Effect of Early Life Exposure of Polystyrene Microplastics on Behavior and DNA Methylation in Later Life Stage of Zebrafish

Microplastic contamination has received increasing attention in recent years, and concern regarding the toxicity of microplastics to the environment and humans has increased. In this study, we investigated the neurodevelopmental toxicity of polystyrene microplastics (PSMPs) in the zebrafish Danio rerio under different exposure scenario. We investigated the effect of early life exposure to PSMPs on responses later in life, under different exposure scenarios. Zebrafish were exposed to PSMPs during embrionic stage, then allowed the fish to recover. The neurodevelopmental toxic responses were investigated using fish behavior and behavior-related gene expression. Early life exposure to PSMPs did not alter fish behavior at the early stage, however, it led to hyperactivity later life-stage. Generally, oxidative stress (i.e. sod2 and nrf2a) and nervous system (i.e. slc6a4b, npy and nrbf2)-related gene expression increased in all PSMPs-exposed fish. DNA hypomethylation was observed in fish challenged for a second time using the same PSMPs. Taken together, the current results imply that PSMPs have neurodevelopmental toxic potential when introduced early in life.

Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields

Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide “standardized” feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.

Early line and hook fishing at the Epipaleolithic site of Jordan River Dureijat (Northern Israel)

Nineteen broken and complete bone fish hooks and six grooved stones recovered from the Epipaleolithic site of Jordan River Dureijat in the Hula Valley of Israel represent the largest collection of fishing technology from the Epipaleolithic and Paleolithic periods. Although Jordan River Dureijat was occupied throughout the Epipaleolithic (~20–10 kya the fish hooks appear only at the later stage of this period (15,000–12,000 cal BP). This paper presents a multidimensional study of the hooks, grooved stones, site context, and the fish assemblage from macro and micro perspectives following technological, use wear, residue and zooarchaeological approaches. The study of the fish hooks reveals significant variability in hook size, shape and feature type and provides the first evidence that several landmark innovations in fishing technology were already in use at this early date. These include inner and outer barbs, a variety of line attachment techniques including knobs, grooves and adhesives and some of the earliest evidence for artificial lures. Wear on the grooved stones is consistent with their use as sinkers while plant fibers recovered from the grooves of one hook shank and one stone suggest the use of fishing line. This together with associations between the grooved stones and hooks in the same archaeological layers, suggests the emergence of a sophisticated line and hook technology. The complexity of this technology is highlighted by the multiple steps required to manufacture each component and combine them into an integrated system. The appearance of such technology in the Levantine Epipaleolithic record reflects a deep knowledge of fish behavior and ecology. This coincides with significant larger-scale patterns in subsistence evolution, namely broad spectrum foraging, which is an important first signal of the beginning of the transition to agriculture in this region.

Automatic Recognition of Fish Behavior with a Fusion of RGB and Optical Flow Data Based on Deep Learning

The rapid and precise recognition of fish behavior is critical in perceiving health and welfare by allowing farmers to make informed management decisions on recirculating aquaculture systems while reducing labor. The conventional recognition methods are to obtain movement information by implanting sensors on the skin or in the body of the fish, which can affect the normal behavior and welfare of the fish. We present a novel nondestructive method with spatiotemporal and motion information based on deep learning for real-time recognition of fish schools’ behavior. In this work, a dual-stream 3D convolutional neural network (DSC3D) was proposed for the recognition of five behavior states of fish schools, including feeding, hypoxia, hypothermia, frightening and normal behavior. This DSC3D combines spatiotemporal features and motion features by using FlowNet2 and 3D convolutional neural networks and shows significant results suitable for industrial applications in automatic monitoring of fish behavior, with an average accuracy rate of 95.79%. The model evaluation results on the test dataset further demonstrated that our proposed method could be used as an effective tool for the intelligent perception of fish health status.

DISTRIBUSI CAHAYA LAMPU DAN TINGKAH LAKU IKAN PADA PROSES PENANGKAPAN BAGAN PERAHU DI PERAIRAN MALUKU TENGAH

The objective of this study was to khow the distribution underwater light intensity and fish behavior in fishing process of boat bagan. The data were collected through observation to fishing process, measurement of under water light intensity using marine under water lux meter, fish behavior using fish finder based on hauling time before mid night (19:00-00:00) and after mid night (01:00-05:00. The data analyzed comprise catching process, distribution of under water light intensity,and fish behavior. The results of the study indicate that illumination of light detected under water is 21 m vertically and 12 m horizontally with the value of each illumination is 0.1 lux and 3.0 lux from the center of light. The choice of illumination zone by the fish before mid night is 8-1,5 lux at the depth range 15 - 20 m and after mid night at the illumination 8 - 1,5 lux at the depth range 10-15 m of illumination 8-1,8 lux. During fishing process, the fishes were concentrated at 8 – 1,8 lux more than others zone