Diet Based on Pereskia aculeata Miller Flour Increases Muscle Volume and Modulates the Expression of Myokines in Mice Subjected to Resistance Training.

The study aimed to evaluate the effects of Pereskia aculeata Miller (ora-pro-nobis [OPN]) flour on body and biochemical parameters, thermogenic activity, and molecular expression of markers in the muscle tissue of mice subjected to resistance training (RT). Twelve mice were randomly assigned to two groups (n=6 animals/group): G1: control (Control) fed a standard diet + RT and G2: experimental (OPN) fed a diet based on OPN flour + RT. The RT consisted of a 6-week program using a vertical ladder combined with a fixed weight attached to the animal. Several parameters were measured, including assessment of body composition, biochemical markers, thermogenic activity, and molecular (mRNA expression of interleukin (IL)-6, fibronectin type III domain-containing protein 5 (FNDC5), peroxisome proliferator-activated receptor γ coactivator 1α (PGC-1α), nuclear respiratory factor 1 (NRF1), and mitochondrial transcription factor A (TFAM). The OPN group exhibited a decrease in body weight and visceral adiposity, higher energy expenditure, and lipid oxidation rate. In addition, it was observed an increase in muscle volume and in mRNA expression levels of IL-6, FNDC5, PGC-1α, and TFAM. These findings suggest that OPN flour could be a nutritional option to enhance performance in RT.

Multilingual end-to-end ASR for low-resource Turkic languages with common alphabets.

To obtain a reliable and accurate automatic speech recognition (ASR) machine learning model, it is necessary to have sufficient audio data transcribed, for training. Many languages in the world, especially the agglutinative languages of the Turkic family, suffer from a lack of this type of data. Many studies have been conducted in order to obtain better models for low-resource languages, using different approaches. The most popular approaches include multilingual training and transfer learning. In this study, we combined five agglutinative languages from the Turkic family-Kazakh, Bashkir, Kyrgyz, Sakha, and Tatar,-in order to provide multilingual training using connectionist temporal classification and an attention mechanism including a language model, because these languages have cognate words, sentence formation rules, and alphabet (Cyrillic). Data from the open-source database Common voice was used for the study, to make the experiments reproducible. The results of the experiments showed that multilingual training could improve ASR performances for all languages included in the experiment, except Bashkir language. A dramatic result was achieved for the Kyrgyz language: word error rate decreased to nearly one-fifth and character error rate decreased to one-fourth, which proves that this approach can be helpful for critically low-resource languages.

Augmented Reality in Maintenance-History and Perspectives.

Augmented Reality (AR) is a technology that allows virtual elements to be superimposed over images of real contexts, whether these are text elements, graphics, or other types of objects. Smart AR glasses are increasingly optimized, and modern ones have features such as Global Positioning System (GPS), a microphone, and gesture recognition, among others. These devices allow users to have their hands free to perform tasks while they receive instructions in real time through the glasses. This allows maintenance professionals to carry out interventions more efficiently and in a shorter time than would be necessary without the support of this technology. In the present work, a timeline of important achievements is established, including important findings in object recognition, real-time operation. and integration of technologies for shop floor use. Perspectives on future research and related recommendations are proposed as well.

Study on Data Partition for Delimitation of Masses in Mammography.

Mammography is the primary medical imaging method used for routine screening and early detection of breast cancer in women. However, the process of manually inspecting, detecting, and delimiting the tumoral massess in 2D images is a very time-consuming task, subject to human errors due to fatigue. Therefore, integrated computer-aided detection systems have been proposed, based on modern computer vision and machine learning methods. In the present work, mammogram images from the publicly available Inbreast dataset are first converted to pseudo-color and then used to train and test a Mask R-CNN deep neural network. The most common approach is to start with a dataset and split the images into train and test set randomly. However, since there are often two or more images of the same case in the dataset, the way the dataset is split may have an impact on the results. Our experiments show that random partition of the data can produce unreliable training, so the dataset must be split using case-wise partition for more stable results. In experimental results, the method achieves an average true positive rate of 0.936 with 0.063 standard deviation using random partition and 0.908 with 0.002 standard deviation using case-wise partition, showing that case-wise partition must be used for more reliable results.

Real-Time Quality Control of Heat Sealed Bottles Using Thermal Images and Artificial Neural Network.

Quality control of heat sealed bottles is very important to minimize waste and in some cases protect people's health. The present paper describes a case study where an automated non invasive and non destructive quality control system was designed to assess the quality of the seals of bottles containing pesticide. In this case study, the integrity of the seals is evaluated using an artificial neural network based on images of the seals processed with computer vision techniques. Because the seals are not directly visible from the bottle exterior, the images are infrared pictures obtained using a thermal camera. The method is non invasive, automated, and can be applied to common conveyor belts currently used in industrial plants. The results show that the inspection process is effective in identifying defective seals with a precision of 98.6% and a recall of 100% and because it is automated it can be scaled up to large bottle processing plants.

Influence of sociodemographic factors on eating motivations - modelling through artificial neural networks (ANN).

This study aimed at investigating the influence of some sociodemographic factors on the eating motivations. A longitudinal study was carried conducted with 11960 participants from 16 countries. Data analysis included t-test for independent samples or ANOVA, and neural network models were also created, to relate the input and output variables. Results showed that factors like age, marital status, country, living environment, level of education or professional area significantly influenced all of the studied types of eating motivations. Neural networks modelling indicated variability in the food choices, but identifying some trends, for example the strongest positive factor determining health motivations was age, while for emotional motivations was living environment, and for economic and availability motivations was gender. On the other hand, country revealed a high positive influence for the social and cultural as well as for environmental and political and also for marketing and commercial motivations.

Resting Tremor after Mild Head Injury: Case Report

Over the past few decades, it has been recognized that traumatic brain injury (TBI) may result in various movement disorders. However, moderate or mild TBI only rarely causes persistent post-traumatic movement disorders. In the present report, we describe a case of secondary tremor due to amild head injury with a transitory loss of consciousness. A 26- year-old man developed an isolated rest tremor of the hands and legs without other neurologic signs. The interval between the head trauma and the onset of the symptomswas 4 months. Neuroimaging studies reveled gliosis in the lentiform nucleus. Haloperidol administration resulted in tremor reduction. A rest tremor, similar to essential tremor, can be a rare complication of head trauma. Haloperidolmay be an effective and safe treatment modality for post-traumatic tremor. Further studies are needed to clarify the optimal drug for the treatment of post-traumatic tremor.

Por onde anda a histeria? / Where is the hysteria?

O termo histeria foi suprimido das classificações diagnósticas vigentes. Este artigo cita e descreve os diagnósticos que estão agora abrigados no guarda-chuva da histeria, além de discutir algumas implicações sobre essa mudança.

The term hysteria was suppressed from the current diagnostic classifications. This article cites and describes the diagnoses that are now housed under the umbrella of hysteria, in addition to discussing some implications on this change.

Artificial neural network modelling of the antioxidant activity and phenolic compounds of bananas submitted to different drying treatments.

Bananas (cv. Musa nana and Musa cavendishii) fresh and dried by hot air at 50 and 70°C and lyophilisation were analysed for phenolic contents and antioxidant activity. All samples were subject to six extractions (three with methanol followed by three with acetone/water solution). The experimental data served to train a neural network adequate to describe the experimental observations for both output variables studied: total phenols and antioxidant activity. The results show that both bananas are similar and air drying decreased total phenols and antioxidant activity for both temperatures, whereas lyophilisation decreased the phenolic content in a lesser extent. Neural network experiments showed that antioxidant activity and phenolic compounds can be predicted accurately from the input variables: banana variety, dryness state and type and order of extract. Drying state and extract order were found to have larger impact in the values of antioxidant activity and phenolic compounds.

Fraturas da patela: uma década de tratamento no IOT-HC-FMUSP - parte 1: análise funcional / Patellar fractures: a decade of treatment at IOT-HC-FMUSP - part I: functional analysis

As fraturas da patela são prevalentes na faixa etária de 20 a 50 anos e representam 1 por cento das fraturas. São classificadas através de 2 aspectos principais: característica do traço de fratura e presença de exposição óssea, o que dirige a escolha do tratamento dentro das diversas técnicas propostas. Este trabalho objetivou estudar a incidência dos padrões de fraturas patelares e analisar os resultados funcionais encontrados com as diferentes modalidades de tratamento no IOT-HC-FMUSP. Levantou-se 103 prontuários de pacientes com fratura de patela tratados no IOT no período de 1988 - 1999 com seguimento mínimo de 5 anos. A média etária foi de 39,4 anos, 68 por cento do sexo masculino, 51,5 por cento de acometimento do lado direito e 2,9 por cento de bilateralidade. Prevaleceram fraturas transversas (36 por cento); 30 por cento de exposição óssea; técnicas mais utilizadas patelectomia parcial (46,6 por cento), banda de tensão (20,4 por cento), imobilização gessada (17,5 por cento), cerclagem (4,8 por cento) e patelectomia total (2,9 por cento); notou-se 57,1 por cento de excelentes e bons resultados com banda de tensão AO e 44,4 por cento nas patelectomias parciais. Concluiu-se que o resultado funcional final do tratamento da fratura de patela é multifatorial; o tipo de fratura, o modo de tratamento - conservador ou cirúrgico, a idade e o mecanismo de trauma não influenciam, isoladamente, o sucesso da terapêutica.