The vertebrate retina is a clearly organized signal-processing system. It contains more than 60 different types of neurons, arranged in three distinct neural layers. Each cell type is believed to serve unique role(s) in encoding visual information. While we now have a relatively good understanding of the constituent cell types in the retina and some general ideas of their connectivity, with few exceptions, how the retinal circuitry performs computation remains poorly understood. Computational modeling has been commonly used to study the retina from the single cell to the network level. In this article, we begin by reviewing retinal modeling strategies and existing models. We then discuss in detail the significance and limitations of these models, and finally, we provide suggestions for the future development of retinal neural modeling.
In this study, the selenium enriched peanuts and the different solubility proteins extracted from them were investigated. The dried defatted selenium enriched peanuts (SeP) powder (0.3147 μg/g) had a 2.5-fold higher mean total selenium concentration than general peanuts (GP) power (0.1233 μg/g). The SeP had higher concentration of selenium, manganese and zinc than that of GP, but less calcium. The rate of extraction of protein was 23.39% for peanuts and alkali soluble protein was the main component of protein in SeP, which accounted for 92.82% of total soluble protein and combined selenium was 77.33% of total selenium protein. In different forms of proteins from SeP, the WSePr due to higher concentration of selenium had higher DPPH free-radical scavenging activity, higher reducing activity and longer induction time than other proteins.
Persimmon (Diospyros kaki Thunb.) is widely cultivated in China. On October 15, 2019, about 10% of persimmon fruits showed fruit rot in the orchards of Guilin, Guangxi, China (24°45' N, 110°24' E), which could cause more than 15% of yield losses. The initial symptoms of fruit rot exhibited irregular brown to black spots (range from 2 to 4 cm in diameter), the areas surrounding the blackened spots would be soft and rotten, and three diseased fruit samples were collected from three orchards, respectively. Tissues (5×5 mm) were cut from infected margins, surface-disinfected in 75% ethanol for 10 s, 2% NaClO for 2 min, rinsed three times in sterilized distilled water, and incubated on potato dextrose agar (PDA) at 25°C under 12/12 h light/darkness for a week. Forty-one tissues yielded morphologically similar cultures, and three representative isolates LPG1-1, LPG1-2, and YSG-1 were selected from three samples for further study, respectively. Their colonies showed wavy edges, white surfaces, and dense aerial hyphae on PDA after two weeks. Conidia were fusiform, straight to slightly curved, and 4-septate; basal cells were conical, hyaline, thin, and verruculose with two or three long and hyaline apical appendages and one short apical appendage; three median cells of LPG1-1 with length 14.06 to 17.69 μm (n=100), and LPG1-2 with length 14.03 to 17.61 μm (n=100) were dark brown to olivaceous, while three median cells of YSG-1 with length 12.54 to 15.58 μm (n=100) were dark brown. The conidial sizes of LPG1-1, LPG1-2, and YSG-1 were 17.41 to 27.68 × 4.63 to 8.55 μm (n=100), 18.06 to 27.41 × 4.33 to 8.21 μm (n=100), and 16.58 to 27.73 × 4.99 to 8.39 μm (n=100), respectively. The morphological characteristics were consistent with Neopestalotiopsis spp. (Maharachchikumbura et al. 2012; Maharachchikumbura et al. 2014). Primer pairs ITS4/ITS5, BT2a/BT2b, and EF1-526F/EF-1567R were used to amplify internal transcribed spacer (ITS), beta-tubulin (TUB2), and translation elongation factor 1 alpha (TEF1-α), respectively (Shu et al., 2020). All DNA fragments were sequenced by Sangon Biotech Co., Ltd. (Shanghai, China). Sequences have been deposited in GenBank (ITS: OM349120 to OM349122, TUB2: OM688188 to OM688190, TEF1-α: OM688191 to OM688193). Based on BLASTn analysis of ITS, TUB2, and TEF1-α sequences, the LPG1-1 and LPG1-2 showed over 99% similarity to N. saprophytica, and YSG-1 showed over 99% similarity to N. ellipsospora. Phylogenetic analysis of the three isolates was performed with MEGA10 (version 10.0) based on sequences of ITS, TUB2, and TEF1-α using maximum parsimony analysis. The results revealed that LPG1-1 and LPG1-2 were clustered with N. saprophytica, and YSG-1 was clustered with N. ellipsospora. Pathogenicity tests of three isolates were conducted on 72 healthy persimmon fruits with and without wounds, and 9 fruits are for each treatment. The wound was made by a sterilized needle. Fruits were pre-processed with 75% ethanol for 10 s, 1% NaClO for 2 min and rinsed three times in sterile water. Conidial suspensions (10 µL, 106 conidia/mL in 0.1% sterile Tween 20) were inoculated on each site (4 sites/fruit). Control group was treated with 0.1% sterile Tween 20. All inoculated sites were covered with wet cotton. The inoculated fruits were placed in a plastic box to maintain humidity at 28℃. After 5 days, all wounded fruits showed fruit rot, whereas unwounded and control fruits remained asymptomatic, there were significant differences (P<0.05) in aggressiveness between N. saprophytica (average lesion diameter 13.1 mm) and N. ellipsospora (average lesion diameter 14.9 mm). Koch's postulates were fulfilled by re-isolating the causal agents from inoculated fruits. N. ellipsospora was previously reported as an endophyte in D. montana in southern India (Reddy et al. 2016). N. saprophytica could cause leaf spot of Erythropalum scandens and Magnolia sp., and fruit rot of Litsea rotundifolia in China and leaf spot of Elaeis guineensis in Malaysia (Yang et al. 2021, Ismail et al. 2017). To our knowledge, this is the first report of N. ellipsospora and N. saprophytica causing fruit rot on persimmon in the world. The results will provide a foundation for controlling fruit rot caused by pestalotioid fungi on persimmon.
Microalgae as the photosynthetic organisms offer enormous promise in a variety of industries, such as the generation of high-value byproducts, biofuels, pharmaceuticals, environmental remediation, and others. With the rapid advancement of gene editing technology, CRISPR/Cas system has evolved into an effective tool that revolutionised the genetic engineering of microalgae due to its robustness, high target specificity, and programmability. However, due to the lack of robust delivery system, the efficacy of gene editing is significantly impaired, limiting its application in microalgae. Nanomaterials have become a potential delivery platform for CRISPR/Cas systems due to their advantages of precise targeting, high stability, safety, and improved immune system. Notably, algal-mediated nanoparticles (AMNPs), especially the microalgae-derived nanoparticles, are appealing as a sustainable delivery platform because of their biocompatibility and low toxicity in a homologous relationship. In addition, living microalgae demonstrated effective and regulated distribution into specified areas as the biohybrid microrobots. This review extensively summarised the uses of CRISPR/Cas systems in microalgae and the recent developments of nanoparticle-based CRISPR/Cas delivery systems. A systematic description of the properties and uses of AMNPs, microalgae-derived nanoparticles, and microalgae microrobots has also been discussed. Finally, this review highlights the challenges and future research directions for the development of gene-edited microalgae.
The human brain possesses a remarkable ability to memorize information with the assistance of a specific external environment. Therefore, mimicking the human brain's environment-enhanced learning abilities in artificial electronic devices is essential but remains a considerable challenge. Here, a network of Ag nanowires with a moisture-enhanced learning ability, which can mimic long-term potentiation (LTP) synaptic plasticity at an ultralow operating voltage as low as 0.01 V, is presented. To realize a moisture-enhanced learning ability and to adjust the aggregations of Ag ions, we introduced a thin polyvinylpyrrolidone (PVP) coating layer with moisture-sensitive properties to the surfaces of the Ag nanowires of Ag ions. That Ag nanowire network was shown to exhibit, in response to the humidity of its operating environment, different learning speeds during the LTP process. In high-humidity environments, the synaptic plasticity was significantly strengthened with a higher learning speed compared with that in relatively low-humidity environments. Based on experimental and simulation results, we attribute this enhancement to the higher electric mobility of the Ag ions in the water-absorbed PVP layer. Finally, we demonstrated by simulation that the moisture-enhanced synaptic plasticity enabled the device to adjust connection weights and delivery modes based on various input patterns. The recognition rate of a handwritten data set reached 94.5% with fewer epochs in a high-humidity environment. This work shows the feasibility of building our electronic device to achieve artificial adaptive learning abilities.
The human body contains trillions of cells, classified into specific cell types, with diverse morphologies and functions. In addition, cells of the same type can assume different states within an individual's body during their lifetime. Understanding the complexities of the proteome in the context of a human organism and its many potential states is a necessary requirement to understanding human biology, but these complexities can neither be predicted from the genome, nor have they been systematically measurable with available technologies. Recent advances in proteomic technology and computational sciences now provide opportunities to investigate the intricate biology of the human body at unprecedented resolution and scale. Here we introduce a big-science endeavour called π-HuB (proteomic navigator of the human body). The aim of the π-HuB project is to (1) generate and harness multimodality proteomic datasets to enhance our understanding of human biology; (2) facilitate disease risk assessment and diagnosis; (3) uncover new drug targets; (4) optimize appropriate therapeutic strategies; and (5) enable intelligent healthcare, thereby ushering in a new era of proteomics-driven phronesis medicine. This ambitious mission will be implemented by an international collaborative force of multidisciplinary research teams worldwide across academic, industrial and government sectors.