Methyl 3,4-dihydroxybenzoate protects retina in a mouse model of acute ocular hypertension through multiple pathways.

Methyl 3,4 dihydroxybenzoate (MDHB) is a small molecule that shows neuroprotective effects in vitro and in a photoreceptor-degenerative mouse model. Here we investigated whether MDHB protects retina in a mouse model of acute ocular hypertension (AOH) and explores the underlying mechanisms. AOH was induced in mice by increasing intraocular pressure to approximately 90 mmHg for 60 min, then MDHB or vehicle was intraperitoneally injected daily up to 7 days. Immunostaining and multi-electrode array recordings were performed to examine the structure and function of retinas receiving the treatments. Western-blotting was applied to test the expression of several proteins related to oxidative stress and brain-derived neurotrophic factor (BDNF)-initiated signaling. Results showed that AOH injury reduced the number of Brn3a-stained retinal ganglion cells (RGCs) and ChAT-amacrine cells; thinned the inner retinal layers and induced apoptosis. Physiologically, AOH decreased the response of OFF and ON-OFF RGCs. All of these changes were reversed by MDHB-treatment. Mechanistically, MDHB appeared to work on three parallel pathways: (1) MDHB decreased the production of reactive oxygen species, the expression of nuclear factor erythroid 2-related factor 2 (Nrf2) and cytosol heme oxygenase 1 (HO-1); (2) It upregulated the expression of BDNF and its receptor tropomyosin-related kinase B (TrkB), and activated the downstream AKT pathways; (3) It inhibited reactive gliosis by reducing GFAP and Iba-1 expression. Thus our results suggest that MDHB protects retina against AOH injury by inhibiting oxidative stress, activating the BDNF/AKT signaling and inhibiting inflammatory pathways. Therefore, MDHB may serve as a promising candidate to treat retinal ischemia.

Structural and visual functional deficits in a rat model of neuromyelitis optica spectrum disorders related optic neuritis.

Neuromyelitis optica spectrum disorders (NMOSD) are a group of autoimmune astrocytopathies in the central nervous system, which are mainly caused by immunoglobulin G (IgG) against astrocyte water channel aquaporin-4 (AQP4). In this study, we aimed to establish a model of NMOSD-related optic neuritis (NMOSD-ON) and to evaluate the progressive changes of the optic nerve and visual function. AQP4 IgG-positive serum from NMOSD patients was injected into the subarachnoid space of the rat optic nerve to induce the NMOSD-ON model (AQP4 + group), and healthy serum was injected as the control. The visual evoked potential, pupillary light reflex and optical coherence tomography were monitored every week for 3 weeks after induction. Compared with the control group, the amplitude of the N1-P1 peak and pupillary light reflex in the AQP4+ group were reduced within the first week and then remained low thereafter. Consistent with the functional deficits, the thickness of the peripapillary retinal nerve fiber layer in the AQP4 + group was also greatly reduced. At the end of 3 weeks, there was a loss of retinal ganglion cells and the optic nerves showed characteristic NMOSD-like pathologic changes, including deposition of AQP4 IgG, local astrocyte damage, demyelination, microglia activation, macrophage infiltration and axonal injury. Thus, we have established an NMOSD-ON rat model with deficits in the optic nerve and visual function that may be a valuable tool for exploring the mechanism of NMOSD-ON and evaluating its potential therapeutic treatment.

An edge cloud and Fibonacci-Diffie-Hellman encryption scheme for secure printer data transmission.

Network printers face increasing security threats from network attacks that can lead to sensitive information leakage and data tampering. To address these risks, we propose a novel Fibonacci-Diffie-Hellman (FIB-DH) encryption scheme using edge cloud collaboration. Our approach utilizes properties of third-order Fibonacci matrices combined with the Diffie-Hellman key exchange to encrypt printer data transmissions. The encrypted data is transmitted via edge cloud servers and verified by the receiver using inverse Fibonacci transforms. Our experiments demonstrate that the FIB-DH scheme can effectively improve printer data transmission security against common attacks compared to conventional methods. The results show reduced vulnerabilities to leakage and tampering attacks in our approach. This work provides an innovative application of cryptographic techniques to strengthen security for network printer communications.

Research on deep learning garbage classification system based on fusion of image classification and object detection classification.

With the development of national economy, the output of waste is also increasing. People's living standards are constantly improving, and the problem of garbage pollution is increasingly serious, which has a great impact on the environment. Garbage classification and processing has become the focus of today. This topic studies the garbage classification system based on deep learning convolutional neural network, which integrates the garbage classification and recognition methods of image classification and object detection. First, the data sets and data labels used are made, and then the garbage classification data are trained and tested through ResNet and MobileNetV2 algorithms, Three algorithms of YOLOv5 family are used to train and test garbage object data. Finally, five research results of garbage classification are merged. Through consensus voting algorithm, the recognition rate of image classification is improved to 2%. Practice has proved that the recognition rate of garbage image classification has been increased to about 98%, and it has been transplanted to the raspberry pie microcomputer to achieve ideal results.

Lightweight blockchain fuzzy decision scheme through MQTT and Fibonacci for sustainable transport.

The unprecedented progress in field of IoT enabled rapid developments in the vehicle intelligent transportation systems and most of these provide services in a centralized way. However, the centralized system architecture is vulnerable to the external attacks as a result both information and equipment are prone to eavesdropping and destruction. Therefore, there is a trend to apply blockchain technology to the vehicle intelligent transportation systems in order to achieve sustainable transportation. Nevertheless, the system is so great and very sophisticated and the ultimate task will be harder to implement. In view of this, an attempt is made in this paper to propose a lightweight fuzzy decision blockchain scheme through MQTT and Fibonacci, and through this scheme, the extent of blockchain server can be scaled and easy to deploy. Also through MQTT, reliable communication and transmission of blockchain can be realized. LF-BC is formed by using DH and Fibonacci transformation to enhance security, and F-PBFT consensus algorithm can reduce the communication overhead and improve the fault tolerance tremendously. Using LF-BC scheme, the experimental results show that the fault tolerance rate is significantly improved by 22.3%, and the sustainable safety and reliability of the vehicle intelligent transportation system is increased consumedly. At the same time, the feasibility of the scheme is also verified by taking specific cases.

Detection system of dead and sick chickens in large scale farms based on artificial intelligence.

With the continuous enrichment of scientific and technological means, the production of most chicken farms has been able to achieve automation, but for the dead and sick chickens in the farm, there is no automatic monitoring step, only through continuous manual inspection and discovery. In the face of this problem, there are many solutions to identify dead and sick chickens through sound and image, but they can not achieve the ideal effect. In this paper, a sensor detection method based on artificial intelligence is proposed. This method 1) The maximum displacement of chicken activity is measured by fastening a foot ring on each chicken, and the three-dimensional total variance is designed and calculated to represent the chicken activity intensity. 2) The detection terminal collects the sensing data of foot ring through ZigBee network. 3) The state of chicken (dead chicken and sick chicken) can be identified by machine learning algorithm. This method of artificial intelligence combined with sensor network not only has high recognition rate, but also can reduce the operation cost. The practical results show that the accuracy of the system to identify dead and sick chickens is 95.6%, and the cost of the system running for 4 years can be reduced by 25% compared with manual operation.

Inhibition of non-NMDA ionotropic glutamate receptors delays the retinal degeneration in rd10 mouse.

Retinitis pigmentosa (RP) is a hereditary blinding disease characterized by neurodegeneration of photoreceptors. Retinal ganglion cells (RGCs) in animal models of RP exhibit an abnormally high spontaneous activity that interferes with signal processing. Blocking AMPA/Kainate receptors by bath application of CNQX decreases the spontaneous firing, suggesting that inhibiting these receptors in vivo may help maintain the function of inner retinal neurons in rd10 mice experiencing photoreceptor degeneration. To test this, rd10 mice were i.p. injected with CNQX or GYKI 52466 (an AMPA receptor antagonist) for 1-2 weeks, and examined for their retinal morphology (by immunocytochemistry), function (by MEA recordings) and visual behaviors (using a black/white box). Our data show that iGluRs were up-regulated in the inner plexiform layer (IPL) of rd10 retinas. Application of CNQX at low doses both in vitro and in vivo, attenuated the abnormal spontaneous spiking in RGCs, and increased the light-evoked response of ON RGCs, whereas GYKI 52466 had little effect. CNQX application also improved the behavioral performance. Interestingly, in vivo administration of CNQX delayed photoreceptor degeneration, evidenced by the increased cell number and restored structure. CNQX also improved the structure of bipolar cells. Together, we demonstrated that during photoreceptor degeneration, blockade of the non-NMDA iGluRs decelerates the progression of RGCs dysfunction, possibly by dual mechanisms including slowing photoreceptor degeneration and modulating signal processing within the IPL. Accordingly, this strategy may effectively extend the time window for treating RP.