Beyond schistosomiasis: unraveling co-infections and altered immunity.

Schistosomiasis is a neglected tropical disease caused by the helminth Schistosoma spp. and has the second highest global impact of all parasites. Schistosoma are transmitted through contact with contaminated fresh water predominantly in Africa, Asia, the Middle East, and South America. Due to the widespread prevalence of Schistosoma, co-infection with other infectious agents is common but often poorly described. Herein, we review recent literature describing the impact of Schistosoma co-infection between species and Schistosoma co-infection with blood-borne protozoa, soil-transmitted helminths, various intestinal protozoa, Mycobacterium, Salmonella, various urinary tract infection-causing agents, and viral pathogens. In each case, disease severity and, of particular interest, the immune landscape, are altered as a consequence of co-infection. Understanding the impact of schistosomiasis co-infections will be important when considering treatment strategies and vaccine development moving forward.

Computer Vision in Human Analysis: From Face and Body to Clothes.

For decades, researchers of different areas, ranging from artificial intelligence to computer vision, have intensively investigated human-centered data, i [...].

New Isolation of Ponticelli III Virus (Bunyavirales: Phenuiviridae) in Emilia-Romagna Region, Italy.

The number of newly described sandfly-borne phleboviruses has been steadily growing in recent years. Some phleboviruses are human pathogens, but their health relevance is largely uncharacterized. We aimed to investigate the circulation of these viruses in the Emilia-Romagna region where several have already been described. A total of 482 sandflies were collected in a site in Reggio Emilia in 2019 and 2020. Sandflies collected in 2020 were grouped in 21 pools with a maximum of 25 sandflies per pool, submitted to real time PCR, and isolated in Vero cell culture. Complete genome sequencing showed the isolation of a strain of a Ponticelli III virus. This virus, which belongs to the species Adana phlebovirus, differed in the M segment from the Ponticelli I and Ponticelli II viruses. Analysis performed on the genomic segments of the newly isolated virus compared with other phleboviruses highlighted a strong purifying selection in the L segments, and different substitution saturation, highest in the M segments. Future research should address the ecological processes driving the occurrence of these novel phleboviruses and their possible impact on public health.

Automatic Estimation of Self-Reported Pain by Trajectory Analysis in the Manifold of Fixed Rank Positive Semi-Definite Matrices.

We propose an automatic method to estimate self-reported pain based on facial landmarks extracted from videos. For each video sequence, we decompose the face into four different regions and the pain intensity is measured by modeling the dynamics of facial movement using the landmarks of these regions. A formulation based on Gram matrices is used for representing the trajectory of landmarks on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. A curve fitting algorithm is used to smooth the trajectories and temporal alignment is performed to compute the similarity between the trajectories on the manifold. A Support Vector Regression classifier is then trained to encode extracted trajectories into pain intensity levels consistent with self-reported pain intensity measurement. Finally, a late fusion of the estimation for each region is performed to obtain the final predicted pain level. The proposed approach is evaluated on two publicly available datasets, the UNBCMcMaster Shoulder Pain Archive and the Biovid Heat Pain dataset. We compared our method to the state-of-the-art on both datasets using different testing protocols, showing the competitiveness of the proposed approach.

Determinants of anthroponotic cutaneous leishmaniasis by case-control study in Morocco.

Leishmaniasis is endemic in Morocco where both cutaneous and visceral forms coexist. To date, anthroponotic cutaneous leishmaniasis (ACL) determinants remain poorly investigated in Morocco. However, the disease risk factors identification is vital to determine the specific preventive process. In this aim, a case-control study was conducted in the main active ACL foci in central Morocco. Epidemiological data were extracted from bulletins, registers and annual reports of the regional direction of Health offices. The socioeconomic and environmental data were collected from epidemiological surveys, completed by a questionnaire intended for accessible positive population and control people selected from the cases' entourage. The study included 258 cases and 395 controls. Our results showed that many socioeconomic factors were associated with ACL in Morocco such as the rural habitation (OR = 4.163; 95% CI: 2.91-5.96), movement to endemic area (OR = 4.53; 95% CI: 3.03-6.77), provenance from leishmaniasis foci (in Essaouira focus OR = 5.34; 95% CI: 1.19-24.03) and poverty. In addition, environmental factors like proximity of vegetation (OR = 2.45; 95% CI: 1.14-5.25), poor domestic hygiene, particularly the absence of sewage system/waste management (OR = 1.63; 95% CI: 1.35-1.96), and presence of animals (OR = 2.67; 95% CI: 1.14-5.25) increase the risk of ACL in Morocco. Except for Matrimonial status (married people, OR = 4.11; 95% CI: 1.80-9.41), there is however no significant association of the disease with the other socio-demographic factors in the study area (p>0.05). These several risk factors must be taken in consideration to prevent this disease through multidisciplinary collaboration and community participation.

Identification of Toscana virus in natural population of sand flies (Diptera: Psychodidae) from Moroccan leishmaniasis foci.

BACKGROUND: Phlebotomine sand flies are known as vectors of various pathogens such as Leishmania sp parasite and Toscana virus (TOSV). Leishmaniasis is endemic in Morocco, and TOSV is increasingly reported. Our objective is to analyze the specific composition of the natural population of sand flies in endemic and non endemic area of leishmaniasis in Morocco, thus evaluated their infection by Toscana virus. METHODS: Sand flies were collected by CDC miniature light traps from seven different localities with an altitude range from 399 m to 1496 m. Synanthropic index was calculated for each sand fly species. The collected female sand flies were grouped in 73 pools, with a maximum of 50 specimens per pool, and submitted to real time PCR for TOSV detection. RESULTS: 8 sand fly species were identified morphologically: 5 of the Phlebotomus genus and 3 of the Sergentomyia genus. Phlebotomus sergenti was the most abundant species comprising of 43,12% of identified sand flies, followed by P. papatasi (18,89%) and P. longicuspis (13,43%). Estimated synanthropic indices for these species were between + 1.1 and + 12.6 suggesting a high preference to anthropogenic environments. A total of 3558 sand fly females were grouped in 73 pools (up to 50 sand flies per pool) for TOSV detection. TOSV was detected in one pool (out of 6 tested) from Lalla Laaziza locality (Chichaoua Province) where P. sergenti was the most abundant sand fly species. CONCLUSION: We reported the TOSV for the first time in a central Morocco, where cutaneous leishmaniasis by L. tropica is endemic. This result has epidemiological importance for both researchers and health authorities to monitor circulation of TOSV and implement a surveillance plan of sand fly-borne phleboviruses in Morocco.

Climate Change Influences on the Potential Distribution of the Sand Fly Phlebotomus sergenti, Vector of Leishmania tropica in Morocco.

BACKGROUND: Leishmaniases are a vector-borne disease, re-emerging in several regions of the world posing a burden on public health. As other vector-borne diseases, climate change is a crucial factor affecting the evolution of leishmaniasis. In Morocco, anthroponotic cutaneous leishmaniasis (ACL) is widespread geographically as many foci across the country, mainly in central Morocco. The objective of this study is to evaluate the potential impacts of climate change on the distribution of ACL due to Leishmania tropica, and its corresponding vector Phlebotomus sergenti in Morocco. METHODS: Using Ecological Niche Modeling (ENM) tool, the estimated geographical range shift of L. tropica and P. sergenti by 2050 was projected under two Representative's Concentration's Pathways (RCPs) to be 2.6 and RCP 8.5 respectively. P. sergenti records were obtained from field collections of the laboratory team and previously published entomological observations, while, epidemiological data for L. tropica were obtained from Moroccan Ministry of Health reports. RESULTS: Our models under present-day conditions indicated a probable expansion for L. tropica as well as for its vector in Morocco, P. sergenti. It showed a concentrated distribution in the west-central and northern area of Morocco. Future predictions anticipate expansion into areas not identified as suitable for P. sergenti under present conditions, particularly in northern and southeastern areas of Morocco. L. tropica is also expected to have high expansion in southern areas for the next 30 years in Morocco. CONCLUSION: This indicates that L. tropica and P. sergenti will continue to find suitable climate conditions in the future. A higher abundance of P. sergenti may indeed result in a higher transmission risk of ACL. This information is essential in developing a control plan for ACL in Morocco. However, future investigations on L. tropica reservoirs are needed to confirm our predictions.

Dynamic Facial Expression Generation on Hilbert Hypersphere With Conditional Wasserstein Generative Adversarial Nets.

In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded as points on a hypersphere. By proposing a conditional version of manifold-valued Wasserstein generative adversarial network (GAN) for motion generation on the hypersphere, we learn the distribution of facial expression dynamics of different classes, from which we synthesize new facial expression motions. The resulting motions can be transformed to sequences of landmarks and then to images sequences by editing the texture information using another conditional Generative Adversarial Network. To the best of our knowledge, this is the first work that explores manifold-valued representations with GAN to address the problem of dynamic facial expression generation. We evaluate our proposed approach both quantitatively and qualitatively on two public datasets; Oulu-CASIA and MUG Facial Expression. Our experimental results demonstrate the effectiveness of our approach in generating realistic videos with continuous motion, realistic appearance and identity preservation. We also show the efficiency of our framework for dynamic facial expressions generation, dynamic facial expression transfer and data augmentation for training improved emotion recognition models.

Automatic Estimation of Self-Reported Pain by Interpretable Representations of Motion Dynamics.

We propose an automatic method for pain intensity measurement from video. For each video, pain intensity was measured using the dynamics of facial movement using 66 facial points. Gram matrices formulation was used for facial points trajectory representations on the Riemannian manifold of symmetric positive semi-definite matrices of fixed rank. Curve fitting and temporal alignment were then used to smooth the extracted trajectories. A Support Vector Regression model was then trained to encode the extracted trajectories into ten pain intensity levels consistent with the Visual Analogue Scale for pain intensity measurement. The proposed approach was evaluated using the UNBC McMaster Shoulder Pain Archive and was compared to the state-of-the-art on the same data. Using both 5-fold cross-validation and leave-one-subject-out cross-validation, our results are competitive with respect to state-of-the-art methods.

Impacts of the invasive species Caulerpa cylindracea Sonder, 1845 on the algae flora of the west coast of Algeria.

The assessment of the impacts of the expansion of the invasive species on taxonomic diversity, the abundance and dominance of groups of algae, the presence and/or absence of species of ecological interest that may or may not be indicative of water quality well mentioned, through the installation of a 20 × 20 cm quadrat representing the minimum area. The observation stations were visited monthly, during a repetitive three-year cycle, during the spring, summer and autumn seasons, periods of maximum growth and development of the algal flora and the results suggest the following facts. The invasive alga Caulerpa cylindracea Sonder, 1845 tends to colonise disturbed ecosystems reflecting a reduction in native algal diversity; in fact, we note a drastic impoverishment of the invaded algal community, represented by a limited number of Macrophyte algae accompanying the invasive taxon in phytosociological surveys and a Shannon-Weaver Diversity Index (H') and Equitability reduced by 4.49 and 0.77 n the heavily affected station. The number of macroalgal species accompanying the invasive species has dropped by 52% in Salamandre. In addition, the multidimensional analysis, represented by the Hierarchical Ascendant Clustering applied to this case, confirms our results.

Face and Gesture Analysis for Health Informatics.

The goal of Face and Gesture Analysis for Health Informatics's workshop is to share and discuss the achievements as well as the challenges in using computer vision and machine learning for automatic human behavior analysis and modeling for clinical research and healthcare applications. The workshop aims to promote current research and support growth of multidisciplinary collaborations to advance this groundbreaking research. The meeting gathers scientists working in related areas of computer vision and machine learning, multi-modal signal processing and fusion, human centered computing, behavioral sensing, assistive technologies, and medical tutoring systems for healthcare applications and medicine.

A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding.

In this paper, we propose a novel space-time geometric representation of human landmark configurations and derive tools for comparison and classification. We model the temporal evolution of landmarks as parametrized trajectories on the Riemannian manifold of positive semidefinite matrices of fixed-rank. Our representation has the benefit to bring naturally a second desirable quantity when comparing shapes-the spatial covariance-in addition to the conventional affine-shape representation. We derived then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the underlying manifold. Specifically, our approach involves three steps: (1) landmarks are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank to build time-parameterized trajectories; (2) a temporal warping is performed on the trajectories, providing a geometry-aware (dis-)similarity measure between them; (3) finally, a pairwise proximity function SVM is used to classify them, incorporating the (dis-)similarity measure into the kernel function. We show that such representation and metric achieve competitive results in applications as action recognition and emotion recognition from 3D skeletal data, and facial expression recognition from videos. Experiments have been conducted on several publicly available up-to-date benchmarks.

Automatic Analysis of Facial Expressions Based on Deep Covariance Trajectories.

In this article, we propose a new approach for facial expression recognition (FER) using deep covariance descriptors. The solution is based on the idea of encoding local and global deep convolutional neural network (DCNN) features extracted from still images, in compact local and global covariance descriptors. The space geometry of the covariance matrices is that of symmetric positive definite (SPD) matrices. By conducting the classification of static facial expressions using a support vector machine (SVM) with a valid Gaussian kernel on the SPD manifold, we show that deep covariance descriptors are more effective than the standard classification with fully connected layers and softmax. Besides, we propose a completely new and original solution to model the temporal dynamic of facial expressions as deep trajectories on the SPD manifold. As an extension of the classification pipeline of covariance descriptors, we apply SVM with valid positive definite kernels derived from global alignment for deep covariance trajectories classification. By performing extensive experiments on the Oulu-CASIA, CK+, static facial expression in the wild (SFEW), and acted facial expressions in the wild (AFEW) data sets, we show that both the proposed static and dynamic approaches achieve the state-of-the-art performance for FER outperforming many recent approaches.

Detecting Depression Severity by Interpretable Representations of Motion Dynamics.

Recent breakthroughs in deep learning using automated measurement of face and head motion have made possible the first objective measurement of depression severity. While powerful, deep learning approaches lack interpretability. We developed an interpretable method of automatically measuring depression severity that uses barycentric coordinates of facial landmarks and a Lie-algebra based rotation matrix of 3D head motion. Using these representations, kinematic features are extracted, preprocessed, and encoded using Gaussian Mixture Models (GMM) and Fisher vector encoding. A multi-class SVM is used to classify the encoded facial and head movement dynamics into three levels of depression severity. The proposed approach was evaluated in adults with history of chronic depression. The method approached the classification accuracy of state-of-the-art deep learning while enabling clinically and theoretically relevant findings. The velocity and acceleration of facial movement strongly mapped onto depression severity symptoms consistent with clinical data and theory.

When your face describes your memories: facial expressions during retrieval of autobiographical memories.

Thanks to the current advances in the software analysis of facial expressions, there is a burgeoning interest in understanding emotional facial expressions observed during the retrieval of autobiographical memories. This review describes the research on facial expressions during autobiographical retrieval showing distinct emotional facial expressions according to the characteristics of retrieved memoires. More specifically, this research demonstrates that the retrieval of emotional memories can trigger corresponding emotional facial expressions (e.g. positive memories may trigger positive facial expressions). Also, this study demonstrates the variations of facial expressions according to specificity, self-relevance, or past versus future direction of memory construction. Besides linking research on facial expressions during autobiographical retrieval to cognitive and affective characteristics of autobiographical memory in general, this review positions this research within the broader context research on the physiologic characteristics of autobiographical retrieval. We also provide several perspectives for clinical studies to investigate facial expressions in populations with deficits in autobiographical memory (e.g. whether autobiographical overgenerality in neurologic and psychiatric populations may trigger few emotional facial expressions). In sum, this review paper demonstrates how the evaluation of facial expressions during autobiographical retrieval may help understand the functioning and dysfunctioning of autobiographical memory.

[Villonodular synovitis of the knee: about 20 cases]. / Les synovites villonodulaires du genou: à propos de 20 cas.

Pigmented villonodular synovitis (PVNS) is a rare benign proliferation of synovial joints, serous bursa, tendinous sheaths of unknown etiopathogeny. We here report 20 cases of PVNS of the knee recorded at the Avicenne Military Hospital, Marrakech over a period of 9 years, from January 2000 to December 2009. This study aimed to identify the specific features of this lesion and to examine its anatomoclinic and prognostic aspects. Annual incidence was 2.2 cases per year: 15 men and 5 women. The average age was 32.5 years. It occurred in the right-hand in 55%, 18 patients had monoarticular presentation of the disease while 1 patient had biarticular presentation of the disease. 80% of cases had pain and swelling, palpable mass was detected in 1 case, meniscal syndrome in 1 case, monoseptic arthritis in 3 cases while popliteal cyst in 2 cases. 14 cases (70%) had diffuse involvement, 6 cases had localized involvement. MRI was evocative in 3 patients out of 5; 2 patients underwent diagnostic arthroscopy. Diagnosis was based on anatomo-pathological examination. Treatment was based on subtotal synovectomy in 15 cases and on tumor excision in patients with localized involvement. 2 cases with osteocartilaginous destruction underwent arthroplasty. Patients' evolution was marked by 2 diffuse recurrences after a mean follow-up of 3-7 years. 3 patients had stiffness associated with quadriceps atrophy, therefore arthrolysis was performed. One case of histologically confirmed PVNS had proved to be a monophasic synovial sarcoma invading the bone 5 months after total synovectomy. Hence, the indication for amputation.

[Role of centro-medullary nailing in fractures of the distal quarter of the leg: about 30 cases]. / Intérêt de l'enclouage centromédullaire dans les fractures du quart distal de la jambe: à propos de 30 cas.

The fractures of the distal quarter of the leg are characterized by fracture line located at the level of the lower quarter of the tibia, according to Gerard and Evrard definition [1]. They are serious and pose problems for consolidation, immobilization and stability. We here describe our experience in the Department of Orthopaedics and Traumatology at the Avicenne Military Hospital, Marrakech. We report 30 cases of closed fractures of the lower quarter of the leg treated with centro-medullary nailing over a period of 10 years (January 2001-December 2010). Locked nailing was performed in 80% of cases and simple nailing was performed in the other cases. The average age of patients was 36 years. There was a clear male predominance (27 men, 3 women). The average time for consolidation was 17 weeks and functional outcomes were satisfactory. A single case of infection occurred 6 months after surgery (3.3%) and no other complication was reported. Malunion was detected in 30% of patients. Our epidemiological data and results were almost identical to those in the literature. Angular results were significantly lower than those obtained with the series of plates. By contrast, data on infections called for caution and some nails produced excellent angular results when nail fixation was stable. In the light of these results, codified indications for locked centro-medullary nailing should be extended to the fractures of the lower quarter of the leg, provided that stable fixation using double screw distal locking and primary osteosynthesis of distal fibula fractures are performed.

[Role of Hackethal bundle nailing in the treatment of humerus fractures: about 80 cases]. / Place de l'embrochage fasciculé selon Hackethal dans le traitement des fractures de l'humérus: à propos de 80 cas.

The main debate regarding humerus fractures focuses on their therapeutic indications because any type of orthopedic or surgical stabilization finds unconditional and sometimes vehement defenders. We conducted a retrospective study of 80 patients treated initially with Hackethal's technique of intramedullary bundle nailing in the Department of Orthopaedics and Traumatology at Avicenne Military Hospital between January 2000 and January 2012. The fractures were classified according to AO classification and Hackethal classification (modified by De La Caffinière). Functional assessment was based on the modified Stewart and Hundley classification. We got 60 very good outcomes, 6 good outcomes, 2 quite good outcomes and 2 poor outcomes. The two cases with poor outcomes had pseudarthrosis. We noted no iatrogenic radial nerve palsy, infection or pin migration. The average time for union was 9 weeks and 6 days. This is a reliable, easy to perform and low cost technique, which provides a good stabilization of the fracture site, allowing early mobilization with excellent functional outcomes.

Gauge Invariant Framework for Shape Analysis of Surfaces.

This paper describes a novel framework for computing geodesic paths in shape spaces of spherical surfaces under an elastic Riemannian metric. The novelty lies in defining this Riemannian metric directly on the quotient (shape) space, rather than inheriting it from pre-shape space, and using it to formulate a path energy that measures only the normal components of velocities along the path. In other words, this paper defines and solves for geodesics directly on the shape space and avoids complications resulting from the quotient operation. This comprehensive framework is invariant to arbitrary parameterizations of surfaces along paths, a phenomenon termed as gauge invariance. Additionally, this paper makes a link between different elastic metrics used in the computer science literature on one hand, and the mathematical literature on the other hand, and provides a geometrical interpretation of the terms involved. Examples using real and simulated 3D objects are provided to help illustrate the main ideas.

3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold.

Recognizing human actions in 3-D video sequences is an important open problem that is currently at the heart of many research domains including surveillance, natural interfaces and rehabilitation. However, the design and development of models for action recognition that are both accurate and efficient is a challenging task due to the variability of the human pose, clothing and appearance. In this paper, we propose a new framework to extract a compact representation of a human action captured through a depth sensor, and enable accurate action recognition. The proposed solution develops on fitting a human skeleton model to acquired data so as to represent the 3-D coordinates of the joints and their change over time as a trajectory in a suitable action space. Thanks to such a 3-D joint-based framework, the proposed solution is capable to capture both the shape and the dynamics of the human body, simultaneously. The action recognition problem is then formulated as the problem of computing the similarity between the shape of trajectories in a Riemannian manifold. Classification using k-nearest neighbors is finally performed on this manifold taking advantage of Riemannian geometry in the open curve shape space. Experiments are carried out on four representative benchmarks to demonstrate the potential of the proposed solution in terms of accuracy/latency for a low-latency action recognition. Comparative results with state-of-the-art methods are reported.