Pain experience reduces social avoidance to others in pain: a c-Fos-based functional connectivity network study in mice.

Pain experience increases individuals' perception and contagion of others' pain, but whether pain experience affects individuals' affiliative or antagonistic responses to others' pain is largely unknown. Additionally, the neural mechanisms underlying how pain experience modulates individuals' responses to others' pain remain unclear. In this study, we explored the effects of pain experience on individuals' responses to others' pain and the underlying neural mechanisms. By comparing locomotion, social, exploration, stereotyped, and anxiety-like behaviors of mice without any pain experience (naïve observers) and mice with a similar pain experience (experienced observers) when they observed the pain-free demonstrator with intraperitoneal injection of normal saline and the painful demonstrator with intraperitoneal injection of acetic acid, we found that pain experience of the observers led to decreased social avoidance to the painful demonstrator. Through whole-brain c-Fos quantification, we discovered that pain experience altered neuronal activity and enhanced functional connectivity in the mouse brain. The analysis of complex network and graph theory exhibited that functional connectivity networks and activated hub regions were altered by pain experience. Together, these findings reveal that neuronal activity and functional connectivity networks are involved in the modulation of individuals' responses to others' pain by pain experience.

LncBook 2.0: integrating human long non-coding RNAs with multi-omics annotations.

LncBook, a comprehensive resource of human long non-coding RNAs (lncRNAs), has been used in a wide range of lncRNA studies across various biological contexts. Here, we present LncBook 2.0 (https://ngdc.cncb.ac.cn/lncbook), with significant updates and enhancements as follows: (i) incorporation of 119 722 new transcripts, 9632 new genes, and gene structure update of 21 305 lncRNAs; (ii) characterization of conservation features of human lncRNA genes across 40 vertebrates; (iii) integration of lncRNA-encoded small proteins; (iv) enrichment of expression and DNA methylation profiles with more biological contexts and (v) identification of lncRNA-protein interactions and improved prediction of lncRNA-miRNA interactions. Collectively, LncBook 2.0 accommodates a high-quality collection of 95 243 lncRNA genes and 323 950 transcripts and incorporates their abundant annotations at different omics levels, thereby enabling users to decipher functional significance of lncRNAs in different biological contexts.

Analysis of size-dependent optoelectronic properties of red AlGaInP micro-LEDs.

We have theoretically investigated the size-dependent optoelectronic properties of InGaP/AlGaInP-based red micro-LEDs through an electro-optical-thermal coupling model. The model considers thermal effects due to current crowding near the electrodes, non-thermal efficiency droop due to electron leakage, and etch defects on the LED sidewall. Sidewall defects reduce the carrier concentration at the light-emitting surface's edge and exacerbate the current crowding effect. In addition, p-side electron leakage at high current densities is the leading cause of the efficiency droop of AlGaInP LEDs. In contrast, the effect of temperature on the overall efficiency degradation of LEDs is even more significant.

The Novel Fluorescent Probe Toward Yttrium(III) and its Bioimaging.

In this paper, the novel fluorescence probe XP based on Schiff-base was designed, synthesized and characterized, which could detect Y3+selectively and sensitively. The recognition mechanism of XP toward Y3+ was studied by Job's plot and HRMS. It was investigated that stoichiometric ratio of the probe XP conjugated with Y3+ was 1:2. And the detection limit was calculated as 0.30 µM. In addition, Y3+ was recognized by the test paper made from XP. And the probe XP could detect  Y3+ selectively in Caenorhabditis elegans and the main organs of mice. Thus, XP was considered to have some potential for application in bioimaging.

A Nanoenzyme Constructed from Manganese and Strandberg-Type Phosphomolybdate with Versatility in Antioxidant and Modulating Conformation of Aß Protein Misfolding Aggregates In Vitro.

Amyloid ß-peptide (Aß) misfolding aggregates with ß-sheet structures and surplus reactive oxygen species (ROS) are both considered to be the culprit of neuronal toxicity in Alzheimer's disease (AD). Therefore, modulating the misfolding mode of Aß and inhibiting ROS simultaneous has become an important method for anti-AD. Herein, a nanoscale manganese-substituted polyphosphomolybdate (H2en)3[Mn(H2O)4][Mn(H2O)3]2[P2Mo5O23]2·14.5H2O (abbreviated as MnPM) (en = ethanediamine) was designed and synthesized by single crystal to single crystal transformation method. MnPM can modulate the ß-sheet rich conformation of Aß aggregates, and thus reduce the formation of toxic species. Moreover, MnPM also possesses the ability to eliminate the free radicals produced by Cu2+-Aß aggregates. It can inhibit the cytotoxicity of ß-sheet-rich species and protect synapses of PC12 cells. MnPM combines the conformation modulating ability of Aß and anti-oxidation ability, which makes a promising multi-funcational molecular with a composite mechanism for the new conceptual designing in treatment of such protein-misfolding diseases.

Strategy for Detecting Carbon Monoxide: Cu2+-Assisted Fluorescent Probe and Its Applications in Biological Imaging.

Herein, a novel strategy was proposed for identifying carbon monoxide (CO), which plays a crucial part in living systems. For the first time, we have managed to design, synthesize, and characterize successfully this new Cu2+-assisted fluorescent probe (DPHP) in detecting CO. Compared with the commonly adopted Pd0-mediated Tsuji-Trost reaction recognition method, such a new strategy did not engage costly palladium (II) salt and generated no leaving group, indicating a satisfactory anti-interference ability. The recognition mechanism was confirmed by IR, 1H NMR titration, HR-MS, cyclic voltammetry, X-ray photoelectron spectroscopy, electron paramagnetic resonance, and optical properties. Surprisingly, it was found that the new method achieved high selectivity and rapid identification of CO with a lower limit of detection (1.7 × 10-8 M). More intriguingly, it could recognize endogenous and exogenous CO in HeLa cells. The cytotoxicity of this new method was so low that it allowed the detection of CO in mice and zebrafish. Basically, our results trigger a novel viewpoint of rationally designing and synthesizing advanced materials for CO detection with unique features, impelling new research in detection chemistry.

Discovery and preclinical evaluations of WX-0593, a novel ALK inhibitor targeting crizotinib-resistant mutations.

ALK gene rearrangements are oncogenic drivers in approximately 5% of NSCLC. Crizotinib, a first generation ALK inhibitor, is widely prescribed for ALK-positive NSCLC in clinic. Resistance to crizotinib and other ALK inhibitors has been problematic. Addressing resistance, here we describe discovery and development of a novel, proprietary spirocyclic diamine-substituted aryl phosphine oxide series of inhibitors, which led to the identification of WX-0593 (16a) as a potent ALK inhibitor. WX-0593 inhibited the activity of both wild type and resistant mutants of ALK in vitro, showed strong antitumor activity in a crizotinib-resistant mouse PDX model. WX-0593 is currently under development in phase II/III clinical trials.

Multispectral radiometric temperature measurement algorithm for turbine blades based on moving narrow-band spectral windows.

This paper addresses the problem of inaccurate emissivity presets for multispectral temperature measurements of aero-engine turbine blades and proposes a narrow-band spectral window moving temperature inversion algorithm that does not rely on an assumed emissivity model. As the emissivity of the measured object changes slowly over the narrow spectral window, the temperature corresponding to the normalized spectral radiation intensity for each window in the set temperature range is calculated using the Mahalanobis distance coefficient. The temperature error is less than 1.33% relative to thermocouple measurements when using this algorithm to perform temperature inversion on the experimental spectrum curves for different types of alloy samples. Furthermore, a two-dimensional spectral temperature field measurement platform was built, and the surface temperature fields of alloy samples were reconstructed using the narrow-band spectral window moving algorithm. The proposed algorithm is shown to provide high-precision inversion of the temperature field without presetting the emissivity model, which gives a new processing concept for the application of infrared spectral temperature measurements.

2000 PPI silicon-based AlGaInP red micro-LED arrays fabricated via wafer bonding and epilayer lift-off.

In this article, 2000 PPI red silicon-based AlGaInP micro-LED arrays were fabricated and investigated. The AlGaInP epilayer was transferred onto the silicon substrate via the In-Ag bonding technique and an epilayer lift-off process. The silicon substrate with a high thermal conductivity could provide satisfactory heat dissipation, leading to micro-LED arrays that had a stable emission spectrum with increasing current density from 20 to 420 A/cm2 along with a red-shift of the peak position from 624.69 to 627.12 nm (Δλ = 2.43 nm). Additionally, increasing the injection current density had little effect on the CIE (x, y) of the micro-LED arrays. Further, the I-V characteristics and light output power of micro-LED arrays with different pixel sizes demonstrated that the AlGaInP red micro-LED array on a silicon substrate had excellent electrical stability and optical output.

Data processing and performance evaluation of a tempo-spatially mixed modulation imaging Fourier transform spectrometer based on stepped micro-mirror.

A novel tempo-spatially mixed modulation imaging Fourier transform spectrometer based on a stepped micro-mirror has the advantages of high throughput, compactness, and stability. In this paper, we present a method of image- and spectrum-processing and performance evaluation, which is utilized to obtain a high-quality reconstructed image without stitching gaps and a reconstructed spectrum with significantly reduced noise and side-lobe oscillation. A theoretical model of instrument line shape and signal-to-noise ratio is established to verify the effectiveness of non-uniformity sampling correction and spectral resolution enhancement. Meanwhile, the performance of the instrument was evaluated combined with experimental results.

48 × 48 pixelated addressable full-color micro display based on flip-chip micro LEDs.

This paper reports on the design and fabrication of a ${48} \times {48}$48×48 full-color pixelated addressable light-emitting diode on silicon (LEDoS) micro display. The metallization pattern was designed and fabricated on a silicon substrate, while red, green, and blue monochromatic micro LEDs were integrated on the silicon substrate using transfer printing. The red, green, and blue micro LEDs are flip-chip structures in which red micro LEDs were fabricated using substrate transfer, mesa etching, metal deposition, and chip dicing. The integration process does not require wire bonding, which reduces the full-color pixel size and increases the integration speed. The LEDoS micro display can be addressed individually for each LED pixel and display representative patterns.

MMSET regulates histone H4K20 methylation and 53BP1 accumulation at DNA damage sites.

p53-binding protein 1 (53BP1) is known to be an important mediator of the DNA damage response, with dimethylation of histone H4 lysine 20 (H4K20me2) critical to the recruitment of 53BP1 to double-strand breaks (DSBs). However, it is not clear how 53BP1 is specifically targeted to the sites of DNA damage, as the overall level of H4K20me2 does not seem to increase following DNA damage. It has been proposed that DNA breaks may cause exposure of methylated H4K20 previously buried within the chromosome; however, experimental evidence for such a model is lacking. Here we found that H4K20 methylation actually increases locally upon the induction of DSBs and that methylation of H4K20 at DSBs is mediated by the histone methyltransferase MMSET (also known as NSD2 or WHSC1) in mammals. Downregulation of MMSET significantly decreases H4K20 methylation at DSBs and the subsequent accumulation of 53BP1. Furthermore, we found that the recruitment of MMSET to DSBs requires the γH2AX-MDC1 pathway; specifically, the interaction between the MDC1 BRCT domain and phosphorylated Ser 102 of MMSET. Thus, we propose that a pathway involving γH2AX-MDC1-MMSET regulates the induction of H4K20 methylation on histones around DSBs, which, in turn, facilitates 53BP1 recruitment.

[Analysis and Design of Interference Imaging System in Fourier Transform Imaging Spectrometer Based on Multi-Micro-Mirror].

To realize the static state and high throughput of Fourier transform imaging spectrometer (FTIS), a temporal spatial mixed modulated FTIS based on multi-micro-mirror was put forward in this paper, whose interference system was based on Michelson interferometer with a multi-micro-mirror to replace the plane mirror. The remarkable characteristics of this FTIS were no movable parts and slit existing in this system, and the interferogram and image of object could be gained at the same time. The fore-optics system imaged the object on the plane mirror and multi-micro-mirror of the interference system, due to the structure feature of multi-micro-mirror, the optical path difference (OPD) of two imaging beam could be modulated. Through the reimaging system, the image of object with different interference order could be obtained. By means of the analysis to the spectrum signal-to-noise ratio (SNR) of interference system, the relationship between spectrum SNR and image SNR was definite, and the characteristic parameters of multi-micro-mirror were determined. To ensure the constancy of OPD corresponding to each step plane, by means of the analysis to the imaging process of fore-optics system, the optical path structure of telecentric in image space was determined. According to the calculation of the relationship between field of view and OPD, the design indexes of fore-optics system were determined and the optical design was completed. To ensure no extra OPD was introduced by reimaging system, through the analysis of the imaging feature by reimaging system, the optical path structure of double telecentric was determined. According to the calculation of the relationship between incidence aperture angle and step number, the optical system that satisfied the system requirement was designed. By means of the theory analysis and optical design to each unit system, this research can provide a novel development strategy for static and high throughput FTIS.

Design of spatio-temporally modulated static infrared imaging Fourier transform spectrometer.

A novel static medium wave infrared (MWIR) imaging Fourier transform spectrometer (IFTS) is conceptually proposed and experimentally demonstrated. In this system, the moving mirror in traditional temporally modulated IFTS is replaced by multi-step micro-mirrors to realize the static design. Compared with the traditional spatially modulated IFTS, they have no slit system and are superior with larger luminous flux and higher energy efficiency. The use of the multi-step micro-mirrors can also make the system compact and light.

Pathways in formulating foods for the elderly.

The growth of the elderly population worldwide is posing significant challenges to human society. The progressive physical and physiological changes occur with aging, including decreased appetite, incomplete digestion, and reduced absorption of nutrients. A common feature of many elderly people's diets is a deficiency in proteins (especially easily digestible ones) and micronutrients (e.g., vitamins, zinc, iron, and calcium). One of the solutions to this problem is the incorporation of these components into suitably texture-modified foods. There is a dearth of products that meet the needs of the elderly with special medical/health conditions such as dysphagia, osteoporosis, diabetes, and cardiovascular disease, as well as those who are in hospital and palliative care. Future research and development of foods for the elderly must address specific dietary needs of different subgroups of elderly people with underlying health conditions. The existence of different physical and physiological stages of the elderly means that their specific dietary requirements must be considered. This review summarizes current knowledge on nutritional requirements including those with underlying health problems and outlines the research and innovation pathways for developing new foods considering nutrition, texture, flavor, and other sensory aspects.

ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network.

Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNN) have demonstrated superior performance compared to conventional machine learning (ML) approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.

Application of improved grey wolf model in collaborative trajectory optimization of unmanned aerial vehicle swarm.

With the development of science and technology and economy, UAV is used more and more widely. However, the existing UAV trajectory planning methods have the limitations of high cost and low intelligence. In view of this, grey Wolf algorithm is being used to achieve collaborative trajectory optimization of UAV groups. However, it is found that the Grey Wolf optimization algorithm (GWO) has the problem of weak cooperation. In this study, based on the traditional GWO pheromone factor is introduced to improve it.. Aiming at the problem of unstable performance of swarm intelligence optimization algorithm under dynamic threat, deep reinforcement learning is used to optimize the model. An unmanned aerial vehicle swarm trajectory planning model was constructed based on the improved grey wolf algorithm. Through experimental analysis, the optimal fitness value of the improved grey wolf algorithm was lower than 0.43 of the grey wolf algorithm. Compared with other algorithms, the fitness value of this algorithm is significantly reduced and the stability is higher. In complex scenarios, the improved grey wolf algorithm had a trajectory length of 70.51 km and a planning time of 5.92 s, which was clearly superior to other algorithms. The path length planned by the research and design model was 58.476 km, which was significantly smaller than the other three models. The planning time was 5.33 s and the number of path extension points was 46. The indicator values of the Unmanned Aerial Vehicle swarm trajectory planning model designed by this research were all smaller than the other three models. By analyzing the results, the model can achieve low-cost trajectory optimization, providing more reasonable technical support for unmanned aerial vehicle mission execution.

EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification.

Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human-computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.

Classification Algorithm for fNIRS-based Brain Signals Using Convolutional Neural Network with Spatiotemporal Feature Extraction Mechanism.

Brain Computer Interface (BCI) is a highly promising human-computer interaction method that can utilize brain signals to control external devices. BCI based on functional near-infrared spectroscopy (fNIRS) is considered a relatively new and promising paradigm. fNIRS is a technique of measuring functional changes in cerebral hemodynamics. It detects changes in the hemodynamic activity of the cerebral cortex by measuring oxyhemoglobin and deoxyhemoglobin (HbR) concentrations and inversely predicts the neural activity of the brain. At the present time, Deep learning (DL) methods have not been widely used in fNIRS decoding, and there are fewer studies considering both spatial and temporal dimensions for fNIRS classification. To solve these problems, we proposed an end-to-end hybrid neural network for feature extraction of fNIRS. The method utilizes a spatial-temporal convolutional layer for automatic extraction of temporally valid information and uses a spatial attention mechanism to extract spatially localized information. A temporal convolutional network (TCN) is used to further utilize the temporal information of fNIRS before the fully connected layer. We validated our approach on a publicly available dataset including 29 subjects, including left-hand and right-hand motor imagery (MI), mental arithmetic (MA), and a baseline task. The results show that the method has few training parameters and high accuracy, providing a meaningful reference for BCI development.

EEG-FMCNN: A fusion multi-branch 1D convolutional neural network for EEG-based motor imagery classification.

Motor imagery (MI) electroencephalogram (EEG) signal is recognized as a promising paradigm for brain-computer interface (BCI) systems and has been extensively employed in various BCI applications, including assisting disabled individuals, controlling devices and environments, and enhancing human capabilities. The high-performance decoding capability of MI-EEG signals is a key issue that impacts the development of the industry. However, decoding MI-EEG signals is challenging due to the low signal-to-noise ratio and inter-subject variability. In response to the aforementioned core problems, this paper proposes a novel end-to-end network, a fusion multi-branch 1D convolutional neural network (EEG-FMCNN), to decode MI-EEG signals without pre-processing. The utilization of multi-branch 1D convolution not only exhibits a certain level of noise tolerance but also addresses the issue of inter-subject variability to some extent. This is attributed to the ability of multi-branch architectures to capture information from different frequency bands, enabling the establishment of optimal convolutional scales and depths. Furthermore, we incorporate 1D squeeze-and-excitation (SE) blocks and shortcut connections at appropriate locations to further enhance the generalization and robustness of the network. In the BCI Competition IV-2a dataset, our proposed model has obtained good experimental results, achieving accuracies of 78.82% and 68.41% for subject-dependent and subject-independent modes, respectively. In addition, extensive ablative experiments and fine-tuning experiments were conducted, resulting in a notable 7% improvement in the average performance of the network, which holds significant implications for the generalization and application of the network.