Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks

Remote sensing images have long been preferred to perform building damage assessments. The recently proposed methods to extract damaged regions from remote sensing imagery rely on convolutional neural networks (CNN). The common approach is to train a CNN independently considering each of the different resolution levels (satellite, aerial, and terrestrial) in a binary classification approach. In this regard, an ever-growing amount of multi-resolution imagery are being collected, but the current approaches use one single resolution as their input. The use of up/down-sampled images for training has been reported as beneficial for the image classification accuracy both in the computer vision and remote sensing domains. However, it is still unclear if such multi-resolution information can also be captured from images with different spatial resolutions such as imagery of the satellite and airborne (from both manned and unmanned platforms) resolutions. In this paper, three multi-resolution CNN feature fusion approaches are proposed and tested against two baseline (mono-resolution) methods to perform the image classification of building damages. Overall, the results show better accuracy and localization capabilities when fusing multi-resolution feature maps, specifically when these feature maps are merged and consider feature information from the intermediate layers of each of the resolution level networks. Nonetheless, these multi-resolution feature fusion approaches behaved differently considering each level of resolution. In the satellite and aerial (unmanned) cases, the improvements in the accuracy reached 2% while the accuracy improvements for the airborne (manned) case was marginal. The results were further confirmed by testing the approach for geographical transferability, in which the improvements between the baseline and multi-resolution experiments were overall maintained.
Mkx‐Deficient Mice Exhibit Hedgehog Signaling–Dependent Ectopic Ossification in the Achilles Tendons

Heterotopic ossification is the abnormal formation of mineralized bone in skin, muscle, tendon, or other soft tissues. Tendon ossification often occurs from acute tendon injury or chronic tendon degeneration, for which current treatment relies heavily on surgical removal of the ectopic bony tissues. Unfortunately, surgery creates additional trauma, which often causes recurrence of heterotopic ossification. The molecular mechanisms of heterotopic ossification are not well understood. Previous studies demonstrate that Mkx is a transcription factor crucial for postnatal tendon fibril growth. Here we report that Mkx–/– mutant mice exhibit ectopic ossification in the Achilles tendon within 1 month after birth and the tendon ossification deteriorates with age. Genetic lineage labeling revealed that the tendon ossification in Mkx–/– mice resulted from aberrant differentiation of tendon progenitor cells. Furthermore, tissue‐specific inactivation of Mkx in tendon cells postnatally resulted in a similar ossification phenotype, indicating that Mkx plays a key role in tendon tissue homeostasis. Moreover, we show that Hedgehog signaling is ectopically activated at early stages of tendon ossification and that tissue‐specific inactivation of Smoothened, which encodes the obligatory transducer of Hedgehog signaling, in the tendon cell lineage prevented or dramatically reduced tendon ossification in Mkx–/– mice. Together, these studies establish a new genetic mouse model of tendon ossification and provide new insight into its pathogenic mechanisms. © 2018 American Society for Bone and Mineral Research.
Comparison of electrocardiogram characteristics and pacing parameters between left bundle branch pacing and right ventricular pacing in patients receiving pacemaker therapy.

AIMS This study explores the feasibility of left bundle branch pacing (LBBP) and characterizes electrocardiogram (ECG) patterns during the pacing in comparison with conventional right ventricular pacing (RVP). METHODS AND RESULTS Forty pacing-indicated patients were prospectively enrolled. Twenty patients underwent LBBP (the LBBP group), and 20 patients underwent RVP (the RVP group). Left bundle branch pacing was achieved by transseptal method in the basal ventricular septum. Electrocardiogram characteristics, pacing parameters, pacing sites, and safety events were assessed at implantation and 3-month follow-up. In the LBBP group, the pacing lead was successfully placed near the endocardium of the left side of the septum. Electrocardiogram pattern during LBBP showed right bundle branch conduction delay. Left bundle branch block (LBBB) in two patients was corrected by LBBP. Post-implantation 3D echocardiography confirmed the pacing location. In the RVP group, ECG showed LBBB pattern. The paced QRS duration was 111.85 ± 10.77 ms in LBBP group and 160.15 ± 15.04 ms in the RVP group (P < 0.001). Pacing thresholds (at implantation: 0.73 ± 0.20 V in the LBBP group and 0.61 ± 0.23 V in the RVP group) remained low and stable at 3-month follow-up. No adverse event was observed during 3-month follow-up. CONCLUSION This study demonstrates the clinical feasibility of LBBP. Left bundle branch pacing that has a low pacing threshold and produces narrow ECG QRS duration may be a new pacing strategy for patients in need of ventricular pacing.
Biomechanics and wear comparison between mechanical and kinematic alignments in total knee arthroplasty

The uses of mechanical and kinematic alignments in total knee arthroplasty are under debate in recent clinical investigations. In this study, the differences in short-term biomechanics and long-term wear volume between mechanical and kinematic alignments in total knee arthroplasty were investigated, based on a subject-specific musculoskeletal multi-body dynamics model during walking gait simulation. An increase of 8.2% in the peak tibiofemoral medial contact force, a posterior contact translation by maximum 4.7 mm and a decrease of 5.5% in the wear volume after a 10-million-cycle simulation were predicted in the kinematic alignment, compared with the mechanical alignment. Nevertheless, the tibiofemoral contact mechanics, the range of motions and the long-term wear were not markedly different between mechanical and kinematic alignments. Furthermore, the mechanical alignment with a posterior tibial slope similar to that under the kinematic alignment was found to produce similar anterior–posterior translation and the range of motion, and an approximate wear volume, compared with the kinematic alignment. The ligament forces under the kinematic alignment were influenced markedly by as much as 25%, 50% and 77% for the medial collateral ligament, lateral collateral ligament and posterior cruciate ligament forces, respectively. And, a maximum increase of 40% for patellofemoral contact force was predicted under the kinematic alignment. These findings suggest that the kinematic alignment is an alternative alignment principle but no marked advantages in biomechanics and wear to the mechanical alignment. The adverse effects of the kinematic alignment on patella loading and soft tissue forces should be noticed.
Temporal and spatial asymmetries during stationary cycling cause different feedforward and feedback modifications in the muscular control of the lower limbs

Motor adaptations are useful for studying the way in which the lower limbs are controlled by the brain. However, motor adaptation paradigms for the lower limbs are typically based on locomotion tasks, where the necessity of maintaining postural stability is the main driver of adaptation and could possibly mask other underlying processes. In this study we investigated whether small temporal or spatial asymmetries can trigger motor adaptations during stationary cycling, where stability is not directly compromised. Fourteen healthy individuals participated in two experiments: in one of the experiments the angle between the crank arms of the pedals was altered by 10° to induce a temporal asymmetry; in the other the length of the right pedal was shortened by 2.4 cm to induce a spatial asymmetry. We recorded the acceleration of the crank arms and the EMG signals of 16 muscles (8 per leg). The analysis of the accelerometer data was used to investigate the presence of motor adaptations. Muscle synergy analysis was performed on each side to quantify changes in neuromuscular control. We found that feedforward motor adaptations are present in response to temporal asymmetries and are obtained by progressively shifting the activation patterns of two synergies on the right leg. Spatial asymmetries appear to trigger a feedback-driven response that does not present an aftereffect and is not consistent with a motor adaptation. This response is characterized by a step-like decrease in activity in the right gastrocnemius when the asymmetry is present and likely reflects the altered task demands. New and Noteworthy The processes driving lower limb motor adaptations are not fully clear, and previous research appears to indicate that adaptations are mainly driven by stability. Here we show that lower limb adaptations can be obtained also in the absence of an explicit balance threat. We also show that adaptations are present also when kinematic error cannot be compensated for, suggesting the presence of intrinsic error measures regulating the timing of activation of the two legs.
Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations

This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.
Person-Based Brain Morphometric Similarity is Heritable and Correlates With Biological Features

Abstract The characterization of the functional significance of interindividual variation in brain morphometry is a core aim of cognitive neuroscience. Prior research has focused on interindividual variation at the level of regional brain measures thus overlooking the fact that each individual brain is a person-specific ensemble of interdependent regions. To expand this line of inquiry we introduce the person-based similarity index (PBSI) for brain morphometry. The conceptual unit of the PBSI is the individual person’s brain structural profile which considers all relevant morphometric measures as features of a single vector. In 2 independent cohorts (total of 1756 healthy participants), we demonstrate the foundational validity of this approach by affirming that the PBSI scores for subcortical volume and cortical thickness in healthy individuals differ between men and women, are heritable, and robust to variation in neuroimaging parameters, sample composition, and regional brain morphometry. Moreover, the PBSI scores correlate with age, body mass index, and fluid intelligence. Collectively, these results suggest that the person-based measures of brain morphometry are biologically and functionally meaningful and have the potential to advance the study of human variation in multivariate brain imaging phenotypes in healthy and clinical populations.
Retinal oximetry and systemic arterial oxygen levels

Continuous peripheral pulse oximetry for monitoring adequacy of oxygenation is probably the most important technological advance for patients’ monitoring and safety in the last decades. Pulse oximetry has the disadvantage of measuring the peripheral circulation, and the only mean to measure oxygen content of the central circulation is by invasive technology. Determination of blood oxyhaemoglobin saturation in the retinal vessels of the eye can be achieved noninvasively through spectrophotometric retinal oximetry which provides access to the central nervous system circulation. The aim of the thesis was to determine whether retinal oximetry technique can be applied for estimation of the central nervous system circulation which until now has only been possible invasively. This was achieved by measuring oxyhaemoglobin saturation in three adult subject study groups: in people with central retinal vein occlusion (CRVO) to observe local tissue hypoxia, in patients with severe chronic obstructive pulmonary disease (COPD) on long‐term oxygen therapy to observe systemic hypoxaemia and in healthy subjects during hyperoxic breathing to observe systemic hyperoxemia. In addition, the fourth study that is mentioned was performed to test whether retinal oximetry is feasible for neonates.
Discriminative and Orthogonal Subspace Constraints-Based Nonnegative Matrix Factorization

Nonnegative matrix factorization (NMF) is one widely used feature extraction technology in the tasks of image clustering and image classification. For the former task, various unsupervised NMF methods based on the data distribution structure information have been proposed. While for the latter task, the label information of the dataset is one very important guiding. However, most previous proposed supervised NMF methods emphasis on imposing the discriminant constraints on the coefficient matrix. When dealing with new coming samples, the transpose or the pseudoinverse of the basis matrix is used to project these samples to the low dimension space. In this way, the label influence to the basis matrix is indirect. Although, there are also some methods trying to constrain the basis matrix in NMF framework, either they only restrict within-class samples or impose improper constraint on the basis matrix. To address these problems, in this article a novel NMF framework named discriminative and orthogonal subspace constraints-based nonnegative matrix factorization (DOSNMF) is proposed. In DOSNMF, the discriminative constraints are imposed on the projected subspace instead of the directly learned representation. In this manner, the discriminative information is directly connected with the projected subspace. At the same time, an orthogonal term is incorporated in DOSNMF to adjust the orthogonality of the learned basis matrix, which can ensure the orthogonality of the learned subspace and improve the sparseness of the basis matrix at the same time. This framework can be implemented in two ways. The first way is based on the manifold learning theory. In this way, two graphs, i.e., the intrinsic graph and the penalty graph, are constructed to capture the intra-class structure and the inter-class distinctness. With this design, both the manifold structure information and the discriminative information of the dataset are utilized. For convenience, we name this method as the name of the framework, i.e., DOSNMF. The second way is based on the Fisher’s criterion, we name it Fisher’s criterion-based DOSNMF (FDOSNMF). The objective functions of DOSNMF and FDOSNMF can be easily optimized using multiplicative update (MU) rules. The new methods are tested on five datasets and compared with several supervised and unsupervised variants of NMF. The experimental results reveal the effectiveness of the proposed methods.
Syllable retrieval precedes sub-syllabic encoding in Cantonese spoken word production

Two experiments were conducted to investigate the time course of syllabic and sub-syllabic processing in Cantonese spoken word production by using the picture-word interference task. Cantonese-speaking participants were asked to name individually presented pictures aloud and ignore an auditory word distractor. The targets and distractors were either phonologically related (i.e., sharing two identical word-initial phonemes) or unrelated. In Experiment 1, the target syllables were all consonant-vowel (CV)-structured. The phonological distractor was either a CV syllable (i.e., Full Syllable Overlap) or a CVC (consonant-vowel-consonant) syllable (i.e., Sub-syllable Overlap). Relative to the unrelated control, Full Syllable Overlap distractors facilitated naming in all stimuli onset asynchronies (SOAs) (-175, 0, or +175 ms) whereas Sub-syllable Overlap distractors exhibited facilitation only at 0-ms and +175-ms SOAs. Experiment 2 adopted a similar design to examine the possible influence of syllabic structure similarity on the results of Experiment 1. The target syllables were all CVC-structured. The phonological distractor was either a CVC (i.e., Syllable-structure Consistent) or CV (i.e., Syllable-structure Inconsistent) syllable. Comparable priming was observed between the two distractor conditions across the three SOAs. These results indicated that an earlier priming effect was observed with full syllable overlap than sub-syllabic overlap when the degree of segmental overlap was held constant (Experiment 1). The earlier syllable priming observed in Experiment 1 could not be attributed to the effect of syllabic-structure (Experiment 2), thereby suggesting that the syllable unit is important in Cantonese and is retrieved earlier than sub-syllabic components during phonological encoding.
The PPARα agonist fenofibrate attenuates disruption of dopamine function in a maternal immune activation rat model of schizophrenia

Prenatal maternal immune activation (MIA) is associated with a risk to develop schizophrenia and affects dopamine systems in the ventral tegmental area (VTA), key region in the neurobiology of psychoses. Considering the well‐described sex differences in schizophrenia, we investigated whether sex affects MIA impact on dopamine system and on schizophrenia‐related behavioral phenotype. Furthermore, considering peroxisome proliferator‐activated receptor‐α (PPARα) expression in the CNS as well as its anti‐inflammatory and neuroprotective properties, we tested if PPARα activation by prenatal treatment with a clinically available fibrate (fenofibrate) may mitigate MIA‐related effects.
Activation of Nrf2/ARE pathway alleviates the cognitive deficits in PS1V97L‐Tg mouse model of Alzheimer’s disease through modulation of oxidative stress

Oxidative stress refers to an imbalance between oxidative and antioxidative systems due to environmental factors. Although oxidative stress is implicated in the pathogenesis of Alzheimer's disease (AD), its precise role is not yet understood. We aimed to investigate the pathogenic mechanisms of the oxidative stress by using in vitro cultured neurons and in vivo AD models of PS1V97L‐transgenic (Tg) mice. Our results showed that when oxidative stress became increasingly evident, the endogenous protective pathway of nuclear factor E2‐related factor 2 (Nrf2)/antioxidant response element (ARE) decreased in 10‐month‐old PS1V97L‐Tg mice. Activating the Nrf2/ARE pathway suppressed oxidative stress, decreased amyloid‐β (Aβ), and improved the cognitive function of the PS1V97L‐Tg mice. In contrast, blocking the Nrf2/ARE pathway augmented oxidative injury and decreased the cell viability of PS1V97L‐Tg neurons. Our results highlight the role of the Nrf2/ARE pathway in regulating oxidative stress of the PS1V97L‐Tg mice and may indicate a potential therapeutic avenue for AD treatment.
Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation

Maize (zee mays L.) is one of the most important grain crops in China. Lodging is a natural disaster that can cause significant yield losses and threaten food security. Lodging identification and analysis contributes to evaluate disaster losses and cultivates lodging-resistant maize varieties. In this study, we collected visible and multispectral images with an unmanned aerial vehicle (UAV), and introduce a comprehensive methodology and workflow to extract lodging features from UAV imagery. We use statistical methods to screen several potential feature factors (e.g., texture, canopy structure, spectral characteristics, and terrain), and construct two nomograms (i.e., Model-1 and Model-2) with better validation performance based on selected feature factors. Model-2 was superior to Model-1 in term of its discrimination ability, but had an over-fitting phenomenon when the predicted probability of lodging went from 0.2 to 0.4. The results show that the nomogram could not only predict the occurrence probability of lodging, but also explore the underlying association between maize lodging and the selected feature factors. Compared with spectral features, terrain features, texture features, canopy cover, and genetic background, canopy structural features were more conclusive in discriminating whether maize lodging occurs at the plot scale. Using nomogram analysis, we identified protective factors (i.e., normalized difference vegetation index, NDVI and canopy elevation relief ratio, CRR) and risk factors (i.e., Hcv1) related to maize lodging, and also found a problem of terrain spatial variability that is easily overlooked in lodging-resistant breeding trials.
Monitoring the Vulnerability of the Dam and Dikes in Germano Iron Mining Area after the Collapse of the Tailings Dam of Fundão (Mariana-MG, Brazil) Using DInSAR Techniques with TerraSAR-X Data

The Fundão tailings dam in the Germano iron mining complex (Mariana, Brazil) collapsed on the afternoon of 5 November 2015, and around 32.6 million cubic meters of mining waste spilled from the dam, causing polluion with mining waste along a trajectory of 668 km, extending to the Atlantic Ocean. The Sela & Tulipa and Selinha dikes, and the main Germano tailings dam, were directly or indirectly affected by the accident. This work presents an investigation using Advanced-Differential Interferometric Synthetic Aperture Radar (A-DInSAR) techniques for risk assessment in these critical structures during 18 months after the catastrophic event. The approach was based on the integration of SBAS (Small Baseline Subset) and PSI (Persistent Scatterer Interferometry) techniques, aiming at detecting linear and nonlinear ground displacements in these mining structures. It used a set of 48 TerraSAR-X images acquired on ascending mode from 11 November 2015 to 15 May 2017. The results provided by the A-DInSAR analysis indicated an overall stability in the dikes and in the main wall of Germano tailings dam, which is in agreement with in situ topographic monitoring. In addition, it was possible to detect areas within the reservoir showing accumulated values of up to −125 mm of subsidence, probably caused by settlements of the waste dry material due to the interruption of the mining waste deposition, and values up to −80 mm on auxiliary dikes, probably caused by continuous traffic of heavy equipment. The spatiotemporal information of surface displacement of this large mining structure can be used for future operational planning and risk control.