Psychiatric manifestations in moyamoya disease: more than a puff of smoke? a systematic review and a case-reports meta-analysis.

Introduction: Moyamoya disease (MMD) is a life-threatening condition characterized by stenosis of intracranial arteries. Despite the frequency and the impact of psychiatric symptoms on the long-term prognosis and quality of life of MMD patients, no systematic review on this topic exists. Methods: This systematic review and meta-analysis included 41 studies (29 being case reports), from PubMed, Scopus, Embase until 27/3/2023, on MMD patients exhibiting psychiatric symptoms. Results: Despite a fair average quality of the articles, quantitative synthesis through logistic regression was possible only for case reports, due to heterogeneity between the other studies. Psychosis, the most frequent psychiatric symptom reported in case reports, was more frequent in MMD patients with left hemisphere involvement. Neurological symptoms occurrence increased the odds of MMD diagnosis preceding psychiatric symptoms. Psychiatric symptoms are highly prevalent in MMD patients and are relatively often the only presenting symptoms. Discussion: We discuss the diagnostic, therapeutic, and prognostic implications of recognizing and characterizing specific psychiatric symptoms in MMD, outlining preliminary guidelines for targeted pharmacological and psychotherapeutic interventions. Lastly, we outline future research and clinical perspectives, striving to enhance the oft-overlooked psychiatric care for MMD patients and to ameliorate their long-term outcome. Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42023406303.

Automatic vectorization of historical maps: A benchmark.

Shape vectorization is a key stage of the digitization of large-scale historical maps, especially city maps that exhibit complex and valuable details. Having access to digitized buildings, building blocks, street networks and other geographic content opens numerous new approaches for historical studies such as change tracking, morphological analysis and density estimations. In the context of the digitization of Paris atlases created in the 19th and early 20th centuries, we have designed a supervised pipeline that reliably extract closed shapes from historical maps. This pipeline is based on a supervised edge filtering stage using deep filters, and a closed shape extraction stage using a watershed transform. It relies on probable multiple suboptimal methodological choices that hamper the vectorization performances in terms of accuracy and completeness. Objectively investigating which solutions are the most adequate among the numerous possibilities is comprehensively addressed in this paper. The following contributions are subsequently introduced: (i) we propose an improved training protocol for map digitization; (ii) we introduce a joint optimization of the edge detection and shape extraction stages; (iii) we compare the performance of state-of-the-art deep edge filters with topology-preserving loss functions, including vision transformers; (iv) we evaluate the end-to-end deep learnable watershed against Meyer watershed. We subsequently design the critical path for a fully automatic extraction of key elements of historical maps. All the data, code, benchmark results are freely available at https://github.com/soduco/Benchmark_historical_map_vectorization.

A marked point process for modeling lidar waveforms.

Lidar waveforms are 1-D signals representing a train of echoes caused by reflections at different targets. Modeling these echoes with the appropriate parametric function is useful to retrieve information about the physical characteristics of the targets. This paper presents a new probabilistic model based upon a marked point process which reconstructs the echoes from recorded discrete waveforms as a sequence of parametric curves. Such an approach allows to fit each mode of a waveform with the most suitable function and to deal with both, symmetric and asymmetric, echoes. The model takes into account a data term, which measures the coherence between the models and the waveforms, and a regularization term, which introduces prior knowledge on the reconstructed signal. The exploration of the associated configuration space is performed by a reversible jump Markov chain Monte Carlo (RJMCMC) sampler coupled with simulated annealing. Experiments with different kinds of lidar signals, especially from urban scenes, show the high potential of the proposed approach. To further demonstrate the advantages of the suggested method, actual laser scans are classified and the results are reported.