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Human pluripotent stem cell-based (hPSC-based) replacement therapy holds great promise for the treatment of Parkinson's disease (PD). However, the heterogeneity of hPSC-derived donor cells and the low yield of midbrain dopaminergic (mDA) neurons after transplantation hinder its broad clinical application. Here, we have characterized the single-cell molecular landscape during mDA neuron differentiation. We found that this process recapitulated the development of multiple but adjacent fetal brain regions including the ventral midbrain, the isthmus, and the ventral hindbrain, resulting in a heterogenous donor cell population. We reconstructed the differentiation trajectory of the mDA lineage and identified calsyntenin 2 (CLSTN2) and protein tyrosine phosphatase receptor type O (PTPRO) as specific surface markers of mDA progenitors, which were predictive of mDA neuron differentiation and could facilitate high enrichment of mDA neurons (up to 80%) following progenitor cell sorting and transplantation. Marker-sorted progenitors exhibited higher therapeutic potency in correcting motor deficits of PD mice. Different marker-sorted grafts had a strikingly consistent cellular composition, in which mDA neurons were enriched, while off-target neuron types were mostly depleted, suggesting stable graft outcomes. Our study provides a better understanding of cellular heterogeneity during mDA neuron differentiation and establishes a strategy to generate highly purified donor cells to achieve stable and predictable therapeutic outcomes, raising the prospect of hPSC-based PD cell replacement therapies.

Considering the greater contact stress of turnout rails during wear and the development of heavy-haul railways, twin-disc sliding–rolling wear tests were performed on U75VH heat-treated rail steels applied for turnouts under high contact stress ranging from 1980 MPa to 2270 MPa. The microstructure of the worn surfaces was analyzed using optical microscope (OM), scanning electron microscope (SEM), 3D microscope, electron backscatter diffraction (EBSD), and hardness tests. The results indicated that after 10 h of wear, the weight loss was 63 mg at a contact stress of 1980 MPa, while it reached 95 mg at a contact stress of 2270 MPa. At a given contact stress, the wear rate increased with increasing wear time, while a nearly linear increase in wear rate was observed with increasing contact stress. As wear time and contact stress increased, the worn surface showed more pronounced wear morphology, leading to greater surface roughness. Crack length significantly increased with wear time, and higher contact stress facilitated crack propagation, resulting in longer, deeper cracks. After 10 h of wear under a contact stress of 2270 MPa, large-scale cracks with a maximum length of 128.29 μm and a maximum depth of 31.10 μm were formed, indicating severe fatigue wear. Additionally, the thickness of the plastic deformation layer increased with the wear time and contact stress. The surface hardness was dependent on the thickness of this layer. After 10 h of wear under the minimum and maximum contact stresses, hardening rates of 0.39 and 0.48 were achieved, respectively.

Somatic sensation is defined by the existence of a diversity of primary sensory neurons with unique biological features and response profiles to external and internal stimuli. However, there is no coherent picture about how this diversity of cell states is transcriptionally generated. Here, we use deep single cell analysis to resolve fate splits and molecular biasing processes during sensory neurogenesis in mice. Our results identify a complex series of successive and specific transcriptional changes in post-mitotic neurons that delineate hierarchical regulatory states leading to the generation of the main sensory neuron classes. In addition, our analysis identifies previously undetected early gene modules expressed long before fate determination although being clearly associated with defined sensory subtypes. Overall, the early diversity of sensory neurons is generated through successive bi-potential intermediates in which synchronization of relevant gene modules and concurrent repression of competing fate programs precede cell fate stabilization and final commitment. The diversity of primary sensory neurons and how fate choice is determined is unclear. Here, the authors use single cell RNA sequencing analysis of early murine somatosensory neurons to show that sensory neuron diversity is achieved by a transition through a bi-potential intermediate state.

Speech Emotion Recognition (SER) is a huge challenge for distinguishing and interpreting the sentiments carried in speech. Fortunately, deep learning is proved to have great ability to deal with acoustic features. For instance, Bidirectional Long Short Term Memory (BLSTM) has an advantage of solving time series acoustic features and Convolutional Neural Network (CNN) can discover the local structure among different features. This paper proposed the BLSTM and CNN Stacking Architecture (BCSA) to enhance the ability to recognition emotions. In order to match the input formats of BLSTM and CNN, slicing feature matrices is necessary. For utilizing the different roles of the BLSTM and CNN, the Stacking is employed to integrate the BLSTM and CNN. In detail, taking into account overfitting problem, the estimates of probabilistic quantities from BLSTM and CNN are combined as new data using K-fold cross validation. Finally, based on the Stacking models, the logistic regression is used to recognize emotions effectively by fitting the new data. The experiment results demonstrate that the performance of proposed architecture is better than that of single model. Furthermore, compared with the state-of-the-art model on SER in our knowledge, the proposed method BCSA may be more suitable for SER by integrating time series acoustic features and the local structure among different features.

The balance between T helper type 1 (T H 1) cells and other T H cells is critical for antiviral and anti-tumour responses 1 – 3 , but how this balance is achieved remains poorly understood. Here we dissected the dynamic regulation of T H 1 cell differentiation during in vitro polarization, and during in vivo differentiation after acute viral infection. We identified regulators modulating T helper cell differentiation using a unique T H 1–T H 2 cell dichotomous culture system and systematically validated their regulatory functions through multiple in vitro and in vivo CRISPR screens. We found that RAMP3, a component of the receptor for the neuropeptide CGRP (calcitonin gene-related peptide), has a cell-intrinsic role in T H 1 cell fate determination. Extracellular CGRP signalling through the receptor RAMP3–CALCRL restricted the differentiation of T H 2 cells, but promoted T H 1 cell differentiation through the activation of downstream cAMP response element-binding protein (CREB) and activating transcription factor 3 (ATF3). ATF3 promoted T H 1 cell differentiation by inducing the expression of Stat1 , a key regulator of T H 1 cell differentiation. After viral infection, an interaction between CGRP produced by neurons and RAMP3 expressed on T cells enhanced the anti-viral IFNγ-producing T H 1 and CD8 + T cell response, and timely control of acute viral infection. Our research identifies a neuroimmune circuit in which neurons participate in T cell fate determination by producing the neuropeptide CGRP during acute viral infection, which acts on RAMP3-expressing T cells to induce an effective anti-viral T H 1 cell response. RAMP3, a component of the receptor for the neuropeptide CGRP, has a cell-intrinsic role in T helper type 1 cell fate determination.

Human pluripotent stem cell (hPSC)-based replacement therapy holds great promise in treating Parkinson's disease (PD). However, the heterogeneity of hPSC-derived donor cells and the low yield of midbrain dopaminergic (mDA) neurons after transplantation hinder its broad clinical application. Here, we depicted the single-cell molecular landscape during mDA neuron differentiation. We found that this process recapitulated the development of multiple but adjacent fetal brain regions including ventral midbrain, isthmus, and ventral hindbrain, resulting in heterogenous donor cell population. We reconstructed the differentiation trajectory of mDA lineage and identified CLSTN2 and PTPRO as specific surface markers of mDA progenitors, which were predictive of mDA neuron differentiation and could facilitate highly enriched mDA neurons (up to 80%) following progenitor sorting and transplantation. Marker sorted progenitors exhibited higher therapeutic potency in correcting motor deficits of PD mice. Different marker sorted grafts had a strikingly consistent cellular composition, in which mDA neurons were enriched, while off-target neuron types were mostly depleted, suggesting stable graft outcomes. Our study provides a better understanding of cellular heterogeneity during mDA neuron differentiation, and establishes a strategy to generate highly purified donor cells to achieve stable and predictable therapeutic outcomes, raising the prospect of hPSC-based PD cell replacement therapies.

The CRISPR/Cas9 system is an efficient gene-editing method, but the majority of gene-edited animals showed mosaicism, with editing occurring only in a portion of cells. Here we show that single gene or multiple genes can be completely knocked out in mouse and monkey embryos by zygotic injection of Cas9 mRNA and multiple adjacent single-guide RNAs （spaced 10-200 bp apart） that target only a single key exon of each gene. Phenotypic analysis of F0 mice following targeted deletion of eight genes on the Y chromosome individually demonstrated the robustness of this approach in generating knockout mice. Importantly, this approach delivers complete gene knockout at high efficien- cies （100% on Arnt[ and 91% on Prrt2） in monkey embryos. Finally, we could generate a complete Prrt2 knockout monkey in a single step, demonstrating the usefulness of this approach in rapidly establishing gene-edited monkey models.

In order to deploy current computer vision (CV) models on resource-constrained low-power devices, recent works have proposed in-sensor and in-pixel computing approaches that try to partly/fully bypass the image signal processor (ISP) and yield significant bandwidth reduction between the image sensor and the CV processing unit by downsampling the activation maps in the initial convolutional neural network (CNN) layers. However, direct inference on the raw images degrades the test accuracy due to the difference in covariance of the raw images captured by the image sensors compared to the ISP-processed images used for training. Moreover, it is difficult to train deep CV models on raw images, because most (if not all) large-scale open-source datasets consist of RGB images. To mitigate this concern, we propose to invert the ISP pipeline, which can convert the RGB images of any dataset to its raw counterparts, and enable model training on raw images. We release the raw version of the COCO dataset, a large-scale benchmark for generic high-level vision tasks. For ISP-less CV systems, training on these raw images result in a 7.1% increase in test accuracy on the visual wake works (VWW) dataset compared to relying on training with traditional ISP-processed RGB datasets. To further improve the accuracy of ISP-less CV models and to increase the energy and bandwidth benefits obtained by in-sensor/in-pixel computing, we propose an energy-efficient form of analog in-pixel demosaicing that may be coupled with in-pixel CNN computations. When evaluated on raw images captured by real sensors from the PASCALRAW dataset, our approach results in a 8.1% increase in mAP. Lastly, we demonstrate a further 20.5% increase in mAP by using a novel application of few-shot learning with thirty shots each for the novel PASCALRAW dataset, constituting 3 classes.

The design of cell-free massive MIMO (CF-mMIMO) systems requires accurate, measurement-based channel models. This paper provides the first results from the by far most extensive outdoor measurement campaign for CF-mMIMO channels in an urban environment. We measured impulse responses between over 20,000 potential access point (AP) locations and 80 user equipments (UEs) at 3.5 GHz with 350 MHz bandwidth (BW). Measurements use a \"virtual array\" approach at the AP and a hybrid switched/virtual approach at the UE. This paper describes the sounder design, measurement environment, data processing, and sample results, particularly the evolution of the power-delay profiles (PDPs) as a function of the AP locations, and its relation to the propagation environment.

Sandstorms are meteorological phenomena common in arid and semi-arid regions and have been recognized severe natural disasters worldwide. The key problem is how to control and mitigate sandstorm natural disasters. This research aims to mitigate their development by improving surface stability and soil water retention properties through soil mineralization. The enzymatic induced carbonate precipitation (EICP) is proposed to solidify desert sands and form a hard crust layer on the surface of desert sands. In contrast to micro-induced carbonate precipitation commonly used at room temperatures, EICP had high production efficiency and productivity at a broader temperature range (10–70 °C ±) and significantly improves material water retention properties, which was more suitable to desert environment. Results demonstrate that the enzyme-catalysed mineralisation method can be better resistance to high winds as the number of spraying times increased.