The introduction of the bacterium Wolbachia (wMel strain) into Aedes aegypti mosquitoes reduces their capacity to transmit dengue and other arboviruses. Evidence of a reduction in dengue case ...incidence following field releases of wMel-infected Ae. aegypti has been reported previously from a cluster randomised controlled trial in Indonesia, and quasi-experimental studies in Indonesia and northern Australia.
Following pilot releases in 2015-2016 and a period of intensive community engagement, deployments of adult wMel-infected Ae. aegypti mosquitoes were conducted in Niterói, Brazil during 2017-2019. Deployments were phased across four release zones, with a total area of 83 km2 and a residential population of approximately 373,000. A quasi-experimental design was used to evaluate the effectiveness of wMel deployments in reducing dengue, chikungunya and Zika incidence. An untreated control zone was pre-defined, which was comparable to the intervention area in historical dengue trends. The wMel intervention effect was estimated by controlled interrupted time series analysis of monthly dengue, chikungunya and Zika case notifications to the public health surveillance system before, during and after releases, from release zones and the control zone. Three years after commencement of releases, wMel introgression into local Ae. aegypti populations was heterogeneous throughout Niterói, reaching a high prevalence (>80%) in the earliest release zone, and more moderate levels (prevalence 40-70%) elsewhere. Despite this spatial heterogeneity in entomological outcomes, the wMel intervention was associated with a 69% reduction in dengue incidence (95% confidence interval 54%, 79%), a 56% reduction in chikungunya incidence (95%CI 16%, 77%) and a 37% reduction in Zika incidence (95%CI 1%, 60%), in the aggregate release area compared with the pre-defined control area. This significant intervention effect on dengue was replicated across all four release zones, and in three of four zones for chikungunya, though not in individual release zones for Zika.
We demonstrate that wMel Wolbachia can be successfully introgressed into Ae. aegypti populations in a large and complex urban setting, and that a significant public health benefit from reduced incidence of Aedes-borne disease accrues even where the prevalence of wMel in local mosquito populations is moderate and spatially heterogeneous. These findings are consistent with the results of randomised and non-randomised field trials in Indonesia and northern Australia, and are supportive of the Wolbachia biocontrol method as a multivalent intervention against dengue, chikungunya and Zika.
Coronavirus (Covid-19) pandemic has imposed a complete shut-down of face-to-face teaching to universities and schools, forcing a crash course for online learning plans and technology for students and ...faculty. In the midst of this unprecedented crisis, video conferencing platforms (e.g., Zoom, WebEx, MS Teams) and learning management systems (LMSs), like Moodle, Blackboard and Google Classroom, are being adopted and heavily used as online learning environments (OLEs). However, as such media solely provide the platform for e-interaction, effective methods that can be used to predict the learner's behavior in the OLEs, which should be available as supportive tools to educators and metacognitive triggers to learners. Here we show, for the first time, that Deep Learning techniques can be used to handle LMS users' interaction data and form a novel predictive model, namely DeepLMS, that can forecast the quality of interaction (QoI) with LMS. Using Long Short-Term Memory (LSTM) networks, DeepLMS results in average testing Root Mean Square Error (RMSE) Formula: see text, and average correlation coefficient between ground truth and predicted QoI values Formula: see text Formula: see text, when tested on QoI data from one database pre- and two ones during-Covid-19 pandemic. DeepLMS personalized QoI forecasting scaffolds user's online learning engagement and provides educators with an evaluation path, additionally to the content-related assessment, enriching the overall view on the learners' motivation and participation in the learning process.
The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven ...efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I
= 79.49%) and 0.83 (CI 0.79-0.87, I
= 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I
= 79.10%) and 0.87 (95% CI 0.80-0.93, I
= 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I
= 50.39%) and 0.82 (95% CI 0.70-0.94, I
= 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.
Objective: Parkinson's Disease (PD) is a progressive neurodegenerative disorder, manifesting with subtle early signs, which, often hinder timely and early diagnosis and treatment. The development of ...accessible, technology-based methods for longitudinal PD symptoms tracking in daily living, offers the potential for transforming disease assessment and accelerating diagnosis. Methods: A privacy-aware method for classifying patients and healthy controls (HC), on the grounds of speech impairment present in PD, is proposed. Voice features from running speech signals were extracted from passively-captured recordings over voice calls. Language-aware training of multiple- and single-instance learning classifiers was employed to fuse and predict on voice features and demographic data from a multilingual cohort of 498 subjects (392/106 self-reported HC/PD patients). Results: By means of leave-one-subject-out cross-validation, the best-performing models yielded 0.69/0.68/0.63/0.83 area under the Receiver Operating Characteristic curve (AUC) for the binary classification of PD patient vs. HC in sub-cohorts of English/Greek/German/Portuguese-speaking subjects, respectively. Out-of sample testing of the best performing models was conducted in an additional dataset, generated by 63 clinically-assessed subjects (24/39 HC/early PD patients). Testing has resulted in 0.84/0.93/0.83 AUC for the English/Greek/German-speaking sub-cohorts, respectively. Conclusions: The proposed approach outperforms other methods proposed for language-aware PD detection considering the ecological validity of the voice data. Significance: This paper introduces for the first time a high-frequency, privacy-aware and unobtrusive PD screening tool based on analysis of voice samples captured during routine phone calls.
Advances in consumer electronics, alongside the fields of microfluidics and nanotechnology have brought to the fore low-cost wearable/portable smart devices. Although numerous smart devices that ...track digital biomarkers have been successfully translated from bench-to-bedside, only a few follow the same fate when it comes to track traditional biomarkers. Current practices still involve laboratory-based tests, followed by blood collection, conducted in a clinical setting as they require trained personnel and specialized equipment. In fact, real-time, passive/active and robust sensing of physiological and behavioural data from patients that can feed artificial intelligence (AI)-based models can significantly improve decision-making, diagnosis and treatment at the point-of-procedure, by circumventing conventional methods of sampling, and in person investigation by expert pathologists, who are scarce in developing countries. This review brings together conventional and digital biomarker sensing through portable and autonomous miniaturized devices. We first summarise the technological advances in each field vs the current clinical practices and we conclude by merging the two worlds of traditional and digital biomarkers through AI/ML technologies to improve patient diagnosis and treatment. The fundamental role, limitations and prospects of AI in realizing this potential and enhancing the existing technologies to facilitate the development and clinical translation of “point-of-care” (POC) diagnostics is finally showcased.
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Traditional methods of vector control have proven insufficient to reduce the alarming incidence of dengue, Zika, and chikungunya in endemic countries. The bacterium symbiont
Wolbachia
has emerged as ...an efficient pathogen-blocking and self-dispersing agent that reduces the vectorial potential of
Aedes aegypti
populations and potentially impairs arboviral disease transmission. In this work, we report the results of a large-scale
Wolbachia
intervention in Ilha do Governador, Rio de Janeiro, Brazil.
w
Mel-infected adults were released across residential areas between August 2017 and March 2020. Over 131 weeks, including release and post-release phases, we monitored the
w
Mel prevalence in field specimens and analyzed introgression profiles of two assigned intervention areas, RJ1 and RJ2. Our results revealed that
w
Mel successfully invaded both areas, reaching overall infection rates of 50–70% in RJ1 and 30–60% in RJ2 by the end of the monitoring period. At the neighborhood-level,
w
Mel introgression was heterogeneous in both RJ1 and RJ2, with some profiles sustaining a consistent increase in infection rates and others failing to elicit the same. Correlation analysis revealed a weak overall association between RJ1 and RJ2 (
r
= 0.2849,
p
= 0.0236), and an association at a higher degree when comparing different deployment strategies, vehicle or backpack-assisted, within RJ1 (
r
= 0.4676,
p
< 0.0001) or RJ2 (
r
= 0.6263,
p
< 0.0001). The frequency
knockdown resistance
(
kdr
) alleles in
w
Mel-infected specimens from both areas were consistently high over this study. Altogether, these findings corroborate that
w
Mel can be successfully deployed at large-scale as part of vector control intervention strategies and provide the basis for imminent disease impact studies in Southeastern Brazil.
Neurologists nowadays no longer view neurodegenerative diseases, like Parkinson's and Alzheimer's disease, as single entities, but rather as a spectrum of multifaceted symptoms with heterogeneous ...progression courses and treatment responses. The definition of the naturalistic behavioral repertoire of early neurodegenerative manifestations is still elusive, impeding early diagnosis and intervention. Central to this view is the role of artificial intelligence (AI) in reinforcing the depth of phenotypic information, thereby supporting the paradigm shift to precision medicine and personalized healthcare. This suggestion advocates the definition of disease subtypes in a new biomarker-supported nosology framework, yet without empirical consensus on standardization, reliability and interpretability. Although the well-defined neurodegenerative processes, linked to a triad of motor and non-motor preclinical symptoms, are detected by clinical intuition, we undertake an unbiased data-driven approach to identify different patterns of neuropathology distribution based on the naturalistic behavior data inherent to populations in-the-wild. We appraise the role of remote technologies in the definition of digital phenotyping specific to brain-, body- and social-level neurodegenerative subtle symptoms, emphasizing inter- and intra-patient variability powered by deep learning. As such, the present review endeavors to exploit digital technologies and AI to create disease-specific phenotypic explanations, facilitating the understanding of neurodegenerative diseases as "bio-psycho-social" conditions. Not only does this translational effort within explainable digital phenotyping foster the understanding of disease-induced traits, but it also enhances diagnostic and, eventually, treatment personalization.
Neurodegenerative Parkinson's Disease (PD) is one of the common incurable diseases among the elderly. Clinical assessments are characterized as standardized means for PD diagnosis. However, relying ...on medical evaluation of a patient's status can be subjective to physicians' experience, making the assessment process susceptible to human errors. The use of ICT-based tools for capturing the status of patients with PD can provide more objective and quantitative metrics. In this vein, the Personalized Serious Game Suite (PGS) and intelligent Motor Assessment Tests (iMAT), produced within the i-PROGNOSIS European project (www.i-prognosis.eu), are explored in the current study. More specifically, data from 27 patients with PD at Stage 1 (9) and Stage 3 (18) produced from their interaction with PGS/iMAT are analyzed. Five feature vector (FV) scenarios are set, including features from PGS or iMAT scores or their combination, after also taking into consideration the age of patients with PD. These FVs are fed into three machine learning classifiers, i.e., K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF), to infer the stage of each patient with PD. A Leave-One-Out Cross-Validation (LOOCV) method is adopted for testing the classification performance. The experimental results show that a high (>90%) classification accuracy is achieved from both data sources (PGS/iMAT), justifying the effectiveness of PGS/iMAT to efficiently reflect the motor skill status of patients with PD and further potentiating PGS/iMAT enhancement with a machine learning a part to infer for the stage of patients with PD. Clearly, this integrated approach provides new opportunities for remote monitoring of the stage of patients with PD, contributing to a more efficient organization and set up of personalized interventions.
•Fuzzy modeling of LMS Moodle users’ Quality of Interaction within b-learning.•FCM-QoI model efficiently identifies LMS interaction trends.•QoI dependencies exist with the time-period of the LMS ...use.•FCM-QoI supports better understanding of the LMS users’ interaction behavior.
Learning Management Systems (LMSs) under blended (b-) learning modality can efficiently support Online Learning Environments (OLEs) at Higher Education Institutions (HEIs). Mining of LMS users’ data, involving artificial intelligence and incertitude modeling, e.g., via fuzzy logic, is a fundamental challenge. This study addresses the hypothesis that the structural characteristics of a Fuzzy Cognitive Map (FCM) can efficiently model the way LMS users interact with it, by estimating their Quality of Interaction (QoI) within a b-learning context. This study introduces the FCM-QoI model (combined with a model visualizer) consisting of 14 input-one output concepts, dependences and trends, considering one academic year of the LMS use from 75 professors and 1037 students at a HEI. The experimental findings have shown that the proposed FCM-QoI model can provide concepts interconnection and causal dependencies representation of Moodle LMS users’ QoI, revealing perspectives of its evolution both at a micro and macro analysis level. Moreover, the FCM-QoI model significantly adds to the evaluation and analysis of the QoI influential concepts’ contribution to self-sustained cycles (static analysis) and their alterations, when the time period of the LMS use is considered (dynamic analysis), showing potential to increase the flexibility and adaptivity of the QoI modeling and feedback approaches. Clearly, based on the FCM-QoI model, pedagogical instructors and decision-makers of HEIs could be assisted to holistically visualize, understand and assess OLEs stakeholders’ needs within the teaching and learning practices.
Design dynamics that evolve during a designer's prototyping process encapsulate important insights about the way the designer is using his or her knowledge, creativity, and reflective thinking. ...Nevertheless, the capturing of such dynamics is not always an easy task, as they are built through alternations between the self-first and self-third person views.
This study aimed at introducing a conceptual framework, namely 2D-ME, to provide an explainable domain that could express the dynamics across the design timeline during a prototyping process of serious games.
Within the 2D-ME framework, the Technological-Pedagogical-Content Knowledge (TPACK), its adaptation to the serious games (TPACK-Game), and the activity theory frameworks were combined to produce dynamic constructs that incorporate self-first and self-third person extension of the TPACK-Game to Games TPACK, rules, division of labor, and object. The dynamic interplay between such constructs was used as an adaptation engine within an optimization prototype process, so each sequential version of the latter could converge to the designer's initial idea of the serious game. Moreover, higher-order thinking is scaffolded with the internal Activity Interview Script proposed in this paper.
An experimental case study of the application of the 2D-ME conceptual framework in the design of a light reflection game was showcased, revealing all the designer's dynamics, both from internal (via a diary) and external (via the prototype version) views. The findings of this case study exemplified the convergence of the prototyping process to an optimized output, by minimizing the mean square error between the conceptual (initial and updated) idea of the prototype, following explainable and tangible constructs within the 2D-ME framework.
The generic structure of the proposed 2D-ME framework allows its transferability to various levels of expertise in serious games mastering, and it is used both for the designer's process exploration and training of the novice ones.
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