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Previous studies have shown that early therapeutic events of neural precursor cells (NPCs) transplantation to animals with acute ischemic stroke readily protected neuronal cell damage and improved behavioral recovery through paracrine mechanisms. In this study, we tested the hypothesis that administration of conditioned medium from NPCs (NPC-CMs) could recapitulate the beneficial effects of cell transplantation. Rats with permanent middle cerebral artery occlusion (pMCAO) were randomly assigned to one of the following groups: PBS control, Vehicle (medium) controls, single (NPC-CM(S)) or multiple injections of NPC-CM(NPC-CM(M)) groups. A single intravenous injection of NPC-CM exhibited strong neuroregenerative potential to induce behavioral recovery, and multiple injections enhanced this activity further by suppressing inflammatory damage and inducing endogenous neurogenesis leading to histopathological and functional recovery. Proteome analysis of NPC-CM identified a number of proteins that are known to be associated with nervous system development, neurogenesis, and angiogenesis. In addition, transcriptome analysis revealed the importance of the inflammatory response during stroke recovery and some of the key hub genes in the interaction network were validated. Thus, our findings demonstrated that NPC-CM promoted functional recovery and reduced cerebral infarct and inflammation with enhanced endogenous neurogenesis, and the results highlighted the potency of NPC-CM in stroke therapy. [ABSTRACT FROM AUTHOR]

This paper proposes a total crop-diagnosis platform (TCP) based on deep learning models in a natural nutrient environment, which collects the weather information based on a farm's location information, diagnoses the collected weather information and the crop soil sensor data with a deep learning technique, and notifies a farm manager of the diagnosed result. The proposed TCP is composed of 1 gateway and 2 modules as follows. First, the optimized farm sensor gateway (OFSG) collects data by internetworking sensor nodes which use Zigbee, Wi-Fi and Bluetooth protocol and reduces the number of sensor data fragmentation times through the compression of a fragment header. Second, the data storage module (DSM) stores the collected farm data and weather data in a farm central server. Third, the crop self-diagnosis module (CSM) works in the cloud server and diagnoses by deep learning whether or not the status of a farm is in good condition for growing crops according to current weather and soil information. The TCP performance shows that the data processing rate of the OFSG is increased by about 7% compared with existing sensor gateways. The learning time of the CSM is shorter than that of the long short-term memory models (LSTM) by 0.43 s, and the success rate of the CSM is higher than that of the LSTM by about 7%. Therefore, the TCP based on deep learning interconnects the communication protocols of various sensors, solves the maximum data size that sensor can transfer, predicts in advance crop disease occurrence in an external environment, and helps to make an optimized environment in which to grow crops. [ABSTRACT FROM AUTHOR]

This paper proposes a self-predictable crop yield platform (SCYP) based on crop diseases using deep learning that collects weather information (temperature, humidity, sunshine, precipitation, etc.) and farm status information (harvest date, disease information, crop status, ground temperature, etc.), diagnoses crop diseases by using convolutional neural network (CNN), and predicts crop yield based on factors such as climate change, crop diseases, and others by using artificial neural network (ANN). The SCYP consists of an image preprocessing module (IPM) to determine crop diseases through the Google Vision API and image resizing, a crop disease diagnosis module (CDDM) based on CNN to diagnose the types and extent of crop diseases through photographs, and a crop yield prediction module (CYPM) based on ANN by using information of crop diseases, remaining time until harvest (based on the date), current temperature, humidity and precipitation (amount of snowfall) in the area, sunshine amount, ground temperature, atmospheric pressure, moisture evaporation in the ground, etc. Four experiments were conducted to verify the efficiency of the SCYP. In the CDMM, the accuracy and operation time of each model were measured using three neural network models: CNN, region-CNN(R-CNN), and you only look once (YOLO). In the CYPM, rectified linear unit (ReLU), Sigmoid, and Step activation functions were compared to measure ANN accuracy. The accuracy of CNN was about 3.5% higher than that of R-CNN and about 5.4% higher than that of YOLO. The operation time of CNN was about 37 s less than that of R-CNN and about 72 s less than that of YOLO. The CDDM had slightly less operation time, but in this paper, we prefer accuracy over operation time to diagnose crop diseases efficiently and accurately. When the activation function of the ANN used in the CYPM was ReLU, the accuracy of the ANN was 2% higher than that of Sigmoid and 7% higher than that of Step. The CYPM prediction was about 34% more accurate when using multiple diseases than when not using them. Therefore, the SCYP can predict farm yields more accurately than traditional methods. [ABSTRACT FROM AUTHOR]

Analysis of protein dynamics using single-molecule fluorescence resonance energy transfer (smFRET) is widely used to understand the structure and function of proteins. Nonetheless, site-specific labeling of proteins with a pair of donor and acceptor dyes still remains a challenge. Here we present a general and facile method for site-specific dual labeling of proteins by incorporating two different, readily available, unnatural amino acids (p-acetylphenylalanine and alkynyllysine) for smFRET. We used newly evolved alkynyllysine-specific aminoacyl-tRNA synthetase/tRNA(UCA) and p-acetylphenylalanyl-tRNA synthetase/tRNA(CUA). The utility of our approach was demonstrated by analyzing the conformational change of dual-labeled calmodulin using smFRET measurements. The present labeling approach is devoid of major limitations in conventional cysteine-based labeling. Therefore, our method will significantly increase the repertoire of proteins available for FRET study and expand our ability to explore more complicated molecular dynamics.

Fas-associated protein with death domain (FADD), an adaptor that bridges death receptor signaling to the caspase cascade, is indispensible for the induction of extrinsic apoptotic cell death. Interest in the non-apoptotic function of FADD has greatly increased due to evidence that FADD-deficient mice or dominant-negative FADD transgenic mice result in embryonic lethality and an immune defect without showing apoptotic features. Numerous studies have suggested that FADD regulates cell cycle progression, proliferation, and autophagy, affecting these phenomena. Recently, programmed necrosis, also called necroptosis, was shown to be a key mechanism that induces embryonic lethality and an immune defect. Supporting these findings, FADD was shown to be involved in various necroptosis models. In this review, we summarize the mechanism of extrinsic apoptosis and necroptosis, and discuss the in vivo and in vitro roles of FADD in necroptosis induced by various stimuli.

In this study, we use promoter analysis to show that interaction between Jab1 and p53 induces suppression of p53 activation in U2OS and H1299 cells. Interaction between p53 and Jab1 was further confirmed by immunoprecipitation and immunofluorescent analyses. In particular, Jab1 was able to induce nuclear export of p53 as previously reported. When Jab1 was overexpressed in U2OS cells followed by etoposide or hydrogen peroxide (H(2)O(2)), cell death induced by such stresses was protected against. On the contrary, when the level of Jab1 was suppressed in U2OS cells, cytotoxicity imposed by etoposide and H(2)O(2) was dramatically increased, suggesting a cell protective role for Jab1. These results indicate that Jab1 is a negative regulator of p53 and a plausible oncogene.