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Neurofibromatosis 1 (NF1) is a rare genetic syndrome that leads to the development of neurofibromas and increases the risk of malignancy, including malignant peripheral nerve sheath tumors. Patients with NF1 often have other orthopaedic manifestations, including short stature, osteopenia, and dysplasia. A 47-year-old patient with a history of NF1 and multiple neurofibromas of the right lower extremity presented with a severe valgus deformity, instability, and osteoarthritis of the right knee that was debilitating to daily life. Over time, the patient lost proprioception and potentially some sensation to the right knee with neurofibroma formation, leading to the development of Charcot arthropathy of the right knee with secondary osteoarthritis. The preoperative workup consisted of a magnetic resonance imaging of the knee to confirm no malignancy was present and templating to ensure the standard implant size was amenable for the patient. A primary total knee arthroplasty was performed with a cemented-stemmed hinged knee implant. At 6 months post-surgery, the patient had a dramatic improvement in her pain and quality of life.

Semantic segmentation of medical images presents an enormous potential for diagnosis and surgery. However, achieving precise results involves designing and training complex Deep Learning (DL) models specifically for this task, which is only available to some. SAM is a model developed by Meta capable of segmenting objects present in virtually any type of image. This paper showcases SAM’s robustness and exceptional performance in medical image segmentation, even in the absence of direct training on these image types (lung Computed Tomographies (CTs) and chest X-rays, in particular). Additionally, it achieves this impressive outcome while requiring minimal user intervention. Although the dataset used to train SAM does not contain a single sample of both medical image types, processing a popular dataset comprised of $\mathrm {20 }$ volumes with a total of $\mathrm {3520 }$ slices using the ViT-L version of the model yields an average Jaccard index of $\mathrm {91.45 \%}$ and an average Dice score of $\mathrm {94.95 \%}$ . The same version of the model achieves a $\mathrm {93.19 \%}$ Dice score and a $\mathrm {87.45 \%}$ Jaccard index when segmenting a frequently-used chest X-ray dataset. The values obtained are above the $\mathrm {70 \%}$ mark recommended in the literature, and close to state-of-the art models developed specifically for medical segmentation. These results are achieved without user interaction by providing the model with positive prompts based on the masks of the dataset used and a negative prompt located in the center of bounding box that contains the masks.

Technology innovation for the sixth generation (6G) era should focus on improving quality of life by addressing societal needs, advancing human experience with the fusion of digital and physical worlds, and achieving a sustainable well-being. The 6G networks are expected to bring higher capacity and coverage combined with dependable real-time properties but must additionally be able to control the balance between quality of service (QoS) and quality of experience (QoE). QoS is the ability to offer reliable performance to connect people and things. QoE is a way to measure the quality of a service as perceived by the end-user, enabling a more customer-centric network design approach for end-user services. To provide excellent QoS, accurate estimation of QoE and corrective measures to adapt QoS to maintain acceptable QoE from the end-user perspective will be needed. In this paper, we propose a linear weighted QoE model in terms of mean opinion score (MOS) and the ability to adapt QoS. We further validate our model with a QoE impairment model and analyze the results with respect to the latency and bit rate metrics, which are critical for our target use case of Virtual Reality (VR) gaming. We also propose to use the QoE model to enable adaptive QoS in the 6G radio access network (RAN), including dynamic learning of QoS metrics.

During the COVID-19 pandemic, the monitoring of SARS-CoV-2 RNA in wastewater was used to track the evolution and emergence of variant lineages and gauge infection levels in the community, informing appropriate public health responses without relying solely on clinical testing. As more sublineages were discovered, it increased the difficulty in identifying distinct variants in a mixed population sample, particularly those without a known lineage. Here, we compare the sequencing technology from Illumina and from Oxford Nanopore Technologies, in order to determine their efficacy at detecting variants of differing abundance, using 248 wastewater samples from various Quebec and Ontario cities. Our study used two analytical approaches to identify the main variants in the samples: the presence of signature and marker mutations and the co-occurrence of signature mutations within the same amplicon. We observed that each sequencing method detected certain variants at different frequencies as each method preferentially detects mutations of distinct variants. Illumina sequencing detected more mutations with a predominant lineage that is in low abundance across the population or unknown for that time period, while Nanopore sequencing had a higher detection rate of mutations that are predominantly found in the high abundance B.1.1.7 (Alpha) lineage as well as a higher sequencing rate of co-occurring mutations in the same amplicon. We present a workflow that integrates short-read and long-read sequencing to improve the detection of SARS-CoV-2 variant lineages in mixed population samples, such as wastewater.

Mechatronics and Robotics (MaR) have recently gained importance in product development and manufacturing settings and applications. Therefore, the Center for Space Emerging Technologies (C-SET) has managed an international multi-disciplinary study to present, historically, the first Latin American general review of industrial, collaborative, and mobile robotics, with the support of North American and European researchers and institutions. The methodology is developed by considering literature extracted from Scopus, Web of Science, and Aerospace Research Central and adding reports written by companies and government organizations. This describes the state-of-the-art of MaR until the year 2023 in the 3 Sub-Regions: North America, Central America, and South America, having achieved important results related to the academy, industry, government, and entrepreneurship; thus, the statistics shown in this manuscript are unique. Also, this article explores the potential for further work and advantages described by robotic companies such as ABB, KUKA, and Mecademic and the use of the Robot Operating System (ROS) in order to promote research, development, and innovation. In addition, the integration with industry 4.0 and digital manufacturing, architecture and construction, aerospace, smart agriculture, artificial intelligence, and computational social science (human-robot interaction) is analyzed to show the promising features of these growing tech areas, considering the improvements to increase production, manufacturing, and education in the Region. Finally, regarding the information presented, Latin America is considered an important location for investments to increase production and product development, taking into account the further proposal for the creation of the LATAM Consortium for Advanced Robotics and Mechatronics, which could support and work on roboethics and education/R+D+I law and regulations in the Region. Doi: 10.28991/ESJ-2023-07-04-025 Full Text: PDF

IntroductionThe use of video games has become widespread worldwide. Excessive use of video games is increasingly becoming a matter of concern, particularly in patients diagnosed with Attention Deficit Hyperactivity Disorder (ADHD). Currently, internet gaming disorder (IGD) is not included within the DSM-5-chapter Disorders related to substances and addictive disorders.MethodsThis is a post-hoc descriptive naturalistic study comparing children and adolescents diagnosed with ADHD with and without IGD. We used the 85% cut-off point of the test ADITEC-V for video game addiction to split our sample of ADHD patients into those with IGD (>=85%) and those without IGD (

BackgroundCompared with traditional approaches, gaming strategies are promising interventions for the treatment of attention-deficit/hyperactivity disorder (ADHD). We developed a serious game, The Secret Trail of Moon (TSTM), for ADHD treatment. ObjectiveThe main objective of this clinical trial was to demonstrate the effectiveness of an add-on, either TSTM or Therapeutic Chess (TC), in previously optimally drug-titrated, clinically stable patients with ADHD. MethodsThis study is a prospective, unicentric, randomized clinical trial in clinically stable patients with ADHD, aged 12 to 22 years. The TSTM (n=35) and TC groups (n=34) performed 12 weekly sessions of their respective treatments. The control group (CG) patients (n=35) were called by phone every week, but they received no cognitive intervention. The primary end point was the change from baseline to end point in the parent “Behavior Rating Inventory of Executive Function-2” (BRIEF-2; patients’ parents) in the per-protocol population (31 serious videogame: 24 TC and 34 CG). ResultsOur study failed to probe clear-cut improvements in the global score of the BRIEF-2. However, the TC group showed improvements in measures of emotional control, emotional regulation, and inattention. The TSTM group showed improvements in measures of emotional regulation, inattention, and school context. ConclusionsTSTM and TC did not improve executive function symptoms, but they improved ADHD symptomatology related to emotional regulation. Further studies with bigger samples are required to confirm these preliminary findings. Trial RegistrationClinicalTrials.gov NCT04355065; https://clinicaltrials.gov/ct2/show/NCT04355065

ABSTRACT Although parasite entry through breaks in the skin or mucosa is one of the main routes of natural transmission of Trypanosoma cruzi, little is known about the host cell types initially invaded nor the ability of those host cells to initiate immune responses at the site of infection. To gain insights into these early events, we studied the fate of fluorescently tagged T. cruzi delivered subcutaneously in mouse footpads or ears. We demonstrate that the majority of parasites introduced into the skin initially proliferate there until 8 to 10 days postinfection, when the parasite load decreases. This decline in parasite numbers is dependent on the presence of an intact T cell compartment and on the ability of hosts to produce gamma interferon (IFN-γ). Many of the parasite-containing cells at the initial infection site display a macrophage/monocyte phenotype but with low expression of activation markers, suggesting that these cells provide an early niche for T. cruzi proliferation, rather than being active in parasite control. It is only after the first round of T. cruzi replication and release from host cells that signs of immune activation and control of parasites become apparent. The delay in the activation and failure to rapidly control parasite replication are observed even when T. cruzi-primed T cells are present, such as in chronically infected mice. This failure of a primed immune system to recognize and react prior to extensive parasite expansion at the infection site likely poses a significant challenge for the development of vaccines aiming to prevent T. cruzi infection. IMPORTANCE Trypanosoma cruzi, the parasite causing Chagas disease, usually infects through the mucosa or breaks in the skin, but little is known about the parasite's fate at the site of entry or the early events involving immune control there. Here, we track the local proliferation and subsequent dissemination of fluorescently tagged T. cruzi and the initial immune response at the point of entry. We show that T. cruzi preferentially infects innate immune cells in the skin and that the stimulation of an adaptive T cell response does not occur until after the release of parasites from this first round of infected host cells. This first immunologically “silent” proliferation occurs even in the presence of a strong immune T cell memory generated by previous infection. This capacity of T. cruzi to establish infections while avoiding initial immune recognition has important implications for the potential to develop vaccines to prevent T. cruzi infection.

High levels of school dropout are a major burden on the educational and professional development of a country’s inhabitants. A country’s prosperity depends, among other factors, on its ability to produce higher education graduates capable of moving a country forward. To alleviate the dropout problem, more and more institutions are turning to the possibilities that artificial intelligence can provide to predict dropout as early as possible. The difficulty of accessing personal data and privacy issues that it entails force the institutions to rely on the Academic Data of their students to create accurate and reliable predictive systems. This work focuses on creating the best possible predictive model based solely on academic data, and accordingly, its capacity to infer knowledge must be maximised. Thus, Feature Engineering and Instance Engineering techniques such as dealing with redundancy, significance of the features, correlation, cardinality features, missing values, creation or elimination of features, data fusion, removal of unuseful instances, binning, resampling, normalisation, or encoding are applied in detail before the construction of well-known models such as Gradient Boosting, Random Forest, and Support Vector Machine along with an Ensemble of them at different stages: prior to enrolment, at the end of the first semester, at the end of the second semester, at the end of the third semester, and at the end of the fourth semester. Through the construction of these predictive models that serve as inputs to a decision support system, the application of effective dropout prevention policies can be applied.

Background. Peritoneal tuberculosis is a rare form of extrapulmonary tuberculosis and presents a challenging diagnosis because of its nonspecific clinical manifestations. Peritoneal TB mimics other pathologies, including abdominal carcinomatosis, especially when the patient presents with ascites and an elevated cancer antigen (CA)-125 levels. Case Presentation. A postpartum 20-year-old Hispanic female recently discharged after transverse cesarean surgery, presented to the ER with fever, chills, edema, abdominal distension, nausea, and vomiting. The patient was febrile, tachycardic, and hypotensive. Chest X-ray demonstrated alveolar and interstitial consolidations; chest CT revealed tree-in-bud opacities in the right lower lobe, suggestive of atypical (TB)/fungal infection. CT of the abdomen and pelvis demonstrated ascites, omental thickening, peritoneal thickening, and mesenteric adenopathy, suggestive of carcinomatosis. She was admitted with a presumed diagnosis of sepsis secondary to pneumonia and started empirically on broad-spectrum antibiotics without clinical improvement. A battery of oncology markers was ordered and revealed a mildly elevated cancer antigen (CA)-125. Diagnostic paracentesis showed lymphocytic predominance with positive mycobacteria PCR, elevated adenosine deaminase (ADA), and no malignant cells. Subsequently, the sputum acid-fast bacilli (AFB) stain returned positive for tuberculosis, confirming the diagnosis of pulmonary tuberculosis. A peritoneal biopsy was obtained and demonstrated caseating granulomas consistent with peritoneal tuberculosis. The patient was started on standard antituberculosis therapy with clinical improvement. Conclusions. This case highlights the need for a high-level of suspicion for peritoneal tuberculosis in a patient with pulmonary tuberculosis who presents with intra-abdominal ascites, omental thickening, peritoneal thickening, and mesenteric lymphadenopathy, despite the presence of an elevated CA-125 level.

The aim of this study was to determine the semen quality, sexual behaviour and testosterone (T) levels in Saint Croix rams under semi-desert climate conditions. Sixteen rams (70.6 ± 12.5 kg; 24.1 ± 0.3 months old) were randomly divided into a grazing group (GG, n = 8) and a supplemented group (supplemented individually with 1 kg.d−1 of concentrate (156 g CP.kg−1 DM and 2.5 Mcal EM.kg−1 DM). SG, n = 8). Each group grazed on buffel, grass. Every two weeks, semen was collected, and sexual behaviour traits were evaluated. SG rams had greater body weight (P

Higher Education plays a principal role in the changing and complex world of today, and there has been rapid growth in the scientific literature dedicated to predicting students' academic success or risk of dropout thanks to advances in Data Mining techniques. Degrees such as Computer Science in particular are in ever greater demand. Although the number of students has increased, the number graduating is still not enough to provide society with as many as it requires. This study contributes to reversing this situation by introducing an approach that not only predicts the dropout risk or students' performance but takes action to help both students and educational institutions. The focus is on maximizing graduation rates by constructing a Recommender System to assist students with their selection of subjects. In particular, the challenge is addressed of constructing reliable Recommender Systems on the basis of data which are both sparse and few in quantity, imbalanced, and anonymized, and which might have been stored under imperfect conditions. This approach is successfully applied to create a Recommender System using a real-world dataset from a public Spanish university containing performance data of a Computer Science degree course, demonstrating its successful application in real environments. The construction of a support system based on that approach is described, its results are evaluated, and its implications for students' academic achievement, and for institutions' graduation rates are discussed. Through the construction of this decision support system for students, we intend to increase the graduation rates and lower the dropout rate.

O objetivo deste trabalho foi avaliar o efeito da poda nos cultivares comerciais de goiabeira-serrana “Alcântara” e “Nonante” nas safras 2016/17 e 2017/18. Os tratamentos foram sem poda de frutificação (SP) e com poda de frutificação (CP), com três repetições por cultivar. Foram avaliadas as características de planta como: o diâmetro do caule, o diâmetro de copa, a altura de planta, índice de área foliar, o número total de frutos por planta e a frutificação efetiva. Também o diâmetro, comprimento e peso total do fruto, rendimento de polpa (%), sólidos solúveis totais, acidez titulável e pH. A poda influenciou o diâmetro, o comprimento e o peso dos frutos nos dois cultivares. Na média, o peso foi 73,1 e 65,9 g para o cv Nonante, 83,5 e 76,9 g no cv Alcântara, nas safras 2016/17 e 2017/18 respectivamente. Demonstrando, assim, a importância da poda de frutificação para a qualidade e produtividade dos frutos de goiabeira-serrana.

Abstract Background Biologic therapy has changed the prognosis of patients with juvenile idiopathic arthritis (JIA). The aim of this study was to examine the pattern of use, drug survival, and adverse events of biologics in patients with JIA during the period from diagnosis to adulthood. Methods All patients included in BIOBADASER (Spanish Registry for Adverse Events of Biological Therapy in Rheumatic Diseases), a multicenter prospective registry, diagnosed with JIA between 2000 and 2015 were analyzed. Proportions, means, and SDs were used to describe the population. Incidence rates and 95% CIs were calculated to assess adverse events. Kaplan-Meier analysis was used to compare the drug survival rates. Results A total of 469 patients (46.1% women) were included. Their mean age at diagnosis was 9.4 ± 5.3 years. Their mean age at biologic treatment initiation was 23.9 ± 13.9 years. The pattern of use of biologics during their pediatric years showed a linear increase from 24% in 2000 to 65% in 2014. Biologic withdrawal for disease remission was higher in patients who initiated use biologics prior to 16 years of age than in those who were older (25.7% vs 7.9%, p

In a solid breeding blanket, ceramic breeder pebbles are in contact with reduced activation ferritic martensitic (RAFM) steel at high temperatures for years and accordingly form a corrosion layer on the blanket structural steel. Present study focuses on corrosion characteristics of EUROFER97 RAFM steel by an advanced breeder material of Li2TiO3 with excess Li (initial ratio of Li/Ti = 2.2) at 623, 823, and 1073 K under sweep gas (He + 0.1% H2) flow. Formation of a thick oxide double layer was found on the surface of the EUROFER plates heated at 823 and 1073 K, while the corrosion layer formed at 673 K was

The purpose of the industrial process of chromium plating is the creation of a hard and wear-resistant layer of chromium over a metallic surface. One of the main properties of chromium plating is its resistance to both wear and corrosion. This research presents an innovative nonparametric machine learning approach that makes use of a hybrid gradient boosted regression tree (GBRT) methodology for hard chromium layer thickness prediction. GBRT is a non-parametric statistical learning technique that produces a prediction model in the form of an ensemble of weak prediction models. The motivation for boosting is a procedure that combines the output of many weak classifiers to produce a powerful committee. In this study, the GBRT hyperparameters were optimized with the help of differential evolution (DE). DE is an optimization technique within evolutionary computing. The results found that this model was able to predict the thickness of the chromium layer formed in this industrial process with a determination coefficient equal to 0.9842 and a root-mean-square error value of 0.01590. The two most important variables of the model were the time of the hard-chromium process and the thickness of the layer removed by electropolishing. Thus, these results provide a foundation for an accurate predictive model of hard chromium layer thickness. The derived model also allowed the ranking of the importance of the independent input variables that were examined. Finally, the high performance and simplicity of the model make the DE/GBRT method attractive compared to conventional forecasting techniques.

ABSTRACT Trypanosomatids (order Kinetoplastida), including the human pathogens Trypanosoma cruzi (agent of Chagas disease), Trypanosoma brucei, (African sleeping sickness), and Leishmania (leishmaniasis), affect millions of people and animals globally. T. cruzi is considered one of the least studied and most poorly understood tropical disease-causing parasites, in part because of the relative lack of facile genetic engineering tools. This situation has improved recently through the application of clustered regularly interspaced short palindromic repeats–CRISPR-associated protein 9 (CRISPR-Cas9) technology, but a number of limitations remain, including the toxicity of continuous Cas9 expression and the long drug marker selection times. In this study, we show that the delivery of ribonucleoprotein (RNP) complexes composed of recombinant Cas9 from Staphylococcus aureus (SaCas9), but not from the more routinely used Streptococcus pyogenes Cas9 (SpCas9), and in vitro-transcribed single guide RNAs (sgRNAs) results in rapid gene edits in T. cruzi and other kinetoplastids at frequencies approaching 100%. The highly efficient genome editing via SaCas9/sgRNA RNPs was obtained for both reporter and endogenous genes and observed in multiple parasite life cycle stages in various strains of T. cruzi, as well as in T. brucei and Leishmania major. RNP complex delivery was also used to successfully tag proteins at endogenous loci and to assess the biological functions of essential genes. Thus, the use of SaCas9 RNP complexes for gene editing in kinetoplastids provides a simple, rapid, and cloning- and selection-free method to assess gene function in these important human pathogens. IMPORTANCE Protozoan parasites remain some of the highest-impact human and animal pathogens, with very limited treatment and prevention options. The development of improved therapeutics and vaccines depends on a better understanding of the unique biology of these organisms, and understanding their biology, in turn, requires the ability to track and manipulate the products of genes. In this work, we describe new methods that are available to essentially any laboratory and applicable to any parasite isolate for easily and rapidly editing the genomes of kinetoplastid parasites. We demonstrate that these methods provide the means to quickly assess function, including that of the products of essential genes and potential targets of drugs, and to tag gene products at their endogenous loci. This is all achieved without gene cloning or drug selection. We expect this advance to enable investigations, especially in Trypanosoma cruzi and Leishmania spp., that have eluded investigators for decades.