Important variables are the flooding events; the soil the sea surface temperatures; the humidity; the wind and the occurence of El Niño-Southern Oscillation events. Fishery productivity presents a large difference in quantity and distribution patterns between the river and floodplain lakes. This variability occurs in the region of the Lower Amazon as well as laterally for each fishery group studied, being dependent on the ecological characteristics and life strategies of each fish group considered here.

This study is about the spatial and temporal variability of the Hypophthalmus catfish fishery in the Amazonian floodplain lakes and the relationship among commercial CPUE, environmental and economic variables. The fishing productivity varies according to the fishing ground which varies due to the contribution of a set of variables. The most outstanding environmental variables are the Amazon River flow, the largescale ENSO and GITA events. This catfish productivity was related to the dynamics of the hydrological cycle, ENSO events and economic factors in Óbidos, mainly with economic variables in Santarém and the dynamics of sea surface temperature, ENSO events and economic factors in Monte Alegre. Regarding this fishing profitability, the main economic factors are the distance to the nearest buyer market location and boat types -ice storage capacity and fuel required. The present study is a contribution to the development of a more sustainable small-scale fishery management policy for Amazon and other floodplain regions around the world. To monitor and deepen understanding of this resource fishing dynamics, we strongly encourage additional studies to offer long-term fishery data set, analyze the fishermen behaviour with changes in the exploitation form and intensity in the floodplain lakes, and address other essential data such as use of floodplain, local community, land and vegetation cover as well as landscape changes.

Human anatomy, morphology, and associated diseases can be studied using medical imaging data. However, access to medical imaging data is restricted by governance and privacy concerns, data ownership, and the cost of acquisition, thus limiting our ability to understand the human body. A possible solution to this issue is the creation of a model able to learn and then generate synthetic images of the human body conditioned on specific characteristics of relevance (e.g., age, sex, and disease status). Deep generative models, in the form of neural networks, have been recently used to create synthetic 2D images of natural scenes. Still, the ability to produce high-resolution 3D volumetric imaging data with correct anatomical morphology has been hampered by data scarcity and algorithmic and computational limitations. This work proposes a generative model that can be scaled to produce anatomically correct, high-resolution, and realistic images of the human brain, with the necessary quality to allow further downstream analyses. The ability to generate a potentially unlimited amount of data not only enables large-scale studies of human anatomy and pathology without jeopardizing patient privacy, but also significantly advances research in the field of anomaly detection, modality synthesis, learning under limited data, and fair and ethical AI. Code and trained models are available at: https://github.com/AmigoLab/SynthAnatomy.

In recent years, the populations of many shark species have been depleted drastically around the world. In the present study, we analyzed the shark bycatch in the monthly landing data of the acoupa weakfish (Cynoscion acoupa) gillnet fisheries of the state of Pará, on the northern coast of Brazil, between January 1995 and December 2007. Based on 4,659 landings, we estimated that a total of 1,972.50 tons of shark were taken as bycatch during the study period. The acoupa weakfish fisheries operate on the Amazon Shelf, an important fishing ground, and we analyzed the shark landings in relation to the Amazon River Discharge anomaly (ARD) and the climatic variability in the Atlantic Ocean. We applied cross-correlation, cross-wavelet, wavelet coherence, and redundancy analysis techniques to the analysis of the data time series. The shark bycatch landings peaked between 1998 and 2000, a period associated with an increase in fishing effort by the acoupa weakfish fisheries, in particular during the dry season of the Amazon basin. The cross-correlation analysis indicated that shark landings were associated with Sea Surface Temperatures (SSTs), the characteristics of the wind, and the Atlantic Multidecadal Oscillation (AMO), while the fishing effort of the acoupa weakfish fisheries was associated with the meridional wind component, the AMO, and the ARD. The cross-wavelet and coherence wavelet analyses indicated that environmental variability was linked systematically with shark landings and acoupa weakfish fishing effort. We observed a phase change in this signal between 1998 and 2000, due to a strong and persistent La Niña event. Despite the resistance from the fishing industry, development and deployment of devices designed to reduce bycatch should be incentivized in order to reduce the unintentional capture of endangered species such as sharks. The findings of the present study highlight the importance of a continuous and accurate fishery database, and the need for continuous fishery statistics to ensure adequate management practices. Adequate public fishery management policies must be implemented urgently to guarantee the survival of shark species, with the effective participation of all the actors involved in the process, including managers, researchers, and fishers.