Miracle-dz/UniFAR-FTUs huggingface dataset:
State-of-the-art video recognition of human action under natural scenes

Human action video recognition focuses on discussing the theories and new algorithms to make computers understand the meanings of variety videos, such as those from TV, sports or family videos, and recently it plays an important role in advanced human-machine interface, security monitor, video retrieval and so on. The step-by-step process of traditional computational algorithm for human action recognition from videos are described in this paper, and some important deep learning models are also addressed by which some advantages and disadvantages of each approach in different tasks are analysed. Finally, some current trends and prospects of video recognition models are concluded.
A bagging ensemble learning traffic demand prediction model based on improved LSTM and transformer

The paper aims to build a traffic prediction model for online car-hailing demand with Improved LSTM and Transformer. There are many factors that affect demand, such as temporal features, spatial features, high signal-to-noise ratio, and so on. In this study, LSTM and Transformer are used to extract the temporal and spatial features of data. The temporal and spatial features are used for bagging ensemble learning to predict the online car-hailing orders. An improved LSTM suitable for Traffic data sets is proposed in this paper. In LSTM, we use wavelet decomposition and reconstruction to extract the small-amplitude high-frequency signal from data. The extracted signal is translated and superimposed into the results of the model. The average MAE of 21.24 is obtained on Online Car-hailing Information Data Sets. Compared with other methods, the results suggest the proposed traffic demand prediction model has better accuracy.
Real-time contactless monitoring of respiration rate and heart rate based on thermal imaging technology

Human temperature characteristics are closely related to physiological activities such as respiration and heart rate. For daily healthcare needs, a real-time contactless monitoring of respiration rate and heart rate based on facial thermal imaging was proposed. A RetinaFace framework was applied to detect the facial region from the video frames of thermal imaging. The facial region was divided equally into 12 areas. For the detection of respiratory rate, the respiratory signal is obtained by detecting the nose area and the feature tracking recognition method based on the optical flow field method. For the heart rate detection, an independent component analysis (ICA) was performed on the 12 areas. The heart rate was calculated based on the region with the higher proportion of energy according to the ICA. Experimental results showed that real-time contactless monitoring system proposed in this study can accurately detect the respiratory rate and heart rate, which may facilitate the daily healthcare examination for a large population.
Research on terminal logistics distribution based on reinforcement learning and pointer network

The path planning problem at the end of the logistics is an important task for logistics companies today. Due to the continuous expansion of the logistics scale and the requirements of customer delivery, there is a higher requirement for the path planning algorithm. This paper proposes a distribution path planning algorithm based on pointer network. For the training based on supervised learning, the final results will depend on the quality of the solution in the training data, and the optimal solution is difficult to obtain. In this paper, we propose a reinforcement learning method to train the model, which solves the problem of high cost of training samples and improves model accuracy. Because the input of the terminal logistics distribution problem has the characteristics of sequence independence, this paper studies the multi-head attention mechanism. By capturing the multiple features in the input sequence information, the speed of network convergence is accelerated. The experimental results and analysis indicate that the model proposed in this paper has great improvement in solving speed and good performance in effect.
Anticipatory attractors, functional neurochemistry and “Throw & Catch” mechanisms as illustrations of constructivism

Abstract This review explores several rarely discussed examples illustrating constructivism principles, generative and selective features of neuronal regulation of behaviour. First, the review highlights Walter Freeman’s experiments and mathematical analysis that uncovered the existence of anticipatory attractors, i.e. non-random dynamical patterns in neurodynamics. Since Freeman’s work did not extend to neurochemistry, this paper then points to the proposed earlier neurochemical framework summarizing the managerial roles of monoaminergic, cholinergic and opioid receptor systems likely contributing to anticipatory attractors in line with functional constructivism. As a third example, neurochemistry’s evidence points to the “Throw & Catch” (T&C) principle in neurodynamics. This principle refers to the pro-active, neurochemically expensive, massive but topical increase of potentials (“Throw”) within electrodynamics and neurotransmission in the brain whenever there is an uncertainty in selection of degrees of freedom (DFs). The T&C also underlines the relay-like processes during the selection of DFs. The “Throw” works as an internally generated “flashlight” that, contrarily to the expectations of entropy reduction, increases entropy and variance observed in processes related to orientation and action-formation. The discussed examples highlight the deficiency of structures-oriented projects and excitation-inhibition concepts in neuroscience. The neural regulation of behaviour appears to be a fluid, constructive process, constantly upgrading the choice of behavioural DFs, to ensure the compatibility between the environmental and individual’s individuals’ needs and capacities.
A Novel Overlapping Community Detection Algorithm of Weighted Networks

The community structure is a vital property of complex networks. As special networks the weighted networks also have community structure. Nowadays the studies of overlapping community draw attentions of researchers. However, the scale of networks become huge, so it requires the algorithm has lower time complexity and higher classification accuracy. Many existing algorithms cannot meet these two requirements at the same time. So we propose a novel overlapping community detection algorithm. Firstly we apply maximum degree node and its some special adjacent nodes as the initial community, and then expand the initial community by adding eligible nodes to it, finally other communities can be found by repeating these two steps. Experiments results show that our algorithm can detect overlapping community structure from weighted networks successfully, and also reveal that our method has higher division accuracy and lower time complexity than many previously proposed methods.
Centrifugally driven winds from contracting molecular disks

We suggest that bipolar outflows in dense molecular clouds associated with young stellar objects are steady, centrifugally driven, hydromagnetic winds that arise from molecular disks (on scales < or =10/sup 16/ cm) in which the infrared source(s) embedded. A disk of mass approx.100 M/sub sun/ and rotational speed of approx.10/sup 6/ cm s/sup -1/ provides a resevoir of 10/sup 47/ ergs, which could power the most energetic outflows observed. Acceleration to supersonic speeds is accomplished by the magnetic field embedded in the disk (paralllel rotational and magnetic axes) and extending outward beyond the wind region to join the galactic field. The wind carries angular momentum and energy from the disk out to large distances. Our analysis treats the problem of magnetic braking and energy transport in a partially ionized (two-fluid) wind. The basic parameter which is shown to govern the flow is the coupling parameter . beta. = T/sub n/-i/T/sub n/, the ratio of the characteristic neutral-ion collision time to flow time. A general analysis of angular momentum and energy transport is given, and the wind equation is solved along flux tubes in the strongly coupled (.beta.<<1) limit. The centrifugally driven wind forms when an embedded protostar begins to ionize themore » disk core region. A disk envelope forms at the wind base at a pressure which adjusts to the wind requirements. Envelope heating may be maintained by magnetic flux loss from the dense core (which has a field approx.10/sup -3/ gauss) that is preferentially along the rotation axis for flattened disks. The structure of the field at the disk core surface is derived. The observational implications are discussed; in particular, we emphasize that such molecular disks with double profiles should be observed. « less
Preclinical Platform Using a Triple-negative Breast Cancer Syngeneic Murine Model to Evaluate Immune Checkpoint Inhibitors

Background/Aim: To evaluate the feasibility of syngeneic mouse models of breast cancer by analyzing the efficacy of immune checkpoint inhibitors (ICIs) and potential predictive biomarkers. Materials and Methods: To establish the murine triple-negative breast cancer (TNBC) models, JC, 4T1, EMT6, and E0771 cells were subcutaneously implanted into female syngeneic mice. When the tumor reached 50-100 mm3, each mouse model was divided into a treatment (using a murine PD-1 antibody) and a no-treatment control group. The treatment group was further divided into the responder and non-responder groups. Potential predictive biomarkers were evaluated by analyzing serum cytokines, peripheral blood T cells and tumor infiltrating immune cells. Results: The EMT6 model showed the highest tumor response rate (54%, 6/11) of the syngeneic models: 4T1 (45%, 5/11), JC (40%, 4/10), or E0771 (23%, 3/13). Early changes in tumor size at 7 days post-PD-1 inhibitor treatment predicted the final efficacy of the PD-1 inhibitor. Peripheral blood CD8+ and CD4+ T cells with or without Ki67 expression at 7 days post-PD-1 inhibitor treatment were higher in the finally designated responder group than in the non-responder group. At the time of sacrifice, analyses of tumor infiltrating lymphocytes consistently supported these results. We also demonstrated that retro-orbital blood sampling procedures (baseline, 7 days post-treatment, time of sacrifice) were safe for serum cytokine analyses, suggesting that our preclinical platform may be used for biomarker research using serum cytokines. Conclusion: Our syngeneic mouse model of TNBC is a feasible preclinical platform to evaluate ICI efficacy combined with other drugs and predictive biomarkers in the screening process of immune-oncology drug development.
The shh limb enhancer is activated in patterned limb regeneration but not in hypomorphic limb regeneration in Xenopus laevis

Xenopus young tadpoles regenerate a limb with the anteroposterior (AP) pattern, but metamorphosed froglets regenerate a hypomorphic limb after amputation. The key gene for AP patterning, shh, is expressed in a regenerating limb of the tadpole but not in that of the froglet. Genomic DNA in the shh limb-specific enhancer, MFCS1 (ZRS), is hypermethylated in froglets but hypomethylated in tadpoles: shh expression may be controlled by epigenetic regulation of MFCS1. Is MFCS1 specifically activated for regenerating the AP-patterned limb? We generated transgenic Xenopus laevis lines that visualize the MFCS1 enhancer activity with a GFP reporter. The transgenic tadpoles showed GFP expression in hoxd13- and shh-expressing domains of developing and regenerating limbs, whereas the froglets showed no GFP expression in the regenerating limbs despite having hoxd13 expression. Genome sequence analysis and co-transfection assays using cultured cells revealed that Hoxd13 can activate Xenopus MFCS1. These results suggest that MFCS1 activation correlates with regeneration of AP-patterned limbs and that re-activation of epigenetically inactivated MFCS1 would be crucial to confer the ability to non-regenerative animals for regenerating a properly patterned limb.
Systematic characterization of Drosophila RhoGEF/GAP localizations uncovers regulators of mechanosensing and junction formation during epithelial cell division

Cell proliferation is central to epithelial tissue development, repair and homeostasis. During cell division, small RhoGTPases control both actomyosin dynamics and cell-cell junction remodelling to faithfully segregate the duplicated genome while maintaining tissue polarity and integrity. To decipher the mechanisms of RhoGTPases spatiotemporal regulation during epithelial cell division, we generated a transgenic fluorescently tagged library for Drosophila Rho Guanine exchange factors (GEF) and GTPase activating proteins (GAP), and systematically characterized their endogenous distributions by time- lapse microscopy. Thereby, we unveiled candidate regulators of the interplay between actomyosin and junctional dynamics during epithelial cell division. Building on these findings, we uncovered that during cytokinesis, Cysts and RhoGEF4 play sequential roles in mechanosensing and de novo junction formation, respectively. We foresee that the RhoGEF/GAP library will be a key resource to understand the broad range of biological processes regulated by RhoGTPases.
A robust deep learning platform to predict CD8+ T-cell epitopes

T-cells play a crucial role in the adaptive immune system by inducing an anti-tumour response, defending against pathogens, and maintaining tolerance against self-antigens, which has sparked interest in the development of T-cell-based vaccines and immunotherapies. Because screening antigens driving the T-cell response is currently low-throughput and laborious, computational methods for predicting CD8+ T-cell epitopes have emerged. However, most immunogenicity algorithms struggle to learn features of peptide immunogenicity from small datasets, suffer from HLA bias and are unable to reliably predict pathology-specific CD8+ T-cell epitopes. Therefore, we developed TRAP (T-cell recognition potential of HLA-I presented peptides), a robust deep learning platform for predicting CD8+ T-cell epitopes from MHC-I presented pathogenic and self-peptides. TRAP uses transfer learning, deep learning architecture and MHC binding information to make context-specific predictions of CD8+ T-cell epitopes. TRAP also detects low-confidence predictions for peptides that differ significantly from those in the training datasets to abstain from making incorrect predictions. To estimate the immunogenicity of pathogenic peptides with low-confidence predictions, we further developed a novel metric, RSAT (relative similarity to autoantigens and tumour-associated antigens), as a complementary to ‘dissimilarity to self’ from cancer studies. We used TRAP to identify epitopes from glioblastoma patients as well as SARS-CoV-2 peptides, and it outperformed other algorithms in both cancer and pathogenic settings. Thus, this study presents a novel computational platform for accurately predicting CD8+ T-cell epitopes to foster a better understanding of antigen-specific T-cell response and the development of effective clinical therapeutics. Highlights HLA bias and out-of-distribution problem are causes of poor performance of current state-of-the-art algorithms Transfer learning, deep learning architecture, context-specific and HLA-generalised approaches improve CD8+ T-cell epitope prediction TRAP reports degree of correctness to improve reliability of the prediction A novel metric termed RSAT estimates immunogenicity of pathogenic peptides, as a complementary to ‘dissimilarity to self’ from cancer studies
Crystal structure of the Arabidopsis SPIRAL2 C-terminal domain reveals a p80-Katanin-like domain

Epidermal cells of dark-grown plant seedlings reorient their cortical microtubule arrays in response to blue light from a net lateral orientation to a net longitudinal orientation with respect to the long axis of cells. The molecular mechanism underlying this microtubule array reorientation involves katanin, a microtubule severing enzyme, and a plant-specific microtubule associated protein called SPIRAL2. Katanin preferentially severs longitudinal microtubules, generating seeds that amplify the longitudinal array. Upon severing, SPIRAL2 binds nascent microtubule minus ends and limits their dynamics, thereby stabilizing the longitudinal array while the lateral array undergoes net depolymerization. To date, no experimental structural information is available for SPIRAL2 to help inform its mechanism. To gain insight into SPIRAL2 structure and function, we determined a 1.8 Å resolution crystal structure of the Arabidopsis thaliana SPIRAL2 C-terminal domain. The domain is composed of seven core α-helices, arranged in an α-solenoid. Amino-acid sequence conservation maps primarily to one face of the domain involving helices α1, α3, α5, and an extended loop, the α6-α7 loop. The domain fold is similar to, yet structurally distinct from the C-terminal domain of Ge-1 (an mRNA decapping complex factor involved in P-body localization) and, surprisingly, the C-terminal domain of the katanin p80 regulatory subunit. The katanin p80 C-terminal domain heterodimerizes with the MIT domain of the katanin p60 catalytic subunit, and in metazoans, binds the microtubule minus-end factors CAMSAP3 and ASPM. Structural analysis predicts that SPIRAL2 does not engage katanin p60 in a mode homologous to katanin p80. The SPIRAL2 structure highlights an interesting evolutionary convergence of domain architecture and microtubule minus-end localization between SPIRAL2 and katanin complexes, and establishes a foundation upon which structure-function analysis can be conducted to elucidate the role of this domain in the regulation of plant microtubule arrays.
The Warburg Effect is the result of faster ATP production by glycolysis than respiration

Many prokaryotic and eukaryotic cells metabolize glucose to organism-specific byproducts instead of fully oxidizing it to carbon dioxide and water–a phenomenon referred to as the Warburg Effect. The benefit to a cell has been unclear, given that partial metabolism of glucose yields an order of magnitude less ATP per molecule of glucose than complete oxidation. We show that glycolysis produces ATP faster per gram of pathway protein than respiration in E. coli, S. cerevisiae, and mammalian cells. A simple mathematical model that uses yield, rate, and proteome occupancy of glycolysis and respiration as the only parameters accurately predicts absolute rates of glycolysis and respiration in all three organisms under diverse conditions. Our study suggests that the Warburg Effect is a consequence of the optimization of the rate of energy generation under the constraint of finite proteome space. One-Sentence Summary The Warburg Effect is a manifestation by which cells across kingdoms of life optimize the rate of energy production.
Development of an antibody fused with an antimicrobial peptide targeting Pseudomonas aeruginosa: A new approach to prevent and treat bacterial infections

The increase of emerging drug resistant Gram-negative bacterial infections is of global concern. In addition, there is growing recognition that compromising the microbiota, through the use of broad spectrum antibiotics, may affect patient health in the long term. Therefore, there is the need to develop new -cidal strategies to combat Gram-negative infections that would consider these specific issues. In this study, we report and characterize one such approach, the antibody-drug conjugates (ADCs) that combine (i) targeting a specific pathogenic organism through a monoclonal antibody with (ii) the high killing activity of antimicrobial peptides. We focused on a major pathogenic Gram-negative bacterium associated with antibacterial resistance: Pseudomonas aeruginosa and designed an ADC by fusing an antimicrobial peptide at the C-terminal end of the VH and/or VL-chain of a monoclonal antibody, VSX, that targets the core of P. aeruginosa lipopolysaccharide (LPS). This ADC demonstrated appropriately minimal levels of toxicity to mammalian cells and rapidly kills P. aeruginosa strains through several mechanisms while protecting mice from P. aeruginosa lung infection when administered therapeutically. Furthermore, we found that the ADC was synergistic with several classes of antibiotics. This approach described in this study may result in a widely useful strategy to target specific pathogenic microorganisms without augmenting further antibiotic resistance. Author Summary The increasing of emerging drug resistant bacterial infections is a worldwide issue and infections caused by antibiotic resistant Gram-negative pathogens are particularly concerning. In addition, there is now growing recognition that disruption of the microbiota, through the use of broad spectrum antibiotics, may affect patient health in the long term. Therefore, there is the need to develop new -cidal strategies to combat Gram-negative infections while preserving the microbiota and also avoid enhancement of antibiotic resistance. We report and characterize here one such approach by using a specific monoclonal antibody associated with the potent killing activity of antimicrobial peptides in the form of an antibody-drug conjugate (ADC). The selected pathogenic bacterium was Pseudomonas aeruginosa, that presents numerous markers for both innate and acquired antibiotic resistance. The ADC lacked significant cytotoxicity against mammalian cells and was shown to be effective both in vitro and in vivo against P. aeruginosa.
Characteristics of Patients With Metastatic Breast Cancer Who Survived more than 10 Years

Abstract Background/Aim: Despite advances in the treatment of breast cancer, metastatic breast cancer (MBC) remains difficult to cure, and few MBC patients survive 10 years after receiving a breast cancer metastasis diagnosis. We collected the cases of patients with MBC who survived >10 years post-metastasis diagnosis and assessed the patients’ characteristics. Patients and Methods: We retrospectively analyzed the cases of 245 consecutive patients diagnosed with MBC between January 2005 and December 2012 at our institution. Among them, 167 patients with confirmed survival of >10 years (i.e., long-term survival) or confirmed death at ≤10 years post-metastasis diagnosis were enrolled. Results: There were 22 patients with MBC who survived >10 years. Regarding the cancer subtypes, 11 patients (50%) with long-term survival were HER2-positive. Seven of the 11 patients with HER2-positive MBC have been without recurrence although anti-HER2 therapy was discontinued. Triple-negative breast cancer (TNBC) was most common in the patients who survived ≤5 years but was not present in the >10-year survival group. In the HER2-negative cases, more cases in the long-term survival group were treated with local therapy (34.4% in the <5-year survival group, 43.8% in the 5-10-year group, and 72.7% in the >10-year group). Conclusion: MBC patients who survive >10 years after being diagnosed with metastasis are more likely to be HER2-positive and treated with local therapy. This suggests the efficacy of anti-HER2 therapy, and, conversely, clarifies unmet needs in TNBC and luminal-type MBC. The usefulness of local therapy was also supported by our findings.
Asymptotic Expansion for Sampling Distribution and Sample Size in Statistical Inference III—The Modified Likelihood Ratio Λ-Test in the Noncentral Case

ABSTRACT This is the third article on the subject stated in the title, treating the asymptotic expansion formula for the OC function of the modified Λ-test. According to the idea and approaching procedure in our first article (Siotani et al., 1995), the reference domain of parameters for making the expansion formula valid was set up. Then an approximate upper bound on the absolute error of the treated expanded OC function was constructed numerically and experimentally over the reference domain in such a way that it provides the guarantee for the practical validity of the expanded OC function and method of determining an effective sample size for designing the test procedure satisfying some requirements on the power of the test.