Miracle-dz/UniFAR-FTUs huggingface dataset:
Whole-Brain Neural Connectivity to Lateral Pontine Tegmentum GABAergic Neurons in Mice

The GABAergic neurons in the lateral pontine tegmentum (LPT) play key roles in the regulation of sleep and locomotion. The dysfunction of the LPT is related to neurological disorders such as rapid eye movement sleep behavior disorder and ocular flutter. However, the whole-brain neural connectivity to LPT GABAergic neurons remains poorly understood. Using virus-based, cell-type-specific, retrograde and anterograde tracing systems, we mapped the monosynaptic inputs and axonal projections of LPT GABAergic neurons in mice. We found that LPT GABAergic neurons received inputs mainly from the superior colliculus, substantia nigra pars reticulata, dorsal raphe nucleus (DR), lateral hypothalamic area (LHA), parasubthalamic nucleus, and periaqueductal gray (PAG), as well as the limbic system (e.g., central nucleus of the amygdala). Further immunofluorescence assays revealed that the inputs to LPT GABAergic neurons were colocalized with several markers associated with important neural functions, especially the sleep-wake cycle. Moreover, numerous LPT GABAergic neuronal varicosities were observed in the medial and midline part of the thalamus, the LHA, PAG, DR, and parabrachial nuclei. Interestingly, LPT GABAergic neurons formed reciprocal connections with areas related to sleep-wake and motor control, including the LHA, PAG, DR, parabrachial nuclei, and superior colliculus, only the LPT-DR connections were in an equally bidirectional manner. These results provide a structural framework to understand the underlying neural mechanisms of rapid eye movement sleep behavior disorder and disorders of saccades.
Applying Uncertain Science to Nature-Based Coastal Protection: Lessons From Shallow Wetland-Dominated Shores

Climate change and associated sea-level rise alongside the potential for alterations in the magnitude and frequency of extreme storm events, rapidly rising coastal populations, and a legacy of coastal land reclamation have led to the consideration of nature-based approaches towards coastal flood and erosion protection over recent years. The descriptions of such approaches highlight the flood protection value of natural coastal features but examples of their implementation remain few and far between and nature-based solutions to the rising coastal flood and erosion risk can certainly not (yet) be considered mainstream. One key problem around the implementation of these types of approaches has, arguably, been the relative lack of perceived scientific certainty, amongst stakeholders, surrounding the efficiency with which natural landforms reduce wave action on landward lying structures and the persistence of such landforms in an uncertain future. This makes nature-inclusive approaches less attractive to more traditional engineering-only approaches that rely solely on one ‘hard’ structure with a well-defined impact on waves and a specified design life. Using existing evidence for the coastal protection function of salt marshes, this paper offers a new perspective on this apparent lack of scientific certainty by offering a ‘no-regret’ application of existing science that allows decision makers and coastal planners to implement nature-inclusive approaches to coastal flood and erosion risk reduction measures with greater confidence.
Bold Hearts Team Description for RoboCup 2019 (Humanoid Kid Size League)

We participated in the RoboCup 2018 competition in Montreal with our newly developed BoldBot based on the Darwin-OP and mostly self-printed custom parts. This paper is about the lessons learnt from that competition and further developments for the RoboCup 2019 competition. Firstly, we briefly introduce the team along with an overview of past achievements. We then present a simple, standalone 2D simulator we use for simplifying the entry for new members with making basic RoboCup concepts quickly accessible. We describe our approach for semantic-segmentation for our vision used in the 2018 competition, which replaced the lookup-table (LUT) implementation we had before. We also discuss the extra structural support we plan to add to the printed parts of the BoldBot and our transition to ROS 2 as our new middleware. Lastly, we will present a collection of open-source contributions of our team.
A Survey: Transformation for Integrating Relational Database with Semantic Web

An early vision in Computer Science was focused on Artificial Intelligence (AI) to produce intelligent systems capable of reasoning on huge amounts of data. Since the huge amount of data on the web are still formed and stored in relational databases (RDBs), such data cannot be used directly via the Semantic Web. Therefore, semantic integration of relational data sources into the Semantic Web is not a trivial task and has been addressed by developing approaches and tools for transformation RDB to ontology. The aim of this survey is to present the existing approaches contributed for integrating RDBs into Semantic Web in order to bridge the gap between RDB model and ontology model and to boost the current Web, by discussing the benefits and drawbacks of these approaches as well as to compare between them in specific terms.
Dissecting the Genetic Basis of Grain Size and Weight in Barley (Hordeum vulgare L.) by QTL and Comparative Genetic Analyses

Grain size and weight are crucial components of barley yield and quality and are the target characteristics of domestication and modern breeding. Despite this, little is known about the genetic and molecular mechanisms of grain size and weight in barley. Here, we evaluated nine traits determining grain size and weight, including thousand grain weight (Tgw), grain length (Gl), grain width (Gw), grain length-width ratio (Lwr), grain area (Ga), grain perimeter (Gp), grain diameter (Gd), grain roundness (Gr), and factor form density (Ffd), in a double haploid (DH) population for three consecutive years. Using five mapping methods, we successfully identified 60 reliable QTLs and 27 hotspot regions that distributed on all chromosomes except 6H which controls the nine traits of grain size and weight. Moreover, we also identified 164 barley orthologs of 112 grain size/weight genes from rice, maize, wheat and 38 barley genes that affect grain yield. A total of 45 barley genes or orthologs were identified as potential candidate genes for barley grain size and weight, including 12, 20, 9, and 4 genes or orthologs for barley, rice, maize, and wheat, respectively. Importantly, 20 of them were located in the 14 QTL hotspot regions on chromosome 1H, 2H, 3H, 5H, and 7H, which controls barley grain size and weight. These results indicated that grain size/weight genes of other cereal species might have the same or similar functions in barley. Our findings provide new insights into the understanding of the genetic basis of grain size and weight in barley, and new information to facilitate high-yield breeding in barley. The function of these potential candidate genes identified in this study are worth exploring and studying in detail.
Localization of Epileptogenic Zone Based on Cortico-Cortical Evoked Potential (CCEP): A Feature Extraction and Graph Theory Approach

Objective Epilepsy is a chronic brain disease, which is prone to relapse and affects individuals of all ages worldwide, particularly the very young and elderly. Up to one-third of these patients are medically intractable and require resection surgery. However, the outcomes of epilepsy surgery rely upon the clear identification of epileptogenic zone (EZ). The combination of cortico-cortical evoked potential (CCEP) and electrocorticography (ECoG) provides an opportunity to observe the connectivity of human brain network and more comprehensive information that may help the clinicians localize the epileptogenic focus more precisely. However, there is no standard analysis method in the clinical application of CCEPs, especially for the quantitative analysis of abnormal connectivity of epileptic networks. The aim of this paper was to present an approach on the batch processing of CCEPs and provide information relating to the localization of EZ for clinical study. Methods Eight medically intractable epilepsy patients were included in this study. Each patient was implanted with subdural grid electrodes and electrical stimulations were applied directly to their cortex to induce CCEPs. After signal preprocessing, we constructed three effective brain networks at different spatial scales for each patient, regarding the amplitudes of CCEPs as the connection weights. Graph theory was then applied to analyze the brain network topology of epileptic patients, and the topological metrics of EZ and non-EZ (NEZ) were compared. Results The effective connectivity network reconstructed from CCEPs was asymmetric, both the number and the amplitudes of effective CCEPs decreased with increasing distance between stimulating and recording sites. Besides, the distribution of CCEP responses was associated with the locations of EZ which tended to have higher degree centrality (DC) and nodal shortest path length (NLP) than NEZ. Conclusion Our results indicated that the brain networks of epileptics were asymmetric and mainly composed of short-distance connections. The DC and NLP were highly consistent to the distribution of the EZ, and these topological parameters have great potential to be readily applied to the clinical localization of the EZ.
Being a Participant Matters: Event-Related Potentials Show That Markedness Modulates Person Agreement in Spanish

The present study uses event-related potentials to examine subject–verb person agreement in Spanish, with a focus on how markedness with respect to the speech participant status of the subject modulates processing. Morphological theory proposes a markedness distinction between first and second person, on the one hand, and third person on the other. The claim is that both the first and second persons are participants in the speech act, since they play the speaker and addressee roles, respectively. In contrast, third person refers to whomever is neither the speaker nor the addressee (i.e., it is unmarked for person). We manipulated speech participant by probing person agreement with both first-person singular subjects (e.g., yo…lloro “I…cry-1ST PERSON-SG”) and third-person singular ones (e.g., la viuda…llora “the widow…cry-3RD PERSON-SG”). We also manipulated agreement by crossing first-person singular subjects with third-person singular verbs (e.g., yo…∗llora “I…cry-3RD PERSON-SG”) and vice versa (e.g., la viuda…∗lloro “the widow…cry-1ST PERSON-SG”). Results from 28 native speakers of Spanish revealed robust positivities for both types of person violations, relative to their grammatical counterparts between 500 and 1000 ms, an effect that shows a central-posterior distribution, with a right hemisphere bias. This positivity is consistent with the P600, a component associated with a number of morphosyntactic operations (and reanalysis processes more generally). No negativities emerged before the P600 (between 250 and 450 ms), although both error types yielded an anterior negativity in the P600 time window, an effect that has been argued to reflect the memory costs associated with keeping the errors in working memory to provide a sentence-final judgment. Crucially, person violations with a marked subject (e.g., yo…∗llora “I…cry-3RD PERSON-SG”) yielded a larger P600 than the opposite error type between 700 and 900 ms. This effect is consistent with the possibility that, upon encountering a subject with marked features, feature activation allows the parser to generate a stronger prediction regarding the upcoming verb. The larger P600 for person violations with a marked subject might index the reanalysis process that the parser initiates when there is a conflict between a highly expected verbal form (i.e., more so than in the conditions with an unmarked subject) and the form that is actually encountered.
Protective Effect Against Toxoplasmosis in BALB/c Mice Vaccinated With Toxoplasma gondii Macrophage Migration Inhibitory Factor

Toxoplasma gondii is an obligate intracellular parasite responsible for toxoplasmosis, which can cause severe disease in the fetus and immunocompromised individuals. Developing an effective vaccine is crucial to control this disease. Macrophage migration inhibitory factor (MIF) has gained substantial attention as a pivotal upstream cytokine to mediate innate and adaptive immune responses. Homologs of MIF have been discovered in many parasitic species, and one homolog of MIF has been isolated from the parasite Toxoplasma gondii. In this study, the recombinant Toxoplasma gondii MIF (rTgMIF) as a protein vaccine was expressed and evaluated by intramuscular injection in BALB/c mice. We divided the mice into different dose groups of vaccines, and all immunizations with purified rTgMIF protein were performed at 0, 2, and 4 weeks. The protective efficacy of vaccination was analyzed by antibody assays, cytokine measurements and lymphoproliferative assays, respectively. The results obtained indicated that the rTgMIF vaccine elicited strong humoral and cellular immune responses with high levels of IgG antibody and IFN-γ production compared to those of the controls, in addition to slight higher levels of IL-4 production. After vaccination, a stronger lymphoproliferative response was also noted. Additionally, the survival time of mice immunized with rTgMIF was longer than that of the mice in control groups after challenge infection with virulent T. gondii RH tachyzoites. Moreover, the number of brain tissue cysts in vaccinated mice was reduced by 62.26% compared with the control group. These findings demonstrated that recombinant TgMIF protein is a potential candidate for vaccine development against toxoplasmosis.
Increased Akt-Driven Glycolysis Is the Basis for the Higher Potency of CD137L-DCs

CD137 ligand-induced dendritic cells (CD137L-DCs) are a new type of dendritic cells (DCs) that induce strong cytotoxic T cell responses. Investigating the metabolic activity as a potential contributing factor for their potency, we find a significantly higher rate of glycolysis in CD137L-DCs than in granulocyte macrophage colony-stimulating factor (GM-CSF) and interleukin 4 induced monocyte-derived DCs (moDCs). Using unbiased screening, Akt-mTORC1 activity was found to be significantly higher throughout the differentiation and maturation of CD137L-DCs than that of moDCs. Furthermore, this higher activity of the Akt-mTORC1 pathway is responsible for the significantly higher glycolysis rate in CD137L-DCs than in moDCs. Inhibition of Akt during maturation or inhibition of glycolysis during and after maturation resulted in suppression of inflammatory DCs, with mature CD137L-DCs being the most affected ones. mTORC1, instead, was indispensable for the differentiation of both CD137L-DCs and moDCs. In contrast to its role in supporting lipid synthesis in murine bone marrow-derived DCs (BMDCs), the higher glycolysis rate in CD137L-DCs does not lead to a higher lipid content but rather to an accumulation of succinate and serine. These data demonstrate that the increased Akt-driven glycolysis underlies the higher activity of CD137L-DCs.
DenseNet Models for Tiny ImageNet Classification

In this paper, we present two image classification models on the Tiny ImageNet dataset. We built two very different networks from scratch based on the idea of Densely Connected Convolution Networks. The architecture of the networks is designed based on the image resolution of this specific dataset and by calculating the Receptive Field of the convolution layers. We also used some non-conventional techniques related to image augmentation and Cyclical Learning Rate to improve the accuracy of our models. The networks are trained under high constraints and low computation resources. We aimed to achieve top-1 validation accuracy of 60%; the results and error analysis are also presented.
DirectShape: Photometric Alignment of Shape Priors for Visual Vehicle Pose and Shape Estimation

3D scene understanding from images is a challenging problem which is encountered in robotics, augmented reality and autonomous driving scenarios. In this paper, we propose a novel approach to jointly infer the 3D rigid-body poses and shapes of vehicles from stereo images of road scenes. Unlike previous work that relies on geometric alignment of shapes with dense stereo reconstructions, our approach works directly on images and infers shape and pose efficiently through combined photometric and silhouette alignment of 3D shape priors with a stereo image. We use a shape prior that represents cars in a low-dimensional linear embedding of volumetric signed distance functions. For efficiently measuring the consistency with both alignment terms, we propose an adaptive sparse point selection scheme. In experiments, we demonstrate superior performance of our method in pose estimation and shape reconstruction over a state-of-the-art approach that uses geometric alignment with dense stereo reconstructions. Our approach can also boost the performance of deep-learning based approaches to 3D object detection as a refinement method. We demonstrate that our method significantly improves accuracy for several recent detection approaches.
Mindfulness-Based Intervention for People With Dementia and Their Partners: Results of a Mixed-Methods Study

Objective Studies have shown preliminary support for mindfulness-based interventions benefitting people with dementia and their caregivers. However, most studies focus on these two groups separately. This study examined whether it would be possible and beneficial for people with dementia and their caregiver to jointly undergo an adjusted Mindfulness-Based Stress Reduction (MBSR) training, named TANDEM. Methods The 8-week MBSR training was adjusted based on a literature review and interviews with experts (clinicians and mindfulness trainers). Seven couples (a person with early-stage dementia and their caregiver) participated together in the 8-week TANDEM program. Semi-structured qualitative interviews were conducted after completion. Questionnaires (administered before and after the intervention) assessed the primary outcomes of quality of life and psychological distress (stress, anxiety and depressive symptoms). Secondary outcomes were mindfulness, self-compassion, positive mental health, worrying, and perceived burden (for caregivers). Results All participants completed the program and reported beneficial effects (relaxation, awareness, acceptance, and resilience). Most managed to integrate exercises into their daily lives and planned to continue their practice. Participating in a group was considered valuable and supportive. Furthermore, it was appreciated that participants could follow the training together (as a couple). The quantitative results showed a small effect on increased quality of life for caregivers. No substantial decrease in psychological distress was apparent. Caregivers displayed a large increase in mindfulness. Conclusion The results of this mixed-methods study suggest that an adjusted mindfulness program is feasible and well-received among couples of persons with early-stage dementia and their caregiver, warranting further research in this area.
Edge Detection using CNN for Roof Images

In image processing detection of edges is a very significant phenomenon as it finds its utility in multiple applications from different areas of life such as security, video surveillance, detection of various diseases, text detection, vehicle detection and many more. Image processing based techniques for edge detection usually requires preprocessing of the images in order to reduce the quantity of noise before edge detection. Even after the detection of edges, most of the times these techniques have to involve various post processing steps in order to generate the fine edges to be used in concerned applications like tumor detection or critical security applications. Image processing based algorithms for edge detection are conventionally based on gradient filters and generally they are not reliable in the case of variable illumination conditions or noise deviations. As soon as there is any variation in the illumination conditions of images or change in the percentage of noise element in images, the efficiency of the traditional edge detection algorithms is affected badly because of increased mask size. This increased mask also results in additional computational load. This paper utilizes Convolutional Neural Networks (CNN) for the detection of edges in images. The dataset composes roof images of diverse types and sizes. Employing CNN for edge detection over conventional methods is more reliable and it is speedier and less complex. Also, CNN is very efficient as it can take compute all features automatically so reduces the burden of manual feature extraction. Moreover, feature detection using CNN is accurate and more precise than manual feature compilation methods. To make use of automatic feature extraction of CNN, this technique employs Visual Geometry Group (VGG) CNN network. Further, to compute the edge map, Roberts edge operator is applied on the automatically computed features. This CNN based edge detection technique results into the generation of very fine edges without the application of any post processing step. Also, the results show that this technique works equally good in the presence of noise and requires no pre-processing algorithms.
A posteriori error estimates for mixed virtual element methods

We present an a posteriori error analysis for the mixed virtual element method (mixed VEM) applied to second order elliptic equations in divergence form with mixed boundary conditions. The resulting error estimator is of residual-type. It only depends on quantities directly available from the VEM solution and applies on very general polygonal meshes. The proof of the upper bound relies on a global inf-sup condition, a suitable Helmholtz decomposition, and the local approximation properties of a Clement-type interpolant. In turn, standard inverse inequalities and localization techniques based on bubble functions are the main tools yielding the lower bound. Via the inclusion of a fully local postprocessing of the mixed VEM solution, we also show that the estimator provides a reliable and efficient control on the broken $\mathrm{H}(\mathrm{div})$-norm error between the exact and the postprocessed flux. Numerical examples confirm the theoretical properties of our estimator, and show that it can be effectively used to drive an adaptive mesh refinement algorithm.
Privacy Preserving Group Membership Verification and Identification

When convoking privacy, group membership verification checks if a biometric trait corresponds to one member of a group without revealing the identity of that member. Similarly, group membership identification states which group the individual belongs to, without knowing his/her identity. A recent contribution provides privacy and security for group membership protocols through the joint use of two mechanisms: quantizing biometric templates into discrete embeddings and aggregating several templates into one group representation. This paper significantly improves that contribution because it jointly learns how to embed and aggregate instead of imposing fixed and hard-coded rules. This is demonstrated by exposing the mathematical underpinnings of the learning stage before showing the improvements through an extensive series of experiments targeting face recognition. Overall, experiments show that learning yields an excellent trade-off between security / privacy and the verification / identification performances.