Miracle-dz/UniFAR-FTUs huggingface dataset:
De-tensioning of collagen fibers optimizes endometrial receptivity and improves the rate of embryo implantation Short title: Collagen de-tensioning improves embryo implantation rate

Successful embryo implantation is associated with intensive endometrium matrix remodeling, in which fibrillar collagens and matrix metalloproteinases play a key role. Here we report that topical treatment with a single dose of human collagenase-1 improves the rate of embryo implantation in murine models regardless of genetic background. Collagenase-1 treatment reduces endometrial collagen-fiber tension affecting their aligned configuration while preserving the overall integrity of the uterine tissue. The collagenase treatment causes enhanced local vascular permeability and creates an inflammatory-like environment, all of which are necessary for successful embryo implantation. We show that this specific treatment rescued heat stress and embryo transfer induced embryo failure in mice. Our findings highlight the clinical feasibility to improve endometrial receptivity for embryo implantation. This treatment approach may be used to improve livestock breeding and for assisted human reproduction medical treatments. Teaser Mild specific collagen remodeling improves endometrial receptivity for natural and embryotransfer-derived embryo implantation.
Erroneous compensation for long-latency feedback delays as origin of Essential Tremor

Essential tremor (ET), a movement disorder characterized by involuntary oscillations of the limbs during movement, remains to date not well understood. It has been recently suggested that the tremor originates from impaired delay compensation, affecting movement representation and online control. Here we tested this hypothesis directly with ET patients (N=24) and neurologically intact (NI) volunteers (N=28) in an upper limb postural perturbation task. After maintaining their hand in a visual target, participants experienced perturbations of unpredictable direction and magnitude, and were instructed to counter the perturbation and steer their hand back to the starting position. In comparison with NI volunteers, ET patients early muscular responses (Short and Long Latency Responses, 20-50 ms and 50-100 ms respectively) were preserved or even slightly increased. However, they exhibited perturbation-dependent deficits when stopping and stabilizing their hand in the final target supporting the hypothesis that the tremor was generated by the feedback controller. We show in a computational model that errors in delay compensation accumulating over time produced the same small increase in initial feedback response followed by oscillations that scaled with the perturbation magnitude as observed in ET population. Our experimental results therefore validate the computational hypothesis that inaccurate delay compensation in long-latency pathways could be the origin of the tremor. Significance Statement Essential Tremor origin remains poorly understood. In the present study, we focused on motor impairments associated with feedback control. Following a mechanical perturbation applied to their arm, patients’ short and initial long latency stretch responses were preserved. However, we observed clear impairments during the stabilization phase that scaled with the perturbation magnitude. These results were reproduced in a computational model where delay compensation was inaccurate, suggesting that the origin of the tremor may lie in an underestimation of the delays impacting the internal monitoring of the motor commands.
Population Bursts in a Modular Neural Network as a Mechanism for Synchronized Activity in KNDy Neurons

The pulsatile activity of gonadotropin-releasing hormone neurons (GnRH neurons) is a key factor in the regulation of reproductive hormones. This pulsatility is orchestrated by a network of neurons that release the neurotransmitters kisspeptin, neurokinin B, and dynorphin (KNDy neurons), and produce episodic bursts of activity driving the GnRH neurons. We show in this computational study that the features of coordinated KNDy neuron activity can be explained by a neural network in which connectivity among neurons is modular. That is, a network structure consisting of clusters of highly-connected neurons with sparse coupling among the clusters. This modular structure, with distinct parameters for intracluster and intercluster coupling, also yields predictions for the differential effects of the co-released transmitters neurokinin B and dynorphin. In particular, it provides one possible explanation for how the excitatory neurotransmitter neurokinin B and the inhibitory neurotransmitter dynorphin can both increase the degree of synchronization among KNDy neurons. Author summary Since the discovery of a small population of hypothalamic neurons that secrete kisspeptin, neurokinin B, and dynorphin (KNDy neurons), there has been interest in their role as a pacemaker for the pulsatile release of key reproductive hormones. A fundamental question is what mechanism coordinates KNDy neuron activity to generate population bursts. Optical imaging of the KNDy network at single-neuron resolution has revealed that individual KNDy neurons participate in many, but not all, population bursts. It has also shown that the order in which the neurons are recruited in each burst could be highly determined in some animals but not in others. We demonstrate here that these observations can be explained by a neural network with a modular structure. We also show how such a structure can explain the paradoxical finding that both the excitatory neurotransmitter neurokinin B and the inhibitory neurotransmitter dynorphin can act to increase the degree of synchronization among the KNDy neurons.
Delineation of signaling routes that underlie differences in macrophage phenotypic states

Macrophages represent a major immune cell type in tumor microenvironments, they exist in multiple functional states and are of a strong interest for therapeutic reprogramming. While signaling cascades defining pro-inflammatory macrophages are better characterized, pathways that drive polarization in immunosuppressive macrophages are incompletely mapped. Here, we performed an in-depth characterization of signaling events in primary human macrophages in different functional states using mass spectrometry-based proteomic and phosphoproteomic profiling. Analysis of direct and indirect footprints of kinase activities has suggested PAK2 and PKCα kinases as important regulators of in vitro immunosuppressive macrophages (IL-4/IL-13 or IL-10 stimulated). Network integration of these data with the corresesponding transcriptome profiles has further highlighted FOS and NCOR2 as central transcription regulators in immunosuppressive states. Furthermore, we retrieved single cell sequencing datasets for tumors from cancer patients and found that the unbiased signatures identified here through proteomic analysis were able to successfully separate pro-inflammatory macrophage populations in a clinical setting and could thus be used to expand state-specific markers. This study contributes to in-depth multi-omics characterizations of macrophage phenotypic landscapes, which could be valuable for assisting future interventions that therapeutically alter immune cell compartments. Highlights Global proteomic characterization of primary human macrophages in different states Mapping of main signaling events through in-depth data analysis PKCα and PAK2 kinases are important regulators of immunosuppressive macrophages Proteomic signatures enable accurate detection of pro-inflammatory macrophages in patient tumors
Forget and Forgive: A Neurocognitive Mechanism for Increased Cooperation During Group Formation

How do group size changes influence cooperation within groups? To examine this question, we performed a dynamic, network-based prisoner’s dilemma experiment with fMRI. Across 83 human subjects, we observed increased cooperation as group size increased. However, our computational modeling analysis of behavior and fMRI revealed that group size itself did not increase cooperation. Rather, interaction between (1) subjects’ stable prosocial tendencies, and (2) dynamic reciprocal strategy weighted by memory confidence, underlies the group size-modulated increase in cooperation because the balance between them shifts toward the prosocial tendency with higher memory demands in the larger groups. We found that memory confidence was encoded in fusiform gyrus and precuneus, whereas its integration with prosocial tendencies was reflected in the left DLPFC and dACC. Therefore, interaction between recall uncertainty during reciprocal interaction (i.e., forgetting) and one’s individual prosocial preference is a core pillar of emergent cooperation in more naturalistic and dynamic group formation.
Integrating an Online International Collaboration Into a Physical Education Teacher Education Program

Abstract As the K-12 student population becomes increasingly diverse, developing cultural competence is essential for educators, including physical education teachers. An online international collaboration is one way to address cultural competence in higher education. The purpose of this article is to discuss steps and suggestions for integrating an online international collaboration in a PETE program. The five steps to adopting such a collaboration within a PETE program are as follows: finding a collaborator (step one), designing a program (step two), setting the scene for preservice teachers (step three), implementing the program (step four), and evaluating the program (step five). It is important to note that a one-time integration of an online international collaboration cannot be the sole solution for developing cultural competence. Developing cultural competence is a lifelong process, and an online international collaboration could provide preservice teachers with the first step to developing this important competence.
Impacts of artificial ocean alkalinization on the carbon cycle and climate in Earth system simulations

Using the state‐of‐the‐art emissions‐driven Max Planck Institute Earth system model, we explore the impacts of artificial ocean alkalinization (AOA) with a scenario based on the Representative Concentration Pathway (RCP) framework. Addition of 114 Pmol of alkalinity to the surface ocean stabilizes atmospheric CO2 concentration to RCP4.5 levels under RCP8.5 emissions. This scenario removes 940 GtC from the atmosphere and mitigates 1.5 K of global warming within this century. The climate adjusts to the lower CO2 concentration preventing the loss of sea ice and high sea level rise. Seawater pH and the carbonate saturation state (Ω) rise substantially above levels of the current decade. Pronounced differences in regional sensitivities to AOA are projected, with the Arctic Ocean and tropical oceans emerging as hot spots for biogeochemical changes induced by AOA. Thus, the CO2 mitigation potential of AOA comes at a price of an unprecedented ocean biogeochemistry perturbation with unknown ecological consequences.
A Seed Expansion Algorithm Based on The CRITIC Method and Node Gravity

Complex networks are composed of nodes and complex relationships between nodes, which are the abstraction of various complex systems in the real world. Community structure is a remarkable characteristic of complex networks.Community detection algorithms can reveal the topological structure of the networks and mine the potential information in complex networks, which can be widely used in personalized recommendation, public security, social science and other fields, and has important research significance. The community detection algorithm based on seeds extension is a classic community detection algorithm, which has high execution efficiency, but there are some problems. Aiming at the problems of poor seed nodes selection and improper community expansion method of seeds expansion algorithm, this paper proposes a seed expansion algorithm based on CRITIC method and node gravity which is called SECNG. The contributions of the algorithm are as follows: At first, we introduced CRITIC method to measure the importance of nodes, which can objectively measure the importance of nodes in the networks. Secondly, we propose node gravity and community gravity to guide the expansion of communities. In addition, modularity maximization strategy is used to merge communities. We verify the effectiveness of the SECNG algorithm through the real networks and artificial generated networks. The results show that the Q value and NMI values of the algorithm are higher than other algorithms.
On the Use of Quality Diversity Algorithms for the Travelling Thief Problem

In real-world optimisation, it is common to face several sub-problems interacting and forming the main problem. There is an inter-dependency between the sub-problems, making it impossible to solve such a problem by focusing on only one component. The travelling thief problem (TTP) belongs to this category and is formed by the integration of the travelling salesperson problem (TSP) and the knapsack problem (KP). In this paper, we investigate the inter-dependency of the TSP and the KP by means of quality diversity (QD) approaches. QD algorithms provide a powerful tool not only to obtain high-quality solutions but also to illustrate the distribution of high-performing solutions in the behavioural space. We introduce a multi-dimensional archive of phenotypic elites (MAP-Elites) based evolutionary algorithm using well-known TSP and KP search operators, taking the TSP and KP score as the behavioural descriptor. MAP-Elites algorithms are QD-based techniques to explore high-performing solutions in a behavioural space. Afterwards, we conduct comprehensive experimental studies that show the usefulness of using the QD approach applied to the TTP. First, we provide insights regarding high-quality TTP solutions in the TSP/KP behavioural space. Afterwards, we show that better solutions for the TTP can be obtained by using our QD approach, and it can improve the best-known solution for a number of TTP instances used for benchmarking in the literature.
A Novel Data Augmentation Method for Robotic Surgical Instrument Small Part Segmentation in Complex Scenes

Accurate and robust segmentation of surgical instrument parts is urgently required by AI enhanced intelligent surgery and automatic surgery skill evaluation. However, it is still a challenging problem due to the multiple small areas of frontal clasper and wrist parts, complicated object occlusion and light variation issues. Increasing high quality labeled data is effective to improve the performance but labor-costing. A novel instrument data augmentation method is proposed to generate high quality training data through planar instrument replacement and orientation adjustment. Moreover, image repainting methods are utilized to recover the occluded parts by instruments and generate clean tissue images. Then joint angle reconfigured instruments are placed on recovered clean tissue images to generate simulated surgery images, and we control the occlusion rates and overlapping areas by which high quality instrument manipulation augmented training data are generated automatically. Detailed experiment results show our proposed data augmentation method make existing methods achieve better segmentation accuracy and robustness of wrist and clasper part, especially the fault detection errors decreased significantly in instrument overlapping and complicated lighting conditions.
Dedicated Hardware Accelerators for Processing of Sparse Matrices and Vectors: A Survey

Performance in scientific and engineering applications such as computational physics, algebraic graph problems or Convolutional Neural Networks (CNN), is dominated by the manipulation of large sparse matrices – matrices with a large number of zero elements. Specialized software using data formats for sparse matrices has been optimized for the main kernels of interest: SpMV and SpMSpM matrix multiplications, but due to the indirect memory accesses, the performance is still limited by the memory hierarchy of conventional computers. Recent work shows that specific hardware accelerators can reduce memory traffic and improve the execution time of sparse matrix multiplication, compared to the best software implementations. The performance of these sparse hardware accelerators depends on the choice of the sparse format, COO, CSR, etc, the algorithm, inner-product, outer-product, Gustavson, and many hardware design choices. In this article, we propose a systematic survey which identifies the design choices of state-of-the-art accelerators for sparse matrix multiplication kernels. We introduce the necessary concepts and then present, compare and classify the main sparse accelerators in the literature, using consistent notations. Finally, we propose a taxonomy for these accelerators to help future designers make the best choices depending of their objectives.
Practical Block-wise Neural Network Architecture Generation

Convolutional neural networks have gained a remarkable success in computer vision. However, most usable network architectures are hand-crafted and usually require expertise and elaborate design. In this paper, we provide a block-wise network generation pipeline called BlockQNN which automatically builds high-performance networks us-ing the Q-Learning paradigm with epsilon-greedy exploration strategy. The optimal network block is constructed by the learning agent which is trained sequentially to choose component layers. We stack the block to construct the whole auto-generated network. To accelerate the generation process, we also propose a distributed asynchronous framework and an early stop strategy. The block-wise generation brings unique advantages: (1) it performs competitive results in comparison to the hand-crafted state-of-the-art networks on image classiﬁcation, additionally, the best network generated by BlockQNN achieves 3 . 54% top-1 error rate on CIFAR-10 which beats all existing auto-generate networks. (2) in the meanwhile, it offers tremendous reduction of the search space in designing networks which only spends 3 days with 32 GPUs, and (3) moreover, it has strong generalizability that the network built on CIFAR also performs well on a larger-scale ImageNet dataset.
Functional Hand Type Prior for 3D Hand Pose Estimation and Action Recognition from Egocentric View Monocular Videos

Current methods for egocentric view action recognition often face challenges in per-ceiving dynamic hand movements relying solely on geometrical or physical information. In this work, we effectively address this problem by gaining insights into the correlation between functional hand configurations and objects, which improves the detailed interpretation of real-world scenarios. To this end, we introduce a practical taxonomy of hand types based on the functioning perspective and utilize it for per-frame hand type labeling on existing datasets. We also propose a novel hand action recognition framework considering semantic details of the hand type as prior. This approach boosts the network’s understanding of the continuous hand interaction throughout the action sequence. Our whole pipeline consists of three main modules: (1) Feature Extraction, (2) Egocentric Knowledge Module, which estimates 3D hand pose, object category, and hand type leveraging short-term cues, and (2) Egocentric Action Module, which aggregates per-frame knowledge, including text embeddings of hand type, over a longer time. In our extensive experiments with large-scale benchmarks, FPHA and H2O, our model outperforms current state-of-the-art methods, demonstrating its superior performance.
Integrating Transient and Long-term Physical States for Depression Intelligent Diagnosis

As social competition intensifies, the number of depression patients has rapidly increased. Many researchers have proposed diagnostic models for depression based on various physiological signals and behavioral information, such as Electroencephalogram (EEG) and facial expressions. However, it should be noted that these signals tend to reflect transient information, which may make them insufficient for accurately diagnosing depression characterized by persistent low mood over a prolonged period of time. Mean-while, traditional Chinese medicine(TCM) believes that different parts of the tongue correspond to different Zang-fu and can indicate the long-term physical condition of the human body. Therefore, we use EEG and tongue images to reflect the subject’s instantaneous state and long-term physical condition respectively, and establish a multimodal model MMTV to assist doctors in diagnosing depression. Specifically, MMTV innova-tively introduces the dual-stream input mechanism and self-attention mechanism to EEG-Net to better extract the spatio-temporal features of EEG. Meanwhile, to obtain higher quality tongue surface images, MMTV introduces a segmentation step before inputting tongue images into the ViT model. Meta-learning techniques are applied to gain better pretrained weights for ViT. Furthermore, we analyze the correlation between tongue images and EEG and subsequently fuse the output features of the brain-tongue branches in MMTV. The ability of MMTV to recognize patients with depression
SHLS: Superfeatures learned from still images for self-supervised VOS

Self-supervised video object segmentation (VOS) aims at eliminating the need for manual annotations to learn VOS. However, existing methods often require extensive training data consisting of hours of videos. In this paper, we introduce a novel approach that combines superpixels and deep learning features through metric learning, enabling us to learn VOS from a small dataset of unlabeled still images. Our method, called su-perfeatures in a highly compressed latent space (SHLS), embeds convolutional features into the corresponding superpixel areas, resulting in ultra-compact image representations. This allowed us to construct an efficient memory mechanism to store and retrieve past information throughout a frame sequence to support current frame segmentation. We evaluate our method on the popular DAVIS dataset and achieve competitive results compared to state-of-the-art self-supervised methods, which were trained with much larger video-based datasets. We have made our code and trained model publicly available at: https://github.com/IvisionLab/SHLS .
Reconstructing Synthetic Lensless Images in the Low-Data Regime

Lensless image reconstruction is a class of inverse problems in computational imaging that is gaining immense popularity in the imaging research community because of its potential to revolutionize traditional imaging systems radically. It is possible to build cameras with extremely flat form factors that enable imaging in challenging scenarios. In this paper, we reconstruct simulated lensless images using a very low number of examples where we pre-train a decoder architecture with the available examples to model the data semantics, and then solve the inverse problem of lensless image reconstruction guided by a physics-informed forward loss function. We contrastively analyze and evaluate the reconstruction performance of our model against its untrained counterpart and show a substantial improvement in the reconstruction quality and convergence time even with a few example images.