Dideriksenmedeiros2725

The CRISPR-Cas9 system from Streptococcus pyogenes has been exploited as a programmable RNA-guided DNA-targeting and DNA-editing platform. This evolutionary tool enables diverse genetic manipulations with unprecedented precision and ease. Cas9 is an allosteric enzyme, which is allosterically regulated in conformational activation, target recognition, and DNA cleavage. Here, we outline the underlying allosteric control over the Cas9 complex assembly and targeting specificity. We further review the strategies for mitigating intrinsic Cas9 off-target effects through allosteric modulations and the advances in engineering controllable Cas9 systems that are responsive to external allosteric signals. Future development of highly specific, tunable CRISPR-Cas9 systems through allosteric modulations would greatly benefit applications that require both conditional control and high precision. BACKGROUND Recent data on the rates of infections among patients with multiple sclerosis (MS) are sparse. The objective of this study was to quantify incidence of infections in patients with MS compared with a matched sample of patients without MS (non-MS). METHODS This study was conducted in two separate electronic medical databases the United States Department of Defense (US-DOD) military health care system and the United Kingdom's Clinical Practice Research Datalink GOLD (UK-CPRD). We identified patients with a first recorded diagnosis of MS between 2001 and 2016 (UK-CPRD) or 2004 and 2017 (US-DOD) and matched non-MS patients. We identified infections recorded after the MS diagnosis date (or the matched date in non-MS patients) and calculated incidence rates (IRs) and incidence rate ratios (IRRs) with 95% confidence intervals (CIs) by infection site and type. RESULTS Relative to non-MS patients, MS patients had higher rates of any infection (US-DOD IRR 1.76; 95% CI 1.72-1.80 and UK-CPRD IRR 1.25; 95% CI 1.21-1.29) and a two-fold higher rate of hospitalized infections (US-DOD IRR 2.43; 95% CI 2.23-2.63 and UK-CPRD IRR 2.00; 95% CI 1.84-2.17). IRs of any infection were higher in females compared with males in both MS and non-MS patients, while IRs of hospitalized infections were similar between sexes in both MS and non-MS patients. The IR of first urinary tract or kidney infection was nearly two-fold higher in MS compared with non-MS patients (US-DOD IRR 1.88; 95% CI 1.81-1.95 and UK-CPRD IRR 1.97; 95% CI 1.86-2.09) with higher rates in females compared with males. IRs for any opportunistic infection, candidiasis and any herpes virus were increased between 20 and 52% among MS patients compared with non-MS patients. IRs of meningitis, tuberculosis, hepatitis B and C were all low. CONCLUSION MS patients have an increased risk of infection, notably infections of the renal tract, and a two-fold increased risk of hospitalized infections compared with non-MS patients. V.Deep neural network (DNN) quantization converting floating-point (FP) data in the network to integers (INT) is an effective way to shrink the model size for memory saving and simplify the operations for compute acceleration. Recently, researches on DNN quantization develop from inference to training, laying a foundation for the online training on accelerators. However, existing schemes leaving batch normalization (BN) untouched during training are mostly incomplete quantization that still adopts high precision FP in some parts of the data paths. Currently, there is no solution that can use only low bit-width INT data during the whole training process of large-scale DNNs with acceptable accuracy. In this work, through decomposing all the computation steps in DNNs and fusing three special quantization functions to satisfy the different precision requirements, we propose a unified complete quantization framework termed as "WAGEUBN" to quantize DNNs involving all data paths including W (Weights), A (Activation), G (Gradient), E (Error), U (Update), and BN. Moreover, the Momentum optimizer is also quantized to realize a completely quantized framework. Experiments on ResNet18/34/50 models demonstrate that WAGEUBN can achieve competitive accuracy on the ImageNet dataset. For the first time, the study of quantization in large-scale DNNs is advanced to the full 8-bit INT level. In this way, all the operations in the training and inference can be bit-wise operations, pushing towards faster processing speed, decreased memory cost, and higher energy efficiency. Our throughout quantization framework has great potential for future efficient portable devices with online learning ability. In uncertain domains, the goals are often unknown and need to be predicted by the organism or system. In this paper, contrastive Excitation Backprop (c-EB) was used in two goal-driven perception tasks - one with pairs of noisy MNIST digits and the other with a robot in an action-based attention scenario. The first task included attending to even, odd, low, and high digits, whereas the second task included action goals, such as "eat", "work-on-computer", "read", and "say-hi" that led to attention to objects associated with those actions. The system needed to increase attention to target items and decrease attention to distractor items and background noise. Because the valid goal was unknown, an online learning model based on the cholinergic and noradrenergic neuromodulatory systems was used to predict a noisy goal (expected uncertainty) and re-adapt when the goal changed (unexpected uncertainty). This neurobiologically plausible model demonstrates how neuromodulatory systems can predict goals in uncertain domains and how attentional mechanisms can enhance the perception for that goal. Chinese sign language (CSL) is one of the most widely used sign language systems in the world. As such, the automatic recognition and generation of CSL is a key technology enabling bidirectional communication between deaf and hearing people. Most previous studies have focused solely on sign language recognition (SLR), which only addresses communication in a single direction. As such, there is a need for sign language generation (SLG) to enable communication in the other direction (i.e., from hearing people to deaf people). To achieve a smoother exchange of ideas between these two groups, we propose a skeleton-based CSL recognition and generation framework based on a recurrent neural network (RNN), to support bidirectional CSL communication. This process can also be extended to other sequence-to-sequence information interactions. The core of the proposed framework is a two-level probability generative model. Compared with previous techniques, this approach offers a more flexible approximate posterior distribution, which can produce skeletal sequences of varying styles that are recognizable to humans. In addition, the proposed generation method compensated for a lack of training data. A series of experiments in bidirectional communication were conducted on the large 500 CSL dataset. The proposed algorithm achieved high recognition accuracy for both real and synthetic data, with a reduced runtime. Furthermore, the generated data improved the performance of the discriminator. These results suggest the proposed bidirectional communication framework and generation algorithm to be an effective new approach to CSL recognition. This paper investigates the event-triggered synchronization control of discrete-time neural networks. The main highlights are threefold (1) a new event-triggered mechanism (ETM) is presented, which can be regarded as a switching between the discrete-time periodic sampled-data control and a continuous ETM; (2) a saturating controller which is equipped with two switching gains is designed to match the switching property of the proposed ETM; (3) a dedicated switching Lyapunov-Krasovskii functional is constructed, which takes the sawtooth constraints of control input into account. Based on these ingredients, the synchronization criteria are derived such that the considered error systems are locally stable. Whereafter, two co-design problems are discussed to maximize the set of admissible initial conditions and the triggering threshold, respectively. Finally, the effectiveness and advantages of the proposed method are validated by two numerical examples. Neural coding is one of the central questions in systems neuroscience for understanding how the brain processes stimulus from the environment, moreover, it is also a cornerstone for designing algorithms of brain-machine interface, where decoding incoming stimulus is highly demanded for better performance of physical devices. Traditionally researchers have focused on functional magnetic resonance imaging (fMRI) data as the neural signals of interest for decoding visual scenes. However, our visual perception operates in a fast time scale of millisecond in terms of an event termed neural spike. There are few studies of decoding by using spikes. Here we fulfill this aim by developing a novel decoding framework based on deep neural networks, named spike-image decoder (SID), for reconstructing natural visual scenes, including static images and dynamic videos, from experimentally recorded spikes of a population of retinal ganglion cells. The SID is an end-to-end decoder with one end as neural spikes and the other end as images, which can be trained directly such that visual scenes are reconstructed from spikes in a highly accurate fashion. Our SID also outperforms on the reconstruction of visual stimulus compared to existing fMRI decoding models. In addition, with the aid of a spike encoder, we show that SID can be generalized to arbitrary visual scenes by using the image datasets of MNIST, CIFAR10, and CIFAR100. Furthermore, with a pre-trained SID, one can decode any dynamic videos to achieve real-time encoding and decoding of visual scenes by spikes. Altogether, our results shed new light on neuromorphic computing for artificial visual systems, such as event-based visual cameras and visual neuroprostheses. Recent findings suggest that acetylcholine mediates uncertainty-seeking behaviors through its projection to dopamine neurons - another neuromodulatory system known for its major role in reinforcement learning and decision-making. In this paper, we propose a leaky-integrate-and-fire model of this mechanism. It implements a softmax-like selection with an uncertainty bonus by a cholinergic drive to dopaminergic neurons, which in turn influence synaptic currents of downstream neurons. The model is able to reproduce experimental data in two decision-making tasks. It also predicts that (i) in the absence of cholinergic input, dopaminergic activity would not correlate with uncertainty, and that (ii) the adaptive advantage brought by the implemented uncertainty-seeking mechanism is most useful when sources of reward are not highly uncertain. Moreover, this modeling work allows us to propose novel experiments which might shed new light on the role of acetylcholine in both random and directed exploration. Overall, this study contributes to a more comprehensive understanding of the role of the cholinergic system and, in particular, its involvement in decision-making. HYPOTHESIS Multi-component supramolecular hydrogels are gaining increasing interest as stimuli-responsive materials. To fully understand and possibly exploit the potential of such complex systems, the hierarchical structure of the gel network needs in-depth investigations across multiple length scales. We show that a thorough structural and rheological study represents a crucial pillar for the exploitation of this class of functional materials. EXPERIMENTS Supramolecular hydrogels are prepared by self-assembly of hexadecyltrimethylammonium bromide (CTAB) and azobenzene-4,4'-dicarboxylic acid (AZO) in alkaline aqueous solution. The CTAB/AZO concentration was varied from ϕ = 0.25 to 4 wt% keeping the CTABAZO molar ratio fixed at 21. The systems were thoroughly studied through a combination of X-ray scattering, microscopy, rheological and spectroscopic analyses. FINDINGS The CTAB/AZO solutions form a self-supporting gel with nanofibrillar structure below ~30 °C. The critical gelation concentration is ϕc = 0.45 wt%.