Mode coupling in FM-EDFAs caused by fiber bending: theory and experiments.

Refractive index perturbation caused by erbium-doped fiber (EDF) bending is inevitable in the fabrication of erbium-doped fiber amplifiers (EDFAs). The resulting mode coupling might bring about the deviation of theoretical results from experimental data. We present a theoretical model of FM-EDFAs with mode coupling due to fiber bending and carry out a proof-of-concept experiment by a 3.2-m-long EDF stretcher. Our experiments show that the fluctuation of modal gain due to fiber bending is about 1.5 dB for L P 01 and L P 11e modes, and about 2.5 dB for L P 11o mode, and the theoretical model is more useful for the FM-EDFA design in the presence of fiber bending.

Optogenetics in taste research: A decade of enlightenment.

The electrophysiological function of the tongue involves complicated activities in taste sense, producing the perceptions of salty, sweet, bitter, and sour. However, therapies and prevention of taste loss arising from dysfunction in electrophysiological activity require further fundamental research. Optogenetics has revolutionized neuroscience and brought the study of sensory system to a higher level in taste. The year 2022 marks a decade of developments of optogenetics in taste since this technology was adopted from neuroscience and applied to the taste research. This review summarizes a decade of advances that define near-term translation with optogenetic tools, and newly-discovered mechanisms with the applications of these tools. The main limitations and opportunities for optogenetics in taste research are also discussed.

Fear in the Theater of the Mind: Differential Fear Conditioning With Imagined Stimuli.

Many symptoms of anxiety and posttraumatic stress disorder are elicited by fearful mental imagery. Yet little is known about how visual imagery of conditioned stimuli (CSs) affects the acquisition of differential fear conditioning. Across three experiments with younger human adults (Experiment 1: n = 33, Experiment 2: n = 27, Experiment 3: n = 26), we observed that participants acquired differential fear conditioning to both viewed and imagined percepts serving as the CSs, as measured via self-reported fear and skin conductance responses. Additionally, this differential conditioning generalized across CS-percept modalities such that differential conditioning acquired in response to visual percepts generalized to the corresponding imagined percepts and vice versa. This is novel evidence that perceived and imagined stimuli engage learning processes in very similar ways and is consistent with the theory that mental imagery is depictive and recruits neural resources shared with visual perception. Our findings also provide new insight into the mechanisms of anxiety and related disorders.

Serum proteomic analysis of novel predictive serum proteins for neurological prognosis following cardiac arrest.

Early prognostication of neurological outcome in comatose patients after cardiac arrest (CA) is vital for clinicians when assessing the survival time of sufferers and formulating appropriate treatment strategies to avoid the withdrawal of life-sustaining treatment (WLST) from patients. However, there is still a lack of sensitive and specific serum biomarkers for early and accurate identification of these patients. Using an isobaric tag for relative and absolute quantitation (iTRAQ)-based proteomic approach, we discovered 55 differentially expressed proteins, with 39 up-regulated secreted serum proteins and 16 down-regulated secreted serum proteins between three comatose CA survivors with good versus poor neurological recovery. Then, four proteins were selected and were validated via an enzyme-linked immunosorbent assay (ELISA) approach in a larger-scale sample containing 32 good neurological outcome patients and 46 poor neurological outcome patients, and it was confirmed that serum angiotensinogen (AGT) and alpha-1-antitrypsin (SERPINA1) were associated with neurological function and prognosis in CA survivors. A prognostic risk score was developed and calculated using a linear and logistic regression model based on a combination of AGT, SERPINA1 and neuron-specific enolase (NSE) with an area under the curve of 0.865 (P < .001), and the prognostic risk score was positively correlated with the CPC value (R = 0.708, P < .001). We propose that the results of the risk score assessment not only reveal changes in biomarkers during neurological recovery but also assist in enhancing current therapeutic strategies for comatose CA survivors.

Millimeter-Wave Array Radar-Based Human Gait Recognition Using Multi-Channel Three-Dimensional Convolutional Neural Network.

At present, there are two obvious problems in radar-based gait recognition. First, the traditional radar frequency band is difficult to meet the requirements of fine identification with due to its low carrier frequency and limited micro-Doppler resolution. Another significant problem is that radar signal processing is relatively complex, and the existing signal processing algorithms are poor in real-time usability, robustness and universality. This paper focuses on the two basic problems of human gait detection with radar and proposes a human gait classification and recognition method based on millimeter-wave array radar. Based on deep-learning technology, a multi-channel three-dimensional convolution neural network is proposed on the basis of improving the residual network, which completes the classification and recognition of human gait through the hierarchical extraction and fusion of multi-dimensional features. Taking the three-dimensional coordinates, motion speed and intensity of strong scattering points in the process of target motion as network inputs, multi-channel convolution is used to extract motion features, and the classification and recognition of typical daily actions are completed. The experimental results show that we have more than 92.5% recognition accuracy for common gait categories such as jogging and normal walking.

The association between familial ASD diagnosis, autism symptomatology and developmental functioning in young children.

Few studies have directly compared individuals with and without a relative diagnosed with ASD on various domains. The present study aimed to examine the relationship between familial ASD diagnosis and the exhibition of ASD symptoms in young children with and without ASD diagnoses. Participants included 8353 children aged 17-37 months old and their families. They were divided into four groups based on individual and family diagnosis, then compared on autism symptomatology and developmental domains. No differences were found between ASD groups on overall scores and each of the factor domains, indicating no association between family ASD diagnosis and ASD symptomatology or developmental functioning. Disparate results were found for atypically developing groups with and without relatives diagnosed with ASD. Implications of these results are discussed.

Rectified Binary Network for Single-Image Super-Resolution.

Binary neural network (BNN) is an effective approach to reduce the memory usage and the computational complexity of full-precision convolutional neural networks (CNNs), which has been widely used in the field of deep learning. However, there are different properties between BNNs and real-valued models, making it difficult to draw on the experience of CNN composition to develop BNN. In this article, we study the application of binary network to the single-image super-resolution (SISR) task in which the network is trained for restoring original high-resolution (HR) images. Generally, the distribution of features in the network for SISR is more complex than those in recognition models for preserving the abundant image information, e.g., texture, color, and details. To enhance the representation ability of BNN, we explore a novel activation-rectified inference (ARI) module that achieves a more complete representation of features by combining observations from different quantitative perspectives. The activations are divided into several parts with different quantification intervals and are inferred independently. This allows the binary activations to retain more image detail and yield finer inference. In addition, we further propose an adaptive approximation estimator (AAE) for gradually learning the accurate gradient estimation interval in each layer to alleviate the optimization difficulty. Experiments conducted on several benchmarks show that our approach is able to learn a binary SISR model with superior performance over the state-of-the-art methods. The code will be released at https://github.com/jwxintt/Rectified-BSR.

Unveiling the multifaceted realm of human papillomavirus: a comprehensive exploration of biology, interactions, and advances in cancer management.

Human Papillomavirus (HPV), an extensive family of DNA viruses, manifests as a persistent global health challenge. Persistent HPV infection is now firmly established as a significant aetiological factor for a spectrum of malignancies. In this review, we examine the latest insights into HPV biology and its intricate relationship with the host. We delve into the complex dynamics of co-infections involving HPV alongside other viruses, such as HIV, EBV, and HSV, as well as the burgeoning role of the microbiome in cancer development. We also explore recent advancements in understanding the specific contributions of HPV in the development of various cancers, encompassing cancers of the anogenital region, head and neck, as well as breast, lung, and prostate. Moreover, we focus on the current preventive strategies, including vaccination and screening methods, and therapeutic interventions that range from traditional approaches like surgery and chemotherapy to emerging modalities such as targeted therapies and immunotherapies. Additionally, we provide a forward-looking view on the future directions of HPV research, highlighting potential areas of exploration to further our understanding and management of HPV and its associated cancers. Collectively, this review is positioned to deepen readers' understanding of HPV biology and its complex interplay with cancer biology. It presents innovative strategies for the prevention, management, and therapeutic intervention of HPV-associated malignancies.

Brain-Derived Exosomal CircRNAs in Plasma Serve as Diagnostic Biomarkers for Acute Ischemic Stroke.

Acute ischemic stroke (AIS), commonly known as stroke, is a debilitating condition characterized by the interruption of blood flow to the brain, resulting in tissue damage and neurological deficits. Early diagnosis is crucial for effective intervention and management, as timely treatment can significantly improve patient outcomes. Therefore, novel methods for the early diagnosis of AIS are urgently needed. Several studies have shown that bioactive molecules contained in extracellular vesicles, especially circRNAs, could be ideal markers for the diagnosis of many diseases. However, studies on the effects of exosomes and their circRNAs on the development and prognosis of AIS have not been reported extensively. Therefore, we explored the feasibility of using circRNAs in plasma brain-derived exosomes as biomarkers for AIS. By high-throughput sequencing, we first identified 358 dysregulated circRNAs (including 23 significantly upregulated circRNAs and 335 significantly downregulated circRNAs) in the plasma brain-derived exosomes of the brain infarct patient group compared to those of the noninfarct control group. Five upregulated circRNAs (hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808, and hsa_circ_0000097) were selected for further validation via Real-Time Quantitative Reverse Transcription PCR (qRT‒PCR) in a larger cohort based on the exclusion criteria of log2FC > 1, p < 0.05 and measurable expression. We found that the expression levels of hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808 and hsa_circ_0000097 were significantly upregulated in AIS patients and could serve as potential biomarkers for AIS with high specificity and sensitivity. Moreover, the expression levels of hsa_circ_0007290, hsa_circ_0049637, hsa_circ_0000607, hsa_circ_0004808 and hsa_circ_0000097 were also found to be positively correlated with National Institutes of Health Stroke Scale (NISS) and modified Rankin scale (mRS) scores, which indicated that the presence of these circRNAs in plasma brain-derived exosomes could also determine the progression of AIS.

3D printed VEGF-CPO biomaterial scaffold to promote subcutaneous vascularization and survival of transplanted islets for the treatment of diabetes.

Diabetes is a complex metabolic disease and islet transplantation is a promising approach for the treatment of diabetes. Unfortunately, the transplanted islets at the subcutaneous site are also affected by various adverse factors such as poor vascularization and hypoxia. In this study, we utilize biocompatible copolymers l-lactide and D,l-lactide to manufacture a biomaterial scaffold with a mesh-like structure via 3D printing technology, providing a material foundation for encapsulating pancreatic islet cells. The scaffold maintains the sustained release of vascular endothelial growth factor (VEGF) and a slow release of oxygen from calcium peroxide (CPO), thereby regulating the microenvironment for islet survival. This helps to improve insufficient subcutaneous vascularization and reduce islet death due to hypoxia post-transplantation. By pre-implanting VEGF-CPO scaffolds subcutaneously into diabetic rats, a sufficiently vascularized site is formed, thereby ensuring early survival of transplanted islets. In a word, the VEGF-CPO scaffold shows good biocompatibility both in vitro and in vivo, avoids the adverse effects on the implanted islets, and displays promising clinical transformation prospects.

Comparative genomics of macaques and integrated insights into genetic variation and population history.

The crab-eating macaques ( Macaca fascicularis ) and rhesus macaques ( M. mulatta ) are widely studied nonhuman primates in biomedical and evolutionary research. Despite their significance, the current understanding of the complex genomic structure in macaques and the differences between species requires substantial improvement. Here, we present a complete genome assembly of a crab-eating macaque and 20 haplotype-resolved macaque assemblies to investigate the complex regions and major genomic differences between species. Segmental duplication in macaques is ∼42% lower, while centromeres are ∼3.7 times longer than those in humans. The characterization of ∼2 Mbp fixed genetic variants and ∼240 Mbp complex loci highlights potential associations with metabolic differences between the two macaque species (e.g., CYP2C76 and EHBP1L1 ). Additionally, hundreds of alternative splicing differences show post-transcriptional regulation divergence between these two species (e.g., PNPO ). We also characterize 91 large-scale genomic differences between macaques and humans at a single-base-pair resolution and highlight their impact on gene regulation in primate evolution (e.g., FOLH1 and PIEZO2 ). Finally, population genetics recapitulates macaque speciation and selective sweeps, highlighting potential genetic basis of reproduction and tail phenotype differences (e.g., STAB1 , SEMA3F , and HOXD13 ). In summary, the integrated analysis of genetic variation and population genetics in macaques greatly enhances our comprehension of lineage-specific phenotypes, adaptation, and primate evolution, thereby improving their biomedical applications in human diseases.

Construction and analysis of functional brain network based on emotional electroencephalogram.

Networks play an important role in studying structure or functional connection of various brain areas, and explaining mechanism of emotion. However, there is a lack of comprehensive analysis among different construction methods nowadays. Therefore, this paper studies the impact of different emotions on connection of functional brain networks (FBNs) based on electroencephalogram (EEG). Firstly, we defined electrode node as brain area of vicinity of electrode to construct 32-node small-scale FBN. Pearson correlation coefficient (PCC) was used to construct correlation-based FBNs. Phase locking value (PLV) and phase synchronization index (PSI) were utilized to construct synchrony-based FBNs. Next, global properties and effects of emotion of different networks were compared. The difference of synchrony-based FBN concentrates in alpha band, and the number of differences is less than that of correlation-based FBN. Node properties of different small-scale FBNs have significant differences, offering a new basis for feature extraction of recognition regions in emotional FBNs. Later, we made partition of electrode nodes and 10 new brain areas were defined as regional nodes to construct 10-node large-scale FBN. Results show the impact of emotion on network clusters on the right forehead, and high valence enhances information processing efficiency of FBN by promoting connections in brain areas.

Prognostic Value of the Average Lung CT Number in Patients with Acute Paraquat Poisoning.

Objective: The chest computed tomography (CT) examination is an important clinical examination in the diagnosis and monitoring of paraquat- (PQ-) induced lung injury. The aim of this study was to explore the prognostic value of the average lung CT number acquired by quantitative CT techniques in patients with acute paraquat poisoning in the early stages of the disease. Methods: 46 patients who suffered from acute PQ poisoning in the emergency department of the Nanjing Drum Tower Hospital from January 2015 to June 2020 were enrolled in the present study. The patients were divided into survival group (n = 21) and nonsurvival group (n = 25). Clinical data were collected from subjects who met the inclusion criteria, including general information, personal disease history, and laboratory test indicators. The average lung CT numbers of each patient were obtained by quantitative CT techniques. Receiver operating characteristic (ROC) analysis was conducted to assess the prognostic value of average lung CT number in patients with acute paraquat poisoning. Results: The average CT numbers of the middle-lung, lower-lung, and whole lung fields in the nonsurvival group were significantly higher than those of the survival group (p < 0.0001). However, the upper-lung field was not significantly different between the two groups (p = 0.7765). The AUCs of different levels ranged from 0.554 to 0.977, among which the lower-lung field presented the largest AUC of 0.977 (95% CI: 0.943∼1; cut-off value: -702Hu; sensitivity 96%; specificity, 90.5%; YI: 0.865), followed by the whole lung field 0.914 (95% CI: 0.830∼0.999; cut-off value: -727Hu; sensitivity 76%; specificity, 95.2%; YI: 0.712) and the middle-lung field 0.87 (95% CI: 0.768∼0.971; cut-off value: -779Hu; sensitivity 80%; specificity, 85.7%; YI: 0.657). Conclusion: The present study indicated that the average lung CT number could be used to evaluate the relationship between the severity of PQ-induced lung injury and prognosis, especially in the lower-lung field. However, further research is needed to draw a clear conclusion.

FABNet: Frequency-Aware Binarized Network for Single Image Super-Resolution.

Remarkable achievements have been obtained with binary neural networks (BNN) in real-time and energy-efficient single-image super-resolution (SISR) methods. However, existing approaches often adopt the Sign function to quantize image features while ignoring the influence of image spatial frequency. We argue that we can minimize the quantization error by considering different spatial frequency components. To achieve this, we propose a frequency-aware binarized network (FABNet) for single image super-resolution. First, we leverage the wavelet transformation to decompose the features into low-frequency and high-frequency components and then employ a "divide-and-conquer" strategy to separately process them with well-designed binary network structures. Additionally, we introduce a dynamic binarization process that incorporates learned-threshold binarization during forward propagation and dynamic approximation during backward propagation, effectively addressing the diverse spatial frequency information. Compared to existing methods, our approach is effective in reducing quantization error and recovering image textures. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed methods could surpass state-of-the-art approaches in terms of PSNR and visual quality with significantly reduced computational costs. Our codes are available at https://github.com/xrjiang527/FABNet-PyTorch.

A high-quality genome assembly highlights the evolutionary history of the great bustard (Otis tarda, Otidiformes).

Conservation genomics often relies on non-invasive methods to obtain DNA fragments which limit the power of multi-omic analyses for threatened species. Here, we report multi-omic analyses based on a well-preserved great bustard individual (Otis tarda, Otidiformes) that was found dead in the mountainous region in Gansu, China. We generate a near-complete genome assembly containing only 18 gaps scattering in 8 out of the 40 assembled chromosomes. We characterize the DNA methylation landscape which is correlated with GC content and gene expression. Our phylogenomic analysis suggests Otidiformes and Musophagiformes are sister groups that diverged from each other 46.3 million years ago. The genetic diversity of great bustard is found the lowest among the four available Otidiformes genomes, possibly due to population declines during past glacial periods. As one of the heaviest migratory birds, great bustard possesses several expanded gene families related to cardiac contraction, actin contraction, calcium ion signaling transduction, as well as positively selected genes enriched for metabolism. Finally, we identify an extremely young evolutionary stratum on the sex chromosome, a rare case among birds. Together, our study provides insights into the conservation genomics, adaption and chromosome evolution of the great bustard.

A second-order accelerated neurodynamic approach for distributed convex optimization.

Based on the theories of inertial systems, a second-order accelerated neurodynamic approach is designed to solve a distributed convex optimization with inequality and set constraints. Most of the existing approaches for distributed convex optimization problems are usually first-order ones, and it is usually hard to analyze the convergence rate for the state solution of those first-order approaches. Due to the control design for the acceleration, the second-order neurodynamic approaches can often achieve faster convergence rate. Moreover, the existing second-order approaches are mostly designed to solve unconstrained distributed convex optimization problems, and are not suitable for solving constrained distributed convex optimization problems. It is acquired that the state solution of the designed neurodynamic approach in this paper converges to the optimal solution of the considered distributed convex optimization problem. An error function which demonstrates the performance of the designed neurodynamic approach, has a superquadratic convergence. Several numerical examples are provided to show the effectiveness of the presented second-order accelerated neurodynamic approach.

Dually Distribution Pulling Network for Cross-Resolution Person Reidentification.

Person reidentification (Re-ID) aims at recognizing the same identity across different camera views. However, the cross resolution of images [high resolution (HR) and low resolution (LR)] is unavoidable in a realistic scenario due to the various distances among cameras and pedestrians of interest, thus leading to cross-resolution person Re-ID problems. Recently, most cross-resolution person Re-ID methods focus on solving the resolution mismatch problem, while the distribution mismatch between HR and LR images is another factor that significantly impacts the person Re-ID performance. In this article, we propose a dually distribution pulling network (DDPN) to tackle the distribution mismatch problem. DDPN is composed of two modules, that is: 1) super-resolution module and 2) person Re-ID module. They attempt to pull the distribution of LR images closer to the distribution of HR images from image and feature aspects, respectively, through optimizing the maximum mean discrepancy losses. Extensive experiments have been conducted on three benchmark datasets and the results demonstrate the effectiveness of DDPN. Remarkably, DDPN shows a great advantage when compared to the state-of-the-art methods, for instance, we achieve rank-1 accuracy of 76.9% on VR-Market1501, which outperforms the best existing cross-resolution person Re-ID method by 10%.

Wavelet-Based Dual Recursive Network for Image Super-Resolution.

Although remarkable progress has been made on single-image super-resolution (SISR), deep learning methods cannot be easily applied to real-world applications due to the requirement of its heavy computation, especially for mobile devices. Focusing on the fewer parameters and faster inference SISR approach, we propose an efficient and time-saving wavelet transform-based network architecture, where the image super-resolution (SR) processing is carried out in the wavelet domain. Different from the existing methods that directly infer high-resolution (HR) image with the input low-resolution (LR) image, our approach first decomposes the LR image into a series of wavelet coefficients (WCs) and the network learns to predict the corresponding series of HR WCs and then reconstructs the HR image. Particularly, in order to further enhance the relationship between WCs and image deep characteristics, we propose two novel modules [wavelet feature mapping block (WFMB) and wavelet coefficients reconstruction block (WCRB)] and a dual recursive framework for joint learning strategy, thus forming a WCs prediction model to realize the efficient and accurate reconstruction of HR WCs. Experimental results show that the proposed method can outperform state-of-the-art methods with more than a 2× reduction in model parameters and computational complexity.

Landscape of Metabolic Fingerprinting for Diagnosis and Risk Stratification of Sepsis.

Background: Sepsis and septic shock, a subset of sepsis with higher risk stratification, are hallmarked by high mortality rates and necessitated early and accurate biomarkers. Methods: Untargeted metabolomic analysis was performed to compare the metabolic features between the sepsis and control systemic inflammatory response syndrome (SIRS) groups in discovery cohort, and potential metabolic biomarkers were selected and quantified using multiple reaction monitoring based target metabolite detection method. Results: Differentially expressed metabolites including 46 metabolites in positive electrospray ionization (ESI) ion mode, 22 metabolites in negative ESI ion mode, and 4 metabolites with dual mode between sepsis and SIRS were identified and revealed. Metabolites 5-Oxoproline, L-Kynurenine and Leukotriene D4 were selected based on least absolute shrinkage and selection operator regularization logistic regression and differential expressed between sepsis and septic shock group in the training and test cohorts. Respective risk scores for sepsis and septic shock based on a 3-metabolite fingerprint classifier were established to distinguish sepsis from SIRS, septic shock from sepsis. Significant relationship between developed sepsis risk scores, septic shock risk scores and Sequential (sepsis-related) Organ Failure Assessment (SOFA), procalcitonin (PCT) and lactic acid were observed. Conclusions: Collectively, our findings demonstrated that the characteristics of plasma metabolites not only manifest phenotypic variation in sepsis onset and risk stratification of sepsis but also enable individualized treatment and improve current therapeutic strategies.

Representation of Cone-Opponent Color Space in Macaque Early Visual Cortices.

In primate vision, the encoding of color perception arises from three types of retinal cone cells (L, M, and S cones). The inputs from these cones are linearly integrated into two cone-opponent channels (cardinal axes) before the lateral geniculate nucleus. In subsequent visual cortical stages, color-preferring neurons cluster into functional domains within "blobs" in V1, "thin/color stripes" in V2, and "color bands" in V4. Here, we hypothesize that, with increasing cortical hierarchy, the functional organization of hue representation becomes more balanced and less dependent on cone opponency. To address this question, we used intrinsic signal optical imaging in macaque V1, V2, and V4 cortices to examine the domain-based representation of specific hues (here referred to as "hue domains") in cone-opponent color space (4 cardinal and 4 intermediate hues). Interestingly, we found that in V1, the relative size of S-cone hue preference domain was significantly smaller than that for other hues. This notable difference was less prominent in V2, and, in V4 was virtually absent, resulting in a more balanced representation of hues. In V2, hue clusters contained sequences of shifting preference, while in V4 the organization of hue clusters was more complex. Pattern classification analysis of these hue maps showed that accuracy of hue classification improved from V1 to V2 to V4. These results suggest that hue representation by domains in the early cortical hierarchy reflects a transformation away from cone-opponency and toward a full-coverage representation of hue.