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Abstract An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 13 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model. As a pilot EEG dataset derived from natural Chinese linguistic stimuli, ChineseEEG can significantly support research across neuroscience, NLP, and linguistics. It establishes a benchmark dataset for Chinese semantic decoding, aids in the development of BCIs, and facilitates the exploration of alignment between large language models and human cognitive processes. It can also aid research into the brain’s mechanisms of language processing within the context of the Chinese natural language.

Abstract Background Chimeric antigen receptor T (CAR‐T) therapy has substantially revolutionized the clinical outcomes of patients with hematologic malignancies, but the cancer‐intrinsic mechanisms underlying resistance to CAR‐T cells remain yet to be fully understood. This study aims to explore the molecular determinants of cancer cell sensitivity to CAR‐T cell‐mediated killing and to provide a better understanding of the underlying mechanisms and potential modulation to improve clinical efficacy. Methods The human whole‐genome CRISPR/Cas9‐based knockout screening was conducted to identify key genes that enable cancer cells to evade CD19 CAR‐T‐cell‐mediated killing. The in vitro cytotoxicity assays and evaluation of tumor tissue and bone marrow specimens were further conducted to confirm the role of the key genes in cancer cell susceptibility to CAR‐T cells. In addition, the specific mechanisms influencing CAR‐T cell‐mediated cancer clearance were elucidated in mouse and cellular models. Results The CRISPR/Cas9‐based knockout screening showed that the enrichment of autophagy‐related genes (ATG3, BECN1, and RB1CC1) provided protection of cancer cells from CD19 CAR‐T cell‐mediated cytotoxicity. These findings were further validated by in vitro cytotoxicity assays in cells with genetic and pharmacological inhibition of autophagy. Notably, higher expression of the three autophagy‐related proteins in tumor samples was correlated with poorer responsiveness and worse survival in patients with relapsed/refractory B‐cell lymphoma after CD19 CAR‐T therapy. Bulk RNA sequencing analysis of bone marrow samples from B‐cell leukemia patients also suggested the clinical relevance of autophagy to the therapeutic response and relapse after CD19 CAR‐T cell therapy. Pharmacological inhibition of autophagy and knockout of RB1CC1 could dramatically sensitize tumor cells to CD19 CAR‐T cell‐mediated killing in mouse models of both B‐cell leukemia and lymphoma. Moreover, our study revealed that cancer‐intrinsic autophagy mediates evasion of CAR‐T cells via the TNF‐α‐TNFR1 axis‐mediated apoptosis and STAT1/IRF1‐induced chemokine signaling activation. Conclusions These findings confirm that autophagy signaling in B‐cell malignancies is essential for the effective cytotoxic function of CAR‐T cells and thereby pave the way for the development of autophagy‐targeting strategies to improve the clinical efficacy of CAR‐T cell immunotherapy.

Treatment of multiple myeloma (MM) has evolved remarkably over the past few decades. Autologous stem cell transplantation, as well as proteasome inhibitors, immunomodulatory drugs, and monoclonal antibodies, has substantially improved the prognosis of patients with MM. Novel therapies, including chimeric antigen receptor-T cells, bispecific T-cell engagers, antibody-drug conjugates, histone deacetylase inhibitors, and nuclear export inhibitors, have provided more options. However, MM remains incurable. T cells are the principal weapons of antitumor immunity, but T cells display a broad spectrum of dysfunctional states during MM. The promising clinical results of T-cell-directed immunotherapies emphasize the significance of enhancing T-cell function in antimyeloma treatment. This review summarizes the potential effects of these antimyeloma agents on T-cell function and discusses possible optimized strategies for MM management by boosting T-cell immunity.

Objective: To optimize the formula process of peony pollen candy in tablets and evaluate its antioxidant activity in vitro. Methods: On the basis of the single-factor experiment, taking the dosage of peony pollen, filler, and sweetener as factors, applying the comprehensive scores of the sensory, hardness, and disintegration time as evaluation indexes, the formula process of the peony pollen candy in tablets was optimized by response surface methodology. The DPPH and ABTS+ free radical scavenging capacity of various digestive products of the peony pollen candy in tablets simulated in the human body were determined. Results: The optimal prescription of the candy was as follows: 0.7 g of peony pollen, 3.3 g of the filler, and 1.7 g of the sweetener. The comprehensive score of this formula process was 6.39. The maxima scavenging rates of DPPH and ABTS+ free radicals by accumulated digestive products simulated in the human body were 91.56% and 96.42%, respectively. Conclusion: The formula of the peony pollen candy in tablets is simple and easy to handle. It retains the unique flavor of the peony pollen and maintains in vitro antioxidant activity.

Abstract Deep learning has demonstrated notable success in mechanical signal processing with a large amount labelled data. However, the systems of the Jiaolong deep‐sea submersible prone to malfunction are typically diverse, due to the high complexity of its structure and operational environment. Consequently, this diversity gives rise to variations in the types of sensor signals and their associated data distributions that require analysis. Unsupervised domain adaptation (UDA) uses transferable knowledge derived from the source domain and applies it to an unlabelled target domain in order to improve the reusability of pre‐existing models and data. Inspired by the diffusion models that have the robust capabilities to transform data distributions across a large gap, we propose a novel diffusion‐based unsupervised domain adaptation (diffusion‐UDA) model, which leverages contrastive learning to alleviate the challenges of cross‐domain analysis for fault diagnosis within different systems of the deep‐sea submersible. Experimental results show the proposed method achieves state‐of‐the‐art performance on various benchmarks.

With the widespread use of the Y chromosome in genetics, a lot of commercially available Y chromosome kits were developed, validated, and applied to forensic science practice. The AGCU YNFS Y Kit is a new Y chromosome system containing forty-four preferred Y short tandem repeats (Y-STRs) and five common Y-InDels. In this study, the AGCU YNFS Y system was validated to verify its performance by following the guidelines of the Scientific Working Group on DNA Analysis Methods (SWGDAM). A series of validation experiments included the following parameters: PCR-based studies, sensitivity studies, species specificity studies, stability studies, mixture studies, precision studies, stutter calculation, mutation and statistical analysis, population study, and case samples and degradation studies. The results suggested that appropriately changing PCR amplification conditions did not affect genotyping; the kit had good sensitivity for trace amounts of DNA (0.0625 ng), mixtures of multiple male individuals (minor: major = 1: 9), and three PCR inhibitors (more than 250 μM hematin, 250 ng/μL humic acid and 50 ng/μL tannic acid). The maximum standard deviation of allele size did not exceed 0.1552 reflecting the high accuracy of the system. By this, 87 DNA-confirmed pairs of father-son pairs were also analyzed for mutations. A total of 18 loci were mutated, with mutation rates ranging from 11.5×10-3 to 34.5×10-3 (95% CI 7.2×10-3-97.5×10-3, DYS627 and DYF404S1). In the population study, the haplotype diversity of 87 unrelated individuals was 0.9997, and discrimination capacity was 0.9885. Degradation studies have demonstrated that UV-C light exposure for up to 120 hours has no effect on male blood and semen-vaginal secretion mixtures. However, complete typing could no longer be obtained after 48 hours of UV exposure in single male saliva and in male saliva and female blood mixed samples. Collectively, the AGCU YNFS Y Kit is sensitive and accurate and can play its application value in forensic science practice.

Abstract Chimeric antigen receptor-T (CAR-T) therapy remains to be investigated in T-cell malignancies. CD7 is an ideal target for T-cell malignancies but is also expressed on normal T cells, which may cause CAR-T cell fratricide. Donor-derived anti-CD7 CAR-T cells using endoplasmic reticulum retention have shown efficacy in patients with T-cell acute lymphoblastic leukemia (ALL). Here we launched a phase I trial to explore differences between autologous and allogeneic anti-CD7 CAR-T therapies in T-cell ALL and lymphoma. Ten patients were treated and 5 received autologous CAR-T therapies. No dose-limiting toxicity or neurotoxicity was observed. Grade 1–2 cytokine release syndrome occurred in 7 patients, and grade 3 in 1 patient. Grade 1–2 graft-versus-host diseases were observed in 2 patients. Seven patients had bone marrow infiltration, and 100% of them achieved complete remission with negative minimal residual disease within one month. Two-fifths of patients achieved extramedullary or extranodular remission. The median follow-up was 6 (range, 2.7–14) months and bridging transplantation was not administrated. Patients treated with allogeneic CAR-T cells had higher remission rate, less recurrence and more durable CAR-T survival than those receiving autologous products. Allogeneic CAR-T cells appeared to be a better option for patients with T-cell malignancies.

BackgroundICSI (intracytoplasmic sperm injection) leads to a reduced male-to-female ratio at birth, whereas blastocyst transfer results in an increased male-to-female ratio. However, limited knowledge exists regarding the impact of these factors on the live birth rate for each gender. This study aimed to investigate the influence of patient characteristics and treatment parameters on the live birth rate for each gender, as well as the ultimate male-to-female ratio at birth in frozen-thawed embryo transfer (FET) cycles.MethodThis retrospective cohort study involved a total of 28,376 FET cycles and 9,217 subsequent deliveries, spanning from January 2003 to December 2015. The study consisted of two parts. First, logistic regression models were constructed to determine the factors influencing the male-to-female ratio among babies born after FET. Second, we aimed to investigate the mechanisms underlying this sex ratio imbalance by analyzing data from all transfer cycles. Generalized estimated equations were employed to assess the impact of risk factors on rates of male and female live births separately.ResultsICSI resulted in a lower proportion of male offspring compared to in vitro fertilization (IVF) (50.1% vs. 53.7%, aOR: 0.87, 95% CI: 0.80-0.96). Conversely, blastocyst transfer yielded a higher proportion of male offspring than cleavage-stage embryo transfer (58.7% vs. 51.6%, aOR: 1.32, 95% CI: 1.17-1.48). Analysis of all cycles indicated that ICSI resulted in a reduced likelihood of male live birth in comparison to IVF (19.8% vs. 21.6%, aOR: 0.90, 95% CI: 0.83-0.97). However, the transfer of blastocysts rather than cleavage-stage embryos not only increased the chance of male live birth (26.9% vs. 20.2%, aOR: 1.70, 95% CI:1.56-1.85) but also facilitated female live birth (20.3% vs. 19.3%, aOR: 1.26, 95% CI: 1.15-1.39).ConclusionICSI was associated with a reduction in the male-to-female sex ratio and a lower rate of male live births, while blastocyst transfer was associated with an increased male-to-female sex ratio at birth and a higher rate of male live births.

A worldwide harmonized meta-connectomic analysis of 5212 healthy young adults across 61 independent cohorts provides consistent, reproducible, and transcriptionally relevant functional connectome hubs in the resting human brain.

Abstract Rapidly mutating Y-STRs (RM Y-STRs) harbor great potential to distinguish male relatives and achieve male identification. However, forensic applications were greatly limited by the small number of the initially identified 14 RM Y-STRs. Recently, with the emergence of 12 novel RM Y-STRs, an integrated panel named RMplex was introduced, which contains all 26 RM Y-STRs and four fast mutating Y-STRs (FM Y-STRs). To obtain the first data on the mutation rates and father-son differentiation rates of the 30 newly proposed Y-STRs in Chinese populations, we performed an empirical mutation study on 307 DNA-confirmed Chinese paternal pairs. Previously reported mutation rates for 14 RM Y-STRs in Chinese and European populations were pooled and merged with our data. The highest meiosis number for the two groups reached 4771 and 2687, respectively. Five loci showed significant differences between the populations (DYS570, DYS399S1, DYS547, DYS612, and DYF403S1b). For the new panel covering 30 Y-STR loci, our results show extensive differences in the mutation rates between the two populations, as well. 10 RM Y-STR loci showed relatively low mutation rates (10–3–10–2 per meiosis) and 2 FM Y-STR loci had rapid mutation rates (> 10–2 per meiosis) in the Chinese population. Several-fold differences in mutation rates were found in nine Y-STR loci between the Chinese and reference populations, with two loci having significantly higher mutation rates and one locus with a significantly lower mutation rate in the Chinese population (P 10–2 per meiosis), 8 FM Y-STR loci (5×10–3-10–2 per meiosis), 3 moderately mutating Y-STRs (MM Y-STRs, 10–3-5×10–3 per meiosis), and one locus with no observed mutation events were identified in the Chinese population. 40.06% of the Chinese paternity pairs were discriminated with RMplex while only 20.84% with the initial 14 RM Y-STRs, indicating that RMplex is beneficial for distinguishing paternally related males. Future studies on populations of different genetic backgrounds are necessary to obtain comprehensive estimates of mutation rates at these new loci.

Abstract It is well known that networks generated by common mechanisms such as preferential attachment and homophily can disadvantage the minority group by limiting their ability to establish links with the majority group. This has the effect of limiting minority nodes’ access to information. We present the results of an empirical study on the equality of information access in network models with different growth mechanisms and spreading processes. For growth mechanisms, we focus on the majority/minority dichotomy, homophily, preferential attachment, and diversity. For spreading processes, we investigate simple versus complex contagions, different transmission rates within and between groups, and various seeding conditions. We observe two phenomena. First, information access equality is a complex interplay between network structures and the spreading processes. Second, there is a trade-off between equality and efficiency of information access under certain circumstances (e.g., when inter-group edges are low and information transmits asymmetrically). Our findings can be used to make recommendations for mechanistic design of social networks with information access equality.

Uncontrolled inflammation is a pathological state that underlies many diseases. Despite the development of numerous anti-inflammatory agents, the treatment of uncontrolled inflammation remains a challenging task. We developed a targeted delivery system for [5-(p-fluorophenyl)-2-ureido]thiophene-3-carboxamide (TPCA-1), a potent inhibitor of the NF-κB signaling pathway. The system comprises TPCA-1-loaded nanoparticles (NPs) functionalized with a monoclonal antibody (mAb) that specifically binds to the break point of the IgD6 region of the platelet/endothelial cell adhesion molecule-1 (PECAM-1) extracellular segment that is overexposed on the injured endothelium and activated macrophages during the pathogenesis of inflammation. In vitro binding and cellular uptake experiments revealed that the mAb modification on NPs could significantly enhance uptake by both Raw264.7 and HUVEC compared with unmodified NPs. In studies conducted at the cellular level focusing on anti-inflammatory and antioxidant effects, this formulation was found to effectively inhibit M1 polarization of macrophages, downregulate the secretion of pro-inflammatory cytokines, and reduce the production of reactive oxygen species (ROS) and nitric oxide (NO). In an animal model of vascular endothelial injury with acute inflammation, these NPs were capable of delivering TPCA-1 to inflammatory lesions in a targeted manner. Compared with the free agent-treated group, the NP-treated group exhibited reduced infiltration of inflammatory cells. In conclusion, our study demonstrates that this targeted delivery of TPCA-1-loaded NPs represents a promising strategy for improved mitigation of uncontrolled inflammation.

Objective This work was aimed to analyze the effect of vitamin energy drink on muscle fatigue by surface electromyography (SEMG) and ultrasonic bioimaging (USBI). Methods 20 healthy men were selected to do increasing load fatigue test. Surface electromyographic signals and ultrasonic biological images were collected based on wavelet threshold function with improved thresholds. Time domain and frequency domain characteristic integrated electromyography (IEMG), root mean square amplitude (RMS), average power frequency (MPF), and surface and deep muscle morphological changes were analyzed. Hemoglobin concentration (HB), red blood cell number (RBC), mean volume of red blood cell (MCV), blood lactic acid (BLA), malondialdehyde (MDA), and phosphocreatine kinase (CK) were measured. Results 1) the Accuracy (94.10%), Sensitivity (94.43%), Specificity (93.75%), and Precision (94.07%) of the long and short-term memory (LSTM) specificity for muscle fatigue recognition were higher than those of other models. 2) Compared with the control group, the levels of BLA, MDA, and CK in the experimental group were decreased and HB levels were increased after exercise (P < 0.05). 3) IEMG and RMS of the experimental group were higher than those of the control group, and increased with time (P < 0.05). 4) The mean amplitude of the response signal decreased with time. Compared with the control group, the surface muscle thickness, deep muscle thickness, total muscle thickness, contrast, and homogeneity (HOM) decreased in the experimental group; while the angular second moment (ASM) and contrast increased, showing great differences (P < 0.05). Conclusion Surface electromyographic signal and ultrasonic biological image can be used as auxiliary monitoring techniques for muscle fatigue during exercise. Drinking vitamin energy drinks before exercise can relieve physical fatigue to a certain extent and promote the maintenance of muscle microstructure.

BackgroundGiven the aging population and the rapid development of the digital society, concerns about promoting older adults' health skills are increasing. Patient-accessible electronic health records (PAEHRs) are implemented globally for aging health safeguards. The demand for using health-related information communication technologies (ICTs) among older adults and the factors that promote their usage intention of PAEHRs need to be studied.MethodsDrawing upon affordance theory, we constructed a research model that integrates four affordance types, aggregation, interactivity, collaboration, and communication, to identify the effects of affordances and attachment to platforms and doctors that contribute to older adults' usage intention on PAEHRs. Online survey data from 498 older adults (above 60 years) were collected and analyzed using partial least square-structural equation modeling.ResultsOur findings demonstrated how PAEHR's affordances facilitate older adults' attachment to platforms and doctors. We found that aggregation (γ = 0.417, P < 0.001) and interactivity (γ = 0.397, P < 0.001) can positively influence older adults' attachment to the PAEHR platform, and collaboration (γ = 0.407, P < 0.001) affordance can positively influence older adults' attachment to doctors on the PAEHR platform. Furthermore, seniors' attachment to the platform (γ = 0.598, P < 0.001) and attachment to the doctor (γ = 0.156, P < 0.01) can both positively influence their usage intention, and attachment to the platform had a positive relationship with attachment to doctors (γ = 0.230, P < 0.001) on the PAEHR.ConclusionThis study enriched the understanding of elders' attachment to doctors on PAEHRs and contributed to the literature on health-related ICT usage targets of older adults. Our findings also shed light on inspiring operators of health-related ICTs to formulate appropriate strategies for aging-friendly design to guide older adults to adopt health-related ICTs in their everyday health information practices.

Human cognition and behaviors depend upon the brain's functional connectomes, which vary remarkably across individuals. However, whether and how the functional connectome individual variability architecture is structurally constrained remains largely unknown. Using tractography- and morphometry-based network models, we observed the spatial convergence of structural and functional connectome individual variability, with higher variability in heteromodal association regions and lower variability in primary regions. We demonstrated that functional variability is significantly predicted by a unifying structural variability pattern and that this prediction follows a primary-to-heteromodal hierarchical axis, with higher accuracy in primary regions and lower accuracy in heteromodal regions. We further decomposed group-level connectome variability patterns into individual unique contributions and uncovered the structural-functional correspondence that is associated with individual cognitive traits. These results advance our understanding of the structural basis of individual functional variability and suggest the importance of integrating multimodal connectome signatures for individual differences in cognition and behaviors.

Abstract Patients with hematological malignancies might be at high risk for renal diseases as evidenced by earlier studies. We aim to investigate the mortality and risk factors of deaths due to renal diseases in this population. A total of 831 535 patients diagnosed with hematological malignancies in the Surveillance, Epidemiology, and End Results (SEER) database in the United States from 1975 to 2016 were identified. Standardized mortality ratio (SMR) was evaluated based on the general population's mortality data gathered by the National Center for Health Statistics. The mortality rate associated with renal diseases was 94.22/100 000 person‐years among patients with hematological malignancies (SMR = 3.59; 95% CI, 3.48‐3.70]). The highest mortality rate of dying from renal diseases was observed among multiple myeloma (MM) patients (307.99/100 000 person‐years; SMR = 7.98; 95% CI, 7.49‐8.50), followed by those with chronic myeloid leukemia (142.57/100 000 person‐years; SMR = 6.54; 95% CI, 5.63‐7.60) and chronic lymphocytic leukemia (103.66/100 000 person‐years; SMR = 2.51; 95% CI, 2.27‐2.77). The SMRs increased with time and were found to be the highest 10 years after cancer diagnosis. Independent predictors associated with death from renal diseases were found to be older age, male gender, blacks, unmarried, and MM, using the Cox proportional hazards model. We call for enhanced coordinated multidisciplinary care between hematologists and nephrologists to reduce the mortality rate of renal diseases among patients with hematological malignancies.

In the present study, ultrasound (400 W, U), microwave heating (75 ℃ for 15 min, M) and ultrasound synergized with microwave heating (UM) pretreatments of whey protein isolate (WPI) were applied to investigate and compare their influence on structure, physicochemical and functional characteristic of transglutaminase (TGase)-induced WPI. From the results of size exclusion chromatography, it could be seen that all three physical pretreatments could promote the formation of polymers in TGase cross-linked WPI, whose polymer amounts were increased by the order of U, UM and M pretreatment. Among three physical methods, M pretreatment had the strongest effect on structure and functional characteristics of TGase-induced WPI. Furthermore, compared with TGase-induced WPI, α-helix and β-turn of M−treated TGase-induced WPI (M−WPI−TGase) were reduced by 7.86% and 2.93%, whereas its β-sheet and irregular curl were increased by 15.37% and 7.23%. Zeta potential, emulsion stability and foaming stability of M−WPI−TGase were increased by 7.8%, 59.27% and 28.95%, respectively. This experiment exhibited that M was a more effective pretreatment method than U, UM for WPI, which could promote its reaction with TGase and improve its functional properties.

Brain extraction (a.k.a. skull stripping) is a fundamental step in the neuroimaging pipeline as it can affect the accuracy of downstream preprocess such as image registration, tissue classification, etc. Most brain extraction tools have been designed for and applied to human data and are often challenged by non-human primates (NHP) data. Amongst recent attempts to improve performance on NHP data, deep learning models appear to outperform the traditional tools. However, given the minimal sample size of most NHP studies and notable variations in data quality, the deep learning models are very rarely applied to multi-site samples in NHP imaging. To overcome this challenge, we used a transfer-learning framework that leverages a large human imaging dataset to pretrain a convolutional neural network (i.e. U-Net Model), and then transferred this to NHP data using a small NHP training sample. The resulting transfer-learning model converged faster and achieved more accurate performance than a similar U-Net Model trained exclusively on NHP samples. We improved the generalizability of the model by upgrading the transfer-learned model using additional training datasets from multiple research sites in the Primate Data-Exchange (PRIME-DE) consortium. Our final model outperformed brain extraction routines from popular MRI packages (AFNI, FSL, and FreeSurfer) across a heterogeneous sample from multiple sites in the PRIME-DE with less computational cost (20 s~10 min). We also demonstrated the transfer-learning process enables the macaque model to be updated for use with scans from chimpanzees, marmosets, and other mammals (e.g. pig). Our model, code, and the skull-stripped mask repository of 136 macaque monkeys are publicly available for unrestricted use by the neuroimaging community at https://github.com/HumanBrainED/NHP-BrainExtraction.

Abstract Reading remains a preferred leisure activity fueling an exceptionally competitive publishing market: among more than three million books published each year, only a tiny fraction are read widely. It is largely unpredictable, however, which book will that be, and how many copies it will sell. Here we aim to unveil the features that affect the success of books by predicting a book’s sales prior to its publication. We do so by employing the Learning to Place machine learning approach, that can predicts sales for both fiction and nonfiction books as well as explaining the predictions by comparing and contrasting each book with similar ones. We analyze features contributing to the success of a book by feature importance analysis, finding that a strong driving factor of book sales across all genres is the publishing house. We also uncover differences between genres: for thrillers and mystery, the publishing history of an author (as measured by previous book sales) is highly important, while in literary fiction and religion, the author’s visibility plays a more central role. These observations provide insights into the driving forces behind success within the current publishing industry, as well as how individuals choose what books to read.

Data aggregation is one of the most important functions provided by wireless sensor networks (WSNs). Among a variety of data aggregation schemes, the coding-based approaches (such as Compressive sensing (CS) and other similar programs) can significantly reduce traffic quantity by encoding the raw sensed data using weight vectors. The critical feature to design a coding-based data aggregation protocol is to construct a weight/measurement matrix for the application scenario. After that, the sink node assigns the column of the matrix, which is treated as the weight vector during the encoding process, to each sensor node respectively. However, for a dynamic scenario where the number of sensor nodes changes frequently, the existing approaches have to reconfigure the network by regenerating the measurement matrix and allocating the new weight vectors for all the existing nodes, which causes a considerable energy consumption and affects the regular monitoring tasks. To solve this problem, we propose a Vandermonde matrix-based scalable data aggregation protocol (VSDA), which preserves the advantages of coding-based schemes and addresses the issues mentioned above. In VSDA, as new nodes join into the scaled-up network, the original weight vectors owned by the original nodes do not need to regenerate the weight vectors entirely but add some new entries by itself at all. It outperforms the existing schemes by saving the energy in network scaling-up. Besides, we propose a concise hardware framework to quantify the data encoding process of VSDA, which provides a performance analysis process that is closer to practical application. The numeric tests validate the performance of VSDA compared with the existing schemes in several aspects, such as, the number of transmissions, energy consumption, and storage space showing the outperformance of VSDA scheme.

We propose a novel signal multiplexing and detection method for multiple-input multiple-output (MIMO) communication systems, especially when the number of transmitting and receiving antennas is limited. Inspired by the idea of Compressive Sensing (CS) which can recover a given signal vector from a vector of measurements with less dimensions, our proposed CS-based multiplexing scheme can deliver a modulated data vector with length l via a MIMO system with fewer transmitting/receiving antennas than l , offering higher multiplexing gain. On the receiving side, our proposed detection scheme has two steps, which resort the BCS algorithm and a Deep-Learning algorithm to recover the original modulated data vector. Analytical and simulation results show that the proposed multiplexing and detection method can achieve larger multiplexing gain while reserving good bit error rate (BER), offering a novel research paradigm to improve the utility rate of multiple antennas.

This paper proposes a scheme for updating the location of the sink node to balance the network topology when a wireless sensor network (WSN) is scaled up. We divide the proposed location update scheme into two steps, namely, searching the optimal location and designing the pathfinding algorithm. For the former, to find the optimal location of the sink node simply and efficiently, we only consider the information of the expanded longer paths and some key nodes instead of the global information of the entire network, which is easy to implement with a low-computational load. Then, considering the general unattended application scenario, we propose an improved reinforcement learning (RL) algorithm for the sink node to calculate a feasible efficient path, and then the sink node follows the path to reach the optimal location. Finally, through simulations, we demonstrate the optimal position of the sink node in expanded scenarios and successfully let the sink node learn the effective pathfinding method to reach the target position. A large number of simulation results verify the efficiency and effectiveness of our proposed scheme from the perspective of the efficiency of the pathfinding algorithm.

Neuroimaging non-human primates (NHPs) is a growing, yet highly specialized field of neuroscience. Resources that were primarily developed for human neuroimaging often need to be significantly adapted for use with NHPs or other animals, which has led to an abundance of custom, in-house solutions. In recent years, the global NHP neuroimaging community has made significant efforts to transform the field towards more open and collaborative practices. Here we present the PRIMatE Resource Exchange (PRIME-RE), a new collaborative online platform for NHP neuroimaging. PRIME-RE is a dynamic community-driven hub for the exchange of practical knowledge, specialized analytical tools, and open data repositories, specifically related to NHP neuroimaging. PRIME-RE caters to both researchers and developers who are either new to the field, looking to stay abreast of the latest developments, or seeking to collaboratively advance the field .

The MNI CIVET pipeline for automated extraction of cortical surfaces and evaluation of cortical thickness from in-vivo human MRI has been extended for processing macaque brains. Processing is performed based on the NIMH Macaque Template (NMT), as the reference template, with the anatomical parcellation of the surface following the D99 and CHARM atlases. The modifications needed to adapt CIVET to the macaque brain are detailed. Results have been obtained using CIVET-macaque to process the anatomical scans of the 31 macaques used to generate the NMT and another 95 macaques from the PRIME-DE initiative. It is anticipated that the open usage of CIVET-macaque will promote collaborative efforts in data collection and processing, sharing, and automated analyses from which the non-human primate brain imaging field will advance.

The chronnectome of the human brain represents dynamic connectivity patterns of brain networks among interacting regions, but its organization principle and related transcriptional signatures remain unclear. Using task-free fMRI data from the Human Connectome Project (681 participants) and microarray-based gene expression data from the Allen Institute for Brain Science (1791 brain tissue samples from six donors), we conduct a transcriptome-chronnectome association study to investigate the spatial configurations of dynamic brain networks and their linkages with transcriptional profiles. We first classify the dynamic brain networks into four categories of nodes according to their time-varying characteristics in global connectivity and modular switching: the primary sensorimotor regions with large global variations, the paralimbic/limbic regions with frequent modular switching, the frontoparietal cortex with both high global and modular dynamics, and the sensorimotor association cortex with limited dynamics. Such a spatial layout reflects the cortical functional hierarchy, microarchitecture, and primary connectivity gradient spanning from primary to transmodal areas, and the cognitive spectrum from perception to abstract processing. Importantly, the partial least squares regression analysis reveals that the transcriptional profiles could explain 28% of the variation in this spatial layout of network dynamics. The top-related genes in the transcriptional profiles are enriched for potassium ion channel complex and activity and mitochondrial part of the cellular component. These findings highlight the hierarchically spatial arrangement of dynamic brain networks and their coupling with the variation in transcriptional signatures, which provides indispensable implications for the organizational principle and cellular and molecular functions of spontaneous network dynamics.

Abstract Reading remains the preferred leisure activity for most individuals, continuing to offer a unique path to knowledge and learning. As such, books remain an important cultural product, consumed widely. Yet, while over 3 million books are published each year, very few are read widely and less than 500 make it to the New York Times bestseller lists. And once there, only a handful of authors can command the lists for more than a few weeks. Here we bring a big data approach to book success by investigating the properties and sales trajectories of bestsellers. We find that there are seasonal patterns to book sales with more books being sold during holidays, and even among bestsellers, fiction books sell more copies than nonfiction books. General fiction and biographies make the list more often than any other genre books, and the higher a book’s initial place in the rankings, the longer the book stays on the list as well. Looking at patterns characterizing authors, we find that fiction writers are more productive than nonfiction writers, commonly achieving bestseller status with multiple books. Additionally, there is no gender disparity among bestselling fiction authors but nonfiction, most bestsellers are written by male authors. Finally we find that there is a universal pattern to book sales. Using this universality we introduce a statistical model to explain the time evolution of sales. This model not only reproduces the entire sales trajectory of a book but also predicts the total number of copies it will sell in its lifetime, based on its early sales numbers. The analysis of the bestseller characteristics and the discovery of the universal nature of sales patterns with its driving forces are crucial for our understanding of the book industry, and more generally, of how we as a society interact with cultural products.

Fermented milks with strong angiotensin I-converting enzyme (ACE)-inhibitory activity were obtained through a culture with Lactobacillus helveticus KLDS.31 and Lactobacillus casei KLDS.105 with a fermentation and storage temperature of 37 °C. Ultrafiltration fractions with a molecular weight less than 3 kDa in fermented milk whey exhibited the strongest inhibitory activity. Correspondingly, a gastrointestinal digestion experiment showed retention of the bioactivity of these fractions with pepsin and trypsin treatment. Four ACE-inhibitory peptides from fermented milk were isolated, purified by two-step reverse chromatography, and sequenced. Furthermore, the interaction mechanisms between ACE and four isolated peptides were investigated by a molecular docking method and the Independent Gradient Model. Experimental determination of IC50 was done to verify theoretical results. The inhibitory peptide interacted with ACE as follows: Lys-Pro-Ala-Gly-Asp-Phe > Lys-Ala-Ala-Leu-Ser-Gly-Met > Lys-Lys-Ala-Ala-Met-Ala-Met > Leu-Asp-His-Val-Pro-Gly-Gly-Ala-Arg.

The recently proposed q-rung picture fuzzy set (q-RPFSs) can describe complex fuzzy and uncertain information effectively. The Hamy mean (HM) operator gets good performance in the process of information aggregation due to its ability to capturing the interrelationships among aggregated values. In this study, we extend HM to q-rung picture fuzzy environment, propose novel q-rung picture fuzzy aggregation operators, and demonstrate their application to multi-attribute group decision-making (MAGDM). First of all, on the basis of Dombi t-norm and t-conorm (DTT), we propose novel operational rules of q-rung picture fuzzy numbers (q-RPFNs). Second, we propose some new aggregation operators of q-RPFNs based on the newly-developed operations, i.e., the q-rung picture fuzzy Dombi Hamy mean (q-RPFDHM) operator, the q-rung picture fuzzy Dombi weighted Hamy mean (q-RPFDWHM) operator, the q-rung picture fuzzy Dombi dual Hamy mean (q-RPFDDHM) operator, and the q-rung picture fuzzy Dombi weighted dual Hamy mean (q-RPFDWDHM) operator. Properties of these operators are also discussed. Third, a new q-rung picture fuzzy MAGDM method is proposed with the help of the proposed operators. Finally, a best project selection example is provided to demonstrate the practicality and effectiveness of the new method. The superiorities of the proposed method are illustrated through comparative analysis.