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Abstract The Zig-zag eel (Mastacembelus armatus) is an economically important species in southern China. Its natural resources have declined year by year due to overfishing. Understanding its genetic diversity and population structure is very important for resource conservations. Here, we first successfully developed 28 polymorphic microsatellite markers for zig-zag eels and ten of them were used to examine the genetic diversity and differentiation of 7 populations collected from the major river systems of south China. In total, 224 alleles were found with the 10 microsatellite loci in 7 populations, ranging from 4.6 (Nandujiang: NDJ) to 11.1 (Xijiang, XJ), with an average of 8.871 alleles. The average observed and expected heterozygosity ranged from 0.550 (NDJ) to 0.964 (Yuangjiang, YJ) and from 0.537 (NDJ) to 0.775 (Tanjiang, TJ), respectively. The average polymorphism-information content ranged from 0.472 (NDJ) to 0.757 (TJ). Hardy–Weinberg equilibrium test results revealed the loci showed differing deviation in different populations. In total, low level of genetic diversity was only found in HJ (Hanjiang) and NDJ populations. Besides, evidence of recent bottleneck was found in the HJ populations. Analysis of molecular variation showed that the percent variation within individuals (75.00%) was higher than that among populations (25%). In addition, population structure and the pairwise FST revealed that there was low differentiation among XJ, TJ and YJ populations. These data provide important genetic resources for understanding the population differentiation and facilitating genetic conservation and utilization of this species.

Abstract Federated Learning (FL) is a recent Machine Learning method for training with private data locally stored in distributed machines without gathering them into one place for central learning. Because FL depends on a central server for repeated aggregation of local training models, this server is prone to become a performance bottleneck. Therefore, one can combine FL with Edge Computing: introduce a layer of edge servers to each serve as a regional aggregator to offload the main server. The scalability is thus improved, however at the cost of learning accuracy. We show that this cost can be alleviated with a proper choice of edge server assignment: which edge servers should aggregate the training models from which local machines. In this paper, we propose an assignment solution for this purpose. Our solution is especially useful for the case of non-IID training data which is well-known to hinder today’s FL performance. Our findings are substantiated with an evaluation study using real-world datasets.

Abstract Heart failure and heart disease prediction in real-time is a highly significant necessity for the patients living under the observation of Internet of Things-based Ambient Assisted Living systems because cardiovascular diseases are the most common fatal chronic diseases. Most of the solutions regarding heart disease prediction in the Internet of Things-based medical systems are relying on server-based predictive analysis which can appear to be complex for generating real-time prediction notifications and unreliable in case of any network interruption occurrences. The suggested edge-based solution for the prediction of heart disease from collected sensor data in real-time using a proposed lightweight deep learning technique called Oversampled Quinary Feed Forward Network (OQFFN) provides a less complex framework and more reliable notification system in case of network failure for the disease prediction which also reduces the need of forwarding all the data to the server resulting in reduced network bottleneck.

Abstract Various new treatments are emerging constantly in anti-tumor therapies, including chemotherapy, immunotherapy, and targeted therapy. However, the efficacy is still not satisfactory. Astragalus polysaccharide is an important bioactive component derived from the dry root of Radix astragali. Studies found that astragalus polysaccharides have gained great significance in increasing the sensitivity of anti-tumor treatment, reducing the side effects of anti-tumor treatment, reversing the drug resistance of anti-tumor drugs, etc. In this review, we focused on the role of astragalus polysaccharides in tumor immune microenvironment. We reviewed the immunomodulatory effect of astragalus polysaccharides on macrophages, dendritic cells, natural killer cells, T lymphocytes, and B lymphocytes. We found that astragalus polysaccharides can promote the activities of macrophages, dendritic cells, natural killer cells, T lymphocytes, and B lymphocytes and induce the expression of a variety of cytokines and chemokines. Furthermore, we summarized the clinical applications of astragalus polysaccharides in patients with digestive tract tumors. We summarized the effective mechanism of astragalus polysaccharides on digestive tract tumors, including apoptosis induction, proliferation inhibition, immunoactivity regulation, enhancement of the anticancer effect and chemosensitivity. Therefore, in view of the multiple functions of astragalus polysaccharides in tumor immune microenvironment and its clinical efficacy, the combination of astragalus polysaccharides with antitumor therapy such as immunotherapy may provide new sparks to the bottleneck of current treatment methods.

Abstract Federated learning (FL) draws attention in academia and industry due to its privacy-preserving capability in training machine learning models. However, there are still some critical security attacks and vulnerabilities, including gradients leakage and interference attacks. Meanwhile, communication is another bottleneck in basic FL schemes since large-scale FL parameter transmission leads to inefficient communication, latency, and slower learning processes. To overcome these shortcomings, different communication efficiency strategies and privacy-preserving cryptographic techniques have been proposed. However, a single method can only partially resist privacy attacks. This paper presents a practical, privacy-preserving scheme combining cryptographic techniques and communication networking solutions. We implement Kafka for message distribution, the Diffie–Hellman scheme for secure server aggregation, and gradient differential privacy for interference attack prevention. The proposed approach maintains training efficiency while being able to addressing gradients leakage problems and interference attacks. Meanwhile, the implementation of Kafka and Zookeeper provides asynchronous communication and anonymous authenticated computation with role-based access controls. Finally, we prove the privacy-preserving properties of the proposed solution via security analysis and empirically demonstrate its efficiency and practicality.

Abstract With the development of the Internet of Things (IoT), more and more devices are connected in edge-cloud environment, and spectrum scarcity has become a major bottleneck for the employment of IoT devices in the end side of cloud computing. In addition, due to the broadcast nature of RF link, the potential eavesdropper may be presented in the wireless environment. To improve spectrum efficiency (SE) and ensure safe transmission of the users, in this paper, the two-way relay cooperative Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) with massive multiple-in multiple-out (MIMO) is proposed. Our objective is to maximize the security energy efficiency (SEE) of the cognitive system subject to the quality of service (QoS) of users. Specifically, the multi-objective optimization problem is decomposed into three subproblems, i.e., the optimization of transmit power, power allocation, and antenna selection, respectively. The Lagrange dual algorithm based on the first-order Taylor series expansion function is proposed to solve the non-convex problem. Moreover, a novel orthogonal antenna selection algorithm is proposed to decrease the cost of radio frequency chains and limit the interference to the primary user. The simulation results show that the proposed scheme has significant performance gain on SEE. It can effectively increase the number of access for cloud computing edge IoT users. In addition, due to the impact of correlation between the antennas, as the number of relay antennas increases, the SEE converges to a specific value, which exists the optimal number of antenna.

With the rapid advancement of modern medical technology, microscopy imaging systems have become one of the key technologies in medical image analysis. However, manual use of microscopes presents issues such as operator dependency, inefficiency, and time consumption. To enhance the efficiency and accuracy of medical image capture and reduce the burden of subsequent quantitative analysis, this paper proposes an improved microscope salient object detection algorithm based on U 2 -Net, incorporating deep learning technology. The improved algorithm first enhances the network's key information extraction capability by incorporating the Convolutional Block Attention Module (CBAM) into U 2 -Net. It then optimizes network complexity by constructing a Simple Pyramid Pooling Module (SPPM) and uses Ghost convolution to achieve model lightweighting. Additionally, data augmentation is applied to the slides to improve the algorithm's robustness and generalization. The experimental results show that the size of the improved algorithm model is 72.5 MB, which represents a 56.85% reduction compared to the original U 2 -Net model size of 168.0 MB. Additionally, the model's prediction accuracy has increased from 92.24 to 97.13%, providing an efficient means for subsequent image processing and analysis tasks in microscopy imaging systems., (© 2024. International Federation for Medical and Biological Engineering.)

The bacterium Streptococcus pneumoniae has become a leading cause of meningitis, sepsis, and bacterial pneumonia worldwide, with increased prevalence of antibiotic-resistant serotypes serving to exacerbate the issue. The main factor responsible for colonization and immune response escape in pneumococcal infections is the secreted molecule pneumolysin, which is a subset within a family of related toxins that form transmembrane pores in biological membranes through cholesterol recognition and binding. The conserved activity and structure of pneumolysin between all observed S. pneumoniae serotypes, along with its requirement for pathogenicity, has made this molecule an attractive target for vaccination, diagnostic, and sequestration platforms, but not yet as a facilitative agent for therapeutic treatment. Consequently, the present work aimed to examine the impact of liposomal cholesterol content for pneumolysin-induced release of the encapsulated antimicrobial peptide nisin. It was determined that a cholesterol content above 45 mol% was necessary to facilitate interactions with both purified pneumolysin toxin and S. pneumoniae culture, demonstrated through enhanced nisin release and a reduction in hemolytic rates upon exposure of the toxin with cholesterol-rich vesicles. Antibacterial testing highlighted the ability of the developed platform to elicit a potent and specific bactericidal response in vitro against cultured S. pneumoniae when compared to a control strain, Staphylococcus epidermidis. It further improved viability of a fibroblast cell line upon S. pneumoniae challenge, outperforming free nisin via the synergistic impact of simultaneous bacterial clearance and pneumolysin neutralization. These findings collectively indicate that cholesterol-rich liposomes hold promise as a selective treatment platform against pneumococcal infections., (© 2024. Controlled Release Society.)

Deep learning is a transformative force in the medical field and it has made significant progress as a pivotal alternative to conventional manual testing methods. Detection of Tubercle Bacilli in sputum samples is faced with the problems of complex backgrounds, tiny and numerous objects, and human observation over a long time not only causes eye fatigue, but also greatly increases the error rate of subjective judgement. To solve these problems, we optimize YOLOv8s model and propose a new detection algorithm, Lite-YOLOv8. Firstly, the Lite-C2f module is used to ensure accuracy by significantly reducing the number of parameters. Secondly, a lightweight down-sampling module is introduced to reduce the common feature information loss. Finally, the NWD loss is utilized to mitigate the impact of small object positional bias on the IoU. On the public Tubercle Bacilli datasets, the mean average precision of 86.3% was achieved, with an improvement of 2.2%, 1.5%, and 2.8% over the baseline model (YOLOv8s) in terms of mAP0.5, precision, and recall, respectively. In addition, the parameters reduced from 11.2 to 5.1 M, and the number of GFLOPs from 28.8 to 13.8. Our model is not only more lightweight, but also more accurate, thus it can be easily deployed on computing-poor medical devices to provide greater convenience to doctors., (© 2024. International Federation for Medical and Biological Engineering.)

Context: Airborne pathogens, defined as microscopic organisms, pose significant health risks and can potentially cause a variety of diseases. Given their ability to spread through diverse transmission routes from infected hosts, there is a critical need for accurate monitoring of these pathogens. This study aims to develop a sensor by investigating the vibrational responses of cantilever and bridged boundary-conditioned single-layer graphene (SLG) sheets with microorganisms, specifically SARS-CoV-2, attached at various positions on the sheet. The dynamic analysis of SLG with different boundary conditions and lengths was conducted using the atomistic finite element method (AFEM). Simulations were performed to evaluate SLG's performance as a sensor for biological entities. Altering the sheet's length and the mass of the attached biological object revealed observable frequency differences. This sensor design shows promise for enhancing the detection capabilities of graphene-based technologies for viruses., Methods: Finite element method (FEM) analysis is employed to model the sensor's performance and optimize its design parameters. The simulation results highlight the sensor's potential for achieving high sensitivity and rapid detection of SARS-CoV-2. Bridged and cantilever boundary conditions are applied at the ends of the SLG structure by using ANSYS software. Simulations have been conducted to observe how SLG behaves when used as sensors. In armchair graphene, under both boundary conditions, an SLG (5, 5) structure with a length of 50 nm displayed the highest frequency when a SARS-CoV-2 molecule with a mass of 2.6594 × 10 -18  g was attached. Conversely, the chiral SLG (17, 1) structure exhibited its lowest frequency at a length of 10 nm. This insight is crucial for grasping detection limits and how factors such as size and boundary conditions influence sensor efficacy. These biosensors hold immense promise in biological sciences and medical applications, revolutionizing patient care by enabling early detection and accurate pathogen identification in clinical settings., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose of Review: Major Depressive Disorder (MDD) is characterized by persistent symptoms such as fatigue, loss of interest in activities, feelings of sadness and worthlessness. MDD often coexist with cardiovascular disease (CVD), yet the precise link between these conditions remains unclear. This review explores factors underlying the development of MDD and CVD, including genetic, epigenetic, platelet activation, inflammation, hypothalamic-pituitary-adrenal (HPA) axis activation, endothelial cell (EC) dysfunction, and blood-brain barrier (BBB) disruption., Recent Findings: Single nucleotide polymorphisms (SNPs) in the membrane-associated guanylate kinase WW and PDZ domain-containing protein 1 (MAGI-1) are associated with neuroticism and psychiatric disorders including MDD. SNPs in MAGI-1 are also linked to chronic inflammatory disorders such as spontaneous glomerulosclerosis, celiac disease, ulcerative colitis, and Crohn's disease. Increased MAGI-1 expression has been observed in colonic epithelial samples from Crohn's disease and ulcerative colitis patients. MAGI-1 also plays a role in regulating EC activation and atherogenesis in mice and is essential for Influenza A virus (IAV) infection, endoplasmic reticulum stress-induced EC apoptosis, and thrombin-induced EC permeability. Despite being understudied in human disease; evidence suggests that MAGI-1 may play a role in linking CVD and MDD. Therefore, further investigation of MAG-1 could be warranted to elucidate its potential involvement in these conditions., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Enhanced phosphorus management, geared towards sustainability, is imperative due to its indispensability for all life forms and its close association with water bodies' eutrophication, primarily stemming from anthropogenic activities. In response to this concern, innovative technologies rooted in the circular economy are emerging, to remove and recover this vital nutrient to global food production. This research undertakes an evaluation of the dead-end filtration performance of a mixed matrix membrane composed of modified bentonite (MB) and polyvinylidene fluoride (PVDF) for efficient phosphorus removal from water media. The MB:PVDF membrane exhibited higher permeability and surface roughness compared to the pristine membrane, showcasing an adsorption capacity (Q) of 23.2 mgP·m -2 . Increasing the adsorbent concentration resulted in a higher removal capacity (from 16.9 to 23.2 mgP·m -2 ) and increased solution flux (from 0.5 to 16.5 L·m -2 ·h -1 ) through the membrane. The initial phosphorus concentration demonstrates a positive correlation with the adsorption capacity of the material, while the system pressure positively influences the observed flux. Conversely, the presence of humic acid exerts an adverse impact on both factors. Additionally, the primary mechanism involved in the adsorption process is identified as the formation of inner-sphere complexes., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Preregistration has gained traction as one of the most promising solutions to improve the replicability of scientific effects. In this project, we compared 193 psychology studies that earned a Preregistration Challenge prize or preregistration badge to 193 related studies that were not preregistered. In contrast to our theoretical expectations and prior research, we did not find that preregistered studies had a lower proportion of positive results (Hypothesis 1), smaller effect sizes (Hypothesis 2), or fewer statistical errors (Hypothesis 3) than non-preregistered studies. Supporting our Hypotheses 4 and 5, we found that preregistered studies more often contained power analyses and typically had larger sample sizes than non-preregistered studies. Finally, concerns about the publishability and impact of preregistered studies seem unwarranted, as preregistered studies did not take longer to publish and scored better on several impact measures. Overall, our data indicate that preregistration has beneficial effects in the realm of statistical power and impact, but we did not find robust evidence that preregistration prevents p-hacking and HARKing (Hypothesizing After the Results are Known)., (© 2023. The Author(s).)

Mortierella alpina is popular for lipid production, but the low carbon conversion rate and lipid yield are major obstacles for its economic performance. Here, external addition of organic acids involved in tricarboxylic acid cycle was used to tune carbon flux and improve lipid production. Citrate was determined to be the best organic acid that can be used for enhancing lipid production. By the addition of citrate, the lipid titer and content were approximately 1.24 and 1.34 times higher, respectively. Meanwhile, citrate supplement also promoted the accumulation of succinate, an important value-added platform chemical. Owing to the improved lipid and succinate production through adding citrate, the carbon conversion rate of M. alpina reached up to 52.17%, much higher than that of the control group (14.11%). The addition of citrate could redistribute carbon flux by regulating the expression level of genes related to tricarboxylic acid cycle metabolism. More carbon fluxes flow to lipid and succinate synthesis, which greatly improved the carbon conversion efficiency of M. alpina. This study provides an effective and straightforward strategy with potential economic benefits to improve carbon conversion efficiency in M. alpina., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Purpose: To enhance the performance of machine learning (ML) models for the post-embolization recanalization of cerebral aneurysms, we evaluated the impact of hemodynamic feature derivation and selection method on six ML algorithms., Methods: We utilized computational fluid dynamics (CFD) to simulate hemodynamics in 66 cerebral aneurysms from 65 patients, including 57 stable and nine recanalized aneurysms. We derived a total of 107 features for each aneurysm, encompassing four clinical features, 12 morphological features, and 91 hemodynamic features. To investigate the influence of feature derivation and selection methods on the ML models, we employed two derivation methods, simplified and fully derived, in combination with four selection methods: all features, statistically significant analysis, stepwise multivariate logistic regression analysis (stepwise-LR), and recursive feature elimination (RFE). Model performance was assessed using the area under the receiver operating characteristic curve (AUROC) and precision-recall curve (AUPRC) on both the training and testing datasets., Results: The AUROC values on the testing dataset exhibited a wide-ranging spectrum, spanning from 0.373 to 0.863. Fully derived features and the RFE selection method demonstrated superior performance in intra-model comparisons. The multi-layer perceptron (MLP) model, trained with RFE-selected fully derived features, achieved the best performance on the testing dataset, with an AUROC value of 0.863 (95% CI: 0.684- 1.000)., Conclusion: Our study demonstrated the importance of feature derivation and selection in determining the performance of ML models. This enabled the development of accurate decision-making models without the need to invade the patient., (© 2024. The Author(s) under exclusive licence to Biomedical Engineering Society.)

When a rhythm makes an event predictable, that event is perceived faster, and typically more accurately. However, the experiments showing this used simple tasks, and most manipulated temporal expectancy by using periodic or aperiodic precursors unrelated to stimulus and task. Three experiments tested the generality of these observations in a complex task in which rhythm was intrinsic to, rather than a precursor of, the information needed to respond: listeners averaged the laterality of a stream of noise bursts. We varied presentation rate, degree of periodicity, and average lateralisation. Decisions following a probe tone were fastest after periodic stimuli, and slowest after the most aperiodic stimuli. Without a probe tone, listeners responded sooner during periodic sequences, thus hearing less information. Periodicity did not benefit accuracy overall. This gain in speed but not accuracy for less information is not reported for simpler tasks. Neural entrainment supplemented by cognitive factors provide a tentative explanation. When the task is inherently complex and demands high attention over long durations, both expected-periodic and unexpected-aperiodic stimuli can increase response amplitude, enhancing stimulus representation, but periodicity increases confidence to respond early. Drift diffusion modelling supports this proposal: aperiodicity modulated the decision threshold, but not the drift rate or non-decision time. Together, these new data and the literature point towards task-dependent effects of temporal expectation on decision-making, showing interactions between rhythmic variance, task complexity, and sources of expectation about stimuli. We suggest the implications are worth exploring to extend understanding of rhythmicity on decision-making to everyday situations., (© 2024. The Psychonomic Society, Inc.)

Peripheral vision is limited due to several factors, such as visual resolution, crowding, and attention. When attention is not directed towards a stimulus, detection, discrimination, and identification are often compromised. Recent studies have found a new phenomenon that strongly limits peripheral vision, "redundancy masking". In redundancy masking, the number of perceived items in repeating patterns is reduced. For example, when presenting three lines in the peripheral visual field and asking participants to report the number of lines, often only two lines are reported. Here, we investigated what role attention plays in redundancy masking. If redundancy masking was due to limited attention to the target, it should be stronger when less attention is allocated to the target, and absent when attention is maximally focused on the target. Participants were presented with line arrays and reported the number of lines in three cueing conditions (i.e., single cue, double cue, and no cue). Redundancy masking was observed in all cueing conditions, with observers reporting fewer lines than presented in the single, double, and no cue conditions. These results suggest that redundancy masking is not due to limited attention. The number of lines reported was closer to the correct number of lines in the single compared to the double and the no cue conditions, suggesting that reduced attention additionally compromised stimulus discrimination, and replicating typical effects of diminished attention. Taken together, our results suggest that the extent of attention to peripherally presented stimuli modulates discrimination performance, but does not account for redundancy masking., Competing Interests: Declarations Competing interests The authors declare no competing interests., (© 2024. The Psychonomic Society, Inc.)

The problem of multi-class classification is always a challenge in the field of EEG (electroencephalogram)-based seizure detection. The traditional studies focus on computing or learning a set of features from EEG to distinguish between different patterns. However, the extraction of characteristic information becomes increasingly difficult as the number of EEG types increases. To address this issue, a creative EEG classification technique is proposed by employing a principal component analysis network (PCANet) coupled with phase space reconstruction (PSR) and power spectrum density (PSD). We have introduced the PSR and PSD to prepare the inputs, where dynamic and frequency information are exposed from deep within PCANet. It is remarkable that a layered cascade strategy is designed to make a powerful deep learner according to the rule of one network vs one task (OVO). The proposed method has achieved greater effects than the individual models and shown superior performance in comparison with state-of-the-art algorithms, which present 98.0% of sensitivity, 99.90% of specificity, and 99.07% of accuracy. Our ensemble PCANet model works in an assembly line-like manner, obviating the need for hand-craft features. Results demonstrate that the proposed scheme can greatly enhances the accuracy and robustness of seizure detection from EEG signals., (© 2024. International Federation for Medical and Biological Engineering.)

The challenge of long-term interaction between humans and robots is still a bottleneck in service robot research. To gain an understanding of sustained relatedness with robots, this study proposes a conceptual framework for bond formation. More specifically, it addresses the dynamics of children bonding with robotic pets as the basis for certain services in healthcare and education. The framework presented herein offers an integrative approach and draws from theoretical models and empirical research in Human Robot Interaction and also from related disciplines that investigate lasting relationships, such as human-animal affiliation and attachment to everyday objects. The research question is how children's relatedness to personified technologies occurs and evolves and what underpinning processes are involved. The subfield of research is child-robot interaction, within the boundaries of social psychology, where the robot is viewed as a social agent, and human-system interaction, where the robot is regarded as an artificial entity. The proposed framework envisions bonding with pet-robots as a socio-affective process towards lasting connectedness and emotional involvement that evolves through three stages: first encounter, short-term interaction and lasting relationship. The stages are characterized by children's behaviors, cognitions and feelings that can be identified, measured and, maybe more importantly, managed. This model aims to integrate fragmentary and heterogeneous knowledge into a new perspective on the impact of robots in close and enduring proximity to children., Author(s): Marta Díaz-Boladeras [sup.1] [sup.2] Author Affiliations: (1) Technical Research Centre for Dependency Care and Autonomous Living (CETpD), , Rambla Exposició, 59., 08800, Vilanova i la Geltrú, Spain (2) grid.6835.8, [...]

Abstract Blur detection is aimed to recognize the blurry pixels from a given image, which is increasingly valued in vision-centered applications. Albeit great improvement achieved by recent deep learning-based methods, the overweight model and rough boundary still pose challenges to blur detection. In this paper, we propose a Hierarchical Edge-guided Region-complemented Network (HER-Net) to tackle the above issues in quest of a favorable accuracy–complexity trade-off. First, we propose novel olive-shaped and pear-shaped inverted bottleneck structures based on large-kernel depth-wise convolutions to build a very concise architecture. Second, we provoke and exploit region-concerned and edge-concerned morphological priors to refine the boundary. To this end, we propose a reverse-region spatial attention to mine the complementary affinities between blurry and sharp regions so as to enrich the residual details around the boundary. In addition, we propose an edge spatial attention to guide the edge-concerned cues to emphasize the features related to the boundary. Both attentions are embedded into the model with hierarchical manners. Extensive experiments on three benchmark datasets demonstrate that the proposed method can achieve better detection performance using fewer parameters and lower floating-point operations compared to competitive methods. It proves the efficiency and effectiveness of our method in blur detection task.

Abstract Purpose Chordoma is a rare and aggressive bone cancer driven by the developmental transcription factor brachyury. Efforts to target brachyury are hampered by the absence of ligand-accessible small-molecule binding pockets. Genome editing with CRISPR systems provides an unprecedented opportunity to modulate undruggable transcription factor targets. However, delivery of CRISPR remains a bottleneck for in vivo therapy development. The aim was to investigate the in vivo therapeutic efficiency of Cas9/guide RNA (gRNA) ribonucleoprotein (RNP) delivery through a novel virus-like particle (VLP) by fusing an aptamer-binding protein to the lentiviral nucleocapsid protein. Methods The p24 based ELISA and transmission electron microscopy were used to determine the characterization of engineered VLP-packaged Cas9/gRNA RNP. The deletion efficiency of brachyury gene in chordoma cells and tissues was measured by genome cleavage detection assay. RT-PCR, Western blot, immunofluorescence staining, and IHC were employed to test the function of brachyury deletion. Cell growth and tumor volume were measured to evaluate the therapeutic efficiency of brachyury deletion by VLP-packaged Cas9/gRNA RNP. Results Our “all-in-one” VLP-based Cas9/gRNA RNP system allows for transient expression of Cas9 in chordoma cells, but maintains efficient editing capacity leading to approximately 85% knockdown of brachyury with subsequent inhibition of chordoma cell proliferation and tumor progression. In addition, this VLP-packaged brachyury-targeting Cas9 RNP avoids systemic toxicities in vivo. Conclusion Our preclinical studies demonstrate the potential of VLP-based Cas9/gRNA RNP gene therapy for the treatment of brachyury-dependent chordoma.

The accurate perception of human crowds is integral to social understanding and interaction. Previous studies have shown that observers are sensitive to several crowd characteristics such as average facial expression, gender, identity, joint attention, and heading direction. In two experiments, we examined ensemble perception of crowd speed using standard point-light walkers (PLW). Participants were asked to estimate the average speed of a crowd consisting of 12 figures moving at different speeds. In Experiment 1, trials of intact PLWs alternated with trials of scrambled PLWs with a viewing duration of 3 seconds. We found that ensemble processing of crowd speed could rely on local motion alone, although a globally intact configuration enhanced performance. In Experiment 2, observers estimated the average speed of intact-PLW crowds that were displayed at reduced viewing durations across five blocks of trials (between 2500 ms and 500 ms). Estimation of fast crowds was precise and accurate regardless of viewing duration, and we estimated that three to four walkers could still be integrated at 500 ms. For slow crowds, we found a systematic deterioration in performance as viewing time reduced, and performance at 500 ms could not be distinguished from a single-walker response strategy. Overall, our results suggest that rapid and accurate ensemble perception of crowd speed is possible, although sensitive to the precise speed range examined.

Abstract Federated learning (FL) enables clients learning a shared global model from multiple distributed devices while keeping training data locally. Due to the synchronous update mode between server and devices, the straggler problem has become a significant bottleneck for efficient FL. Existing approaches attempt to tackle this issue by using asynchronous-based model aggregation. However, these researches are only from the perspective of changing global model updating manner to mitigate straggler effect. They do not investigate the intrinsic reasons for the generation of the straggler effect, which could not fundamentally solve this problem. Furthermore, asynchronous-based approaches usually ignore those slow-responding but important local updates while frequently aggregating fast-responding ones during the whole training process, which may come with degradation in model accuracy. Thus, we propose FedTCR, a novel Federated learning approach via Taming Computing Resources. FedTCR includes a coarse-grained logical computing cluster construction algorithm (LCC) and a fine-grained intra-cluster collaborative training mechanism (ICT) as part of the FL process. The computing resource heterogeneity among devices and the communication frequency between devices and the server are indirectly tamed during this process, which substantially resolves the straggler problem and significantly improves the communication efficiency for FL. Experimental results show that FedTCR achieves much faster training performance, reducing the communication cost by up to $$8.59\,\times $$ 8.59 × while improving $$13.85\%$$ 13.85 % model accuracy, compared to state-of-the-art FL methods.

Abstract Autoencoder-based anomaly detection approaches can be used for precluding scope compliance failures of the automotive perception. However, the applicability of these approaches for the automotive domain should be thoroughly investigated. We study the capability of two autoencoder-based approaches using reconstruction errors and bottleneck-values for detecting semantic anomalies in automotive images. As a use-case, we consider a specific highway driving scenario identifying if there are any vehicles in the field of view of a front-looking camera. We conduct a series of experiments with two simulated driving scenario datasets and measure anomaly detection performance for different cases. We systematically test different autoencoders and training parameters, as well as the influence of image colors. We show that the autoencoder-based approaches demonstrate promising results for detecting semantic anomalies in highway driving scenario images in some cases. However, we also observe the variability of anomaly detection performance between different experiments. The autoencoder-based approaches are capable of detecting semantic anomalies in highway driving scenario images to some extent. However, further research with other use-cases and real datasets is needed before they can be safely applied in the automotive domain.

Cancer metastasis accounts for a majority of cancer-related deaths worldwide. Metastasis occurs when the primary tumor sheds cells into the blood and lymphatic circulation, thereby becoming circulating tumor cells (CTCs) that transverse through the circulatory system, extravasate the circulation and establish a secondary distant tumor. Accumulating evidence suggests that circulating effector CD 8 + T cells are able to recognize and attack arrested or extravasating CTCs, but this important antitumoral effect remains largely undefined. Recent studies highlighted the supporting role of activated platelets in CTCs's extravasation from the bloodstream, contributing to metastatic progression. In this work, a simple mathematical model describes how the primary tumor, CTCs, activated platelets and effector CD 8 + T cells participate in metastasis. The stability analysis reveals that for early dissemination of CTCs, effector CD 8 + T cells can present or keep secondary metastatic tumor burden at low equilibrium state. In contrast, for late dissemination of CTCs, effector CD 8 + T cells are unlikely to inhibit secondary tumor growth. Moreover, global sensitivity analysis demonstrates that the rate of the primary tumor growth, intravascular CTC proliferation, as well as the CD 8 + T cell proliferation, strongly affects the number of the secondary tumor cells. Additionally, model simulations indicate that an increase in CTC proliferation greatly contributes to tumor metastasis. Our simulations further illustrate that the higher the number of activated platelets on CTCs, the higher the probability of secondary tumor establishment. Intriguingly, from a mathematical immunology perspective, our simulations indicate that if the rate of effector CD 8 + T cell proliferation is high, then the secondary tumor formation can be considerably delayed, providing a window for adjuvant tumor control strategies. Collectively, our results suggest that the earlier the effector CD 8 + T cell response is enhanced the higher is the probability of preventing or delaying secondary tumor metastases., (© 2024. The Author(s), under exclusive licence to the Society for Mathematical Biology.)

Anaerobic digestion (AD) emerges as a pivotal technique in climate change mitigation, transforming organic materials into biogas, a renewable energy form. This process significantly impacts energy production and waste management, influencing greenhouse gas emissions. Traditional research has largely focused on anaerobic bacteria and methanogens for methane production. However, the potential of anaerobic lignocellulolytic fungi for degrading lignocellulosic biomass remains less explored. In this study, buffalo rumen inocula were enriched and acclimatized to improve lignocellulolytic hydrolysis activity. Two consortia were established: the anaerobic fungi consortium (AFC), selectively enriched for fungi, and the anaerobic lignocellulolytic microbial consortium (ALMC). The consortia were utilized to create five distinct microbial cocktails-AF0, AF20, AF50, AF80, and AF100. These cocktails were formulated based on varying of AFC and ALMC by weights (w/w). Methane production from each cocktail of lignocellulosic biomasses (cassava pulp and oil palm residues) was evaluated. The highest methane yields of CP, EFB, and MFB were obtained at 337, 215, and 54 mL/g VS, respectively. Cocktails containing a mix of anaerobic fungi, hydrolytic bacteria (Sphingobacterium sp.), syntrophic bacteria (Sphaerochaeta sp.), and hydrogenotrophic methanogens produced 2.1-2.6 times higher methane in cassava pulp and 1.1-1.2 times in oil palm empty fruit bunch compared to AF0. All cocktails effectively produced methane from oil palm empty fruit bunch due to its lipid content. However, methane production ceased after 3 days when oil palm mesocarp fiber was used, due to long-chain fatty acid accumulation. Anaerobic fungi consortia showed effective lignocellulosic and starchy biomass degradation without inhibition due to organic acid accumulation. These findings underscore the potential of tailored microbial cocktails for enhancing methane production from diverse lignocellulosic substrates., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

The integration of precision medicine principles into bone tissue engineering has ignited a wave of research focused on customizing intricate scaffolds through advanced 3D printing techniques. Bioceramics, known for their exceptional biocompatibility and osteoconductivity, have emerged as a promising material in this field. This article aims to evaluate the regenerative capabilities of a composite scaffold composed of 3D-printed gelatin combined with hydroxyapatite/tricalcium phosphate bioceramics (G/HA/TCP), incorporating human dental pulp-derived stem cells (hDPSCs). Using 3D powder printing, we created cross-shaped biphasic calcium phosphate scaffolds with a gelatin layer. The bone-regenerating potential of these scaffolds, along with hDPSCs, was assessed through in vitro analyses and in vivo studies with 60 rats and critical-sized calvarial defects. The assessment included analyzing cellular proliferation, differentiation, and alkaline phosphatase activity (ALP), and concluded with a detailed histological evaluation of bone regeneration. Our study revealed a highly favorable scenario, displaying not only desirable cellular attachment and proliferation on the scaffolds but also a notable enhancement in the ALP activity of hDPSCs, underscoring their pivotal role in bone regeneration. However, the histological examination of calvarial defects at the 12-wk mark yielded a rather modest level of bone regeneration across all experimental groups. The test and cell group exhibited significant bone formation compared to all other groups except the control and cell group. This underscores the complexity of the regenerative process and paves the way for further in-depth investigations aimed at improving the potential of the composite scaffolds., (© 2024. The Society for In Vitro Biology.)

Ticks can transmit viruses, bacteria, and parasites to humans, livestock, and pet animals causing tick-borne diseases (TBDs) mechanically or biologically in the world. Lumpy skin disease virus, Anaplasma marginale, and Theileria annulata inflict severe infections in cattle, resulting in significant economic losses worldwide. The study investigated the potential transmissions of LSDV, A. marginale, and T. annulata through male Hyalomma anatolicum ticks in cattle calves. Two 6-month-old Holstein crossbred calves designated as A and B were used. On day 1, 15 uninfected female ticks (IIa) and infected batch of 40 male ticks (I) were attached on calf A for 11 days. Filial transmission of the infections was observed in female ticks (IIb) collected from calf A, where 8 female ticks had been co-fed with infected male ticks. The blood sample of calf B was found positive through PCR for the infections. The larvae and egg pools obtained from the infected ticks were also tested positive in PCR. The study confirmed the presence of these mixed pathogens and potential intra-stadial and transovarial transmissions of A. marginale, T. annulata, and LSDV in male and female ticks of H. anatolicum and experimental calves to establish the feasibility of infections through an in vivo approach., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

This study aimed to investigate seven outbreaks of A. marginale infection in two regions of Brazil, affecting taurine, zebu, and crossbred cattle. We assessed the possible causes, treatment measures, and genetic diversity of A. marginale. These outbreaks occurred in two states (Goiás: outbreaks 1-7; Mato Grosso do Sul: outbreak 3), breeds (Holstein, Nellore, and crossbreed), age groups (beef cattle: 18-25 days old and 7-8 months; dairy cattle: 18-25 days old, 13-14 months, and cow after the first birth) and rearing systems (feedlot, pasture, pen in a wood shaving bedding system and compost bedded-pack barns). Metaphylactic or prophylactic treatments varied according to outbreak (imidocarb dipropionate: outbreaks 1-4 and 6; enrofloxacin: outbreaks 5 and 7; diminazene diaceturate: outbreak 5). In outbreaks 6 and 7, the packed cell volume was monitored. In all outbreaks, the practice of needle/syringe sharing was discontinued. For outbreaks 1-3, clinical signs and mortality (range, 4.8-13.3%) occurred 36-45 days after entry into the feedlot. In outbreak 4, A. marginale was diagnosed in 66.2% of the calves (bacteremia, 0-4.5%), with a mortality of 8.6%. Among nursing calves aged 60 days during outbreak 5, 53.8% were infected with A. marginale, with average bacteremia of 2.7% (range, 0-21.3%), and a mortality of 13.8%. In dairy heifers aged 14 months, raised in paddocks lacking vegetation cover and infested with R. microplus, then transitioned to a rotational grazing system also infested with R. microplus, the A. marginale bacteremia ranged from 3.2 to 6.7%, with a mortality of 20%. Before monitoring during outbreak 7, the mortality was 17.9%, but no further deaths were observed after monitoring initiation. In conclusion, possible causes triggering the outbreaks included primary tick infestation, needle/syringe sharing, and stress factors which may have affected the immunological statues of animals in the feedlots. Control measures performed in all outbreaks were effective. The partial msp4 gene sequences of A. marginale generated herein belonged to two haplotypes, but further research would be needed to investigate if this finding has any clinical significance., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

Earthworms play an important role in the soil environment. To explore the difference in earthworms influence on various media in different soil-plant systems, the abundance of tetracycline, sulfonamide and quinolone resistance genes and the structure of the bacterial community were analysed from five different media including non-rhizosphere soil, rhizosphere soil, phyllosphere, root endophytes and earthworm intestine by real-time quantitative PCR and high-throughput 16S rRNA sequencing. Studies have shown that earthworms can reduce the absolute abundance of antibiotic resistance genes (ARGs) in non-rhizosphere soil. Root endophytes in the soil-cabbage system and rhizosphere soil in the soil-setaria system had the same findings. Earthworms can change the bacterial community structure, especially that of Proteobacteria and Cyanobacteria in the phyllosphere and root endophytes. Redundancy analysis (RDA) results that bacterial community change was the main factor affecting ARGs. In addition, earthworms increased the proportion of Cyanobacteria in root endophytes, and Cyanobacteria was significantly positively correlated with sul3. This study provides a scientific basis for controlling the migration and diffusion of ARGs and reducing environmental risks in soil-plant systems in the future., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The recently published DISCHARGE-1 trial supports the observations of earlier autopsy and neuroimaging studies that almost 70% of all focal brain damage after aneurysmal subarachnoid hemorrhage are anemic infarcts of the cortex, often also affecting the white matter immediately below. The infarcts are not limited by the usual vascular territories. About two-fifths of the ischemic damage occurs within ~ 48 h; the remaining three-fifths are delayed (within ~ 3 weeks). Using neuromonitoring technology in combination with longitudinal neuroimaging, the entire sequence of both early and delayed cortical infarct development after subarachnoid hemorrhage has recently been recorded in patients. Characteristically, cortical infarcts are caused by acute severe vasospastic events, so-called spreading ischemia, triggered by spontaneously occurring spreading depolarization. In locations where a spreading depolarization passes through, cerebral blood flow can drastically drop within a few seconds and remain suppressed for minutes or even hours, often followed by high-amplitude, sustained hyperemia. In spreading depolarization, neurons lead the event, and the other cells of the neurovascular unit (endothelium, vascular smooth muscle, pericytes, astrocytes, microglia, oligodendrocytes) follow. However, dysregulation in cells of all three supersystems-nervous, vascular, and immune-is very likely involved in the dysfunction of the neurovascular unit underlying spreading ischemia. It is assumed that subarachnoid blood, which lies directly on the cortex and enters the parenchyma via glymphatic channels, triggers these dysregulations. This review discusses the neuroglial, neurovascular, and neuroimmunological dysregulations in the context of spreading depolarization and spreading ischemia as critical elements in the pathogenesis of cortical infarcts after subarachnoid hemorrhage., (© 2024. The Author(s).)

Drug repositioning or repurposing has gained worldwide attention as a plausible way to search for novel molecules for the treatment of particular diseases or disorders. Drug repurposing essentially refers to uncovering approved or failed compounds for use in various diseases. Cancer is a deadly disease and leading cause of mortality. The search for approved non-oncologic drugs for cancer treatment involved in silico modeling, databases, and literature searches. In this review, we provide a concise account of the existing non-oncologic drug molecules and their therapeutic potential in chemotherapy. The mechanisms and modes of action of the repurposed drugs using computational techniques are also highlighted. Furthermore, we discuss potential targets, critical pathways, and highlight in detail the different challenges pertaining to drug repositioning for cancer immunotherapy., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Biotin, also known as vitamin H or B 7 , acts as a crucial cofactor in the central metabolism processes of fatty acids, amino acids, and carbohydrates. Biotin has important applications in food additives, biomedicine, and other fields. While the ability to synthesize biotin de novo is confined to microorganisms and plants, humans and animals require substantial daily intake, primarily through dietary sources and intestinal microflora. Currently, chemical synthesis stands as the primary method for commercial biotin production, although microbial biotin production offers an environmentally sustainable alternative with promising prospects. This review presents a comprehensive overview of the pathways involved in de novo biotin synthesis in various species of microbes and insights into its regulatory and transport systems. Furthermore, diverse strategies are discussed to improve the biotin production here, including mutation breeding, rational metabolic engineering design, artificial genetic modification, and process optimization. The review also presents the potential strategies for addressing current challenges for industrial-scale bioproduction of biotin in the future. This review is very helpful for exploring efficient and sustainable strategies for large-scale biotin production., (© 2024. The Author(s), under exclusive licence to Springer Nature B.V.)

The production of extended spectrum β-lactamases (ESBLs) in extensively drug-resistant (XDR) strains of Acinetobacter baumannii has created havoc amongst clinicians making the treatment procedure challenging. Carbapenem-resistant strains have displayed total ineffectiveness towards newer combinations of β-lactam-β-lactamase inhibitors (βL-βLI) in tertiary healthcare settings. Therefore, the present study was aimed to design potential β-lactamase antimicrobial peptide (AMP) inhibitors against ESBLs produced by the strains. We have constructed an AMP mutant library with higher antimicrobial efficacy (range: ~ 15 to 27%) than their parent peptides. The mutants were thoroughly screened based on different physicochemical and immunogenic properties revealing three peptides, namely SAAP-148, HFIAP-1, myticalin-C6 and their mutants with safe pharmacokinetics profile. Molecular docking highlighted SAAP-148_M15 displaying maximum inhibitory potential with lowest binding energies against NDM1 (- 1148.7 kcal/mol), followed by OXA23 (- 1032.5 kcal/mol) and OXA58 (- 925.3 kcal/mol). The intermolecular interaction profiles displayed SAAP-148_M15 exhibiting hydrogen bonds and van der Waals hydrophobic interactions with the crucial residues of metallo β-lactamase [IPR001279] and penicillin-binding transpeptidase [IPR001460] domains. Coarse-grained clustering and molecular dynamics simulations (MDS) further validated the stable backbone profile and minimal residue-level fluctuations of the protein-peptide complex that were maintained throughout the simulation timeframe. The present study hypothesised that the combination of sulbactam (βL) with SAAP-148_M15 (βLI) holds immense potential in inhibiting the ESBLs alongside restoration of sulbactam activity. The current in silico findings upon further experimental validations can pave path towards designing of successful therapeutic strategy against XDR strains of A. baumannii., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Segmentation of intervertebral discs and vertebrae from spine magnetic resonance (MR) images is essential to aid diagnosis algorithms for lumbar disc herniation. Convolutional neural networks (CNN) are effective methods, but often require high computational costs. Designing a lightweight CNN is more suitable for medical sites lacking high-computing power devices, yet due to the unbalanced pixel distribution in spine MR images, the segmentation is often sub-optimal. To address this issue, a lightweight spine segmentation CNN based on a self-adjusting loss function, which is named SALW-Net, is proposed in this study. For SALW-Net, the self-adjusting loss function could dynamically adjust the loss weights of the two branches according to the differences in segmentation results and labels during the training; thus, the ability for learning unbalanced pixels is enhanced. Two separate datasets are used to evaluate the proposed SALW-Net. Specifically, the proposed SALW-Net has fewer parameter numbers than U-net (only 2%) but achieves higher evaluation scores than that of U-net (the average DSC score of SALW-Net is 0.8781, and that of U-net is 0.8482). In addition, the practicality validation for SALW-Net is also proceeding, including deploying the model on a lightweight device and producing an aid diagnosis algorithm based on segmentation results. This means our SALW-Net has clinical application potential for assisted diagnosis in low computational power scenarios., (© 2024. International Federation for Medical and Biological Engineering.)

The human visual system is very sensitive to the presence of faces in the environment, so much so that it can produce the perception of illusory faces in everyday objects. Growing research suggests that illusory faces and real faces are processed by similar perceptual and neural mechanisms, but whether this similarity extends to visual attention is less clear. A visual search study showed that illusory faces have a search advantage over objects when the types of objects vary to match the objects in the illusory faces (e.g., chair, pepper, clock) (Keys et al., 2021). Here, we examine whether the search advantage for illusory faces over objects remains when compared against objects that belong to a single category (flowers). In three experiments, we compared visual search of illusory faces, real faces, variable objects, and uniform objects (flowers). Search for real faces was best compared with all other types of targets. In contrast, search for illusory faces was only better than search for variable objects, not uniform objects. This result shows a limited visual search advantage for illusory faces and suggests that illusory faces may not be processed like real faces in visual attention., (© 2024. The Psychonomic Society, Inc.)

Abstract The computational bottleneck in distributed optimization methods, which is based on projected gradient descent, is due to the computation of a full gradient vector and projection step. This is a particular problem for large datasets. To reduce the computational complexity of existing methods, we combine the randomized block-coordinate descent and the Frank–Wolfe techniques, and then propose a distributed randomized block-coordinate projection-free algorithm over networks, where each agent randomly chooses a subset of the coordinates of its gradient vector and the projection step is eschewed in favor of a much simpler linear optimization step. Moreover, the convergence performance of the proposed algorithm is also theoretically analyzed. Specifically, we rigorously prove that the proposed algorithm can converge to optimal point at rate of $${\mathcal {O}}(1/t)$$ O ( 1 / t ) under convexity and $${\mathcal {O}}(1/t^2)$$ O ( 1 / t 2 ) under strong convexity, respectively. Here, t is the number of iterations. Furthermore, the proposed algorithm can converge to a stationary point, where the “Frank-Wolfe” gap is equal to zero, at a rate $${\mathcal {O}}(1/\sqrt{t})$$ O ( 1 / t ) under non-convexity. To evaluate the computational benefit of the proposed algorithm, we use the proposed algorithm to solve the multiclass classification problems by simulation experiments on two datasets, i.e., aloi and news20. The results shows that the proposed algorithm is faster than the existing distributed optimization algorithms due to its lower computation per iteration. Furthermore, the results also show that well-connected graphs or smaller graphs leads to faster convergence rate, which can confirm the theoretical results.