Miracle-dz/UniFAR-FTUs huggingface dataset:
A Fully 3D Interconnected Graphene–Carbon Nanotube Web Allows the Study of Glioma Infiltration in Bioengineered 3D Cortex‐Like Networks

Currently available 3D assemblies based on carbon nanotubes (CNTs) lag far behind their 2D CNT‐based bricks and require major improvements for biological applications. By using Fe nanoparticles confined to the interlamination of graphite as catalyst, a fully 3D interconnected CNT web is obtained through the pores of graphene foam (GCNT web) by in situ chemical vapor deposition. This 3D GCNT web has a thickness up to 1.5 mm and a completely geometric, mechanical and electrical interconnectivity. Dissociated cortical cells cultured inside the GCNT web form a functional 3D cortex‐like network exhibiting a spontaneous electrical activity that is closer to what is observed in vivo. By coculturing and fluorescently labeling glioma and healthy cortical cells with different colors, a new in vitro model is obtained to investigate malignant glioma infiltration. This model allows the 3D trajectories and velocity distribution of individual infiltrating glioma to be reconstructed with an unprecedented precision. The model is cost effective and allows a quantitative and rigorous screening of anticancer drugs. The fully 3D interconnected GCNT web is biocompatible and is an ideal tool to study 3D biological processes in vitro representing a pivotal step toward precise and personalized medicine.
Assessing Study-Specific Regional Variations in fMRI Signal

In this paper, we present a post hoc method for identifying regions where functional MRI data are subject to signal reduction that may compromise sensitivity to activations. The motivation for developing this technique derives from recent language studies that showed responses in the inferior temporal lobe that could be detected by PET but not by fMRI. Reduced signal is due mostly to susceptibility artifacts and can be identified by comparing the T2* images (which are subject to susceptibility artifacts) with T2 images (which are not). However, T2 images are not usually acquired in fMRI studies. Therefore, we propose that areas with reduced signal can be identified by comparing T2* images that are corrected for nonuniformity with the original uncorrected images. The technique provides a voxel-wise characterization of signal reduction that pertains to the particular data that enter into a statistical model. It requires only the functional data and can thus be applied post hoc and without any additional scans.
Distribución vertical de hongos en hojas de tres especies de pinos en Nuevo León, México

The vertical distribution of fungi in seven types of pine leaves of Pinus arizonica, P. cembroides, and P. pseudostrobus was studied during one year by the method of isolation in culture medium in Petri dishes (indirect method) and by the use of moist chambers (direct method). 57 taxa of fungi were identified by the culture medium method, 56 taxa are adscribed to the Phyla Ascomycota and only one species (Gymnopus androsaceus) belongs to the Phyla Basidiomycota; Lophodermium australe, Pestalotia stevensonii and Rhizosphaera kalkhoffii were the most abundant species; Cladosporium cladosporioides, Alternaria alternata and Ceuthospora sp. were the most frequent taxa. By the moist chamber method 28 taxa were determined; Lophodermium australe, Preussia sp. , and Pestalotia stevensonii were the most abundant species; Preussia sp. , and Coniochaeta ligniaria were the most frequent species. Well defined patterns of fungi were observed in both methods for the three pine species and for the studied leave types. Pinus arizonica, registered the bigger fungal diversity followed by P. pseudostrobus and P. cembroides. As the leaf degradation rate grows, the number of species of fungi decrease.
Perception below the Objective Threshold?

Abstract The experiments reported by Snodgrass, Shevrin, and Kopka (1993) appear to demonstrate that words are perceived even when overall forced-choice discrimination performance does not deviate from chance. We replicated their critical finding in two separate experiments; our results indicated that the subjects′ preferences for one of the two strategy conditions (i.e., pop or look) predicted significant deviations from chance performance in the pop condition, even though the overall performance in this condition did not differ from chance. In addition, we found that task preference had no effect when blank cards rather than words were presented and that the words could not be detected when subjects were simply asked to decide on each trial whether a stimulus had been presented. These results suggest that it is possible to demonstrate perception below an objective threshold defined in terms of overall discrimination performance. As such, the results highlight the importance of considering individual differences in studies of perception without awareness.
PCR and Infectious Diseases

Since the 1950s, the medical community has been faced with infectious diseases, which have brought significant public health and financial challenges. Currently, rou-tine testing for the laboratory diagnosis for infectious agents is based on cell culture, serological, and molecular methods. However, cell culture-based methods are used mainly in research laboratories and are less sensitive methods when compared with serological and molecular methods. The diagnosis of infectious diseases has been revolutionized by the development of molecular techniques, mainly with the applications of polymerase chain reaction (PCR). The high sensitivity, specificity, and ease with which the PCR can be used to detect genetic sequences known have led to your wide application in science. A great number of qualitative and quantitative molecular assays are mostly based on what have been described such as real-time PCR, multiplex PCR, LAMP-PCR, and digital PCR. These assays could identify active infection by detecting infectious agents and nucleic acid in various clinical conditions including arboviruses, sexually transmitted infections, and bacterial infections. Further advancement of molecular technology is needed to improve the capacity to detect infectious agents in order to control the spread of infectious diseases and lead to appropriate actions which help to benefit patients and health-care workers themselves.
Toxoplasma gondii – obsessive –compulsive disorder relationship: is it different in children?

Abstract Background and aim: Obsessive–compulsive disorder (OCD) is a common neuropsychiatric illness. Although the etiology of OCD is still unknown, recent investigations have associated development of OCD with infectious illness. Toxoplasma gondii (T. gondii) is a neurotropic protozoan parasite that causes infection of the central nervous system. In the last decade, a lot of researches have focused on the possible relationship between exposure to T. gondii and neuropsychiatric disorders such as schizophrenia. Therefore, in this study, it was aimed to investigate a possible association between Toxoplasma infection and OCD in children and adolescents. Methods: We selected 55 patients with OCD (aged between 7 and 16 years) and 59 healthy children and adolescents (aged between 7 and 16 years), and investigated the seropositivity rate for anti-Toxoplasma IgG antibodies by enzyme-linked immunosorbent assay. Results: The seropositivity rate for anti-T. gondii IgG antibodies among OCD patients (21.82%) was found to be higher than the rate in control group (15.25%). However, the difference between the OCD group and the control group was not statistically significant (p > .05). Conclusion: In contrast to studies in adult patients, the results of this study do not support the relationship between T. gondii and OCD children and adolescents.
Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes

Multi-chamber heart segmentation is a prerequisite for quantification of the cardiac function. In this paper, we propose an automatic heart chamber segmentation system. There are two closely related tasks to develop such a system: heart modeling and automatic model fitting to an unseen volume. The heart is a complicated non-rigid organ with four chambers and several major vessel trunks attached. A flexible and accurate model is necessary to capture the heart chamber shape at an appropriate level of details. In our four-chamber surface mesh model, the following two factors are considered and traded-off: 1) accuracy in anatomy and 2) easiness for both annotation and automatic detection. Important landmarks such as valves and cusp points on the interventricular septum are explicitly represented in our model. These landmarks can be detected reliably to guide the automatic model fitting process. We also propose two mechanisms, the rotation-axis based and parallel-slice based resampling methods, to establish mesh point correspondence, which is necessary to build a statistical shape model to enforce priori shape constraints in the model fitting procedure. Using this model, we develop an efficient and robust approach for automatic heart chamber segmentation in 3D computed tomography (CT) volumes. Our approach is based on recent advances in learning discriminative object models and we exploit a large database of annotated CT volumes. We formulate the segmentation as a two step learning problem: anatomical structure localization and boundary delineation. A novel algorithm, Marginal Space Learning (MSL), is introduced to solve the 9-dimensional similarity transformation search problem for localizing the heart chambers. After determining the pose of the heart chambers, we estimate the 3D shape through learning-based boundary delineation. Extensive experiments demonstrate the efficiency and robustness of the proposed approach, comparing favorably to the state-of-the-art. This is the first study reporting stable results on a large cardiac CT dataset with 323 volumes. In addition, we achieve a speed of less than eight seconds for automatic segmentation of all four chambers.
Creating a virtual human that visualizes skin strain distribution for apparel wearing simulation

This paper describes the first step of our research collaboration to create a virtual human that simulates strain of the skin to aid the design of comfortable, ergonomic sportswear. We apply insights from sports science and computer graphics. For the former, human's kinematic properties, such as the angular rotation for each joint or the magnitude of strain on the subject's skin, play an important role in understanding and simulating human movements. Contrarily, during the creation of computer graphics characters, artists mainly focus on a plausible appearance instead of these properties. Therefore, creating such a virtual human model poses several significant interdisciplinary challenges. We demonstrate several collaborative efforts and initial research results, focusing on how to visualize human skin strain distribution of lower body of a 3D human model. The human surface model, skin strain shader, and its rig are also briefly described. Finally, we discuss our early results and future scope of our collaborative work.
Brushing element fields

Aggregate elements following certain directions have a variety of applications in graphics, design, and visualization. However, authoring oriented elements in various output domains, especially in 3D, remains challenging. We propose a novel brushing system to facilitate interactive authoring of aggregate elements with diverse properties over given output domains via an element synthesis approach. To increase output quality and reduce input workload, we further propose element fields that can automatically orient the entire elements in better alignments over the output domains according to partially user-specified strokes. The proposed system can effectively synthesize distinct types of elements within various output domains in higher quality and efficiency and offer more user friendliness than existing practices. Our method can be applied to practical applications such as graphic design, artistic collage, and aggregate modeling.
Production ray tracing of feature lines

Automated feature line drawing of virtual 3D objects helps artists depict shapes and allows for creating stylistic rendering effects. High-fidelity drawing of lines that are very thin or have varying thickness and color, or lines of recursively reflected and refracted objects, is a challenging task. In this paper we describe an image-based feature detection and line drawing method that integrates naturally into a ray tracing renderer and runs as a post-process, after the pixel sampling stage. Our method supports arbitrary camera projections and surface shaders, and its performance does not dependent on the geometric complexity of the scene but on the pixel sampling rate. By leveraging various attributes stored in every pixel sample, which are typically available in production renderers, e.g. for arbitrary output variables (AOVs), feature lines of reflected and refracted objects can be obtained with relative ease. The color and width of the lines can be driven by the surface shaders, which allows for achieving a wide variety of artistic styles.
Head-tracked off-axis perspective projection improves gaze readability of 3D virtual avatars

Virtual avatars have been employed in many contexts, from simple conversational agents to communicating the internal state and intentions of large robots when interacting with humans. Rarely, however, are they employed in scenarios which require non-verbal communication of spatial information or dynamic interaction from a variety of perspectives. When presented on a flat screen, many illusions and visual artifacts interfere with such applications, which leads to a strong preference for physically-actuated heads and faces. By adjusting the perspective projection used to render 3D avatars to match a viewer's physical perspective, they could provide a useful middle ground between typical 2D/3D avatar representations, which are often ambiguous in their spatial relationships, and physically-actuated heads/faces, which can be difficult to construct or impractical to use in some environments. A user study was conducted to determine to what extent a head-tracked perspective projection scheme was able to mitigate the issues in readability of a 3D avatar's expression or gaze target compared to use of a standard perspective projection. To the authors' knowledge, this is the first user study to perform such a comparison, and the results show not only an overall improvement in viewers' accuracy when attempting to follow the avatar's gaze, but a reduction in spatial biases in predictions made from oblique viewing angles.
FlyingHand: extending the range of haptic feedback on virtual hand using drone-based object recognition

This paper presents a Head Mounted Display (HMD) integrated system, that uses a drone and a virtual hand to help the users explore remote environment. The system allows the users to use hand gestures to control the drone and identify the Objects of Interest (OOI) through tactile feedback. The system uses a Convolutional Neural Network to perform object classification with the drone captured image and provides a virtual hand to realize interaction with the object. Accodingly, tactile feedback is also provided to users' hands to enhance the virtual hand body ownership. The system aims to help users assess space and objects regardless of body limitations, which could not only benefit elderly or handicapped people, but make potential contributions in environment measurement and daily life as well.
Coded skeleton: shape changing user interface with mechanical metamaterial

We propose a design method for fabricating a novel shape-changing user interface, called "Coded Skeleton", by computationally integrating actuators and sensors using a mechanical metamaterial. This design method realizes the deformation of various curves using simple expansion and contraction actuators, leveraging the fact that the Coded Skeleton is flexible in one deformation mode but stiff in other. We describe the design method and structural analysis of the mechanical metamaterial that can uniquely define deformation along with outlining the creation and control method of the Coded Skeleton using this structure. Finally, we propose three applications of the Coded Skeleton.
FIST: a fast, implicit model of the human hand with semi-anatomical structures

There are many opportunities to draw human hands in computer graphics. The motion of internal organs within a human hand has a non-negligible effect on the natural change in the appearance of the hand's surface. In this work, we propose a method for expressing this change interactively with the use of an implicit model of a human hand that has semi-anatomical structures. The model is referred to as Fast, Implicit model with Semi-anatomical sTructures or FIST. In the FIST model, bones are modeled anatomically based on computed tomography imaging, while soft tissues are modeled artificially. It can be controlled only by specifying the angles of the joints. The proposed method can contribute to a compelling expression of the dynamism in such hand motions as grasping, pinching, and scratching in immersive virtual reality and games.
Learning photo enhancement by black-box model optimization data generation

We address the problem of automatic photo enhancement, in which the challenge is to determine the optimal enhancement for a given photo according to its content. For this purpose, we train a convolutional neural network to predict the best enhancement for given picture. While such machine learning techniques have shown great promise in photo enhancement, there are some limitations. One is the problem of interpretability, i.e., that it is not easy for the user to discern what has been done by a machine. In this work, we leverage existing manual photo enhancement tools as a black-box model, and predict the enhancement parameters of that model. Because the tools are designed for human use, the resulting parameters can be interpreted by their users. Another problem is the difficulty of obtaining training data. We propose generating supervised training data from high-quality professional images by randomly sampling realistic de-enhancement parameters. We show that this approach allows automatic enhancement of photographs without the need for large manually labelled supervised training datasets.
A magic wand for motion capture editing and edit propagation

This paper introduces a new method for editing character animation, by using a data-driven pose distance as a falloff to interpolate edited poses seamlessly into a motion sequence. This pose distance is defined using Green's function of the pose space Laplacian. The resulting falloff shape and timing are derived from and reflect the motion itself, replacing the need for a manually adjusted falloff spline. This data-driven region of influence is somewhat analogous to the difference between a generic 2D spline and the "magic wand" selection in an image editor, but applied to the animation domain. It also supports powerful non-local edit propagation in which edits are applied to all similar poses in the entire animation sequence.
Realistic AR makeup over diverse skin tones on mobile

We propose a novel approach to the application of realistic makeup over a diverse set of skin tones in mobile phones using augmented reality. The method we developed mimics the real world layering techniques and application that makeup artists use. We can accurately represent the five most commonly used materials found in commercial makeup products- Matte, Velvet, Glossy, Glitter, and Metallic. We apply skin smoothing to even out the natural skin tone and tone-mapping to further blend source and synthetic layers.
PPConv: polypod convolution for 3D point cloud description

3D point cloud is a collection of unordered sparse 3D points that is different from densely structured color image. Therefore, applying a fixed structure of deep learning network on 3D point cloud is a challenging task in computer vision and graphics problems. Recently, researchers have proposed deep learning methods for 3D point cloud based on data conversion or simplification. However, they lose either local 3D shape information for the simplicity of method or geometric locality for using array as an input. In this paper we propose a new convolution technique, named Polypod convolution, for 3D point cloud description that is distribution independent and maintains both local and global 3D shapes. Quantitative and qualitative evaluation results show the potential of our new network for 3D point cloud based deep learning applications.
A Rprop-Neural-Network-Based PV Maximum Power Point Tracking Algorithm with Short-Circuit Current Limitation

This paper proposes a resilient-backpropagation-neural-network-(Rprop-NN) based algorithm for Photovoltaic (PV) maximum power point tracking (MPPT). A supervision mechanism is proposed to calibrate the Rprop-NN-MPPT reference and limit short-circuit current caused by incorrect prediction. Conventional MPPT algorithms (e.g., perturb and observe (P&O), hill climbing, and incremental conductance (Inc-Cond) etc.) are trial-and-error-based, which may result in steady-state oscillations and loss of tracking direction under fast changing ambient environment. In addition, partial shading is also a challenge due to the difficulty of finding the global maximum power point on a multi-peak characteristic curve. As an attempt to address the aforementioned issues, a novel Rprop-NN MPPT algorithm is developed and elaborated in this work. Multiple case studies are carried out to verify the effectiveness of proposed algorithm.
Digital reproduction of hair waving based on animator technique

Even in animation production, which was once a hand-drawn creation, it has been increased speed and reduced costs by applying NPR technology. For example, there are researches and products for generating animations of waving hairs from a single image in which a still scene of the animated characters are drawn. However, animations created by previous methods often provide a feeling of strangeness compared to the hand-drawn animations. Because, in the previous method, tequniques of experienced animators are not considered, a huge amount of time and high skill are necessary to make the generated scene closer to the hand-drawn animation. Therefore, we developed a tool that can reproduce the tequniques used by animators to discribe the waving behavior of hair, for the purpose of generating an animation without the strangeness. The parameters need for our method can be automatically determined by using a kind of genetic algorithm.
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models

Generative models are becoming a tool of choice for exploring the molecular space. These models learn on a large training dataset and produce novel molecular structures with similar properties. Generated structures can be utilized for virtual screening or training semi-supervized predictive models in the downstream tasks. While there are plenty of generative models, it is unclear how to compare and rank them. In this work, we introduce a benchmarking platform called Molecular Sets (MOSES) to standardize training and comparison of molecular generative models. MOSES provides training and testing datasets, and a set of metrics to evaluate the quality and diversity of generated structures. We have implemented and compared several molecular generation models and suggest to use our results as reference points for further advancements in generative chemistry research. The platform and source code are available at https://github.com/molecularsets/moses.