Miracle-dz/UniFAR-FTUs huggingface dataset:
Recent Advances in Classification of Brain Tumor from MR Images - State of the Art Review from 2017 to 2021.

BACKGROUND The task of identifying a tumor in the brain is a complex problem that requires sophisticated skills and inference mechanisms to accurately locate the tumor region. The complex nature of the brain tissue makes the problem of locating, segmenting, and ultimately classifying Magnetic Resonance (MR) images a complex problem. The aim of this review paper is to consolidate the details of the most relevant and recent approaches proposed in this domain for the binary and multi-class classification of brain tumors using brain MR images. OBJECTIVE In this review paper, a detailed summary of the latest techniques used for brain MR image feature extraction and classification is presented. A lot of research papers have been published recently with various techniques proposed for identifying an efficient method for the correct recognition and diagnosis of brain MR images. The review paper allows researchers in the field to familiarize themselves with the latest developments and be able to propose novel techniques that have not yet been explored in this research domain. In addition, the review paper will facilitate researchers, who are new to machine learning algorithms for brain tumor recognition, to understand the basics of the field and pave the way for them to be able to contribute to this vital field of medical research. RESULTS In this paper, the review is performed for all recently proposed methods for both feature extraction and classification. It also identifies the combination of feature extraction methods and classification methods that when combined would be the most efficient technique for the recognition and diagnosis of brain tumor from MR images. In addition, the paper presents the performance metrics particularly the recognition accuracy, of selected research published between 2017- 2021.
Zebrafish larvae: A new model to study behavioural effects and metabolism of fentanyl, in comparison to a traditional mice model

In an effort to find alternatives to study in vivo the so-called New Psychoactive Substances (NPS), the present work was undertaken to investigate the use of zebrafish larvae as animal model in pharmaco-toxicology, providing behavioural and metabolism information. For this purpose, fentanyl, the progenitor of an extremely dangerous group of NPS, was administered at different doses to zebrafish larvae (1, 10, 50, 100 µM) in comparison to mice (0.1, 1, 6, 15 mg/kg), as a well-established animal model. A behavioural assay was performed at the time of the peak effect of fentanyl, showing that the results in larvae are consistent with those observed in mice. On the other hand, several morphological abnormalities (namely yolk sac edema, abnormal pericardial edema, jaw defect and spinal curvature) were found in larvae mostly at high fentanyl doses (50, 100 µM). Larva extract and mice urine were analyzed by using liquid chromatography coupled to high resolution mass spectrometry to identify the metabolic pathways of fentanyl. The main metabolites detected were norfentanyl and hydroxyfentanyl in both the tested models. In conclusion, the present study provides evidence that fentanyl effects on zebrafish larvae and metabolism are similar to rodents and consequently support the hypothesis of using zebrafish larvae as a suitable rapid screening tool to investigate new drugs, and particularly NPS.
Identifying Electronic Nicotine Delivery System Brands and Flavors on Instagram: Natural Language Processing Analysis

Background Electronic nicotine delivery system (ENDS) brands, such as JUUL, used social media as a key component of their marketing strategy, which led to massive sales growth from 2015 to 2018. During this time, ENDS use rapidly increased among youths and young adults, with flavored products being particularly popular among these groups. Objective The aim of our study is to develop a named entity recognition (NER) model to identify potential emerging vaping brands and flavors from Instagram post text. NER is a natural language processing task for identifying specific types of words (entities) in text based on the characteristics of the entity and surrounding words. Methods NER models were trained on a labeled data set of 2272 Instagram posts coded for ENDS brands and flavors. We compared three types of NER models—conditional random fields, a residual convolutional neural network, and a fine-tuned distilled bidirectional encoder representations from transformers (FTDB) network—to identify brands and flavors in Instagram posts with key model outcomes of precision, recall, and F1 scores. We used data from Nielsen scanner sales and Wikipedia to create benchmark dictionaries to determine whether brands from established ENDS brand and flavor lists were mentioned in the Instagram posts in our sample. To prevent overfitting, we performed 5-fold cross-validation and reported the mean and SD of the model validation metrics across the folds. Results For brands, the residual convolutional neural network exhibited the highest mean precision (0.797, SD 0.084), and the FTDB exhibited the highest mean recall (0.869, SD 0.103). For flavors, the FTDB exhibited both the highest mean precision (0.860, SD 0.055) and recall (0.801, SD 0.091). All NER models outperformed the benchmark brand and flavor dictionary look-ups on mean precision, recall, and F1. Comparing between the benchmark brand lists, the larger Wikipedia list outperformed the Nielsen list in both precision and recall. Conclusions Our findings suggest that NER models correctly identified ENDS brands and flavors in Instagram posts at rates competitive with, or better than, others in the published literature. Brands identified during manual annotation showed little overlap with those in Nielsen scanner data, suggesting that NER models may capture emerging brands with limited sales and distribution. NER models address the challenges of manual brand identification and can be used to support future infodemiology and infoveillance studies. Brands identified on social media should be cross-validated with Nielsen and other data sources to differentiate emerging brands that have become established from those with limited sales and distribution.
Epidemiological characteristics and genetic diversity of Bartonella species in rodents from southeastern China

Rodents are the primary hosts of Bartonella species and carry more than 22 Bartonella species. However, the information on epidemiological characteristics and genetic diversity of Bartonella species in rodents in southeastern China is limited. From 2015 to 2020, 1,137 rodents were captured. Bartonella‐positive DNA was detected in 14.9% (169/1,137) of rodents by PCR for both the ssrA and gltA genes. A highest Bartonella prevalence was detected in Apodemus agrarius (33.5%) and lowest in B. indica (1.8%). The probability of Bartonella infection in summer (20.1%) was higher than in spring (14.6%; p = .011, OR = 1.756). Sequencing and phylogenetic analysis revealed that nine known Bartonella species were identified in rodents, including B. tribocorum, B. grahamii, B. rattimassiliensis, B. queenslandensis, B. elizabethae, B. phoceensis, B. coopersplainsensis, B. japonica and B. rochalimae. In our study, Bartonella species exhibited a strong association with their hosts. Zoonotic B. tribocorum, B. grahamii, B. elizabethae and B. rochalimae were found in synanthropic rodent species in southeastern China, which pose a potential threat to the public health. To prevent the spread of zoonotic Bartonella species to humans, preventive and control measures should be implemented, and more research is needed to confirm the pathogen's association with human disease.
Automated recognition of functioning, activity and participation in COVID-19 from electronic patient records by natural language processing: a proof- of- concept

Abstract Purpose To address the feasibility, reliability and internal validity of natural language processing (NLP) for automated functional assessment of hospitalised COVID-19 patients in key International Classification of Functioning, Disability and Health (ICF) categories and levels from unstructured text in electronic health records (EHR) from a large teaching hospital. Materials and methods Eight human annotators assigned four ICF categories to relevant sentences: Emotional functions, Exercise tolerance, Walking and Moving, Work and Employment and their ICF levels (Functional Ambulation Categories for Walking and Moving, metabolic equivalents for Exercise tolerance). A linguistic neural network-based model was trained on 80% of the annotated sentences; inter-annotator agreement (IAA, Cohen’s kappa), a weighted score of precision and recall (F1) and RMSE for level detection were assessed for the remaining 20%. Results In total 4112 sentences of non-COVID-19 and 1061 of COVID-19 patients were annotated. Average IAA was 0.81; F1 scores were 0.7 for Walking and Moving and Emotional functions; RMSE for Walking and Moving (5- level scale) was 1.17 for COVID-19 patients. Conclusion Using a limited amount of annotated EHR sentences, a proof-of-concept was obtained for automated functional assessment of COVID-19 patients in ICF categories and levels. This allows for instantaneous assessment of the functional consequences of new diseases like COVID-19 for large numbers of patients. Key messages Hospitalised Covid-19 survivors may persistently suffer from low physical and mental functioning and a reduction in overall quality of life requiring appropriate and personalised rehabilitation strategies. For this, assessment of functioning within multiple domains and categories of the International Classification of Function is required, which is cumbersome using structured data. We show a proof-of-concept using Natural Language Processing techniques to automatically derive the aforementioned information from free-text notes within the Electronic Health Record of a large academic teaching hospital.
Nutrients and herbivores impact grassland stability across spatial scales through different pathways

Nutrients and herbivores are well‐known drivers of grassland diversity and stability in local communities. However, whether they interact to impact the stability of aboveground biomass and whether these effects depend on spatial scales remain unknown. It is also unclear whether nutrients and herbivores impact stability via different facets of plant diversity including species richness, evenness, and changes in community composition through time and space. We used a replicated experiment adding nutrients and excluding herbivores for 5 years in 34 global grasslands to explore these questions. We found that both nutrient addition and herbivore exclusion alone reduced stability at the larger spatial scale (aggregated local communities; gamma stability), but through different pathways. Nutrient addition reduced gamma stability primarily by increasing changes in local community composition over time, which was mainly driven by species replacement. Herbivore exclusion reduced gamma stability primarily by decreasing asynchronous dynamics among local communities (spatial asynchrony). Their interaction weakly increased gamma stability by increasing spatial asynchrony. Our findings indicate that disentangling the processes operating at different spatial scales may improve conservation and management aiming at maintaining the ability of ecosystems to reliably provide functions and services for humanity.
An investigation of the effects of modeling application workloads and path characteristics on network performance

Network testbeds and simulators remain the dominant platforms for evaluating networking technologies today. Central to the problem of network emulation or simulation is the problem modeling and generating realistic, synthetic Internet traffic as the results of such experiments are valid to the extent that the traffic generated to drive these experiments accurately represents the traffic carried in real production networks. Modeling and generating realistic Internet traffic remains a complex and not well-understood problem in empirical networking research. When modeling production network traffic, researchers lack a clear understanding about which characteristics of the traffic must be modeled, and how these traffic characteristics affect the results of their experiments. In this dissertation, we developed and analyzed a spectrum of empirically-derived traffic models with varying degrees of realism. For TCP traffic, we examined several choices for modeling the internal structure of TCP connections (the pattern of request/response exchanges), and the round trip times of connections. Using measurements from two different production networks, we constructed nine different traffic models, each embodying different choices in the modeling space, and conducted extensive experiments to evaluate these choices on a 10Gbps laboratory testbed. As a result of this study, we demonstrate that the old adage of "garbage-in-garbage-out" applies to empirical networking research. We conclude that the structure of traffic driving an experiment significantly affects the results of the experiment. And we demonstrate this by showing the effects on four key network performance metrics: connection durations, response times, router queue lengths, and number of active connections in the network.
MECOM gene overexpression in pediatric patients with acute myeloid leukemia

Abstract Background Acute myeloid leukemia (AML) is characterized by blocked or aberrant differentiation of hematopoietic stem cells. The MECOM gene overexpression in hematopoietic progenitors induces myeloid differentiation block, resulting in increased self-renewal and survival of these transformed progenitors. However, its exact role in AML remains unclear. We aimed to estimate the prevalence of MECOM overexpression among pediatric AML patients, and assess its impact on clinical outcome. Patients and Methods Real-time quantitative polymerase chain reaction and Livak method (2ΔΔCt) were used to determine relative MECOM expression level among 243 pediatric patients with AML. MECOM overexpression was considered if the cumulative relative expression was above 1 (2-ΔΔCt) and was designated as MECOMpos . Results Of 243 AML patients tested 57(23.5%) demonstrated MECOMpos . Patients with MECOMpos had significantly lower median age. The frequency of MECOMpos was significantly higher among AML patients with 11q23 abnormalities, complex karyotypes and among high- and intermediate-risk groups compared to low-risk group (p = .014). MECOMpos patients had significantly lower overall survival (OS) (38.7 vs. 78.9%, p < .001), event-free survival (EFS) (37.3% vs. 68.4%, p < .001), and had higher cumulative incidence of relapse (49.5% vs. 23.5%, p = .002) at 36 months compared to MECOMneg patients. Multivariate analysis revealed that MECOMpos was an adverse prognostic factor for OS (hazards ratio (HR) = 2.11, 95% confidence interval (CI) 1.24–3.60, p = . 006) and EFS (HR= 1.71, 95% CI 1.07–2.75, p = .025). The logistic regression model showed that MECOMpos was an independent prognostic factor regardless of minimal residual disease status post first induction therapy in the intermediate-risk group (odds ratio 2.89; 95% CI 1.19–6.57, p = .018). Conclusion The aberrant MECOM gene expression is an adverse prognostic factor, especially in patients without previously known cytogenetic risk factors. Our results suggest the potential benefit from pretreatment screening for MECOM gene overexpression in newly diagnosed AML patients for better risk stratification and treatment adjustment.
Evaluation of antibody serology to determine current helminth and Plasmodium falciparum infections in a co-endemic area in Southern Mozambique

Background: Soil-transmitted helminths (STH), Schistosoma spp. and Plasmodium falciparum are parasites of major public health importance and co-endemic in many sub-Saharan African countries. Management of these infections requires detection and treatment of infected people and evaluation of large-scale measures implemented. Diagnostic tools are available but their low sensitivity, especially for low intensity helminth infections, leaves room for improvement. Antibody serology could be a useful approach thanks to its potential to detect both current infection and past exposure. Methodology: We evaluated total IgE responses and specific-IgG levels to 9 antigens from STH, 2 from Schistosoma spp. , and 16 from P. falciparum, as potential markers of current infection in a population of children and adults from Southern Mozambique (N = 715). Antibody responses were measured by quantitative suspension array Luminex technology and their performance was evaluated by ROC curve analysis using microscopic and molecular detection of infections as reference. Principal findings: IgG against the combination of EXP1, AMA1 and MSP2 (P. falciparum) in children and NIE (Strongyloides stercoralis) in adults and children had the highest accuracies (AUC = 0.942 and AUC = 0.872, respectively) as markers of current infection. IgG against the combination of MEA and Sm25 (Schistosoma spp.) were also reliable markers of current infection (AUC = 0.779). In addition, IgG seropositivity against 20 out of the 27 antigens in the panel differentiated the seropositive endemic population from the non-endemic population, suggesting a possible role as markers of exposure. Conclusions: We provided evidence for the utility of antibody serology to detect current infection with parasites causing tropical diseases in endemic populations. In addition, most of the markers could be used as markers of exposure. We also showed the feasibility of measuring antibody serology with a platform that allows the integration of control and elimination programs for different pathogens.
PLWHA's Perspective on Community Stigma for Getting Social Support and Improving Life Quality in the Digital Era

Community stigma against HIV/AIDS is still a problem that must be faced by people living with HIV/AIDS (PLWHA). The existence of stigmatization and discrimination received by PLWHA causes the lack of social support they get. Social support is very important for PLWHA to improve their quality of life. In this digital era where the development of technology and social media is an alternative for PLWHA to get social support online through their community, by sharing information and experiences. This systematic review was conducted through Scopus, Science Direct, ProQuest, and SAGE. The selection of articles followed inclusion criteria, including articles published in the last five years and published in English and discussing perspectives on stigma. Articles published not in English and articles with unclear literature reviews were excluded from this study. The PRISMA flow chart and JBI assessment checklist were used to assess the risk of bias and article quality. 20 relevant articles will be reviewed. The results of the study found that in this digital era PLWHA can take advantage of technological developments and social media to improve their quality of life by getting social support through the online communities they follow.
Suppression of Interuser Interference for Fiber-optic Synchronous Code Division Multiple Access Networks

In this paper, a new coding architecture is proposed for fiber-optic synchronous code division multiple access (S/CDMA) network by using parallel cancellation scheme and bit-stuffed unipolar m-sequence. The well-known maximal length code (m-sequence) with one bit stuffing which is called bit-stuffed unipolar m-sequence in this paper can achieve unique cross-correlation value Due to the uniqueness of cross-correlation value of unipolar m-sequence, the proposed parallel cancellation scheme can completely suppress the interuser interference in the negligible effects of noise. In the proposed architecture, the correlator employs avalanche photodiode (APD) for photodetection, and the correlator output can be modeled as a Gaussian random process [7-8]. In addition to the suppression of interuser interference, the inevitable effects of APD noise and thermal noise in the process of photodetection are also investigated in this paper.
Effects of circuit elements and harmonic oscillation power on fundamental PM noise power of FET oscillator

Analytical expressions for the PM noise in FET oscillators are derived in terms of the FET equivalent circuit elements and the passive circuitry. Efficient methods to reduce the PM noise in fundamental and in harmonic mode are suggested and implements. The effects of the different FET equivalent circuit parameters on large-signal, small-signal, and noise behaviour of FET oscillators are thoroughly investigated. Finally, the effects of harmonic signal on both fundamental and harmonic output noise are determined.
Synthesis and Reconstruction of Fingerprints using Generative Adversarial Networks

Deep learning-based models have been shown to improve the accuracy of fingerprint recognition. While these algorithms show exceptional performance, they require large-scale fingerprint datasets for training and evaluation. In this work, we propose a novel fingerprint synthesis and reconstruction framework based on the StyleGan2 architecture, to address the privacy issues related to the acquisition of such large-scale datasets. We also derive a computational approach to modify the attributes of the generated fingerprint while preserving their identity. This allows synthesizing multiple different fingerprint images per finger. In particular, we introduce the SynFing synthetic fingerprints dataset consisting of 100K image pairs, each pair corresponding to the same identity. The proposed framework was experimentally shown to outperform contemporary state-of-the-art approaches for both fingerprint synthesis and reconstruction. It significantly improved the realism of the generated fingerprints, both visually and in terms of their ability to spoof fingerprint-based verification systems. The code and fingerprints dataset are publicly available: https://github.com/rafaelbou/fingerprint_generator.
How Bad Are Artifacts?: Analyzing the Impact of Speech Enhancement Errors on ASR

It is challenging to improve automatic speech recognition (ASR) performance in noisy conditions with single-channel speech enhancement (SE). In this paper, we investigate the causes of ASR performance degradation by decomposing the SE errors using orthogonal projection-based decomposition (OPD). OPD decomposes the SE errors into noise and artifact components. The artifact component is defined as the SE error signal that cannot be represented as a linear combination of speech and noise sources. We propose manually scaling the error components to analyze their impact on ASR. We experimentally identify the artifact component as the main cause of performance degradation, and we find that mitigating the artifact can greatly improve ASR performance. Furthermore, we demonstrate that the simple observation adding (OA) technique (i.e., adding a scaled version of the observed signal to the enhanced speech) can monotonically increase the signal-to-artifact ratio under a mild condition. Accordingly, we experimentally confirm that OA improves ASR performance for both simulated and real recordings. The findings of this paper provide a better understanding of the influence of SE errors on ASR and open the door to future research on novel approaches for designing effective single-channel SE front-ends for ASR.