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The polygonal shape of cells in proliferating epithelia is a result of the tensile forces of the cytoskeletal cortex and packing geometry set by the cell cycle. In the larval Drosophila epidermis, two cell populations, histoblasts and larval epithelial cells, compete for space as they grow on a limited body surface. They do so in the absence of cell divisions. We report a striking morphological transition of histoblasts during larval development, where they change from a tensed network configuration with straight cell outlines at the level of adherens junctions to a highly folded morphology. The apical surface of histoblasts shrinks while their growing adherens junctions fold, forming deep lobules. Volume increase of growing histoblasts is accommodated basally, compensating for the shrinking apical area. The folded geometry of apical junctions resembles elastic buckling, and we show that the imbalance between the shrinkage of the apical domain of histoblasts and the continuous growth of junctions triggers buckling. Our model is supported by laser dissections and optical tweezer experiments together with computer simulations. Our analysis pinpoints the ability of histoblasts to store mechanical energy to a much greater extent than most other epithelial cell types investigated so far, while retaining the ability to dissipate stress on the hours time scale. Finally, we propose a possible mechanism for size regulation of histoblast apical size through the lateral pressure of the epidermis, driven by the growth of cells on a limited surface. Buckling effectively compacts histoblasts at their apical plane and may serve to avoid physical harm to these adult epidermis precursors during larval life. Our work indicates that in growing nondividing cells, compressive forces, instead of tension, may drive cell morphology. During the development of the larval Drosophila epidermis, histoblasts and epithelial cells compete for space as they grow and undergo morphological changes. This study shows that compressive forces play a key role in controlling the shape of histoblasts, leading to the buckling of their apical junctions and changing the morphology of the epidermis from straight to highly folded outlines. [ABSTRACT FROM AUTHOR]

Virtual reality (VR) enables the development of virtual training frameworks suitable for various domains, especially when real-world conditions may be hazardous or impossible to replicate because of unique additional resources (e.g., equipment, infrastructure, people, locations). Although VR technology has significantly advanced in recent years, methods for evaluating immersion (i.e., the extent to which the user is engaged with the sensory information from the virtual environment or is invested in the intended task) continue to rely on self-reported questionnaires, which are often administered after using the virtual scenario. Having an objective method to measure immersion is particularly important when using VR for training, education, and applications that promote the development, fine-tuning, or maintenance of skills. The level of immersion may impact performance and the translation of knowledge and skills to the real-world. This is particularly important in tasks where motor skills are combined with complex decision making, such as surgical procedures. Efforts to better measure immersion have included the use of physiological measurements including heart rate and skin response, but so far they do not offer robust metrics that provide the sensitivity to discriminate different states (idle, easy, and hard), which is critical when using VR for training to determine how successful the training is in engaging the user's senses and challenging their cognitive capabilities. In this study, electroencephalography (EEG) data were collected from 14 participants who completed VR jigsaw puzzles with two different levels of task difficulty. Machine learning was able to accurately classify the EEG data collected during three different states, obtaining accuracy rates of 86% and 97% for differentiating easy versus hard difficulty states and baseline vs. VR states. Building on these results may enable the identification of robust biomarkers of immersion in VR, enabling real-time recognition of the level of immersion that can be used to design more effective and translative VR-based training. This method has the potential to adjust aspects of VR related to task difficulty to ensure that participants are immersed in VR. [ABSTRACT FROM AUTHOR]

Effective features play an important role in synthetic aperture radar (SAR) image interpretation. However, since SAR images contain a variety of terrain types, it is not easy to extract effective features of different terrains from SAR images. Deep learning methods require a large amount of labeled data, but the difficulty of SAR image annotation limits the performance of deep learning models. SAR images have inevitable geometric distortion and coherence speckle noise, which makes it difficult to extract effective features from SAR images. If effective semantic context features cannot be learned for SAR images, the extracted features struggle to distinguish different terrain categories. Some existing terrain classification methods are very limited and can only be applied to some specified SAR images. To solve these problems, a jigsaw puzzle self-supervised learning (JPSSL) framework is proposed. The framework comprises a jigsaw puzzle pretext task and a terrain classification downstream task. In the pretext task, the information in the SAR image is learned by completing the SAR image jigsaw puzzle to extract effective features. The terrain classification downstream task is trained using only a small number of labeled data. Finally, fully connected conditional random field processing is performed to eliminate noise points and obtain a high-quality terrain classification result. Experimental results on three large-scene high-resolution SAR images confirm the effectiveness and generalization of our method. Compared with the supervised methods, the features learned in JPSSL are highly discriminative, and the JPSSL achieves good classification accuracy when using only a small amount of labeled data. [ABSTRACT FROM AUTHOR]

Modeling is coming to the fore as it is now widely accepted and indeed expected during drug discovery and development. Modeling integrates knowledge, increases understanding and provides the ability to predict an outcome either before it occurs or when it is not possible to measure. This makes modeling an attractive option for inhaled drugs as it is not possible to routinely measure what is occurring to the drug (pharmacokinetics) and what effect the drug is having (pharmacodynamics) at local microscopic sites of such a diverse and complex organ as the lung. Many pieces of information (data and knowledge) exist like the pieces of a jigsaw puzzle and modeling brings the pieces together in a scientific and mechanistically coherent manner to increase understanding of both the efficacy and safety of inhaled drugs. [ABSTRACT FROM AUTHOR]

Is it possible to experience healing and growth when you grieve? How and where do you find meaning again? During the COVID-19 pandemic, many people were looking for answers to these questions. This autoethnography explores how I experienced personal transformation through the method of Music and Imagery (MI) therapy in the midst of the pandemic and huge personal loss. This transformation also impacted my faith in Christ. Through documenting my journey using music listening, artwork, journaling, memories, and peers' feedback, I realized just how possible it was to grow and find healing in trying times. I pieced all the information together like a jigsaw puzzle until I could see the complete picture. [ABSTRACT FROM AUTHOR]

This article explores the relationship between hemodynamic forces and atherosclerosis, with a focus on the role of HEG1 as a sensor of shear stress on endothelial cells. It explains that disturbed shear stress in certain areas of blood vessels is associated with the development of atherosclerotic plaques, while laminar shear stress is protective. The study by Tamargo et al. identifies HEG1 as the sensor that activates the ERK pathway and leads to the activation of KLF2, which is important for vascular health. The article also discusses the role of HEG1 in regulating the activation of KLF2/4 and the MEKK3 cascade in endothelial cells under laminar flow conditions. The authors demonstrate that HEG1 is a cell surface sensor that promotes the activation of KLF2/4 and regulates the MEKK3 cascade, and that suppression of the MEKK3 cascade is controlled by KRIT1 in a HEG1-dependent manner. The findings suggest that HEG1 plays a crucial role in maintaining endothelial homeostasis and protecting against atherosclerosis, but there are still unanswered questions about the mechanisms of HEG1 and KRIT1 secretion, as well as the relationship between HEG1 and NOTCH1. [Extracted from the article]

A jigsaw puzzle is a recreational activity that involves assembling a certain number of pieces into a combined and well-fitting unit without creating gaps between adjacent pieces. Two-dimensional puzzles are divided into two main categories, the "apictorial" in which the only information available is the shape of the pieces and the "pictorial" which may take into account not only the shape of the pieces, but also their content. Jigsaw puzzles are considered as one of the most popular category of puzzles. The majority of them are accompanied by a guiding image and there is only one "counterpart" for each side of each piece (pictorial jigsaw puzzles), although some more difficult variants have blank pieces, the so-called apictorial jigsaw puzzles. In this paper, we will examine the open problem of solving pictorial and apictorial jigsaw puzzles, and their various applications, such as the reconstruction of two-dimensional fragmented objects, the restoration of fragmented wall-paintings and the repair of shredded documents. We will also present an evaluation of the state-of-the- art jigsaw puzzle reassembly techniques in pictorial and apictorial puzzles. [ABSTRACT FROM AUTHOR]

Background: Reconstruction of large scalp defects after tumor resection is common and challenging. Free skin grafting or free flap transfer is always performed. However, these techniques can result in poor functional and cosmetic outcomes. Methods: In the Second Affiliated Hospital of Soochow University and the Second Affiliated Hospital of Chengdu Medical College, 35 patients underwent jigsaw puzzle flap procedures based on a free-style perforator to repair large scalp defects after tumor resection from May 2013 to November 2022. The key to this procedure was to divide a large defect into several smaller parts that correspond to adjacent free-style perforators around the scalp defect. The free-style perforator flaps were designed in different ways, such as propeller flaps, rotation flaps, and V-Y advancement flaps, which were pedicled with free-style perforators. All of the small flaps were then mobilized to the defect and sutured to each other to create a new, large jigsaw puzzle-like flap to cover the large scalp defect. Finally, all the donor sites were closed directly. Results: Overall, 35 patients were treated. The average defect size was 72 cm² (range, 25-91 cm²). All flaps had a satisfactory appearance after follow-up periods of 6 months--10 years. Local tumor recurrence occurred in one case. Conclusion: The application of a jigsaw puzzle flap based on a free-style perforator is a suitable option for the reconstruction of large scalp defects after resection, yielding satisfactory functional and cosmetic results. [ABSTRACT FROM AUTHOR]

An enumeration of spatial autocorrelation's (SA's) polyvalent forms occurred nearly three decades ago. Attempts to conceive and disseminate a clearer explanation of it employ metaphors seeking to better relate SA to a student's or spatial scientist's personal knowledge databank. However, not one of these uses the jigsaw puzzle metaphor appearing in this paper, which exploits an analogy between concrete visual content organization and abstract map patterns of attributes. It not only makes SA easier to understand, which furnishes a useful pedagogic tool for teaching novices and others about it, but also discloses that many georeferenced data should contain a positive–negative SA mixture. Empirical examples corroborate this mixture's existence, as well as the tendency for marked positive SA to characterize remotely sensed and moderate (net) positive SA to characterize socio-economic/demographic, georeferenced data. [ABSTRACT FROM AUTHOR]

This study proposed two algorithms for reconstructing jigsaw puzzles by using a color compatibility feature. Two realistic application cases were examined: one involved using the original image, while the other did not. We also calculated the transformation matrix to obtain the real positions of each puzzle piece and transmitted the positional information to the robotic arm, which then put each puzzle piece in its correct position. The algorithms were tested on 35-piece and 70-piece puzzles, achieving an average success rate of 87.1%. Compared with the human visual system, the proposed methods demonstrated enhanced accuracy when handling more complex textural images. [ABSTRACT FROM AUTHOR]

We present the case of a 55-year-old male patient who came to our attention because of a basal cell carcinoma of the right retroauricolar area, near the mastoid-auricle border. The tumor had a size of about 1.5 cm. Repairing retroauricolar surgical defects may be actually very challenging, firstly because an incorrect reconstruction may result in severe deformities which are extremely hard to conceal; so, we decided to employ the "jigsaw puzzle" advancement flap, a versatile flap, firstly successfully used in the reconstruction of a nasal ala defect. [ABSTRACT FROM AUTHOR]

Both Irenaeus and Tertullian address the issue of "the rule of faith" in a context where they fight adversaries or heretics. The argument of both writers reaches a climax when they point out how cento-writers composed new stories or texts by lifting lines verbatim from classical epics. Thus, they kept the words (verba) and altered the stories (res or sensus). This article takes this analogy as point of departure in a way not yet done, and finds that ancient rhetoric, with ideals of literary composition, arrangement and order, is decisive for how both Irenaeus and Tertullian present the rule of faith. This applies to both terminology and the illustrations they use. The rule of faith is the intended story of Scripture, which is naturally discerned from there in a way analogous to how historians were expected to arrange their stories. Lucian of Samosata and Dionysius of Halicarnassus provide helpful analogies. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel attack on image encryption for privacy-preserving deep neural networks (DNNs). Although several encryption schemes have been proposed for privacy-preserving DNNs, existing cipher-text-only attacks (COAs) have succeeded in restoring visual information from encrypted images. Image encryption using the Vision Transformer (ViT) is known to be robust against existing COAs due to the operations of block scrambling and pixel shuffling, which permute divided blocks and pixels in an encrypted image. However, the correlation between blocks in the encrypted image can still be exploited for reconstruction. Therefore, in this paper, a novel jigsaw puzzle solver-based attack that utilizes block correlation is proposed to restore visual information from encrypted images. In the experiments, we evaluated the security of image encryption for privacy-preserving deep neural networks using both conventional and proposed COAs. The experimental results demonstrate that the proposed attack is able to restore almost all visual information from images encrypted for being applied to ViTs. [ABSTRACT FROM AUTHOR]

This paper proposes a data-driven method to predict mechanical responses for structures directly from full-field observations obtained on previously tested structures, with minimal introduction of arbitrary models. The fundamental concept is to directly use raw data, called patches from hereon, comprising displacement fields over large domains, obtained during data harvesting through full-field measurement. These displacement fields have been observed on domains of real structures, and hence are naturally viable solutions from static, kinematic, and constitutive viewpoint. We compile a library of such patches to compute response for new structures. Patches are assembled as pieces of a jigsaw puzzle, similar to how Frankenstein put his monster together from human patches. The approach is illustrated using a traditional beam problem for simplicity. However, the approach is not limited to beam or even solid mechanics, the concept can be applied to predict a wide range of physics and multi-physics phenomena. [ABSTRACT FROM AUTHOR]

We present a method for determining which of a large set of pixels are inside or on the boundary of a polygon. The method works much quicker than the standard method used for points in general and also provides an ordering of the boundary pixels. Once the edge pixels have been chosen and ordered, we move on to the characterization of the edge. We present a method for characterizing the edge slope of digitized images, which is—to a desired tolerance—invariant to rotation. We demonstrate that this characterization is good enough for subsequent use in edge identification and matching algorithms. This allows us to continue, e.g., to juxtaposing corresponding pieces of a jigsaw puzzle, or "sewing" back together, ripped pieces of material, paper or glass. [ABSTRACT FROM AUTHOR]

• Introduce jigsaw puzzle solving auxiliary loss into vision transformer-based models. • Removing positional embeddings, randomly masking patches as techniques. • Improve vision transformers' generalization on large-scale image classification. • Improve vision transformers' robustness against label noise. • Improve vision transformers' robustness against adversarial examples. The success of Vision Transformer (ViT) in various computer vision tasks has promoted the ever-increasing prevalence of this convolution-free network. The fact that ViT works on image patches makes it potentially relevant to the problem of jigsaw puzzle solving, which is a classical self-supervised task aiming at reordering shuffled sequential image patches back to their original form. Solving jigsaw puzzle has been demonstrated to be helpful for diverse tasks using Convolutional Neural Networks (CNNs), such as feature representation learning, domain generalization and fine-grained classification. In this paper, we explore solving jigsaw puzzle as a self-supervised auxiliary loss in ViT for image classification, named Jigsaw-ViT. We show two modifications that can make Jigsaw-ViT superior to standard ViT: discarding positional embeddings and masking patches randomly. Yet simple, we find that the proposed Jigsaw-ViT is able to improve on both generalization and robustness over the standard ViT, which is usually rather a trade-off. Numerical experiments verify that adding the jigsaw puzzle branch provides better generalization to ViT on large-scale image classification on ImageNet. Moreover, such auxiliary loss also improves robustness against noisy labels on Animal-10N, Food-101N, and Clothing1M, as well as adversarial examples. Our implementation is available at https://yingyichen-cyy.github.io/Jigsaw-ViT. [ABSTRACT FROM AUTHOR]

For manipulating heat flow according to human desire, thermal metamaterial structures (meta-structures) have attracted growing interest. Based on the transformation thermotics and the effective medium approximation theory, we designed and fabricated a convertible thermal meta-structural device to demonstrate that multiple different heat-flow manipulations could be conducted using a single thermal meta-structural device. The convertible meta-structures were designed by discretizing a two-dimensional plane and separating multiple square unit modules with stripe-shaped dissimilar materials of the Polydimethylsiloxane (PDMS) and solid resin with surface metallized copper (Cu). The convertible thermal meta-structure device with a relatively high geometric accuracy was fabricated via a proposed hybrid manufacturing path of "Stereolithography Apparatus (SLA) 3D printing—electroless plating—electroplating—thermally insulated packaging". The thermal manipulation features were numerically simulated and preliminarily verified by experimental testing. Using multiple dispersed square unit modules to replace an annular region of the thermal meta-structure exhibited different thermal flow functions, including thermal cloak, thermal concentration, thermal rotation, and thermal dispersion, through the unique geometric design of the heat-flow transfer direction within each module. By rotating each square unit module at a specific angle and arranging the modules, similar to a "jigsaw puzzle", the convertibility among different thermal manipulation functions was achieved. This path is anticipated to provide a new strategy for multifunctional meta-structures in thermo-physics and its potential engineering applications. [ABSTRACT FROM AUTHOR]

Introduction: Students of medical and paramedical sciences often perceive medical terminologies as a foreign language. They find it difficult to memorize, spell and recall, the words. Incorporation of active learning methods in adjunct to didactic lectures favors critical thinking, encourages students' participation and helps in reinforcing the material acquired during lectures and also develops communication skills. Materials and Methods: The study was conducted in the Department of anatomy after taking permission from Institutional Ethics Committee. The activity was performed on 225 students after the topics were taught in the class. Crossword puzzles were designed by the faculty and the content validity was ensured by linking the clues and answers to specific learning objectives. Students were divided into small groups and time given was 20 minutes. Student's perception was noted through a prevalidated feedback questionnaire with a five-point Likert scale. Results: Most of the students enjoyed learning through recreation. 75% of the students were of the view that crossword puzzles should be incorporated in other subjects also, as it encourages active learning. Conclusions: The active learning methodologies like crossword, jigsaw puzzle, evokes interest in the subject, motivates students and relieve the tedium of traditional teaching methods. In our study, we concluded, that crossword puzzles made learning fun as the students were interactive, and it also provided a nice change from typically dry nature of the subject, so it should be included in curriculum. [ABSTRACT FROM AUTHOR]

As in a jigsaw puzzle, the pieces put together place before us the drama of the proliferation of new conflicts. Such are the times of today when people try to justify war on the grounds of belonging to different and incompatible cultures. Hence, there is an urgent need for a revival of intercultural dialogue and adequate education to foster it. Aware of the current challenge to peace, the two authors, who come from different geographical areas and cultures, seek through their research to highlight the educational value of Chiara Lubich's charism, her work and that of the Focolare Movement she founded. To this end, they make use of the hermeneutic method of "text" analysis, understood as everything the person said, did and wrote. For this, they focus on what, with respect to the epistemology of education, consists in the real innovation, namely: the Lubich synoran, the Golden Rule as the axiological foundation of intercultural dialogue education, the Art of Loving as a method and the Cube of Peace as a teaching tool. [ABSTRACT FROM AUTHOR]

Conflict is often colouring the diversities of theology, besides church divisions, especially in the Indonesian context. In minimising the conflict, adequate knowledge is needed to understand that the diversity of theology is the essence of church unity. Apostle Paul explained the diversities in church unity using the metaphor of the body of Christ. This metaphor is inadequate to explain the differences in theological teachings as the essence of the unity of the church, especially in this postmodern era. This article offered a jigsaw puzzle metaphor to quickly understand the nature of the church's unity in its diversity. This puzzle is a well-known game type played by many people in various age groups worldwide. By using descriptive analysis and analogy methods, it resulted in an understanding of the description of the church's diversity through jigsaw pieces as an essential element in forming a complete and perfect image, namely the one and universal church, by arranging each jigsaw piece according to the pattern formed as the blueprint. In conclusion, theology can adopt this metaphor as a discourse in establishing church unity in the ecumenical project. Intradisciplinary and/or interdisciplinary implications: This article discusses the diversities of theological and dogmatic forms that represent every church denomination as the essence of the one and universal church. This study offers a constructive theological model to understand diversity as the essence of church unity in an ecumenical project. [ABSTRACT FROM AUTHOR]

In this article, we try to explore and understand the neurodynamics of the decision-making process for mobile application downloading. We begin the model development in a rather unorthodox fashion. Patterns of brain activation regions are identified, across participants, at different time instance of the decision-making process. Region-wise activation knowledge from previous studies is used to put together the entire process model like a cognitive jigsaw puzzle. We find that there are indeed a common dynamic set of activation patterns that are consistent across people and apps. That is to say that not only are there consistent patterns of activation there is a consistent change from one pattern to another across time as people make the app adoption decision. Moreover, this pattern is clearly different for decisions that end in adoption than for decisions that end with no adoption. [ABSTRACT FROM AUTHOR]

• An interactive framework for reconstruction of strip-shredded documents. • The user lock and forbid pairs automatically selected by the recommender module. • Four query strategies for recommending the pairs of shreds to be annotated. • A novel methodology to assess the human impact on the quality of a reconstruction. • Annotating 25% of the shreds can yield an error reduction of more than 40%. [Display omitted] The advances in machine learning – particularly in deep learning – have enabled automatizing the reconstruction of shredded documents with significant accuracy. However, despite the recent remarkable results, the state-of-the-art on fully automatic reconstruction still has room for improvement, mainly due to imprecision on the evaluation of how the shreds fit each other (compatibility/cost evaluation). To tackle this problem, we propose a human-in-the-loop reconstruction framework that takes user inputs to improve the solutions (permutation of shreds). In our approach, the user verifies whether adjacent shreds of a solution are also adjacent in the original document. Unlike the current literature, our framework includes a recommender module that automatically selects pairs of shreds to be analyzed by a human. Four recommendation strategies were proposed and evaluated. Results achieved by coupling deep learning reconstruction methods into our framework have shown that introducing the human in the loop can reduce errors by more than 40 %. [ABSTRACT FROM AUTHOR]

The Sec61 complex is the major protein translocation channel of the endoplasmic reticulum (ER), where it plays a central role in the biogenesis of membrane and secretory proteins. Whilst Sec61‐mediated protein translocation is typically coupled to polypeptide synthesis, suggestive of significant complexity, an obvious characteristic of this core translocation machinery is its surprising simplicity. Over thirty years after its initial discovery, we now understand that the Sec61 complex is in fact the central piece of an elaborate jigsaw puzzle, which can be partly solved using new research findings. We propose that the Sec61 complex acts as a dynamic hub for co‐translational protein translocation at the ER, proactively recruiting a range of accessory complexes that enhance and regulate its function in response to different protein clients. It is now clear that the Sec61 complex does not have a monopoly on co‐translational insertion, with some transmembrane proteins preferentially utilising the ER membrane complex instead. We also have a better understanding of post‐insertion events, where at least one membrane‐embedded chaperone complex can capture the newly inserted transmembrane domains of multi‐span proteins and co‐ordinate their assembly into a native structure. Having discovered this array of Sec61‐associated components and competitors, our next challenge is to understand how they act together in order to expand the range and complexity of the membrane proteins that can be synthesised at the ER. Furthermore, this diversity of components and pathways may open up new opportunities for targeted therapeutic interventions designed to selectively modulate protein biogenesis at the ER. [ABSTRACT FROM AUTHOR]

Structures matter in single image super-resolution (SISR). Benefiting from generative adversarial networks (GANs), recent studies have promoted the development of SISR by recovering photo-realistic images. However, there are still undesired structural distortions in the recovered images. In this paper, we propose a structure-preserving super-resolution (SPSR) method to alleviate the above issue while maintaining the merits of GAN-based methods to generate perceptual-pleasant details. First, we propose SPSR with gradient guidance (SPSR-G) by exploiting gradient maps of images to guide the recovery in two aspects. On the one hand, we restore high-resolution gradient maps by a gradient branch to provide additional structure priors for the SR process. On the other hand, we propose a gradient loss to impose a second-order restriction on the super-resolved images, which helps generative networks concentrate more on geometric structures. Second, since the gradient maps are handcrafted and may only be able to capture limited aspects of structural information, we further extend SPSR-G by introducing a learnable neural structure extractor (NSE) to unearth richer local structures and provide stronger supervision for SR. We propose two self-supervised structure learning methods, contrastive prediction and solving jigsaw puzzles, to train the NSEs. Our methods are model-agnostic, which can be potentially used for off-the-shelf SR networks. Experimental results on five benchmark datasets show that the proposed methods outperform state-of-the-art perceptual-driven SR methods under LPIPS, PSNR, and SSIM metrics. Visual results demonstrate the superiority of our methods in restoring structures while generating natural SR images. Code is available at https://github.com/Maclory/SPSR. [ABSTRACT FROM AUTHOR]

The issue of treating Multiple Sclerosis (MS) begins with disease-modifying treatments (DMTs) which may cause lymphopenia, dyspnea, and many other adverse effects. Consequently, further identification and evaluation of alternative treatments are crucial to monitoring their long-term outcomes and hopefully, moving toward personalized approaches that can be translated into clinical treatments. In this article, we focused on the novel therapeutic modalities that alter the interaction between the cellular constituents contributing to MS onset and progression. Furthermore, the studies that have been performed to evaluate and optimize drugs' efficacy, and particularly, to show their limitations and strengths are also presented. The preclinical trials of novel approaches for multiple sclerosis treatment provide promising prospects to cure the disease with pinpoint precision. Considering the fact that not a single treatment could be effective enough to cover all aspects of MS treatment, additional researches and therapies need to be developed in the future. Since the pathophysiology of MS resembles a jigsaw puzzle, researchers need to put a host of pieces together to create a promising window towards MS treatment. Thus, a combination therapy encompassing all these modules is highly likely to succeed in dealing with the disease. The use of different therapeutic approaches to re-induce self-tolerance in autoreactive cells contributing to MS pathogenesis is presented. A Combination therapy using these tools may help to deal with the clinical disabilities and symptoms of the disease in the future. [ABSTRACT FROM AUTHOR]

Virtual test driving is a methodology to evaluate vehicle performance and environmental impacts under realistic driving conditions. This approach can generate comprehensive traffic information and trajectory data that reflect the dynamic and stochastic properties of transportation systems. Despite the benefits of virtual test driving, it is challenging to ensure a sufficient correlation between virtual and various real-life traffic situations. In this study, the simulation environment comprises a microscopic traffic network in the district of Aachen and a drivetrain model representing the longitudinal dynamics of a real car. In addressing the research challenge of sufficient correlation between virtual and real traffic, this work aims to develop a methodology to calibrate and validate virtual test driving within a selected study site by comparing simulation results with multi-source field measurements. The comparison between the field data and simulation is threefold: We used traffic counts to evaluate the simulated traffic volume at the macroscopic level; the drone-captured trajectory data to characterize the individual vehicle movements on highways, roundabouts, and intersections; and the real driving data of probe vehicles to investigate the influence of vehicle trajectories on energy demand. We also developed a generic approach to investigate the correlation between driving cycles and drivetrain performance, accounting for different combinations of real and virtual objects in three calibration and validation stages. Furthermore, the concept of mapping jigsaw puzzle scenarios is proposed to replicate the drone-captured concrete scenarios in the simulation and extend the calibrated results to a large-scale network. The results indicate that the proposed toolchain can effectively cope with the validation complexity of virtual test driving. A comparison with additional field measurements reveals the benefits of virtual test driving in providing a wide spectrum of realistic driving conditions in a cost-efficient and time-saving manner. [ABSTRACT FROM AUTHOR]

Fidelity Coverage Analysis Analysis of a fidelity claim will vary depending on whether it covers employee dishonesty or employee theft and based on the circumstances surrounding the fraudulent scheme perpetrated by the employee. 3d 512, 518 (D.N.J. 2015) (holding that kickbacks an employee received from a colluding vendor satisfied the employee theft insuring agreement but that padded amounts paid to the vendor for legitimate services due to the employee misconduct were not covered). Further, identifying the steps in the employee's scheme is necessary to determine whether a loss results directly from employee dishonesty or employee theft. However, issues can arise where a defalcating employee is involved in a kickback scheme containing a mix of legitimate and illegitimate services.24 Query what an insured loses if an employee retains a vendor that renders legitimate services to the insured at a fair price but pays a kickback to the employee for the procurement of the service contract. [Extracted from the article]

Phytosterols are vital structural and regulatory components in plants. Zea mays produces a series of phytosterols that are specific to corn. However, the underline biosynthetic mechanism remains elusive. In this study, we identified a novel sterol methyltransferase from Z. mays (ZmSMT1–2) which showed a unique feature compared with documented plant SMTs. ZmSMT1–2 showed a substrate preference for cycloartenol. Using S-adenosyl- L -methionine (AdoMet) as a donor, ZmSMT1–2 converted cycloartenol into alkylated sterols with unique side-chain architectures, including Δ25(27) (i.e., cyclolaudenol and cycloneolitsol) and Δ24(25) (i.e., cyclobranol) sterols. Cycloneolitsol is identified as a product of SMTs for the first time. Our discovery provides a previously untapped mechanism for phytosterol biosynthesis and adds another layer of diversity of sterol biosynthesis. [Display omitted] • ZmSMT1–2 shows a unique feature with a substrate preference for cycloartenol • ZmSMT1–2 converts cycloartenol into sterols with unique side-chain architectures • Cyclolaudenol was the major product of ZmSMT1–2 • Cycloneolitsol is the first time to be identified as the product of SMTs [ABSTRACT FROM AUTHOR]

Mentally visualizing objects, understanding relationships between two- or three- dimensional objects, and manipulating objects in space are some examples of visuospatial abilities. Numerous studies have shown that male participants outperform female participants in visuospatial tasks, particularly in mental rotation. One exception is solving jigsaw puzzles. Performance by seven- to eight-year-old girls was found to be superior to that of boys of the same age (Kocijan et al. 2017). No study, however, has confirmed this finding in an adult population, where sex differences are often detectable. Seventy-nine young adult participants were given four different jigsaw puzzles and the Shepard and Metzler mental rotation test (MRT) with two main goals: First, to investigate possible sex differences in jigsaw puzzle solving, and second, to explore a potential relationship between mental rotation and jigsaw puzzle solving. We hypothesized that female participants would outperform males in the jigsaw puzzles but males would outperform females in the MRT. The findings confirmed this hypothesis. Notably, the male performance in jigsaw puzzle solving was attributed to their sex and mediated by their higher MRT scores. These results yielded two key insights. First, they indicate a dissociation between these two visuospatial abilities, jigsaw puzzle solving and mental rotation; and second, female and male participants capitalize on their distinct cognitive strengths when solving visuospatial tasks. [ABSTRACT FROM AUTHOR]

Cartoon face recognition is challenging as they typically have smooth color regions and emphasized edges, the key to recognizing cartoon faces is to precisely perceive their sparse and critical shape patterns. However, it is quite difficult to learn a shape-oriented representation for cartoon face recognition with convolutional neural networks (CNNs). To mitigate this issue, we propose the GraphJigsaw that constructs jigsaw puzzles at various stages in the classification network and solves the puzzles with the graph convolutional network (GCN) in a progressive manner. Solving the puzzles requires the model to spot the shape patterns of the cartoon faces as the texture information is quite limited. The key idea of GraphJigsaw is constructing a jigsaw puzzle by randomly shuffling the intermediate convolutional feature maps in the spatial dimension and exploiting the GCN to reason and recover the correct layout of the jigsaw fragments in a self-supervised manner. The proposed GraphJigsaw avoids training the classification model with the deconstructed images that would introduce noisy patterns and are harmful for the final classification. Specially, GraphJigsaw can be incorporated at various stages in a top-down manner within the classification model, which facilitates propagating the learned shape patterns gradually. GraphJigsaw does not rely on any extra manual annotation during the training process and incorporates no extra computation burden at inference time. Both quantitative and qualitative experimental results have verified the feasibility of our proposed GraphJigsaw, which consistently outperforms other face recognition or jigsaw-based methods on two popular cartoon face datasets with considerable improvements. [ABSTRACT FROM AUTHOR]

The paper proposes a solution based on Generative Adversarial Network (GAN) for solving jigsaw puzzles. The problem assumes that an image is divided into equal square pieces, and asks to recover the image according to information provided by the pieces. Conventional jigsaw puzzle solvers often determine the relationships based on the boundaries of pieces, which ignore the important semantic information. In this paper, we propose JigsawGAN, a GAN-based auxiliary learning method for solving jigsaw puzzles with unpaired images (with no prior knowledge of the initial images). We design a multi-task pipeline that includes, (1) a classification branch to classify jigsaw permutations, and (2) a GAN branch to recover features to images in correct orders. The classification branch is constrained by the pseudo-labels generated according to the shuffled pieces. The GAN branch concentrates on the image semantic information, where the generator produces the natural images to fool the discriminator, while the discriminator distinguishes whether a given image belongs to the synthesized or the real target domain. These two branches are connected by a flow-based warp module that is applied to warp features to correct the order according to the classification results. The proposed method can solve jigsaw puzzles more efficiently by utilizing both semantic information and boundary information simultaneously. Qualitative and quantitative comparisons against several representative jigsaw puzzle solvers demonstrate the superiority of our method. [ABSTRACT FROM AUTHOR]

We present a simple algorithm for synthesizing 3D jigsaw puzzles from arbitrary 3D freeform 2-manifold geometric models represented with trimmed NURBS surfaces. The construction algorithm is based on a few conventional geometric operations on freeform curves and surfaces. In particular, we need to compute the offset of freeform NURBS surfaces (for thickening the 2-manifold surfaces) and the functional composition of a univariate curve representation to a bivariate rational surface (for breaking up a 3D model into curved jigsaw tiles). It is thus almost straightforward to convert the proposed algorithm to a practical system using standard tools available in B-rep based geometric modeling systems, that employ trimmed NURBS surfaces. We demonstrate the effectiveness of the proposed approach by fabricating several test sets of 3D jigsaw puzzles for freeform solids consisting of trimmed NURBS surface models. [Display omitted] • Simple algorithm for synthesizing 3D jigsaw puzzles over freeform 2-manifolds. • Based on a few conventional geometric operations on freeform curves and surfaces. • Almost straightforward to convert the proposed algorithm to a practical system. [ABSTRACT FROM AUTHOR]

Don't Forget, This Is Not You I Speak to My Twin Sister Graph: Shirin Neshat, Identified, 1995. When I am a little girl I learn to feel the space between me and my twin sister as if it is an atomic bond and we are ions - the relative connection only shifts by push and pull. Ink for Shirin Neshat The ink of my eyes is the same you used to write me out of the story. [Extracted from the article]

Under global change, how biological diversity and ecosystem services are maintained in time is a fundamental question. Ecologists have long argued about multiple mechanisms by which local biodiversity might control the temporal stability of ecosystem properties. Accumulating theories and empirical evidence suggest that, together with different population and community parameters, these mechanisms largely operate through differences in functional traits among organisms. We review potential trait-stability mechanisms together with underlying tests and associated metrics. We identify various trait-based components, each accounting for different stability mechanisms, that contribute to buffering, or propagating, the effect of environmental fluctuations on ecosystem functioning. This comprehensive picture, obtained by combining different puzzle pieces of trait-stability effects, will guide future empirical and modeling investigations. The main mechanisms by which biodiversity affects the stability of ecosystem functions (dominant species, compensatory dynamics, and insurance effects) all act through the functional traits of organisms that form local communities. These mechanisms can be assessed using different components of trait value distributions within and between species [collectively referred as trait probability density (TPD)]. Variation in local populations can result in overall changes in community structure, which may or may not be propagated into changes in ecosystem functions. The extent of such propagation depends on: (i) the extent of TPD shifts, and (ii) the overlap between response and effect traits, and these effects will vary depending on the biotic mechanisms being assessed. [ABSTRACT FROM AUTHOR]

Weakly supervised temporal action localization is a challenging yet interesting task. Existing methods usually apply a few temporal convolutional layers or linear layers to predict classification scores, where the model capacity is limited. Inspired by counterpart researches, increasing model capacity is the potential to improve the localization performance. However, under the weakly supervised paradigm, the video-level classification label is insufficient to learn large-capacity models. The essential reason lies in that most of the inputs to action localization networks are high-level features extracted by video recognition models. In lack of off-the-shelf initialization weights, the action localization networks have to train from scratch and can only explore low-capacity models. In this work, we are inspired by the self-supervised learning paradigm and propose to learn high-quality representative models via solving the feature jigsaw puzzle task. The proposed self-supervised pretraining process can explore networks with large kernel size and deeper layers, which can provide valuable initialization to action localization networks. In the implementation, we first discover potential action scopes via calculating motion intensity. Then, we cut features into snippets and permute them into out-of-order status. We randomly discard frames for boundaries between two snippets to guide the network learning high-level representations and prevent information leakage. Moreover, because the potential permutation number factorially rises with the increase of snippet number, we select a fixed number of permutation operations via the maximum hamming distance criterion, which eases the learning process. Comprehensive experiments on two benchmarks demonstrate the efficiency of pretraining to weakly supervised action localization task, and the proposed method builds new state-of-the-art performance. [ABSTRACT FROM AUTHOR]

• The new middle Triangle Method filters bad Ketpoints, selecting the best ones. • With the new Retention Layres Method we got even frescoes reconstructions. • A rejected fragment by one layer is analyzed by the next one, improving performance. This work presents the development of a new method for solving puzzles, based on sequence of layers, called Retention Layers Method, using feature extraction algorithms with a new method to select the corresponding keypoints between puzzle pieces and reference image, called the Middle Triangle Method. Promising results, both in the correct positioning of the fragments and in the disposal of spurious fragments, can be seen by the high ratio of correctly positioned fragments, reaching up to 100%, surpassing the analyzed puzzle solvers. [ABSTRACT FROM AUTHOR]

The new genus Sinuicella, an early successional lichen, was found on bare soil in Oregon, USA. The thallus is minute fruticose, grey to nearly black, branching isotomic dichotomous, branches round, 20–90 μm wide in water mount. The cortex is composed of interlocking cells shaped like jigsaw puzzle pieces. Spores are hyaline, 1-septate, 25–40(–50) × 6.5–9(–11) μm. Maximum likelihood phylogenetic analyses on multilocus data sets, first spanning the entire order Peltigerales and then restricted to Peltigeraceae with extended sampling from Solorina and Peltigera, revealed the placement of Sinuicella outside of currently recognized genera, sister to Peltigera, with high support. Based on the phylogenetic, morphological and ecological distinctness of Sinuicella, we formally introduce a new genus represented by the single species S. denisonii. The cyanobiont of S. denisonii is Nostoc from phylogroup XL, Clade 2, Subclade 3 based on the rbcLX marker. [ABSTRACT FROM AUTHOR]

Summary: Apart from posing various mechanical and medical issues compromising general health, obesity is a major factor for respiratory tract infections, due to specific inflammation and immunological compromise. The burden of obesity on morbidity and mortality of SARS‐CoV‐2 infection/COVID‐19 is considerable. Herein, we aimed to search the literature and present to the readers pathophysiologic pathways that may associate obesity and COVID‐19. We present potential mechanisms, which might partly explain why patients with obesity are more prone to suffer from respiratory infections in the context of COVID‐19. Better understanding of these pathways could eventually guide management strategies and therapies for COVID‐19 in the future. [ABSTRACT FROM AUTHOR]

Currently, weakly supervised semantic segmentation approaches adopt the Class Activation Map (CAM) to generate the initial attention maps from the standard classification backbone network, with only image-level class labels as training supervision. In this paper, we propose a novel "destruction learning" method via self-supervised manner, producing the CAM attention maps better covering the whole object rather than only the most discriminative regions as previous approaches. Region destruction mechanism is proposed to deliberately "destruct" the global structure in both Mid-Level and Low-Level feature learning following jigsaw puzzle operation, for better local feature extraction of the classification network. Specifically, a Mid-Level Distribution-and-Collection Module is proposed to firstly independently process local patches in parallel and then aggregate them, achieving enhanced local part sensitivity. For low-level "destruction learning", a Low-Level Destruction Module is proposed to partition the original image into patches and shuffle them randomly to obtain a destructed image, which enforces the network to find weak information from the scattered discriminative small parts. The two destruction modules can be conveniently embedded into arbitrary backbone networks and trained in an end-to-end manner, without any extra annotations. Our proposed method achieves outstanding performance on the PASCAL VOC 2012 dataset (e.g. , with 69.1% mIoU on the testing set), which surpasses existing state-of-the-art weakly supervised semantic segmentation methods. [Display omitted] • A novel "destruction learning" method via self-supervised manner. • The MDC module is with stronger sensitivity to the Mid-Level local parts. • The LD Module explores the local feature details from the original images. [ABSTRACT FROM AUTHOR]

A polymer-film inertial microfluidic jigsaw (PIMJ) sorter with trapezoidal spiral channels using the jigsaw puzzle method was proposed to realize precise and high-throughput rare cell separation. The PIMJ sorter was fabricated by assembling laser-patterned polymer-film layers of different thicknesses. After illustrating the conceptual design and fabrication process, the effects of the cross-sectional dimension, particle size, and operational flow rate on particle focusing were systematically explored under a broad flow rate range. Then, the separation performances of the PIMJ sorter were characterized using the binary particle mixture and the blood samples spiked with four types of tumor cells. The results indicated that the complete separation of the binary particles with a minimum size difference of 2 μm was successfully realized at a high throughput up to 3000 μL/min. A high recovery ratio of 90.57%-94.14% and a high purity of 48.67%-79.05% were achieved for the separation of rare tumor cells from white blood cells (WBCs). Finally, the PIMJ sorter was successfully employed for separating rare circulating tumor cells (CTCs) from the metastatic breast and lung cancer patients with a capture ratio of 7–226 CTCs per 5 mL sample. The results proved the high sensitivity and high reliability of the PIMJ sorter. The PIMJ sorter is expected to be a potential device for precise CTC separation towards the clinical applications. Image 1 • A trapezoidal spiral inertial microfluidic sorter was fabricated by polymer films using the jigsaw puzzle method. • Complete separation of the binary particles with a minimum size difference of 2 μm was realized at 3000 μL/min. • A high recovery ratio and purity can be realized for the separation of tumor cells. • The device was successfully applied for the separation of rare CTCs from the blood samples of cancer patients. [ABSTRACT FROM AUTHOR]

Jigsaw puzzles are ubiquitous developmental toys in Western societies, used here to examine the development of metarepresentation. For jigsaw puzzles this entails understanding that individual pieces, when assembled, produce a picture. In Experiment 1, 3‐ to 5‐year‐olds (N = 117) completed jigsaw puzzles that were normal, had no picture, or comprised noninterlocking rectangular pieces. Pictorial puzzle completion was associated with mental and graphical metarepresentational task performance. Guide pictures of completed pictorial puzzles were not useful. In Experiment 2, 3‐ to 4‐year‐olds (N = 52) completed a simplified task, to choose the correct final piece. Guide‐use associated with age and specifically graphical metarepresentation performance. We conclude that the pragmatically natural measure of jigsaw puzzle completion ability demonstrates general and pictorial metarepresentational development at 4 years. [ABSTRACT FROM AUTHOR]