The cortisol stress response to capture was investigated in two species of fish (Perca flavescens and Esox lucius) from sites polluted by high levels of polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and mercury, and from reference sites in the St. Lawrence river system. Fish from the reference sites exhibited the normal elevation of serum cortisol in response to the acute stress of capture and had large pituitary corticotropes. In contrast, fish from the most polluted sites were unable to increase their serum cortisol in response to the acute stress of capture and their pituitary corticotropes were atrophied. These results suggest that a life-long exposure to chemical pollutants may lead to an exhaustion of the cortisol-producing endocrine system, possibly as a result of prolonged hyperactivity of the system.

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Human activity recognition (HAR) has become a popular topic in research because of its wide application. With the development of deep learning, new ideas have appeared to address HAR problems. Here, a deep network architecture using residual bidirectional long short-term memory (LSTM) cells is proposed. The advantages of the new network include that a bidirectional connection can concatenate the positive time direction (forward state) and the negative time direction (backward state). Second, residual connections between stacked cells act as highways for gradients, which can pass underlying information directly to the upper layer, effectively avoiding the gradient vanishing problem. Generally, the proposed network shows improvements on both the temporal (using bidirectional cells) and the spatial (residual connections stacked deeply) dimensions, aiming to enhance the recognition rate.When tested with the Opportunity data set and the public domain UCI data set, the accuracy was increased by 4.78% and 3.68%, respectively, compared with previously reported results. Finally, the confusion matrix of the public domain UCI data set was analyzed. IntroductionIn real life, many problems can be described as time series problems. Indeed, human activity recognition (HAR) is of value in both theoretical research and actual practice. It can be used widely, including in health monitoring [1][2], smart homes [3][4], and human-computer interactions [5][6]; for example, LSTM cells are a good choice for solving HAR problems. Unlike traditional algorithms, LSTM can catch relationships in data on the temporal dimension without having to mix the time steps together as a 1D convolutional neural network (CNN) would do. As more of what is commonly called "big data" emerges, LSTM architecture can offer great performance and many potential applications. More specifically, HAR is the process of obtaining action data with sensors; it symbolizes the action information and then allows understanding and extraction of the motion characteristics, which is what activity recognition refers to. Because of the spatial complexity and temporal divergence of behavior, there is no unified recognition method. A public domain benchmark of HAR has been introduced, and different methods of recognition have been analyzed [7]. The results showed that the K-Nearest Neighbor (KNN) algorithm outperforms other algorithms in most recognition tasks. Support Vector Machine (SVM) is another outstanding algorithm. A Multi-Class Hardware-Friendly Support Vector Machine (MC-HF-SVM), which uses fixed-point arithmetic for HAR instead of the typical floating-point arithmetic, has been proposed for sensor data [8]. Unlike the manual filtering features in previous algorithms, a systematic feature learning method that combines feature extraction with CNN training has also been proposed [9]. Subsequently, DeepConvLSTM networks outperformed previous algorithms in the Opportunity Challenge by an average of 4% of the F1 score [10]; the effects of parameters on the final result were...

A quantitative time study of the incorporation of choline-SH, le~cine-~H and gala~tose-~H in lung epithelial cells, was undertaken in vivo with electron microscopic autoradiography. Type I1 pneumocytes were selectively labeled with ~holine-~H, a specific precursor of phosphatidylcholine, which is the main component of pulmonary surfactant. Choline was initially localized in endoplasmic reticulum, then was rapidly transferred through the Golgi complex and stored in lamellar bodies. Previously undescribed small lamellar bodies are suggested as phospholipid carriers between Golgi complex and lamellar bodies.After initial incorporation in the endoplasmic reticulum, the leucine label migrated through the Golgi complex into lamellar bodies by fusion of multivesicular bodies, which are the carriers between the two structures. Galactose was modestly incorporated into lamellar bodies via the Golgi complex. Intra-alveolar myelin figures, recognized as excreted surfactant, were labeled 120 minutes after injection with the three precursors.These findings indicate that the synthesis of a secretory product by type I1 pneumocytes involves phospholipid, protein and polysaccharide precursors; the secretory product is segregated as lamellar bodies which are destined to be excreted into the alveolar cavity to become part of the lining layer.

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