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BackgroundPersistent motor or vocal tic disorder (PMVT) has been hypothesized to be a forme fruste of Tourette syndrome (TS). Although the primary diagnostic criterion for PMVT (presence of motor or vocal tics, but not both) is clear, less is known about its clinical presentation.ObjectiveThe goals of this study were to compare the prevalence and number of comorbid psychiatric disorders, tic severity, age at tic onset, and family history for TS and PMVT.MethodsWe analyzed data from two independent cohorts using generalized linear equations and confirmed our findings using meta-analyses, incorporating data from previously published literature.ResultsRates of obsessive-compulsive disorder (OCD) and attention deficit hyperactivity disorder (ADHD) were lower in PMVT than in TS in all analyses. Other psychiatric comorbidities occurred with similar frequencies in PMVT and TS in both cohorts, although meta-analyses suggested lower rates of most psychiatric disorders in PMVT compared with TS. ADHD and OCD increased the odds of comorbid mood, anxiety, substance use, and disruptive behaviors, and accounted for observed differences between PMVT and TS. Age of tic onset was approximately 2 years later, and tic severity was lower in PMVT than in TS. First-degree relatives had elevated rates of TS, PMVT, OCD, and ADHD compared with population prevalences, with rates of TS equal to or greater than PMVT rates.ConclusionsOur findings support the hypothesis that PMVT and TS occur along a clinical spectrum in which TS is a more severe and PMVT a less severe manifestation of a continuous neurodevelopmental tic spectrum disorder. © 2021 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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Deep learning (DL) models have shown promise for the accurate detection of atrial fibrillation (AF) from electrocardiogram/photoplethysmography (ECG/PPG) data, yet deploying these on resource-constrained wearable devices remains challenging. This study proposes integrating a customized channel attention mechanism to compress DL neural networks for AF detection, allowing the model to focus only on the most salient time-series features. The results demonstrate that applying compression through channel attention significantly reduces the total number of model parameters and file size while minimizing loss in detection accuracy. Notably, after compression, performance increases for certain model variants in key AF databases (ADB and C2017DB). Moreover, analyzing the learned channel attention distributions after training enhances the explainability of the AF detection models by highlighting the salient temporal ECG/PPG features most important for its diagnosis. Overall, this research establishes that integrating attention mechanisms is an effective strategy for compressing large DL models, making them deployable on low-power wearable devices. We show that this approach yields compressed, accurate, and explainable AF detectors ideal for wearables. Incorporating channel attention enables simpler yet more accurate algorithms that have the potential to provide clinicians with valuable insights into the salient temporal biomarkers of AF. Our findings highlight that the use of attention is an important direction for the future development of efficient, high-performing, and interpretable AF screening tools for wearable technology.

Invasive mucormycosis is an aggressive fungal infection characterized by rapid progression, primarily impacting immunocompromised individuals. Herein, we report a case of splenic infarction in association with gastrointestinal fistula and brain abscess as a rare presentation of mucormycosis biopsy, proven in a 56-year-old patient diagnosed with acute myeloid leukemia. The patient initially sought medical attention with a 3-week history of fever, night sweats, and malaise. Considering the chest computed tomography findings compatible with fungal disease and neutropenia, he underwent broad-spectrum antifungal therapy. Following the occurrence of splenic infarctions and a gastric fistula, the patient underwent a partial gastrectomy and splenectomy. Despite the interventions, the patient did not have a successful outcome and died on the second postoperative day. This case highlights the importance of timely suspicion, immediate antifungal therapy, and surgical intervention to improve the survival prospects of patients with multifaceted manifestations of mucormycosis.

Los criterios diagnósticos, los tratamientos en el momento de la admisión y los fármacos utilizados en pacientes con síndrome coronario agudo están bien definidos en innumerables guías. Sin embargo, existe incertidumbre acerca de las medidas para recomendar durante la planificación del egreso de los pacientes. Este documento reúne las evidencias más recientes y el tratamiento estandarizado y óptimo para los pacientes al momento del egreso de una hospitalización por un síndrome coronario agudo, para un cuidado integral y seguro en la transición del paciente entre la atención del evento agudo y el cuidado ambulatorio, con el objetivo de optimizar la recuperación de miocardio viable, garantizar la prevención secundaria más adecuada, reducir el riesgo de un nuevo evento coronario y la mortalidad, así como la adecuada reinserción de los pacientes en la vida cotidiana.

Neurenteric cysts (NECs) are rare congenital, benign lesions of the central nervous system (CNS), predominantly located within the spinal cord. However, they may also occur less frequently within the brainstem, fourth ventricle, or cerebellopontine angle (CPA). Originating from anomalous interactions between embryonic layers, NECs are recognized for their potential to compress adjacent structures. We report a unique case of disseminated NECs exhibiting few to absent symptoms, which represents an unusual presentation of this disease, with only six similar reports in the literature. A 22-year-old female presented to our institute with chronic headaches resistant to nonsteroidal anti-inflammatory drugs (NSAIDs). Initially treated for intracranial hypertension (ICH) secondary to a cyst in the quadrigeminal cistern at the age of 17 via neuroendoscopic surgery and subsequent ventriculoperitoneal shunting, she experienced transient relief. However, follow-up at 22 years of age revealed multiple cysts in the basal and spinal cisterns, with MRI findings suggestive of neurocysticercosis. Despite treatment with albendazole and corticosteroids, subsequent MRIs showed no change in the size or number of the cysts. Six years later, symptoms had worsened, previously identified cysts had grown, and the detection of new cysts prompted surgical intervention. Histopathological examination confirmed the presence of NECs. This case highlights the diagnostic challenges posed by NECs, especially in regions endemic for neurocysticercosis, where clinical and radiological findings may initially suggest this condition. It underscores the importance of considering NECs in the differential diagnosis of cystic lesions in the CNS, even in the absence of typical symptoms of spinal cord compression. The recurrence and spread of NECs post-treatment demand a comprehensive management approach, encompassing surgical intervention and close monitoring.

BackgroundIn the burgeoning area of clinical digital phenotyping research, there is a dearth of literature that details methodology, including the key challenges and dilemmas in developing and implementing a successful architecture for technological infrastructure, patient engagement, longitudinal study participation, and successful reporting and analysis of diverse passive and active digital data streams. ObjectiveThis article provides a narrative rationale for our study design in the context of the current evidence base and best practices, with an emphasis on our initial lessons learned from the implementation challenges and successes of this digital phenotyping study. MethodsWe describe the design and implementation approach for a digital phenotyping pilot feasibility study with attention to synthesizing key literature and the reasoning for pragmatic adaptations in implementing a multisite study encompassing distinct geographic and population settings. This methodology was used to recruit patients as study participants with a clinician-validated diagnostic history of unipolar depression, bipolar I disorder, or bipolar II disorder, or healthy controls in 2 geographically distinct health care systems for a longitudinal digital phenotyping study of mood disorders. ResultsWe describe the feasibility of a multisite digital phenotyping pilot study for patients with mood disorders in terms of passively and actively collected phenotyping data quality and enrollment of patients. Overall data quality (assessed as the amount of sensor data obtained vs expected) was high compared to that in related studies. Results were reported on the relevant demographic features of study participants, revealing recruitment properties of age (mean subgroup age ranged from 31 years in the healthy control subgroup to 38 years in the bipolar I disorder subgroup), sex (predominance of female participants, with 7/11, 64% females in the bipolar II disorder subgroup), and smartphone operating system (iOS vs Android; iOS ranged from 7/11, 64% in the bipolar II disorder subgroup to 29/32, 91% in the healthy control subgroup). We also described implementation considerations around digital phenotyping research for mood disorders and other psychiatric conditions. ConclusionsDigital phenotyping in affective disorders is feasible on both Android and iOS smartphones, and the resulting data quality using an open-source platform is higher than that in comparable studies. While the digital phenotyping data quality was independent of gender and race, the reported demographic features of study participants revealed important information on possible selection biases that may result from naturalistic research in this domain. We believe that the methodology described will be readily reproducible and generalizable to other study settings and patient populations given our data on deployment at 2 unique sites.

Selection in the Iowa Stiff Stalk Synthetic (BSSS) maize population for high yield, grain moisture, and root and stalk lodging has indirectly modified plant architecture traits that are important for adaptation to high plant density. In this study, we developed doubled haploid (DH) lines from the BSSS maize population in the earliest cycle of recurrent selection (BSSS), cycle 17 of reciprocal recurrent selection, [BSSS(R)17] and the cross between the two cycles [BSSS/BSSS(R)C17]. We aimed to determine the phenotypic variation and changes in agronomic traits that have occurred through the recurrent selection program in this population and to identify genes or regions in the genome associated with the plant architecture changes observed in the different cycles of selection. We conducted a per se evaluation of DH lines focusing on high heritability traits important for adaptation to high planting density and grain yield. Trends for reducing flowering time, anthesis-silking interval, ear height, and the number of primary tassel branches in BSSS(R)17 DH lines compared to BSSS and BSSS/BSSS(R)C17 DH lines were observed. Additionally, the BSSS(R)C17 DH lines showed more upright flag leaf angles. Using the entire panel of DH lines increased the number of SNP markers identified within candidate genes associated with plant architecture traits. The genomic regions identified for plant architecture traits in this study may help to elucidate the genetic basis of these traits and facilitate future work about marker-assisted selection or map-based cloning in maize breeding programs.

The evolutionarily conserved splicing regulator neuro-oncological ventral antigen 1 (NOVA1) plays a key role in neural development and function. NOVA1 also includes a protein-coding difference between the modern human genome and Neanderthal and Denisovan genomes. To investigate the functional importance of an amino acid change in humans, we reintroduced the archaic allele into human induced pluripotent cells using genome editing and then followed their neural development through cortical organoids. This modification promoted slower development and higher surface complexity in cortical organoids with the archaic version of NOVA1 Moreover, levels of synaptic markers and synaptic protein coassociations correlated with altered electrophysiological properties in organoids expressing the archaic variant. Our results suggest that the human-specific substitution in NOVA1, which is exclusive to modern humans since divergence from Neanderthals, may have had functional consequences for our species' evolution.

Response to lithium varies widely between individuals with bipolar disorder (BD). Polygenic risk scores (PRSs) can uncover pharmacogenomics effects and may help predict drug response. Patients (N = 2,510) with BD were assessed for long-term lithium response in the Consortium on Lithium Genetics using the Retrospective Criteria of Long-Term Treatment Response in Research Subjects with Bipolar Disorder score. PRSs for attention-deficit/hyperactivity disorder (ADHD), major depressive disorder (MDD), and schizophrenia (SCZ) were computed using lassosum and in a model including all three PRSs and other covariates, and the PRS of ADHD (β = -0.14; 95% confidence interval [CI]: -0.24 to -0.03; p value = 0.010) and MDD (β = -0.16; 95% CI: -0.27 to -0.04; p value = 0.005) predicted worse quantitative lithium response. A higher SCZ PRS was associated with higher rates of medication nonadherence (OR = 1.61; 95% CI: 1.34-1.93; p value = 2e-7). This study indicates that genetic risk for ADHD and depression may influence lithium treatment response. Interestingly, a higher SCZ PRS was associated with poor adherence, which can negatively impact treatment response. Incorporating genetic risk of ADHD, depression, and SCZ in combination with clinical risk may lead to better clinical care for patients with BD.

Lithium is the gold standard therapy for Bipolar Disorder (BD) but its effectiveness differs widely between individuals. The molecular mechanisms underlying treatment response heterogeneity are not well understood, and personalized treatment in BD remains elusive. Genetic analyses of the lithium treatment response phenotype may generate novel molecular insights into lithium's therapeutic mechanisms and lead to testable hypotheses to improve BD management and outcomes. We used fixed effect meta-analysis techniques to develop meta-analytic polygenic risk scores (MET-PRS) from combinations of highly correlated psychiatric traits, namely schizophrenia (SCZ), major depression (MD) and bipolar disorder (BD). We compared the effects of cross-disorder MET-PRS and single genetic trait PRS on lithium response. For the PRS analyses, we included clinical data on lithium treatment response and genetic information for n = 2283 BD cases from the International Consortium on Lithium Genetics (ConLi+Gen; www.ConLiGen.org ). Higher SCZ and MD PRSs were associated with poorer lithium treatment response whereas BD-PRS had no association with treatment outcome. The combined MET2-PRS comprising of SCZ and MD variants (MET2-PRS) and a model using SCZ and MD-PRS sequentially improved response prediction, compared to single-disorder PRS or to a combined score using all three traits (MET3-PRS). Patients in the highest decile for MET2-PRS loading had 2.5 times higher odds of being classified as poor responders than patients with the lowest decile MET2-PRS scores. An exploratory functional pathway analysis of top MET2-PRS variants was conducted. Findings may inform the development of future testing strategies for personalized lithium prescribing in BD.

Lithium remains the gold standard for the treatment of bipolar disorder (BD); however, its use has declined over the years mainly due to the side effects and the subjective experience of cognitive numbness reported by patients. In the present study, we aim to methodically test the effects of lithium on neurocognitive functioning in the largest single cohort (n = 262) of BD patients reported to date by harnessing the power of a multi-site, ongoing clinical trial of lithium monotherapy. At the cross-sectional level, multivariate analysis of covariance (MANCOVA) was conducted to examine potential group differences across neurocognitive tests [California Verbal Learning Test (CVLT trials 1-5,CVLT delayed recall), Wechsler Digit Symbol, Trail-making Test parts A and B (TMT-A; TMT-B), and a global cognition index]. At the longitudinal level, on a subset of patients (n = 88) who achieved mood stabilization with lithium monotherapy, we explored the effect of lithium treatment across time on neurocognitive functioning. There were no differences at baseline between BD patients that were taking lithium compared with those that were not. At follow-up a significant neurocognitive improvement in the global cognitive index score [F = 31.69; p

Quantifying changes in Earth's ice sheets and identifying the climate drivers are central to improving sea level projections. We provide unified estimates of grounded and floating ice mass change from 2003 to 2019 using NASA's Ice, Cloud and land Elevation Satellite (ICESat) and ICESat-2 satellite laser altimetry. Our data reveal patterns likely linked to competing climate processes: Ice loss from coastal Greenland (increased surface melt), Antarctic ice shelves (increased ocean melting), and Greenland and Antarctic outlet glaciers (dynamic response to ocean melting) was partially compensated by mass gains over ice sheet interiors (increased snow accumulation). Losses outpaced gains, with grounded-ice loss from Greenland (200 billion tonnes per year) and Antarctica (118 billion tonnes per year) contributing 14 millimeters to sea level. Mass lost from West Antarctica's ice shelves accounted for more than 30% of that region's total.

General personality dimensions are associated with clinical severity and treatment response in individuals with depression and many anxiety disorders, but little is known about these relationships in individuals with obsessive-compulsive disorder (OCD). Individuals in the current study included 705 adults with OCD who had participated in family and genetic studies of the disorder. Participants self-completed the Neuroticism, Extraversion, Openness Personality Inventory or Neuroticism, Extraversion, Openness Five-Factor Inventory-3. Relationships between personality scores, and subjective impairment and OCD treatment response, were evaluated. The odds of subjective impairment increased with (unit increase in) the neuroticism score (odds ratio, OR = 1.03; 95% CI = 1.01-1.04; p < 0.01) and decreased with extraversion scores (OR = 0.98; 95% CI = 0.96-0.99; p < 0.01). The odds of reporting a good response to serotonin/selective serotonin reuptake inhibitors (OR = 1.02; 95% CI = 1.01-1.04; p < 0.01) or cognitive behavioural therapy (OR = 1.03; 95% CI = 1.01-1.05; p < 0.01) increased with the extraversion score. The magnitude of these relationships did not change appreciably after adjusting for other clinical features related to one or more of the personality dimensions. The findings suggest that neuroticism and extraversion are associated with subjective impairment, and that extraversion is associated with self-reported treatment response, in individuals with OCD. © 2019 John Wiley & Sons, Ltd.

Autonomous mobile robots use computational techniques of great complexity so that to allow navigation in various types of dynamic environments, avoiding collisions with obstacles and always seeking to optimize the best route, ultimately enabling them to operate in a safe and precise manner. In order for navigation at this level to be possible, a variety of computer vision and intelligent sensing techniques are used. The potential of an intelligent computer vision system to detect and predict the actions of dynamic agents on the streets is applied to increase traffic safety with intelligent robotic vehicles. In this paper we present a systematic review of computer vision models for the detection and tracking of obstacles in traffic environments. Specifically, we cover works involving 2D and 3D (stereo vision) data fusion for both internal and external perception, as well as current trends regarding efficient model design and temporally-aware architectures. We provide a thorough discussion on the main positive and negative points of the state-of-the-art in Visual Robotic Attention, as well as share our experience and contributions in applying visual perception for external obstacle detection and tracking, and internal (driver) monitoring. The results presented should serve as a compilation of the history of visual perception for autonomous mobile robots (specifically, Advanced Driver Assistance Systems (ADAS) and Autonomous Vehicles), thus providing the reader with a comprehensive basis on both the main contributions and the state-of-the-art in the field.

Teaching Machine Learning in school helps students to be better prepared for a society rapidly changing due to the impact of Artificial Intelligence. This requires age-appropriate tools that allow students to develop a comprehensive understanding of Machine Learning in order to become creators of smart solutions. Following the trend of visual languages for introducing algorithms and programming in K-12, we present a ten-year systematic mapping of emerging visual tools that support the teaching of Machine Learning at this educational stage and analyze the tools concerning their educational characteristics, support for the development of ML models as well as their deployment and how the tools have been developed and evaluated. As a result, we encountered 16 tools targeting students mostly as part of short duration extracurricular activities. Tools mainly support the interactive development of ML models for image recognition tasks using supervised learning covering basic steps of the ML process. Being integrated into popular block-based programming languages (primarily Scratch and App Inventor), they also support the deployment of the created ML models as part of games or mobile applications. Findings indicate that the tools can effectively leverage students' understanding of Machine Learning, however, further studies regarding the design of the tools concerning educational aspects are required to better guide their effective adoption in schools and their enhancement to support the learning process more comprehensively.

Abstract Because many vessels use the Automatic Identification System (AIS) to broadcast GPS positions, recent advances in satellite technology have enabled us to map global fishing activity. Understanding of human activity at sea, however, is limited because an unknown number of vessels do not broadcast AIS. Those vessels can be detected by satellite-based Synthetic Aperture Radar (SAR) imagery, but this technology has not yet been deployed at scale to estimate the size of fleets in the open ocean. Here we combine SAR and AIS for large-scale open ocean monitoring, developing methods to match vessels with AIS to vessels detected with SAR and estimate the number of non-broadcasting vessels. We reveal that, between September 2019 and January 2020, non-broadcasting vessels accounted for about 35% of the longline activity north of Madagascar and 10% of activity near French Polynesia and Kiribati’s Line Islands. We further demonstrate that this method could monitor half of the global longline activity with about 70 SAR images per week, allowing us to track human activity across the oceans.

Computing education in schools faces several problems, such as a lack of computing teachers and time in an already overloaded curriculum. A solution can be a multidisciplinary approach, integrating the teaching of computing within other subjects, creating the need to motivate teachers from other disciplines to teach computing in middle school. Therefore, the motivation and training of in-service teachers becomes crucial, as they need to have computing content and technological knowledge as well as pedagogical content knowledge. Yet, so far there exist very few training programs. Thus, as part of a comprehensive outreach program, we present a study on a one-day taster workshop for middle school teachers on physical computing education. Participants learn computer programming practice and computational thinking by programming an interactive robot. The workshop also approaches pedagogical aspects for teaching computing and technical issues regarding the installation and preparation of the required hardware/software. Preliminary results of its application with public school teachers in Florianopolis/Brazil are positive, motivating the majority of participants to introduce computing into their classes. However, our results also highlight that in order to enable teacher to apply the workshops effectively, longer training courses and ongoing support is required.

Bacteria of the sister genera Ehrlichia and Anaplasma (Anaplasmataceae) are obligate intracellular Alphaproteobacteria that are transmitted mostly through arthropod vectors. These agents can infect different vertebrate cells, depending on the species involved, and can cause diseases in animals and humans. In this study, we evaluated the presence of Anaplasmataceae bacteria in Amblyomma calcaratum ticks collected from a road-killed Tamandua tetradactyla in the Rainforest ecoregion in Argentina. All samples were screened for Anaplasmataceae DNA using a real-time PCR assay targeting the 16S rRNA gene. Evidence of Anaplasmataceae DNA was detected in three out of thirty-nine Am. calcaratum ticks. Phylogenetic analysis of a portion of 16S rRNA gene positioned one sample (Ehrlichia sp. strain Ac124) with Ehrlichia sequences and the other two samples with Anaplasma sequences; Anaplasma sp. strain Ac145 close to Anaplasma odocoilei and Anaplasma sp. strain Ac152 in an ancestral position to most Anaplasma species. The groEL sequence obtained showed that Ehrlichia sp. strain Ac124 was phylogenetically related to Ehrlichia sp. strain Iberá reported infecting Amblyomma tigrinum from Iberá wetlands in Argentina. Phylogenetic analysis using the rpoB sequence positioned Anaplasma sp. strain Ac145 close to the canine pathogen Anaplasma platys, while Anaplasma sp. strain Ac152 was positioned close to the bovine pathogen Anaplasma marginale.In this study, three Anaplasmataceae agents were detected in adults of Am. calcaratum associated with a T. tetradactyla. These results suggest that the number of Anaplasmataceae species, as well as their distribution, is largely unknown.