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A high-contrast luminescent material exhibits dual-emission and pressure-induced bidirectional shifting, originating from the cooperative effects of TADF and excimers. It provides a novel mechanism for the piezochromic behavior.

Utilizes machine learning to propose an absorption bandwidth and structural parameters prediction approach for the design of patterned graphene metasurface absorber, which provides a new direction for the precision design of optical devices.

The medial pallium (MP) is the major forebrain region underlying learning and memory, spatial navigation, and emotion; however, the mechanisms underlying the specification of its principal neuron subtypes remain largely unexplored. Here, by postmitotic deletion of FOXG1 (a transcription factor linked to autism spectrum disorders and FOXG1 syndrome) and single-cell RNA sequencing of E17.5 MP in mice, we found that FOXG1 controls the specification of upper-layer retrosplenial cortical pyramidal neurons [RSC-PyNs (UL)], subiculum PyNs (SubC-PyNs), CA1-PyNs, CA3-PyNs, and dentate gyrus granule cells (DG-GCs) in the MP. We uncovered subtype-specific and subtype-shared FOXG1-regulated transcriptomic networks orchestrating MP neuron specification. We further demonstrated that FOXG1 transcriptionally represses Zbtb20 , Prox1 , and Epha4 to prevent CA3-PyN and DG-GC identities during the specification of RSC-PyNs (UL) and SubC-PyNs; FOXG1 directly activates Nr4a2 to promote SubC-PyN identity. We showed that TBR1, controlled by FOXG1 during CA1-PyN specification, was down-regulated. Thus, our study illuminates MP principal neuron subtype specification and related neuropathogenesis.

Although the design of graphene-based tunable broadband terahertz (THz) absorbers has attracted much attention, how to improve the functionality of the absorber to adapt to different scenarios is still worth...

In this work, we improve the performance of multi-atlas segmentation (MAS) by integrating the recently proposed VoteNet model with the joint label fusion (JLF) approach. Specifically, we first illustrate that using a deep convolutional neural network to predict atlas probabilities can better distinguish correct atlas labels from incorrect ones than relying on image intensity difference as is typical in JLF. Motivated by this finding, we propose VoteNet+, an improved deep network to locally predict the probability of an atlas label to differs from the label of the target image. Furthermore, we show that JLF is more suitable for the VoteNet framework as a label fusion method than plurality voting. Lastly, we use Platt scaling to calibrate the probabilities of our new model. Results on LPBA40 3D MR brain images show that our proposed method can achieve better performance than VoteNet., Comment: Accepted by ISBI-2020

Smoke detection is of great significance for fire location and fire behavior analysis in a fire video surveillance system. Smoke image classification methods based on a deep convolution network have achieved high accuracy. However, the combustion of different types of fuel can produce smoke with different colors, such as black smoke, grey smoke, and white smoke. Additionally, the diffusion characteristic of smoke can lead to transparent smoke regions accompanied by colors and textures of background objects. Therefore, compared with smoke image classification, smoke region detection is a challenging task. This paper proposes a two-stream convolutional neural network based on spatio-temporal attention mechanism for smoke region segmentation (STCNNsmoke). The spatial stream extracts spatial features of foreground objects using the semi-supervised ranking model. The temporal stream uses optical flow characteristics to represent the dynamic characteristics of smoke such as diffusion and flutter features. Specifically, the spatio-temporal attention mechanism is presented to fuse the spatial and temporal characteristics of smoke and pay more attention to the moving regions with smoke colors and textures by predicting attention weights of channels. Furthermore, the spatio-temporal attention model improves the channel response of smoke-moving regions for the segmentation of complete smoke regions. The proposed method is evaluated and analyzed from multiple perspectives such as region detection accuracy and anti-interference. The experimental results showed that the proposed method significantly improved the ability of segmenting thin smoke and small smoke.

Objective Understanding the heterogeneous pathology in Alzheimer's disease and related tauopathies is one of the most urgent and fundamental challenges facing the discovery of novel disease-modifying therapies. Through monitoring ensembles of toxic and nontoxic tau oligomers spontaneously formed in cells, our biosensor technology can identify tool compounds that modulate tau oligomer structure and toxicity, providing much needed insight into the nature and properties of toxic tau oligomers. Background Tauopathies are a group of neurodegenerative disorders characterized by pathologic aggregation of the microtubule binding protein tau. Recent studies suggest that tau oligomers are the primary toxic species in tauopathies. New/Updated Hypothesis We hypothesize that tau biosensors capable of monitoring tau oligomer conformation are able to identify tool compounds that modulate the structure and conformation of these tau assemblies, providing key insight into the unique structural fingerprints of toxic tau oligomers. These fingerprints will provide gravely needed biomarker profiles to improve staging of early tauopathy pathology and generate lead compounds for potential new therapeutics. Our time-resolved fluorescence resonance energy transfer biosensors provide us an exquisitely sensitive technique to monitor minute structural changes in monomer and oligomer conformation. In this proof-of-concept study, we identified a novel tool compound, MK-886, which directly binds tau, perturbs the conformation of toxic tau oligomers, and rescues tau-induced cytotoxicity. Furthermore, we show that MK-886 alters the conformation of tau monomer at the proline-rich and microtubule binding regions, stabilizing an on-pathway oligomer. Major Challenges for the Hypothesis Our approach monitors changes in the ensemble of assemblies that are spontaneously formed in cells but does not specifically isolate or enrich unique toxic tau species. However, time-resolved fluorescence resonance energy transfer does not provide high-resolution, atomic scale information, requiring additional experimental techniques to resolve the structural features stabilized by different tool compounds. Linkage to Other Major Theories Our biosensor technology is broadly applicable to other areas of tauopathy therapeutic development. These biosensors can be readily modified for different isoforms of tau, specific post-translational modifications, and familial Alzheimer's disease–associated mutations. We are eager to explore tau interactions with chaperone proteins, monitor cross-reactivity with other intrinsically disordered proteins, and target seeded oligomer pathology.

As an important way for power distribution, the distribution network based on various types of wire and cable products is directly related to the safety of power transportation. Based on the 2331 test results of extruded insulated power cables and overhead insulated wires of the testing center in recent years, this paper analyzes and studies the change of the unqualified rate and the possible causes and hazards of unqualified products. The study found that the unqualified items of the distribution network wire and cable products mainly focused on the structural size, insulation thermal shrinkage, thermal extension and 20 °C conductor direct- current resistance. Further investigation shows that the factors that lead to the quality failure of the distribution network wire and cable products can be basically attributed to two aspects: one is the raw material problem, some manufacturers use substandard insulation materials and conductor materials when producing cables; the second is the processing technology problem, steel tape winding process, insulation cross-linking process and conductor drawing process is not under strict control. Based on the above research, this work puts forward measures to improve the quality of distribution network wire and cable products, hoping to attract manufacturers' attention to product quality and provide analytical support for the quality control of distribution network materials.

A 8-chanenel interface IC for ambient vibration energy harvesting from broadband MEMS transducer array is presented. A rectifier-free synchronized electrical charging extraction (RF-SECE) architecture is proposed to harvest low voltage ambient vibration. A nW zero-crossing detector with compensation loop calibrates the comparator offset adaptively. Conflict-free inductor sharing by multiple channels is achieved with an asynchronous arbiter. The chip works in 100 Hz – 2KHz range with 15 nA/channel quiescent power and 580% efficiency improvement compared to the full-bridge rectifier at 0.3V input.

Low-cost and efficient multifunctional electrodes play an important part in promoting the practical application of energy conversion and storage. Herein, we report the facile synthesis of N-graphyne, with a novel structure, by one-step ball milling of CaC2 and pyrazine. The accurate doping of nitrogen atoms at the controllable sites of the molecular skeleton of γ-graphyne was achieved using the nitrogenous precursor (pyrazine) as a reactant. Various techniques were adopted for the investigation of the composition, structure, and morphology of the obtained samples. The electrochemical measurements demonstrated that N-graphyne can serve as an excellent electrode material for both electrocatalysis and supercapacitors. As an electrocatalyst, N-graphyne exhibited an overpotential of 280 mV at 100 mA cm-2 and a Tafel slope of 122 mV dec-1 for the oxygen evolution reaction with highly stable morphology and electrocatalytic performance. As a supercapacitor electrode, N-graphyne showed a maximum capacitance of 235 F g-1 at 1 A g-1, and capacitance retention of 87% after 3000 cycles. The superior electrochemical performance of N-graphyne is due to the nitrogen heteroatomic defects, large electrochemical active surface areas and fast electron migration. Our studies provide a facile synthesis of novel N-graphyne with controllable doping sites and promote its potential applications in electrocatalysis and supercapacitors.

Huntington's disease (HD) is the most common inherited neurodegenerative disorder and one of the nine polyglutamine (polyQ) diseases. HD is characterized by the pathological aggregation of the misfolded huntingtin exon 1 protein (Httex1) with abnormally long polyQ expansion due to genetic mutation. While there is currently no effective treatment for HD, inhibition of aggregate formation represents a direct approach in mediating the toxicity associated with Httex1 misfolding. To exploit this therapeutic window, we engineered two fluorescence resonance energy transfer (FRET) based biosensors that monitor the aggregation of Httex1 with different expanded Q-lengths (Q39 and Q72) in living cells. These FRET biosensors, together with a high-precision fluorescence lifetime detection platform, enable high-throughput screening of small molecules that target Httex1 aggregation. We found six small molecules that decreased the FRET of the biosensors and reduced Httex1-Q72-induced neuronal cytotoxicity in N2a cells with nanomolar potency. Using advanced SPR and EPR techniques, we confirmed that the compounds directly bind to Httex1 fibrils and inhibit aggregate formation. This strategy in targeting the Httex1 aggregates can be applicable to other proteins involved in polyQ related diseases.

Herein, the alkynyl groups are grafted on g-C3N4 via ball milling of g-C3N4 and CaC2. The successful functionalization of g-C3N4 by alkynyl is demonstrated by Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. Ultrathin wrinkle-like structure with abundant pores is observed for the resulted alkynyl functionalized g-C3N4 nanosheets, which can furnish abundant photocatalytic active sites. The photocatalytic NH3 production rate of 0.12 mmol·gcat-1·h-1 is gained for alkynyl functionalized g-C3N4 under UV-visible irradiation, which is 2.0-folds higher than pristine g-C3N4 (0.058 mmol·gcat-1·h-1). A maximum of 1.9 and 1.5 folds enhancements are achieved for the photodegradation of rhodamine B and levofloxacin over functionalized g-C3N4 compared to pristine g-C3N4. The improved mechanism is investigated by linear sweep voltammetry, transient photo-current, Tafel plots, electrochemical impedance spectroscopy, Mott-Schottky curves, turnover frequency, transient decay time, photoluminescence spectra, etc. The modification technique proposed in this paper can also be adopted to prepare other alkynyl functionalized semiconductors for photocatalytic application.

Herein, a series of TiO2/Bi0.5Na0.5TiO3 heterojunctions with different mass ratio are first constructed under ultrasound irradiation, which shows superior sonocatalytic and photoelectrochemical properties. The improved performance is attributed to the internal electric field and close interfacial contact of TiO2 and Bi0.5Na0.5TiO3, which promote the transfer of photoinduced electrons from TiO2 to Bi0.5Na0.5TiO3. The spontaneous polarization of ferroelectrics in the heterostructures can be adjusted by ultrasonic treatment durations, which further influences the internal electric field. The TiO2/Bi0.5Na0.5TiO3 sample with mass ratios of 5:1 and ultrasonic treatment for 15 min presents the highest sonocatalytic degradation of Rhodamine B. Moreover, the sonocatalytic activity also shows a close relevant to the ultrasonic temperature and frequency of degradation process. And, the •OH radicals are mainly responsible for Rhodamine B sonodegradation. This work provides a novel TiO2/Bi0.5Na0.5TiO3 heterojunctions and promotes the application of ferroelectrics modified materials in sono-/photo-catalysis.

Herein, a series of N-TiO2/Ti3C2 (N-TTCx) composites are constructed via ultrasonically treating pre-synthesized N doped TiO2 and Ti3C2 at room temperature. The intimate contact between N-TiO2 and Ti3C2 is verified by the transmission electron microscopy, scanning electron microscopy, and X-ray photoelectron spectroscopy. And, the N-TTC0.06 composites exhibit the best sonophotocatalytic performance, which is 1.9 times and 8.8 folds higher than that of TiO2 for pollutant degradation and nitrogen fixation, respectively. What’s more, sonophotocatalytic degradation of pollutants and nitrogen fixation shows larger efficiency compared with that of photocatalysis and sonocatalysis, which is attributed to the synergistic effect between sonocatalysis and photocatalysis in the sonophotocatalytic process. Moreover, taking N-TTC0.06 for example, the three cycles of sonophotocatalytic degradation of Rh-B and nitrogen fixation as well as the unchanged X-ray diffraction before and after sonophotocatalytic nitrogen fixation all confirm the stable sonophotocatalytic behavior of N-TiO2/Ti3C2 composites. The improved sonophotocatalytic activity is attributed to the decrease of band gap by N 2p doping level and the separation/transfer improvement of photoinduced carriers by Ti3C2, which are demonstrated by UV–vis absorption spectra, photoelectrochemical tests, Nyquists plots, and Mott-schottky plots. This study confirms that the designed N-TiO2/Ti3C2 composites exhibit great potential application in sonophotocatalytic pollutant degradation and nitrogen fixation.

A paradigm for material synthesis is demonstrated under the guidance of machine learning as well as the designed wet-lab experiments. This work begins with a series of well-tailored experiments, including the synthesis of C/N-codoped TiO2 nanoparticles with exposed anatase {001} facets and its photocatalytic performance evaluations. In advance of applying the machine learning, the underlying synergistic effects are revealed and expounded in detail purely based on wet-lab experiments. Then, the idea of active learning is adopted by implementing a multi-step workflow to iterate the experiments around preferred synthesis conditions, and unravel the optimum synthesis parameters with the assistance of machine learning. In this work, the prediction models are built on a solid physical and chemical understanding, rather than a ‘black box approach’. To this end, the designed experiments as well as the bootstrapping technique together guarantee a good prediction with less training. The compatibility of the artificial neural network and wet-lab experiment also endows the current strategy with several innate advantages. Moreover, controlled experiments with different starting materials were carried out. The results suggest that, in an effort to ensure more accurate predictions, further restrictions could be placed on data collection prior to the stage of introducing machine learning, and the design of wet-lab experiment plays a crucial role in facilitating machine learning. Lastly, the future prospect for machine learning-assisted material synthesis is briefly discussed based on the current work.