Trends in bioactivity: inducing and detecting mineralization of regenerative polymeric scaffolds.

Due to limitations of biological and alloplastic grafts, regenerative engineering has emerged as a promising alternative to treat bone defects. Bioactive polymeric scaffolds are an integral part of such an approach. Bioactivity importantly induces hydroxyapatite mineralization that promotes osteoinductivity and osseointegration with surrounding bone tissue. Strategies to confer bioactivity to polymeric scaffolds utilize bioceramic fillers, coatings and surface treatments, and additives. These approaches can also favorably impact mechanical and degradation properties. A variety of fabrication methods are utilized to prepare scaffolds with requisite morphological features. The bioactivity of scaffolds may be evaluated with a broad set of techniques, including in vitro (acellular and cellular) and in vivo methods. Herein, we highlight contemporary and emerging approaches to prepare and assess scaffold bioactivity, as well as existing challenges.

Teaching Video NeuroImage: Isolated Undulating Tongue Hyperkinesia Following a Basilar Stroke.

An 82-year-old man with a history of hypertension and coronary revascularization presented with sudden-onset right hemiparesis and disorientation lasting 5 hours. On admission, he was intubated because of gasping and a Glasgow Coma Scale of 3. Hemorrhagic stroke was suspected, but ruled out by the initial head CT, which revealed old cerebellar lacunae. The following day, the comatose, now unsedated patient exhibited tetraparesis; fixed, nonreactive pupils; and corneal reflex, but no oculocephalic reflex. Rhythmic undulating tongue movements without palatal or limb involvement were first observed (Video 1). EEG revealed no epileptiform activity. Follow-up head CT showed acute ischemic lesions in the thalamocapsular region, midbrain, and pons while angiotomography revealed distal basilar artery occlusion (Figure). Involuntary tongue movements, though rare, have been associated with various conditions such as stroke, trauma, and epilepsy.1,2 These movements may result from disinhibition within the inhibitory reticular formation projecting to hypoglossal neurons, suggesting the pontine reticular formation as a central pacemaker.2.

Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal.

BACKGROUND: Autism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets. METHODS: The previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm. RESULTS: The test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset. CONCLUSION: In summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.

Improving explainable AI with patch perturbation-based evaluation pipeline: a COVID-19 X-ray image analysis case study.

Recent advances in artificial intelligence (AI) have sparked interest in developing explainable AI (XAI) methods for clinical decision support systems, especially in translational research. Although using XAI methods may enhance trust in black-box models, evaluating their effectiveness has been challenging, primarily due to the absence of human (expert) intervention, additional annotations, and automated strategies. In order to conduct a thorough assessment, we propose a patch perturbation-based approach to automatically evaluate the quality of explanations in medical imaging analysis. To eliminate the need for human efforts in conventional evaluation methods, our approach executes poisoning attacks during model retraining by generating both static and dynamic triggers. We then propose a comprehensive set of evaluation metrics during the model inference stage to facilitate the evaluation from multiple perspectives, covering a wide range of correctness, completeness, consistency, and complexity. In addition, we include an extensive case study to showcase the proposed evaluation strategy by applying widely-used XAI methods on COVID-19 X-ray imaging classification tasks, as well as a thorough review of existing XAI methods in medical imaging analysis with evaluation availability. The proposed patch perturbation-based workflow offers model developers an automated and generalizable evaluation strategy to identify potential pitfalls and optimize their proposed explainable solutions, while also aiding end-users in comparing and selecting appropriate XAI methods that meet specific clinical needs in real-world clinical research and practice.

Early and fair COVID-19 outcome risk assessment using robust feature selection.

Personalized medicine plays an important role in treatment optimization for COVID-19 patient management. Early treatment in patients at high risk of severe complications is vital to prevent death and ventilator use. Predicting COVID-19 clinical outcomes using machine learning may provide a fast and data-driven solution for optimizing patient care by estimating the need for early treatment. In addition, it is essential to accurately predict risk across demographic groups, particularly those underrepresented in existing models. Unfortunately, there is a lack of studies demonstrating the equitable performance of machine learning models across patient demographics. To overcome this existing limitation, we generate a robust machine learning model to predict patient-specific risk of death or ventilator use in COVID-19 positive patients using features available at the time of diagnosis. We establish the value of our solution across patient demographics, including gender and race. In addition, we improve clinical trust in our automated predictions by generating interpretable patient clustering, patient-level clinical feature importance, and global clinical feature importance within our large real-world COVID-19 positive patient dataset. We achieved 89.38% area under receiver operating curve (AUROC) performance for severe outcomes prediction and our robust feature ranking approach identified the presence of dementia as a key indicator for worse patient outcomes. We also demonstrated that our deep-learning clustering approach outperforms traditional clustering in separating patients by severity of outcome based on mutual information performance. Finally, we developed an application for automated and fair patient risk assessment with minimal manual data entry using existing data exchange standards.

Performance Analysis of Relative GPS Positioning for Low-Cost Receiver-Equipped Agricultural Rovers.

Global navigation satellite systems (GNSSs) became an integral part of all aspects of our lives, whether for positioning, navigation, or timing services. These systems are central to a range of applications including road, aviation, maritime, and location-based services, agriculture, and surveying. The Global Positioning System (GPS) Standard Position Service (SPS) provides position accuracy up to 10 m. However, some modern-day applications, such as precision agriculture (PA), smart farms, and Agriculture 4.0, have demanded navigation technologies able to provide more accurate positioning at a low cost, especially for vehicle guidance and variable rate technology purposes. The Society of Automotive Engineers (SAE), for instance, through its standard J2945 defines a maximum of 1.5 m of horizontal positioning error at 68% probability (1σ), aiming at terrestrial vehicle-to-vehicle (V2V) applications. GPS position accuracy may be improved by addressing the common-mode errors contained in its observables, and relative GNSS (RGNSS) is a well-known technique for overcoming this issue. This paper builds upon previous research conducted by the authors and investigates the sensitivity of the position estimation accuracy of low-cost receiver-equipped agricultural rovers as a function of two degradation factors that RGNSS is susceptible to: communication failures and baseline distances between GPS receivers. The extended Kalman filter (EKF) approach is used for position estimation, based on which we show that it is possible to achieve 1.5 m horizontal accuracy at 68% probability (1σ) for communication failures up to 3000 s and baseline separation of around 1500 km. Experimental data from the Brazilian Network for Continuous Monitoring of GNSS (RBMC) and a moving agricultural rover equipped with a low-cost GPS receiver are used to validate the analysis.

Escaping through virtual gaming-what is the association with emotional, social, and mental health? A systematic review.

Background: The realm of virtual games, video games, and e-sports has witnessed remarkable and substantial growth, captivating a diverse and global audience. However, some studies indicate that this surge is often linked to a desire to escape from real life, a phenomenon known as escapism. Much like substance abuse, escapism has been identified as a significant motivator, leading to adverse outcomes, including addiction. Therefore, it is crucial to comprehend the existing research on the connection between escapism and engagement in virtual gaming. This understanding can shed light on the reasons behind such practices and their potential impact on mental and public health. Purpose: The objective of this systematic review is investigate the findings pertaining to association between escapism and the practice of virtual games, such as video-games and e-sport. Methods: PUBMED and SCOPUS database were systematically searched. Six independent researchers screened articles for relevance. We extracted data regarding escapism-related measures, emotional/mental health-related measures and demographic information relevant to the review purpose. Results: The search yielded 357 articles, 36 were included. Results showed that: (i) Escapist motivation (EM) is one of the main motives for playing virtual games; (ii) EM is related to negative clinical traits; (iii) EM predicts negative psychological/emotional/mental health outcomes; (iv) EM is associated with impaired/negative perception of the real-world life; (v) EM predicts non-adaptive real social life; and (vi) EM is associated with dysfunctional gaming practices in some cases. However, EM can have beneficial effects, fostering confidence, determination, a sense of belonging in virtual communities, and representation through avatars. Furthermore, the reviewed findings suggest that EM was positively linked to mitigating loneliness in anxious individuals and promoting social activities that preserved mental health among typical individuals during the pandemic. Conclusion: Our review reinforces the evidence linking EM in the context of virtual games to poor mental health and non-adaptive social behavior. The ensuing discussion explores the intricate connection between escapism and mental health, alongside examining the broad implications of virtual gaming practices on underlying motivations for escapism in the realms of social cognition, health promotion, and public health.

Pathological beige remodeling induced by cancer cachexia depends on the disease severity and involves mainly the trans-differentiation of mature white adipocytes.

In cancer associated cachexia (CAC), white adipose tissue undergoes morphofunctional and inflammatory changes that lead to tissue dysfunction and remodeling. In addition to metabolic changes in white adipose tissues (WAT), adipose tissue atrophy has been implicated in several clinical complications and poor prognoses associated with cachexia. Adipocyte atrophy may be associated with increased beige remodeling in human CAC as evidenced by the "beige remodeling" observed in preclinical models of CAC. Even though beige remodeling is associated with CAC-induced WAT dysfunction, there are still some open questions regarding their cellular origins. In this study, we investigated the development of beige remodeling in CAC from a broader perspective. In addition, we used a grading system to identify the scAT as being affected by mice weight loss early and intensely. Using different in vitro and ex-vivo techniques, we demonstrated that Lewis LLC1 cells can induce a switch from white to beige adipocytes, which is specific to this type of tumor cell. During the more advanced stages of CAC, beige adipocytes are mainly formed from the transdifferentiation of cells. According to our results, humanizing the CAC classification system is an efficient approach to defining the onset of the syndrome in a more homogeneous manner. Pathological beige remodeling occurred early in the disease course and exhibited phenotypic characteristics specific to LLC cells' secretomes. Developing therapeutic strategies that recruit beige adipocytes in vivo may be better guided by an understanding of the cellular origins of beige adipocytes emitted by CAC.

Accelerated body size evolution in upland environments is correlated with recent speciation in South American freshwater fishes.

Speciation rates vary greatly among taxa and regions and are shaped by both biotic and abiotic factors. However, the relative importance and interactions of these factors are not well understood. Here we investigate the potential drivers of speciation rates in South American freshwater fishes, the most diverse continental vertebrate fauna, by examining the roles of multiple biotic and abiotic factors. We integrate a dataset on species geographic distribution, phylogenetic, morphological, climatic, and habitat data. We find that Late Neogene-Quaternary speciation events are strongly associated with body-size evolution, particularly in lineages with small body sizes that inhabit higher elevations near the continental periphery. Conversely, the effects of temperature, area, and diversity-dependence, often thought to facilitate speciation, are negligible. By evaluating multiple factors simultaneously, we demonstrate that habitat characteristics associated with elevation, as well as body size evolution, correlate with rapid speciation in South American freshwater fishes. Our study emphasizes the importance of integrative approaches that consider the interplay of biotic and abiotic factors in generating macroecological patterns of species diversity.

Explainable synthetic image generation to improve risk assessment of rare pediatric heart transplant rejection.

Expert microscopic analysis of cells obtained from frequent heart biopsies is vital for early detection of pediatric heart transplant rejection to prevent heart failure. Detection of this rare condition is prone to low levels of expert agreement due to the difficulty of identifying subtle rejection signs within biopsy samples. The rarity of pediatric heart transplant rejection also means that very few gold-standard images are available for developing machine learning models. To solve this urgent clinical challenge, we developed a deep learning model to automatically quantify rejection risk within digital images of biopsied tissue using an explainable synthetic data augmentation approach. We developed this explainable AI framework to illustrate how our progressive and inspirational generative adversarial network models distinguish between normal tissue images and those containing cellular rejection signs. To quantify biopsy-level rejection risk, we first detect local rejection features using a binary image classifier trained with expert-annotated and synthetic examples. We converted these local predictions into a biopsy-wide rejection score via an interpretable histogram-based approach. Our model significantly improves upon prior works with the same dataset with an area under the receiver operating curve (AUROC) of 98.84% for the local rejection detection task and 95.56% for the biopsy-rejection prediction task. A biopsy-level sensitivity of 83.33% makes our approach suitable for early screening of biopsies to prioritize expert analysis. Our framework provides a solution to rare medical imaging challenges currently limited by small datasets.

Toward a mechanistic understanding of adsorption behavior of phenol onto a novel activated carbon composite.

In this research, the solid-liquid adsorption systems for MSAC (PbFe2O4 spinel-activated carbon)-phenol and pristine activated carbon-phenol were scrutinized from the thermodynamics and statistical physics (sta-phy) viewpoints. Experimental results indicated that MSAC composite outperformed pristine AC for the uptake of phenol from waste streams. By increasing the process temperature, the amount of phenol adsorbed onto both adsorbents, MSAC composite and pristine AC, decreased. Thermodynamic evaluations for MSAC demonstrated the spontaneous and exothermic characteristics of the adsorption process, while positive values of ΔG for pristine AC indicated a non-spontaneous process of phenol adsorption in all temperatures. In a mechanistic investigation, statistical physics modeling was applied to explore the responsible mechanism for phenol adsorption onto the MSAC composite and pristine AC. The single-layer model with one energy was the best model to describe the experimental data for both adsorbents. The adsorption energies of phenol onto both adsorbents were relatively smaller than 20 kJ/mol, indicating physical interactions. By increasing temperature from 298 to 358 K, the value of the absorbed amount of phenol onto the MSAC composite and pristine AC at saturation (Qsat) decreased from 158.94 and 138.91 to 115.23 and 112.34 mg/g, respectively. Mechanistic studies confirm the significant role of metallic hydroxides in MSAC to facilitate the removal of phenol through a strong interaction with phenol molecules, as compared with pristine activated carbon.

Size reduction and skull shape parallelism following the evolutionary forest-to-savanna transition in Platyrrhini monkeys.

There are strong physiological and behavioral differences that allow animals to live in forests versus savannas. For example, terrestrial forest-dwelling mammals tend to be small compared to species living in savannas. Robust capuchin monkeys (genus Sapajus) are widespread in tropical South America, occurring in both forest and savanna environments, with forest species considered basal in an evolutionary context. Whether or not skull shape and size variations are associated with variation in resource use remains unknown, particularly for the two species living in savanna (Sapajus libidinosus and S. cay). Here we show that savanna species present convergent size and skull characteristics that may facilitate living in this new environment. Geometric morphometric methods were used to assess skull size and shape variation for 184 Sapajus specimens distributed across South America. We used phylogenetic generalized least squares to test size against environmental variables and multivariate morphological trajectories/partial least square analyses on the skull shape to detect shape differences in specimens between forest and savanna biomes. Our findings reveal Sapajus size reduction in the evolutionary transition from forest to savanna, a process related to increasing seasonality. Moreover, we found morphological parallelism in the skull (e.g., muzzle shortening) and (large) molars in the two savanna species, features that may facilitate the processing of harder food such as fallback resources. We associate these phenotypic differences to the evolutionary process of colonizing the savannas by primates (including early hominins), leading to morphological adaptations to tolerate stressful, seasonal environments, such as body size reduction and ingestion and mastication of tough foods.

Distributed I/O as a service: A data acquisition solution to Industry 4.0.

Data acquisition is a required step for any industrial application. Industrial automation traditionally relies on Supervisory Control and Data Acquisition (SCADA), and remote I/O is an important element of this infrastructure. The remote I/O is responsible for the data acquisition of sensors and actuators of industrial processes. The distributed I/O represents an evolution of the remote I/O to include processing and support network communication. With the adoption of recent technologies within the industry 4.0, automation solutions are facing servitization and being develop as a service. This paper describes the development of an open-source and low-cost distributed I/O as a service for industrial automation. The solution is based on a single board computer with I/O expansion modules, a microservice framework and a customized software to enable the I/O data acquisition to function in a networked, standardized, and scalable manner. Some application examples of the distributed I/O service are presented such as the I/O redundancy and historian, process control, and IIoT monitoring.

Boom and bust: Simulating the effects of climate change on the population dynamics of a global invader near the edge of its native range.

Despite the increasing awareness of climate change, few studies have used the Intergovernmental Panel on Climate Change (IPCC) scenarios to simulate the effects of climate change on estuarine populations of crustaceans. The objective of this study was to investigate the effects of temperature and salinity fluctuations on the population dynamics of the shore crab Carcinus maenas at the southern edge of its native range. To this end, a population dynamics model was developed based on experimental and literature data on the biology, ecology and physiology of the species. Results showed that the shore crab will be more affected by changes in temperature than in salinity. The parameter sensitivity analysis revealed that the larval phase of the species is the most sensitive stage of the shore crab life cycle. Three IPCC scenarios (SSP1-2.6, SSP2-4.5, and SSP3-8.5) were used to simulate the effects of temperature increase on the population of C. maenas in the near- (2040), mid- (2060), and long-term (2100). Two scenarios of drought conditions accompanied by the estimated salinity change were also simulated (10 % and 40 % drought). Results suggested that slight increases in temperature (up to 2 °C) lead to a strong increase on the density of C. maenas in the mid-term, while further temperature increases lead to a decline or local extinction of the shore crab population at the southern edge of its distribution range. Results indicated that a salinity increase in the estuary had a negative effect on the shore crab population. Given the importance of the species to temperate coastal ecosystems, both population increase and local extinction are likely to have significant impacts on estuarine communities and food webs, with unknown ecological and socioeconomic consequences.

Influence of Integration Schemes and Maneuvers on the Initial Alignment and Calibration of AUVs: Observability and Degree of Observability Analyses.

The use of autonomous underwater vehicles (AUV) has increased in a wide range of sectors, including the oil and gas industry, military, and marine research. The AUV capabilities to operate without a direct human operator and untethered to a support vessel are features that have aroused interest in the marine environment. The localization of AUV is significantly affected by the initial alignment and the calibration of the navigation sensors. In this sense, this paper proposes a thorough observability analysis applied to the latter problem. The observability analysis is carried out considering three types of sensor fusion integration and a set of maneuvers, and the results are validated through numerical simulations. As main contribution of this paper, it is shown how the addition of position errors in the observation vector can decouple some gyro and accelerometer biases from the latitude and altitude errors, particularly in the stationary observability analysis. The influence of oscillations in the diving plane and typical AUV maneuvers are analyzed, showing their relative impacts on the degree of observability of the inertial measurement unit (IMU)/Doppler velocity log (DVL) misalignment and DVL scale factor error. Finally, the state's estimation accuracy is also analyzed, showing the limitation of the degree of observability as an assessment tool for the estimability of the states.

Comparative evaluation of mesenchymal stromal cell growth and osteogenic differentiation on a shape memory polymer scaffold.

Trauma-induced, critical-size bone defects pose a clinical challenge to heal. Albeit autografts are the standard-of-care, they are limited by their inability to be shaped to various defect geometries and often incur donor site complications. Herein, the combination of a "self-fitting" shape memory polymer (SMP) scaffold and seeded mesenchymal stromal cells (MSCs) was investigated as an alternative. The porous SMP scaffold, prepared from poly(ε-caprolactone) diacrylate (PCL-DA) and coated with polydopamine, provided conformal shaping and cell adhesion. MSCs from five tissues, amniotic (AMSCs), chorionic tissue (CHSCs), umbilical cord (UCSCs), adipose (ADSCs), and bone marrow (BMSCs) were evaluated for viability, density, and osteogenic differentiation on the SMP scaffold. BMSCs exhibited the fastest increase in cell density by day 3, but after day 10, CHSCs, UCSCs, and ADSCs approached similar cell density. BMSCs also showed the greatest calcification among the cell types, followed closely by ADSCs, CHSCs and AMSCs. Alkaline phosphatase (ALP) activity peaked at day 7 for AMSCs, UCSCs, ADSCs and BMSCs, and at day 14 for CHSCs, which had the greatest overall ALP activity. Of all the cell types, only scaffolds cultured with ADSCs in osteogenic media had increased hardness and local modulus as compared to blank scaffolds after 21 days of cell culture and osteogenic differentiation. Overall, ADSCs performed most favorably on the SMP scaffold. The SMP scaffold was able to support key cellular behaviors of MSCs and could potentially be a viable, regenerative alternative to autograft.

Suitability of EtO Sterilization for Polydopamine-coated, Self-fitting Bone Scaffolds.

Irregularly shaped craniomaxillofacial (CMF) defects may be advantageously treated by "self-fitting" shape memory polymer (SMP) scaffolds, namely those prepared from poly(ε-caprolactone)diacrylate (PCL-DA) networks and PCL-DA/poly(L-lactic acid) (PLLA) (75:25 wt%) semi-interpenetrating polymer networks (semi-IPNs). In addition to achieving good scaffold-tissue contact, a polydopamine (PD) coating can be leveraged to enhance bioactivity for improved osseointegration. Sterilization with ethylene oxide (EtO) represents a logical choice due to its low operating temperature and humidity. Herein, for the first time, the impact of EtO sterilization on the material properties of PD-coated SMP scaffolds was systematically assessed. Morphological features (i.e., pore size and pore interconnectivity), and in vitro bioactivity were preserved as were PCL crystallinity, PLLA crystallinity, and crosslinking. These latter features led to sustained shape memory properties, and compressive modulus. EtO-sterilized, PD-coated scaffolds displayed similar in vitro degradation behaviors versus analogous non-sterilized scaffolds. This included maintenance of compression modulus following 28 days of exposure to non-accelerated degradation conditions.

Effect of Productivity on Community Size Explains the Latitudinal Diversity Gradient of South American Small Mammals.

AbstractAlthough many studies have shown that species richness increases from high to low latitudes (the latitudinal diversity gradient), the mechanisms responsible for generating and maintaining higher species richness in the tropics remain intensely debated. Here we investigate how the effects of temperature on speciation rates (kinetic effects) and the effects of productivity on community size (chemical effects) explain the latitudinal diversity gradient of South American small mammals. We implemented Bayesian models that integrate processes from the neutral and metabolic theories, comparing model predictions with empirical richness patterns. The neutral-metabolic model predicted the latitudinal richness gradient in South American small mammals. We found evidence that the effects of productivity on community size are more important for explaining differences in species richness than the effects of temperature on speciation rates. These results suggest that differences in species richness along latitudinal gradients are regulated primarily by the chemical effects of productivity on speciation-extinction dynamics.

Evaluation of a self-fitting, shape memory polymer scaffold in a rabbit calvarial defect model.

Self-fitting scaffolds prepared from biodegradable poly(ε-caprolactone)-diacrylate (PCL-DA) have been developed for the treatment of craniomaxillofacial (CMF) bone defects. As a thermoresponsive shape memory polymer (SMP), with the mere exposure to warm saline, these porous scaffolds achieve a conformal fit in defects. This behavior was expected to be advantageous to osseointegration and thus bone healing. Herein, for an initial assessment of their regenerative potential, a pilot in vivo study was performed using a rabbit calvarial defect model. Exogenous growth factors and cells were excluded from the scaffolds. Key scaffold material properties were confirmed to be maintained following gamma sterilization. To assess scaffold integration and neotissue infiltration along the defect perimeter, non-critically sized (d = 8 mm) bilateral calvarial defects were created in 12 New Zealand white rabbits. Bone formation was assessed at 4 and 16 weeks using histological analysis and micro-CT, comparing defects treated with an SMP scaffold (d = 9 mm x t = 1 or 2 mm) to untreated defects (i.e. defects able to heal without intervention). To further assess osseointegration, push-out tests were performed at 16 weeks and compared to defects treated with poly(ether ether ketone) (PEEK) discs (d = 8.5 mm x t = 2 mm). The results of this study confirmed that the SMP scaffolds were biocompatible and highly conducive to bone formation and ingrowth at the perimeter. Ultimately, this resulted in similar bone volume and surface area versus untreated defects and superior performance in push-out testing versus defects treated with PEEK discs. STATEMENT OF SIGNIFICANCE: Current treatments of craniomaxillofacial (CMF) bone defects include biologic and synthetic grafts but they are limited in their ability to form good contact with adjacent tissue. A regenerative engineering approach using a biologic-free scaffold able to achieve conformal fitting represents a potential "off-the-shelf" surgical product to heal CMF bone defects. Having not yet been evaluated in vivo, this study provided the preliminary assessment of the bone healing potential of self-fitting PCL scaffolds using a rabbit calvarial defect model. The study was designed to assess scaffold biocompatibility as well as bone formation and ingrowth using histology, micro-CT, and biomechanical push-out tests. The favorable results provide a basis to pursue establishing self-fitting scaffolds as a treatment option for CMF defects.

Computer-aided autism diagnosis based on visual attention models using eye tracking.

An advantage of using eye tracking for diagnosis is that it is non-invasive and can be performed in individuals with different functional levels and ages. Computer/aided diagnosis using eye tracking data is commonly based on eye fixation points in some regions of interest (ROI) in an image. However, besides the need for every ROI demarcation in each image or video frame used in the experiment, the diversity of visual features contained in each ROI may compromise the characterization of visual attention in each group (case or control) and consequent diagnosis accuracy. Although some approaches use eye tracking signals for aiding diagnosis, it is still a challenge to identify frames of interest when videos are used as stimuli and to select relevant characteristics extracted from the videos. This is mainly observed in applications for autism spectrum disorder (ASD) diagnosis. To address these issues, the present paper proposes: (1) a computational method, integrating concepts of Visual Attention Model, Image Processing and Artificial Intelligence techniques for learning a model for each group (case and control) using eye tracking data, and (2) a supervised classifier that, using the learned models, performs the diagnosis. Although this approach is not disorder-specific, it was tested in the context of ASD diagnosis, obtaining an average of precision, recall and specificity of 90%, 69% and 93%, respectively.