Artificial Intelligence in Eye Movements Analysis for Alzheimer's Disease Early Diagnosis.

As the world's population ages, Alzheimer's disease is currently the seventh most common cause of death globally; the burden is anticipated to increase, especially among middle-class and elderly persons. Artificial intelligence-based algorithms that work well in hospital environments can be used to identify Alzheimer's disease. A number of databases were searched for English-language articles published up until March 1, 2024, that examined the relationships between artificial intelligence techniques, eye movements, and Alzheimer's disease. A novel non-invasive method called eye movement analysis may be able to reflect cognitive processes and identify anomalies in Alzheimer's disease. Artificial intelligence, particularly deep learning, and machine learning, is required to enhance Alzheimer's disease detection using eye movement data. One sort of deep learning technique that shows promise is convolutional neural networks, which need further data for precise classification. Nonetheless, machine learning models showed a high degree of accuracy in this context. Artificial intelligence-driven eye movement analysis holds promise for enhancing clinical evaluations, enabling tailored treatment, and fostering the development of early and precise Alzheimer's disease diagnosis. A combination of artificial intelligence-based systems and eye movement analysis can provide a window for early and non-invasive diagnosis of Alzheimer's disease. Despite ongoing difficulties with early Alzheimer's disease detection, this presents a novel strategy that may have consequences for clinical evaluations and customized medication to improve early and accurate diagnosis.

Feeding Ecology of the Cuvier's Gazelle (Gazella cuvieri, Ogilby, 1841) in the Sahara Desert.

Knowledge of the feeding ecology of ungulates in arid biomes offers an interesting model for understanding the drought resistance of large desert-adapted herbivores, a crucial issue in the face of increasing desertification due to climate change. To assess the feeding ecology of the endangered Cuvier's gazelle (Gazella cuvieri) in the Sahara desert, we used a multi-method approach combining faecal samples, direct observations, and the recording of indirect signs of feeding. We hypothesised that browser behaviour is the best foraging strategy for species living in hyper-arid environments, mainly due to long periods without grazing opportunities. Complementarily, we explored the effects of the main environmental descriptors (rainfalls and NDVI) on feeding patterns and diet quality. We found that Cuvier's diets are based mainly on acacias (Vachellia tortilis, V. flava) and occasionally on the annual forb Anastatica hierochuntica. In total, eighteen species (five trees, nine shrubs, three herbs, and one grass) belonging to fifteen families were recorded. Our result confirmed the browsers' characteristic of this species, reaffirming its ability to settle in a hostile environment. Acacias stand out as key species consumed at the southernmost limit of their range; hence, future conservation plans and strategies should take this into account for the survival of Cuvier's gazelle in desert environments.

Frequency and Longitudinal Course of Motor Signs In Genetic Frontotemporal Dementia.

BACKGROUND AND OBJECTIVES: Frontotemporal dementia (FTD) is a highly heritable disorder. The majority of genetic cases are caused by autosomal dominant pathogenic variants in the c9orf72, GRN and MAPT gene. As motor disorders are increasingly recognized as part of the clinical spectrum the current study aimed to describe motor phenotypes caused by genetic FTD, quantify their temporal association, and investigate their regional association with brain atrophy. METHODS: We analyzed baseline visit data of known carriers of a pathogenic variant in the c9orf72, GRN or MAPT gene from the Genetic Frontotemporal dementia Initiative cohort study. Principal component analysis with varimax rotation was performed to identify motor sign clusters that were compared with respect to frequency and severity between groups. Associations with cross-sectional atrophy patterns were determined using voxel-wise regression. We applied linear mixed effects models to assess whether groups differed in the association between motor signs and estimated time to symptom onset. RESULTS: 322 pathogenic variant carriers were included in the analysis: 122 c9orf72 (79 presymptomatic), 143 GRN (112 presymptomatic) and 57 MAPT (43 presymptomatic) pathogenic variant carriers. Principal component analysis revealed five motor clusters, which we call progressive supranuclear palsy like (PSP-like), bulbar amyotrophic lateral sclerosis (ALS) like, mixed/ALS-like, Parkinson's disease like (PD-like), and corticobasal syndrome like motor phenotypes. There was no significant group difference in the frequency of signs of different motor phenotypes. However, mixed/ALS-like motor signs were most frequent, followed by PD-like motor signs. While the PSP-like phenotype was associated with mesencephalic atrophy, the mixed/ALS-like phenotype was associated with motor cortex and corticospinal tract atrophy. The PD-like phenotype was associated with widespread cortical and subcortical atrophy. Estimated time to onset, genetic group and their interaction, influenced motor signs. In c9orf72 pathogenic variant carriers, motor signs could be detected up to 25 years prior to expected symptom onset. DISCUSSION: These results indicate the presence of multiple natural clusters of motor signs in genetic FTD, each correlated with specific atrophy patterns. Their motor severity depends on time and the affected gene. These clinico-genetic associations can guide diagnostic evaluations and the design of clinical trials for new disease-modifying and preventive treatments.

Galectin-1 Cooperates with Yersinia Outer Protein (Yop) P to Thwart Protective Immunity by Repressing Nitric Oxide Production.

Yersinia enterocolitica (Ye) inserts outer proteins (Yops) into cytoplasm to infect host cells. However, in spite of considerable progress, the mechanisms implicated in this process, including the association of Yops with host proteins, remain unclear. Here, we evaluated the functional role of Galectin-1 (Gal1), an endogenous ß-galactoside-binding protein, in modulating Yop interactions with host cells. Our results showed that Gal1 binds to Yops in a carbohydrate-dependent manner. Interestingly, Gal1 binding to Yops protects these virulence factors from trypsin digestion. Given that early control of Ye infection involves activation of macrophages, we evaluated the role of Gal1 and YopP in the modulation of macrophage function. Although Gal1 and YopP did not influence production of superoxide anion and/or TNF by Ye-infected macrophages, they coordinately inhibited nitric oxide (NO) production. Notably, recombinant Gal1 (rGal1) did not rescue NO increase observed in Lgals1-/- macrophages infected with the YopP mutant Ye ∆yopP. Whereas NO induced apoptosis in macrophages, no significant differences in cell death were detected between Gal1-deficient macrophages infected with Ye ∆yopP, and WT macrophages infected with Ye wt. Strikingly, increased NO production was found in WT macrophages treated with MAPK inhibitors and infected with Ye wt. Finally, rGal1 administration did not reverse the protective effect in Peyer Patches (PPs) of Lgals1-/- mice infected with Ye ∆yopP. Our study reveals a cooperative role of YopP and endogenous Gal1 during Ye infection.

Publisher Correction: Identifying priority conservation areas in a Saharan environment by highlighting the endangered Cuvier's Gazelle as a flagship species.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Galectin-1 Facilitates Macrophage Reprogramming and Resolution of Inflammation Through IFN-ß.

During the resolution of acute inflammation, macrophages undergo reprogramming from pro-inflammatory, to anti-inflammatory/reparative, and eventually to pro-resolving macrophages. Galectin-1 (Gal-1) is a bona fide pro-resolving lectin while interferon ß (IFN-ß) was recently shown to facilitate macrophage reprogramming and resolution of inflammation. In this study, we found Gal-1null mice exhibit a hyperinflammatory phenotype during the resolution of zymosan A-induced peritonitis but not during the early inflammatory response. This phenotype was characterized by reduced macrophage numbers, increased secretion of pro-inflammatory cytokines, such as interleukin-12 (IL-12), and reduced secretion of anti-inflammatory cytokines, such as interleukin-10 (IL-10). In addition, we found a delayed expression of the pro-resolving enzyme 12/15-lipoxygenase in macrophages and heightened levels of the inflammatory protease proteinase-3 (PR3) in peritoneal fluids from Gal-1null mice. Moreover, we observed sex-dependent differences in the inflammatory profile of Gal-1null mice. Notably, we found that IFN-ß levels were reduced in resolution-phase exudates from Gal-1null mice. Administration of IFN-ß in vivo or ex vivo treatment was able to rescue, at least in part, the hyperinflammatory profile of Gal-1null mice. In particular, IFN-ß recovered a subset of F4/80+GR-1+ macrophages, restored IL-12 and IL-10 secretion from macrophages to WT values and diminished abnormal peritoneal PR3 levels in Gal-1null mice. In conclusion, our results revealed a new Gal-1-IFN-ß axis that facilitates the resolution of inflammation and might restrain uncontrolled inflammatory disorders.

Identifying priority conservation areas in a Saharan environment by highlighting the endangered Cuvier's Gazelle as a flagship species.

Monitoring populations and designing effective conservation actions for endangered species present significant challenges. An accurate understanding of current distribution, ecological traits and habitat requirements is imperative in formulating conservation strategies. Recent surveys on the southernmost Cuvier's Gazelle (Gazella cuvieri) population, an ungulate endemic to North Africa, showcase its importance in terms of numbers and genetic diversity. This population inhabits a remote region in the extreme north-western portion of the Sahara Desert and has not been well studied. Here, we examine the potential distribution of Cuvier's Gazelle and the environmental factors limiting the species in a Saharan environment, by combining broad-scale field survey data and species distribution models. Our objective was to identify high priority conservation areas in the southernmost known portion of the species' distribution by modelling habitat selection at the landscape scale using a predictive distribution map. Our results show that the distribution of Cuvier's Gazelle is strongly related to mountainous areas with heterogeneous terrain and remoteness from large human settlements over other ecological factors that had less impact on the species' presence and distribution. We also provide a quantitative estimate of the potential distribution range of Cuvier's Gazelle in southern Morocco, identifying two well-demarcated key areas. The two core areas currently contain enough rugged terrain isolated from human encroachment to support the endangered species in this harsh desert environment. We encourage the implementation of conservation planning for Cuvier's Gazelle as an "umbrella species", which will confer effective protection to higher-quality habitat zones and co-occurring species, leading to sustainable and ecologically responsible development in the region.

Quantification of gait parameters with inertial sensors and inverse kinematics.

Measuring human gait is important in medicine to obtain outcome parameter for therapy, for instance in Parkinson's disease. Recently, small inertial sensors became available which allow for the registration of limb-position outside of the limited space of gait laboratories. The computation of gait parameters based on such recordings has been the subject of many scientific papers. We want to add to this knowledge by presenting a 4-segment leg model which is based on inverse kinematic and Kalman filtering of data from inertial sensors. To evaluate the model, data from four leg segments (shanks and thighs) were recorded synchronously with accelerometers and gyroscopes and a 3D motion capture system while subjects (n = 12) walked at three different velocities on a treadmill. Angular position of leg segments was computed from accelerometers and gyroscopes by Kalman filtering and compared to data from the motion capture system. The four-segment leg model takes the stance foot as a pivotal point and computes the position of the remaining segments as a kinematic chain (inverse kinematics). Second, we evaluated the contribution of pelvic movements to the model and evaluated a five segment model (shanks, thighs and pelvis) against ground-truth data from the motion capture system and the path of the treadmill. RESULTS: We found the precision of the Kalman filtered angular position is in the range of 2-6° (RMS error). The 4-segment leg model computed stride length and length of gait path with a constant undershoot of 3% for slow and 7% for fast gait. The integration of a 5th segment (pelvis) into the model increased its precision. The advantages of this model and ideas for further improvements are discussed.

Diagnosis of Neurodegenerative Diseases: The Clinical Approach.

There are a number of clinical questions for which there are no easy answers, even for well-trained doctors. The diagnostic tool commonly used to assess cognitive impairment in neurodegenerative diseases is based on established clinical criteria. However, the differential diagnosis between disorders can be difficult, especially in early phases or atypical variants. This takes on particular importance when it is still possible to use an appropriate treatment. To solve this problem, physicians need to have access to an arsenal of diagnostic tests, such as neurofunctional imaging, that allow higher specificity in clinical assessment. However, the reliability of diagnostic tests may vary from one to the next, so the diagnostic validity of a given investigation must be estimated by comparing the results obtained from "true" criteria to the "gold standard" or reference test. While pathological analysis is considered to be the gold standard in a wide spectrum of diseases, it cannot be applied to neurological processes. Other approaches could provide solutions, including clinical patient follow-up, creation of a data bank or use of computer-aided diagnostic algorithms. In this article, we discuss the development of different methodological procedures related to analysis of diagnostic validity and present an example from our own experience based on the use of I-123-ioflupane-SPECT in the study of patients with movement disorders. The aim of this chapter is to approach the problem of diagnosis from the point of view of the clinician, taking into account specific aspects of neurodegenerative disease.

Aerial obstacle detection with 3-D mobile devices.

In this paper, we present a novel approach for aerial obstacle detection (e.g., branches or awnings) using a 3-D smartphone in the context of the visually impaired (VI) people assistance. This kind of obstacles are especially challenging because they cannot be detected by the walking stick or the guide dog.The algorithm captures the 3-D data of the scene through stereo vision. To our knowledge, this is the first work that presents a technology able to obtain real 3-D measures with smartphones in real time. The orientation sensors of the device (magnetometer and accelerometer) are used to approximate the walking direction of the user, in order to look for the obstacles only in such a direction. The obtained 3-D data are compressed and then linearized for detecting the potential obstacles. Potential obstacles are tracked in order to accumulate enough evidence to alert the user only when a real obstacle is found.In the experimental section, we show the results of the algorithm in several situations using real data and helped by VI users.

Efficient plasmid-mediated gene transfection of ovine bone marrow mesenchymal stromal cells.

BACKGROUND AIMS: Given the close similarity between ovine and human cardiomyocytes, sheep models of myocardial infarction and heart failure are increasingly used in studies of stem cell-mediated heart regeneration. In these studies, mesenchymal stromal cells (MSCs) are frequently employed. To enhance the paracrine effects of these MSCs, ex vivo transfection with genes encoding growth factors has been proposed. Although viral vectors exhibit higher transfection efficiency than plasmids, they entail the risks of uncontrolled transgene expression and immune reactions that preclude repeated administration. Our aim was to optimize the efficiency of plasmid-mediated transfection of ovine MSCs, while preserving cell viability. METHODS: Varying amounts of diverse cationic lipids were used to obtain the reagent-to-DNA mass ratio showing highest luciferase activity. Transfection efficiency (flow cytometry) was tested on plasmid-green fluorescent protein-transfected MSCs at increasing DNA mass. RESULTS: Lipofectamine LTX 5 µL and Plus reagent 4 µL with 2 µg of DNA yielded 42.3 ± 4.7% transfection efficiency, while preserving cell viability. Using these transfection conditions, we transfected MSCs with a plasmid encoding human vascular endothelial growth factor (VEGF) and found high VEGF protein concentrations in the culture supernatant from day 2 (1968 ± 324 pg/mL per µg DNA) through at least day 12 (888 ± 386 pg/mL per µg DNA) after transfection. CONCLUSIONS: Plasmid-mediated transfection of ovine MSCs to over-express paracrine heart-regenerative growth factors is feasible and efficient and overcomes the risks and limitations associated with the use of viral vectors.

Learning Gaussian mixture models with entropy-based criteria.

In this paper, we address the problem of estimating the parameters of Gaussian mixture models. Although the expectation-maximization (EM) algorithm yields the maximum-likelihood (ML) solution, its sensitivity to the selection of the starting parameters is well-known and it may converge to the boundary of the parameter space. Furthermore, the resulting mixture depends on the number of selected components, but the optimal number of kernels may be unknown beforehand. We introduce the use of the entropy of the probability density function (pdf) associated to each kernel to measure the quality of a given mixture model with a fixed number of kernels. We propose two methods to approximate the entropy of each kernel and a modification of the classical EM algorithm in order to find the optimum number of components of the mixture. Moreover, we use two stopping criteria: a novel global mixture entropy-based criterion called Gaussianity deficiency (GD) and a minimum description length (MDL) principle-based one. Our algorithm, called entropy-based EM (EBEM), starts with a unique kernel and performs only splitting by selecting the worst kernel attending to GD. We have successfully tested it in probability density estimation, pattern classification, and color image segmentation. Experimental results improve the ones of other state-of-the-art model order selection methods.