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.

Investigation of interactions between Jiuzao glutelin with resveratrol, quercetin, curcumin, and azelaic and potential improvement on physicochemical properties and antioxidant activities.

The interactions among small molecular functional components (FCTs) within a food matrix have become a focal point for enhancing their stability and bioactivities. Jiuzao glutelin (JG) is a mixed plant protein within Jiuzao (a protein-rich baijiu distillation by-product). This study aimed to explore the interactions between JG and selected FCTs, including resveratrol (RES), quercetin (QUE), curcumin (CUR), and azelaic acid (AZA), and the consequential impact on stability and antioxidant activity of the complexes. The findings conclusively demonstrated that the interactions between JG and the FCTs significantly enhanced the storage stability of the complexes. Moreover, the antioxidant activity of the complexes exhibited improvement compared to their individual counterparts. This study underscores the notion that JG and FCTs mutually reinforce, exerting positive effects on stability and antioxidant activity. This symbiotic relationship can be strategically employed to augment the quality of proteins and enhance the functional properties of bioactive components through these interactions.

Sexually dimorphic control of affective state processing and empathic behaviors.

Recognizing the affective states of social counterparts and responding appropriately fosters successful social interactions. However, little is known about how the affective states are expressed and perceived and how they influence social decisions. Here, we show that male and female mice emit distinct olfactory cues after experiencing distress. These cues activate distinct neural circuits in the piriform cortex (PiC) and evoke sexually dimorphic empathic behaviors in observers. Specifically, the PiC → PrL pathway is activated in female observers, inducing a social preference for the distressed counterpart. Conversely, the PiC → MeA pathway is activated in male observers, evoking excessive self-grooming behaviors. These pathways originate from non-overlapping PiC neuron populations with distinct gene expression signatures regulated by transcription factors and sex hormones. Our study unveils how internal states of social counterparts are processed through sexually dimorphic mechanisms at the molecular, cellular, and circuit levels and offers insights into the neural mechanisms underpinning sex differences in higher brain functions.

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.

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.

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.

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.

A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation.

Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases.

Classification Algorithm for Electroencephalogram-based Motor Imagery Using Hybrid Neural Network with Spatio-temporal Convolution and Multi-head Attention Mechanism.

Motor imagery (MI) is a brain-computer interface (BCI) technique in which specific brain regions are activated when people imagine their limbs (or muscles) moving, even without actual movement. The technology converts electroencephalogram (EEG) signals generated by the brain into computer-readable commands by measuring neural activity. Classification of motor imagery is one of the tasks in BCI. Researchers have done a lot of work on motor imagery classification, and the existing literature has relatively mature decoding methods for two-class motor tasks. However, as the categories of EEG-based motor imagery tasks increase, further exploration is needed for decoding research on four-class motor imagery tasks. In this study, we designed a hybrid neural network that combines spatiotemporal convolution and attention mechanisms. Specifically, the data is first processed by spatiotemporal convolution to extract features and then processed by a Multi-branch Convolution block. Finally, the processed data is input into the encoder layer of the Transformer for a self-attention calculation to obtain the classification results. Our approach was tested on the well-known MI datasets BCI Competition IV 2a and 2b, and the results show that the 2a dataset has a global average classification accuracy of 83.3% and a kappa value of 0.78. Experimental results show that the proposed method outperforms most of the existing methods.

Differences in the neural basis and transcriptomic patterns in acute and persistent pain-related anxiety-like behaviors.

Background: Both acute and persistent pain is associated with anxiety in clinical observations, but whether the underlying neural mechanisms differ is poorly understood. Methods: We used formalin or complete Freund's adjuvant (CFA) to induce acute or persistent pain. Behavioral performance was assessed by the paw withdrawal threshold (PWT), open field (OF), and elevated plus maze (EPM) tests. C-Fos staining was used to identify the activated brain regions. Chemogenetic inhibition was further performed to examine the necessity of brain regions in behaviors. RNA sequencing (RNA-seq) was used to identify the transcriptomic changes. Results: Both acute and persistent pain could lead to anxiety-like behavior in mice. The c-Fos expression indicates that the bed nucleus of the stria terminalis (BNST) is activated only in acute pain, whereas the medial prefrontal cortex (mPFC) is activated only in persistent pain. Chemogenetic manipulation reveals that the activation of the BNST excitatory neurons is required for acute pain-induced anxiety-like behaviors. In contrast, the activation of the prelimbic mPFC excitatory neurons is essential for persistent pain-induced anxiety-like behaviors. RNA-seq reveals that acute and persistent pain induces differential gene expression changes and protein-protein interaction networks in the BNST and prelimbic mPFC. The genes relevant to neuronal functions might underline the differential activation of the BNST and prelimbic mPFC in different pain models, and be involved in acute and persistent pain-related anxiety-like behaviors. Conclusion: Distinct brain regions and gene expression patterns are involved in acute and persistent pain-related anxiety-like behaviors.

Microfluidic static droplet generated quantum dot arrays as color conversion layers for full-color micro-LED displays.

This paper presents an easy and intact process based on microfluidics static droplet array (SDA) technology to fabricate quantum dot (QD) arrays for full-color micro-LED displays. A minimal sub-pixel size of 20 µm was achieved, and the fluorescence-converted red and green arrays provide good light uniformity of 98.58% and 98.72%, respectively.

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.

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.

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.

A novel label-free universal biosensing platform based on CRISPR/Cas12a for biomarker detection.

The development of a biosensing platform with high sensitivity, high specificity, and low cost for the detection of biomarkers, especially one that is programmable and universal, is critical for disease surveillance and diagnosis, yet it remains a difficulty. Herein, we combined the clustered regularly interspaced short palindromic repeats (CRISPR)/Cas system with a fluorescent label-free biosensor platform for sensitive and specific detection of disease-related protein, small molecule and nucleic acid. In this strategy, we designed an exonuclease III-mediated target cycle and released a universal trigger chain to stimulate the enzyme activity of CRISPR/Cas12a for additional signal amplification. The hydrolysis of ssDNA-templated silver nanoclusters (ssDNA-Ag NCs) as the reporter probe resulted in a significant decrease of fluorescence intensity. This biosensing platform can be flexibly used to the sensitive and specific determination of protein, small molecule, or microRNA in biological samples by simply transforming the target recognized sequences in the DNA hairpin. In this work, a new label-free sensing system used the fluorescent ssDNA-Ag NCs as the signal output does not need to be marked in advance and has no background signal. In addition, the method has the advantages of low cost, simple operation and high speed, and provides an innovative idea for the development of a powerful clinical diagnosis tool.

A MnOx-nucleic acid nanoprobe with enzyme-free cascade signal amplification for ultrasensitive intracellular microRNA imaging.

A novel MnOx-nucleic acid nanoprobe was constructed for catalytic imaging of microRNA in living cells based on the combination of catalytic hairpin assembly, hybridization chain reaction, and DNAzyme amplification. This nanoprobe exhibited ultrahigh sensitivity and specificity, and could distinguish tumor cells and normal cells by live cell microRNA imaging.

Therapeutic drug monitoring of perampanel in children diagnosed with epilepsy: Focus on influencing factors on the plasma concentration-to-dose ratio.

OBJECTIVE: This study aimed to investigate the efficacy and tolerability of perampanel (PER) therapy and to optimize a specific plasma reference range for PER in children. Another major aim was to evaluate the potential determinators of PER concentration. METHODS: Concentrations obtained from 80 children were analyzed for routine therapeutic drug monitoring (TDM) between 2021 and 2022. We retrospectively reviewed the clinical data of these patients and assessed the efficacy at 3 months after treatment initiation. Trough concentration-to-dose ratio (C0 /Dose ratio) of PER was compared among patients on various potential influencing factors. RESULTS: A 3-month PER therapy produced a ≥50% reduction in seizure frequency in 58.8% of patients. Twelve patients reported at least one adverse effect (AE), mainly dizziness. The monitoring data showed that the median C0 was 325.5 ng/mL. Under maintenance dosages, approximately 75% of the C0 values were 180.0-610.0 ng/mL. The C0 /Dose ratio in patients aged 1 to <4 was significantly lower by twofold than in those aged 4 to ≤12 years (P = 0.001). Enzyme-inducing ASMs (EIASMs) decreased the C0 /Dose ratio of PER by 25.9% (P = 0.165). In addition, seizure frequency reduction in responders was achieved at a median PER C0 value of 357 ng/mL, which was similar to the value of 314 ng/mL found in nonresponders (P = 0.288). No significant difference was found in PER C0 values between patients with and without AEs (P = 0.082). SIGNIFICANCE: In this study, PER treatment showed acceptable efficacy and tolerance in Chinese children with epilepsy. Contributing factors like age to variable C0 /Dose ratios were identified, and complex PER-ASMs interactions were observed. Notably, the reference range, that is, 180.0-610.0 ng/mL, for routine PER monitoring may be more applicable for them. Routine TDM should be considered a positive attempt to manage the effectiveness and safety of PER.

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.

FACS-Based Neuronal Cell Type-Specific RNA Isolation and Alternative Splicing Analysis.

Alternative splicing of pre-mRNAs expands the coding abilities of genomes by generating distinct transcription variants from individual genes. It contributes to the marvelous complexity of the transcriptome in neurons. Given the differential expression of alternative splicing regulators and diversity in alternative splicing programs in neuronal subpopulations, it is urgent and necessary to develop methods to efficiently isolate diverse subgroups of neurons and analyze their transcriptomic diversity. Here, we describe a protocol to isolate RNA from specific neuronal types using a fluorescence-activated cell sorting (FACS)-based method to analyze alternative splicing events in a cell type-specific manner. The method is universally applicable to analyze alternative splicing in fluorescent protein-labeled neuronal types. It was optimized to preserve the transcription state and improve efficiency in cell suspension purification. With our protocol, fluorescent protein-labeled neurons could be efficiently purified. The transcription states suitable for gene expression and alternative splicing analysis could be well-preserved.

A sensitive and facile microRNA detection based on CRISPR-Cas12a coupled with strand displacement amplification.

MicroRNAs (miRNAs) are important biomarkers that are closely associated with certain diseases. The detection of miRNA is critical because it provides the necessary information for Disease Diagnosis. In this study, we achieved miRNA determination by coupling the CRISPR-Cas (Clustered regularly interspaced short palindromic repeats-CRISPR-associated) system with strand displacement amplification (SDA). In the experiment, miRNA was used as the initiator of SDA, and the activator of Cas12a nuclease activity was amplified by SDA. Subsequently, the unique nuclease activity of Cas12a was exploited to carry out trans cleaving on the ssDNA reporting probe modified with carboxyfluorescein(FAM) and BHQ1(dark Quencher: 480-580 nm) to achieve a signal output. In addition to chain design and reaction simplification, this method is lofty sensitive and selective for the determination of miRNA with a good linear range of 250 fmol·L-1 âˆ¼ 40 pmol·L-1, the detection limit of 150 fmol·L-1 (S/N = 3), and the method showed good recovery in spiked human serum. Overall, this method is expected to be applied to diagnosis with miRNA biomarkers because of its rapidity, high sensitivity, and high selectivity.