Detecting at-risk mental states for psychosis (ARMS) using machine learning ensembles and facial features.

AIMS: Our study aimed to develop a machine learning ensemble to distinguish "at-risk mental states for psychosis" (ARMS) subjects from control individuals from the general population based on facial data extracted from video-recordings. METHODS: 58 non-help-seeking medication-naïve ARMS and 70 healthy subjects were screened from a general population sample. At-risk status was assessed with the Structured Interview for Prodromal Syndromes (SIPS), and "Subject's Overview" section was filmed (5-10 min). Several features were extracted, e.g., eye and mouth aspect ratio, Euler angles, coordinates from 51 facial landmarks. This elicited 649 facial features, which were further selected using Gradient Boosting Machines (AdaBoost combined with Random Forests). Data was split in 70/30 for training, and Monte Carlo cross validation was used. RESULTS: Final model reached 83 % of mean F1-score, and balanced accuracy of 85 %. Mean area under the curve for the receiver operator curve classifier was 93 %. Convergent validity testing showed that two features included in the model were significantly correlated with Avolition (SIPS N2 item) and expression of emotion (SIPS N3 item). CONCLUSION: Our model capitalized on short video-recordings from individuals recruited from the general population, effectively distinguishing between ARMS and controls. Results are encouraging for large-screening purposes in low-resource settings.

Gesticulation in individuals with at risk mental states for psychosis.

Nonverbal communication (NVC) is a complex behavior that involves different modalities that are impaired in the schizophrenia spectrum, including gesticulation. However, there are few studies that evaluate it in individuals with at-risk mental states (ARMS) for psychosis, mostly in developed countries. Given our prior findings of reduced movement during speech seen in Brazilian individuals with ARMS, we now aim to determine if this can be accounted for by reduced gesticulation behavior. Fifty-six medication-naïve ARMS and 64 healthy controls were filmed during speech tasks. The frequency of specifically coded gestures across four categories (and self-stimulatory behaviors) were compared between groups and tested for correlations with prodromal symptoms of the Structured Interview for Prodromal Syndromes (SIPS) and with the variables previously published. ARMS individuals showed a reduction in one gesture category, but it did not survive Bonferroni's correction. Gesture frequency was negatively correlated with prodromal symptoms and positively correlated with the variables of the amount of movement previously analyzed. The lack of significant differences between ARMS and control contradicts literature findings in other cultural context, in which a reduction is usually seen in at-risk individuals. However, gesture frequency might be a visual proxy of prodromal symptoms, and of other movement abnormalities. Results show the importance of analyzing NVC in ARMS and of considering different cultural and sociodemographic contexts in the search for markers of these states.

Ethical Implications of the Use of Language Analysis Technologies for the Diagnosis and Prediction of Psychiatric Disorders.

Recent developments in artificial intelligence technologies have come to a point where machine learning algorithms can infer mental status based on someone's photos and texts posted on social media. More than that, these algorithms are able to predict, with a reasonable degree of accuracy, future mental illness. They potentially represent an important advance in mental health care for preventive and early diagnosis initiatives, and for aiding professionals in the follow-up and prognosis of their patients. However, important issues call for major caution in the use of such technologies, namely, privacy and the stigma related to mental disorders. In this paper, we discuss the bioethical implications of using such technologies to diagnose and predict future mental illness, given the current scenario of swiftly growing technologies that analyze human language and the online availability of personal information given by social media. We also suggest future directions to be taken to minimize the misuse of such important technologies.

Nurturing care indicators for the Brazilian Early Childhood Friendly Municipal Index (IMAPI).

The Nurturing Care Framework (NCF) calls for establishing a global monitoring and accountability systems for early childhood development (ECD). Major gaps to build low-cost and large-scale ECD monitoring systems at the local level remain. In this manuscript, we describe the process of selecting nurturing care indicators at the municipal level from existing routine information systems to develop the Brazilian Early Childhood Friendly Index (IMAPI). Three methodological steps developed through a participatory decision-making process were followed. First, a literature review identified potential indicators to translate the NCF domains. Four technical panels composed of stakeholders from federal, state and municipal levels were consulted to identify data sources, their availability at the municipal level and the strengths and weakness of each potential indicator. Second, national and international ECD experts participated in two surveys to score, following a SMART approach, the expected performance of each nurturing care indicator. This information was used to develop analytical weights for each indicator. Third, informed by strengths and weaknesses pointed out in the previous steps, the IMAPI team reached consensus on 31 nurturing care indicators across the five NCF domains (Good health [n = 14], Adequate nutrition [4], Responsive caregiving [1], Opportunities for early learning [7] and Security and safety [4]). IMAPI represents the first attempt to select nurturing care indicators at the municipal level using data from existing routine information systems.

Nurturing care indicators for the Brazilian Early Childhood Friendly Municipal Index (IMAPI)

The Nurturing Care Framework (NCF) calls for establishing a global monitoring and accountability systems for early childhood development (ECD). Major gaps to build low-cost and large-scale ECD monitoring systems at the local level remain. In this manuscript, we describe the process of selecting nurturing care indicators at the municipal level from existing routine information systems to develop the Brazilian Early Childhood Friendly Index (IMAPI). Three methodological steps developed through a participatory decision-making process were followed. First, a literature review identified potential indicators to translate the NCF domains. Four technical panels composed of stakeholders from federal, state and municipal levels were consulted to identify data sources, their availability at the municipal level and the strengths and weakness of each potential indicator. Second, national and international ECD experts participated in two surveys to score, following a SMART approach, the expected performance of each nurturing care indicator. This information was used to develop analytical weights for each indicator. Third, informed by strengths and weaknesses pointed out in the previous steps, the IMAPI team reached consensus on 31 nurturing care indicators across the five NCF domains (Good health [n = 14], Adequate nutrition [4], Responsive caregiving [1], Opportunities for early learning [7] and Security and safety [4]). IMAPI represents the first attempt to select nurturing care indicators at the municipal level using data from existing routine information systems.

Preventing epidemics by wearing masks: An application to COVID-19.

The goal of this work is to consider widespread use of face masks as a non-pharmaceutical control strategy for the COVID-19 pandemic. A SEIR model that divides the population into individuals that wear masks and those that do not is considered. After calculating the basic reproductive number by a next generation approach, a criterion for determining when an epidemic can be prevented by the use of masks only and the critical percentage of mask users for disease prevention in the population are derived. The results are then applied to real world data from the United States, Brazil and Italy.

Optimal quarantine strategies for the COVID-19 pandemic in a population with a discrete age structure.

The goal of this work is to study the optimal controls for the COVID-19 epidemic in Brazil. We consider an age-structured SEIRQ model with quarantine compartment, where the controls are the quarantine entrance parameters. We then compare the optimal controls for different quarantine lengths and distributions of the total control cost by assessing their respective reductions in deaths in comparison to the same period without quarantine. The best strategy provides a calendar of when to relax the isolation measures for each age group. Finally, we analyse how a delay in the beginning of the quarantine affects this calendar by changing the initial conditions.

Supplanting resistance of the Mi gene by root-knot nematode in industrial tomato in the Cerrado in Goiás State of Brazil / Suplantação de resistência do gene Mi por nematoide-das-galhas em tomate industrial no Cerrado de Goiás, Brasil

ABSTRACT: Problems with root-knot nematodes, caused by genus Meloidogyne, can be controlled through the introduction of resistance genes in commercial varieties of tomatoes. However, plants of the hybrid 'Heinz 9992' for industrial processing, carrying the Mi gene, were reported with their roots severely infected in experimental cultivation in Goiás State of Brazil with symptoms typical of galls caused by Meloidogyne spp. After dissecting the galls present in the root samples, the females of the nematodes were removed and afterwards, performed the analysis of the perineal pattern and the biochemical characterization by electrophoresis of isoenzymes, besides the pathogenicity test. The pathogen analyzed was identified as Meloidogyne incognita. The pathogenicity test confirmed the supplanting resistance of the tomato hybrid with the Mi gene by this virulent population of M. incognita.

RESUMO: Problemas causados pelos nematoides-das-galhas, pertencentes ao gênero Meloidogyne podem ser controlados com a introdução de genes de resistência em cultivares comerciais de tomateiro. No entanto, plantas do híbrido 'Heinz 9992' adequadas ao processamento industrial, portadoras do gene Mi, foram encontradas com suas raízes severamente infectadas, em um cultivo experimental no município de Morrinhos (GO) com sintomas típicos de galhas causadas por Meloidogyne spp. Após dissecar as galhas presentes nas amostras de raízes, as fêmeas do nematoide foram retiradas para a análise do padrão perineal e a caracterização bioquímica por eletroforese de isoenzimas, além do teste de patogenicidade. O patógeno em estudo foi identificado como Meloidogyne incognita. O teste de patogenicidade confirmou que ocorreu a suplantação de resistência do híbrido de tomateiro, portador do gene Mi, por esta população virulenta de M. incognita.

A Methodological Approach for Assessing Amplified Reflection Distributed Denial of Service on the Internet of Things.

Concerns about security on Internet of Things (IoT) cover data privacy and integrity, access control, and availability. IoT abuse in distributed denial of service attacks is a major issue, as typical IoT devices' limited computing, communications, and power resources are prioritized in implementing functionality rather than security features. Incidents involving attacks have been reported, but without clear characterization and evaluation of threats and impacts. The main purpose of this work is to methodically assess the possible impacts of a specific class-amplified reflection distributed denial of service attacks (AR-DDoS)-against IoT. The novel approach used to empirically examine the threat represented by running the attack over a controlled environment, with IoT devices, considered the perspective of an attacker. The methodology used in tests includes that perspective, and actively prospects vulnerabilities in computer systems. This methodology defines standardized procedures for tool-independent vulnerability assessment based on strategy, and the decision flows during execution of penetration tests (pentests). After validation in different scenarios, the methodology was applied in amplified reflection distributed denial of service (AR-DDoS) attack threat assessment. Results show that, according to attack intensity, AR-DDoS saturates reflector infrastructure. Therefore, concerns about AR-DDoS are founded, but expected impact on abused IoT infrastructure and devices will be possibly as hard as on final victims.

Bioinformatics of the Paracoccidioides brasiliensis EST Project.

Paracoccidioides brasiliensis is the etiological agent of paracoccidioidomycosis, an endemic mycosis of Latin America. This fungus presents a dimorphic character; it grows as a mycelium at room temperature, but it is isolated as yeast from infected individuals. It is believed that the transition from mycelium to yeast is important for the infective process. The Functional and Differential Genome of Paracoccidioides brasiliensis Project--PbGenome Project was developed to study the infection process by analyzing expressed sequence tags--ESTs, isolated from both mycelial and yeast forms. The PbGenome Project was executed by a consortium that included 70 researchers (professors and students) from two sequencing laboratories of the midwest region of Brazil; this project produced 25,741 ESTs, 19,718 of which with sufficient quality to be analyzed. We describe the computational procedures used to receive process, analyze these ESTs, and help with their functional annotations; we also detail the services that were used for sequence data exploration. Various programs were compared for filtering and grouping the sequences, and they were adapted to a user-friendly interface. This system made the analysis of the differential transcriptome of P. brasiliensis possible.