Miracle-dz/UniFAR-FTUs huggingface dataset:
AN IMPROVED TRANSFER LEARNING APPROACH TOWARDS BREAST CANCER CLASSIFICATION ON DEEP RESIDUAL NETWORK

Consultant Breast Radiologist, Sri Shankara Cancer Hospital & Research Centre, Bengaluru, Karnataka,560004, INDIA. divyasantosh@gmail.com Abstract Breast Cancer is considered a significant health threat in women of all ages, and the incidence has been rapidly increasing, particularly in the last decade. Usually, for diagnosis, characteristics such as skin texture, color (redness), size variation, pain, and gene mutation are used. Binary classification is the most frequent classification type to classify benign and malignant tumors. A deep learning-based mechanism has been hugely popular and significant improvisation in binary classification, especially CNN-based mechanism. Moreover, to take CNN advantage, several breakthroughs have been observed for image classification, such as integrating layer-based features with the classifier. However, due to limited images available for medical research, several areas are exposed, such as overfitting issues, memory issues, and network architecture issues. Hence, in this research work, the design and development of an ImprovisedDeepResidualNetwork, aka IDRN, for binary classification on mammogram. IDRN adopts the Deep Residual Network and enforces the improved transfer learning approach for training and testing the model. IDRN follows the novel architecture, forcing the transfer learning-based task-specific layer to be integrated to enhance the features and improvise the classification process. Further, IDRN is evaluated considering the INbreast dataset considering the important metrics like accuracy, precision, recall, F1-score, and AUC; a comparative analysis is also carried out with a state-of-art model of deep learning model.
Production of Niobium Carbide Layers on High‐Strength Bainitic Steels by Thermochemical Treatment of Thermoreactive Diffusion Followed by Austempering

The development of third‐generation advanced high‐strength steels aims for the production of low‐alloy steels with a high strength‐to‐weight ratio through innovative heat treatments. Thermochemical treatments that eliminate reheating steps are an excellent alternative for the surface hardening of this type of alloy. Herein, the incorporation of a thermoreactive diffusion (TRD) thermochemical treatment along with austempering treatment to obtain carbide‐coated high‐strength bainitic steels is evaluated. For this purpose, samples of bainitic steels containing ≈1.5 wt% silicon are immersed in an austenitizing bath composed of ferro‐niobium and sodium borate for 2, 4, and 6 h at 1100 °C. Subsequently, they are held isothermally in a 360 °C salt bath for 1 h. After treatment, the samples are characterized by optical microscopy, scanning electron microscopy, X‐ray diffraction, dispersive energy spectroscopy, Knoop microhardness, and microadhesive wear tests. Microstructural analysis shows the formation of niobium carbide layers at the surface of a substrate composed of granular and plate bainite, characteristic of austempered high‐silicon steels. These layers show thickness in the range of 4−9 μm, hardness from 2333 to 2599 HK, presenting hardness up to 6 times higher than the substrates, and wear resistance up to 8 times greater.
Direct Use of Clinical Tolerance Limits for Assessing Agreement: A Robust Nonparametric Approach

Clinical agreement between two quantitative measurements on a group of subjects is generally assessed with the help of the Bland-Altman (B-A) limits. The interpretation regarding agreement is based on whether B-A limits are within the pre-specified clinical tolerance. Thus, clinical tolerance limits are necessary for this method. We argue in this communication that such limits of clinical tolerance can be directly used for assessing agreement and plead that this nonparametric approach is simple and robust to the distribution pattern and outliers. Such direct use of clinical tolerance limits has more flexibility, and it is more effective in assessing the extent of agreement.
Respondent Burden Effects on Item Non-Response and Careless Response Rates: An Analysis of Two Types of Surveys

The respondent burden refers to the effort required by a respondent to answer a questionnaire. Although this concept was introduced decades ago, few studies have focused on the quantitative detection of such a burden. In this paper, a face-to-face survey and a telephone survey conducted in Valencia (Spain) are analyzed. The presence of burden is studied in terms of both item non-response rates and careless response rates. In particular, two moving-window statistics based on the coefficient of unalikeability and the average longstring index are proposed for characterizing careless responding. Item non-response and careless response rates are modeled for each survey by using mixed-effects models, including respondent-level and question-level covariates and also temporal random effects to assess the existence of respondent burden during the questionnaire. The results suggest that the sociodemographic characteristics of the respondents and the typology of the question impact item non-response and careless response rates. Moreover, the estimates of the temporal random effects indicate that item non-response and careless response rates are time-varying, suggesting the presence of respondent burden. In particular, an increasing trend in item non-response rates in the telephone survey has been found, which supports the hypothesis of the burden. Regarding careless responding, despite the presence of some temporal variation, no clear trend has been identified.
Review of Spider Ecology During Carrion Decomposition, with a Field Study on Density and Taxa Richness in Pitfall Trap Captures of Geophilic Spiders (Aranea: Anyphaenidae, Corinnidae, Linyphiidae, Lycosidae, Salticidae)

Abstract During a recent pig carrion succession study in northwest Florida, United States, we noticed an abundance of spiders but found that literature on spider ecology at carrion is sparse and scattered. We compiled a literature review of 569 carrion succession studies, of which 37 studies specifically mentioned the presence of spiders, with less than a third providing species-level identifications and only half providing family-level identifications. Nineteen spider families have been reported at carrion in the literature. Spiders are recognized as generalist, opportunistic predators in carrion succession studies, but only 38% of the studies that mentioned spiders during carrion succession included any ecological information. Data on spiders in the present experiment were compared to background samples, finding that the difference in abundance was statistically significant for all spider species combined. Seven species of spiders from five families were identified from both background and carrion succession samples; statistical differences in abundance were found for three species: the linyphiid Florinda coccinea and the lycosids Tigrosa annexa and Pirata seminolus. The family Corinnidae is reported from carrion for the first time. This research reviews the existing literature on spider ecology during carrion succession, provides additional data on species identity and abundance, and demonstrates the potential significance of spiders in carrion ecology and forensic investigations.
Seasonality in Kgalagadi Lizards: Inferences from Legacy Data

An ecological issue can best be studied by gathering original data that are specifically targeted for that issue. But ascertaining—a priori—whether a novel issue will be worth exploring can be problematic without background data. However, an issue’s potential merit can sometimes be evaluated by repurposing legacy or other data that had been gathered for unrelated purposes but that are nonetheless relevant. Our present project was initially motivated by an ecological trade-off—proposed eight decades ago—involving the depth at which desert reptiles overwintered. To address those and related issues, we repurposed our five-decades-old natural history data for 18 species of Kgalagadi lizards and then explored the seasonal ecology of these lizards, emphasizing winter. Our data were not gathered for a study of seasonal ecology but nonetheless inform diverse seasonal patterns for a major community of lizards. However, repurposed data (whether recent or legacy) present challenges and ambiguities, and we suggest targeted, next-step studies of seasonal ecology that can circumvent limitations and ambiguities.
Multimodal Tucker Decomposition for Gated RBM Inference

Gated networks are networks that contain gating connections in which the output of at least two neurons are multiplied. The basic idea of a gated restricted Boltzmann machine (RBM) model is to use the binary hidden units to learn the conditional distribution of one image (the output) given another image (the input). This allows the hidden units of a gated RBM to model the transformations between two successive images. Inference in the model consists in extracting the transformations given a pair of images. However, a fully connected multiplicative network creates cubically many parameters, forming a three-dimensional interaction tensor that requires a lot of memory and computations for inference and training. In this paper, we parameterize the bilinear interactions in the gated RBM through a multimodal tensor-based Tucker decomposition. Tucker decomposition decomposes a tensor into a set of matrices and one (usually smaller) core tensor. The parameterization through Tucker decomposition helps reduce the number of model parameters, reduces the computational costs of the learning process and effectively strengthens the structured feature learning. When trained on affine transformations of still images, we show how a completely unsupervised network learns explicit encodings of image transformations.
Sexual dimorphism in cranial shape and size in geomyoid rodents: multivariate and evolutionary perspectives

Abstract Geomyoid rodents provide a great study system for the analysis of sexual dimorphism. They are polygynic and many inhabit harsh arid environments thought to promote sexual dimorphism. In fact, there has been extensive work published on the sexual size dimorphism of individual populations and species within this rodent clade. However, little work has been undertaken to assess the evolutionary patterns and processes associated with this sexual dimorphism. We use multivariate analyses of cranial measurements in a phylogenetic framework to determine the distribution of size and shape dimorphism among geomyoids and test for Rensch’s rule. Our results suggest that sexual dimorphism is more common in geomyids than heteromyids, but it is not in fact universal. There is evidence for variation in sexual dimorphism across populations. Additionally, in many taxa, geographic variation appears to overwhelm existing sexual dimorphism. We find support for the repeated independent evolution of shape and size dimorphism across geomyoid taxa, but we do not find support for an association between size and shape dimorphism. There is no evidence for Rensch’s rule in geomyoids, whether at the superfamily or family level. Together, our findings suggest that there is no single explanation for the evolution of sexual dimorphism in geomyoids and that, instead, it is the product of numerous evolutionary events. Future studies incorporating phylogenetic relationships will be necessary to paint a more complete picture of the evolution of sexual dimorphism in geomyoids.
Recent trends in the timing of the growing season in New Zealand’s natural and semi-natural grasslands

ABSTRACT We investigate the temporal dynamics of shifts in phenological responses of a range of key stages of the growing season in New Zealand’s three indigenous grassland types over the last 16 years (2001–2016). A near-daily Normalized Difference Vegetation Index (NDVI) time series from MODerate Resolution Imaging Spectroradiometer (MODIS) was used to extract five annual growth phenology indices, namely the Start, End, Length, Peak and Peak NDVI of a growing season. The start of the growing season advanced (i.e. happened earlier) by a median of 7.2, 6.0 and 8.8 days per decade in Alpine, Tall Tussock and Low Producing grassland, whereas the end of the season advanced by a median of 4.5, 0.4 and 0.4 days in the three types respectively. The length of growing season was extended by 3.2, 5.2 and 7.1 days per decade in these three grassland types. Over 86% of the investigated grassland areas showed an advancing (earlier) start of the growing season, and 74% of Alpine grassland showed a trend toward an earlier end of season. Over 63% of all grassland types showed an increase in growing season length. A trend toward earlier growing season peak and overall increasing NDVI in the three grassland types indicate a tendency for increasing vegetation vitality in grassland ecosystems in recent years. The start of growing season was correlated with atmospheric pressure (negatively) and precipitation (positively) changes in winter–spring months, while the timing of the season end is positively correlated with air temperature and solar radiation in summer–autumn months. Our study shows that different grassland types differ in magnitude – but not in direction – of their recent shifts in timing of key growing season stages with high-alpine grasslands showing the strongest response. This study highlights the usefulness of remote sensing for monitoring ecosystem-level phenological shifts over large areas and long time periods.
Progressive Learning With Anchoring Regularization For Vehicle Re-Identification

Vehicle re-identification (re-ID) aims to automatically find vehicle identity from a large number of vehicle images captured from multiple cameras. Most existing vehicle re-ID approaches rely on fully supervised learning methodologies, where large amounts of annotated training data are required, which is an expensive task. In this paper, we focus our interest on semi-supervised vehicle re-ID, where each identity has a single labeled and multiple unlabeled samples in the training. We propose a framework which gradually labels vehicle images taken from surveillance cameras. Our framework is based on a deep Convolutional Neural Network (CNN), which is progressively learned using a feature anchoring regularization process. The experiments conducted on various publicly available datasets demonstrate the efficiency of our framework in re-ID tasks. Our approach with only 20% labeled data shows interesting performance compared to the state-of-the-art supervised methods trained on fully labeled data.
Modularisation of published and novel models toward a complex KIR2DL4 pathway in pbNK cell

KIR2DL4 is an interesting receptor expressed on the peripheral blood natural killer (pbNK) cell as it can be either activating or inhibitory depending on the amino acid residues in the domain. This model uses mathematical modelling to investigate the downstream effects of natural killer cells’ activation (KIR2DL4) receptor after stimulation by key ligand (HLA-G) on pbNK cells. Development of this large pathway is based on a comprehensive qualitative description of pbNKs’ intracellular signalling pathways leading to chemokine and cytotoxin secretion, obtained from the KEGG database (https://www.genome.jp/pathway/hsa04650). From this qualitative description we built a quantitative model for the pathway, reusing existing curated models where possible and implementing new models as needed. This model employs a composite approach for generating modular models. The approach allows for the construction of large-scale complex model by combining component of sub-models that can be modified individually. This large pathway consists of two published sub-models; the Ca2+ model and the NFAT model, and a newly built FCεRIγ sub-model. The full pathway was fitted to published dataset and fitted well to one of two secreted cytokines. The model can be used to predict the production of IFNγ and TNFα cytokines. • Development of pathway and mathematical model• Reusing existing curated models and implementing new models• Model optimization and analysis
Impact of the COVID19 pandemic on the implementation of Health Information Technologies (HIT): a case study in Maternity Hospital of Lubango-Angola

Background COVID19 pandemic has shown the importance of data to manage health crises. Therefore, countries that were more mature regarding using Information Systems (IS) were better prepared to respond to their population needs. Unfortunately, in Angola, such Health Information Systems (HIS) maturity is very low, so new Health Information Technology (HIT) projects must change this scenario. Objective Describe the impact of COVID19 on a new health information technology project called "ObsCare Lubango" to collect essential data on deliveries and births at the Maternity Hospital in Lubango-Angola. Methods Retrieve data from the notes, communication events of the project management. Also, the collected data regarding obstetrics (pregnancies and childbirth) was from Jan 2019 to Apr 2021 (14 months before COVID19 and 14 months after the beginning of COVID19). The data analyzed were collected from the utilization audit trail that stores the sessions and clicks in the application logs. These logs are then presented in aggregated and anonymized form in a web interface. Results The start of COVID19 in Maternity halted the evolution of the health information project implementation. At the beginning of 2020, the usage of ObsCare Lubango was growing steadily (5.9%, 7.5%, 9.4% in the first three months), but rapidly dropped to 0% in the following months after the first of COVID arrived in late March. Conclusion COVID19 had a significant impact on the evolution of ObsCare Lubango and heavily impacted the quality of the data collected in the paper. COVID19 will probably increase the digital divide in health care between nations.
Do Playfulness and University Support Facilitate the Adoption of Online Education in a Crisis? COVID-19 as a Case Study Based on the Technology Acceptance Model

A large number of universities worldwide are paying more and more attention to the application and exploration of online education. As the group with the most significant number of online education users, their participation attitude and participation intention directly determine the teaching performance of online education. This research will incorporate playfulness teaching and scenario variables that reflect the universities’ ability to respond to emergencies. Based on the technology acceptance model, this research proposes an integrated research model of online education participation intention to investigate university students’ online education participation intention to reveal the key factors and mechanisms that affect online education participation intention. A structural equation model of participation intention is constructed, and 342 valid samples are obtained by questionnaire survey. The empirical results of PLS-SEM show that: (1) students’ participation attitude positively affects their participation intention; (2) the perceived ease of use and usefulness positively affect their participation attitude, and the perceived usefulness and ease of use affect their participation intention through the complete mediation of participation attitude; (3) the perceived playfulness does not have a significant impact on participation attitude but has a positive impact on participation intention; (4) the innovative discovery university support positively moderates the relationship between participation attitude and intention during such emergencies. The research found that improving students’ attitudes toward participation, perceived ease of use, usefulness, playfulness, and strengthening university support are all helpful to optimize students’ participation intention in online education. At the same time, it also explored operability suggestions for improving the quality of online education and optimizing future education.
Towards Deep Learning-Based Sarcopenia Screening with Body Joint Composition Analysis

Sarcopenia, a newly recognized geriatric syndrome, now prevalent in the rapidly aging region of Asia, is characterized by the age-related decline of skeletal muscle mass plus relatively low muscle strength and/or physical performance. Doctors screen for sarcopenia by observing patients’ habitual gait features without quantification and the performance of gait disturbances differ in various people that are considered to be sarcopenic, which is an important basis along with reduced physical functioning for the diagnosis of sarcopenia. Such a subjective diagnosis has been seen as a problem because diagnostic results may differ among doctors and factors such as fatigue may affect diagnosis. To strengthen and aid the use of these observations, we built a novel automatic deep learning model based on random forest for real-time human body joint detection coupled with a modified Long Short-Term Memory (LSTM) to recognize gait features for further clinical analysis. Aligned with the Asian Working Group for Sarcopenia (AWGS) [1] aims, our goal is to facilitate the implementation of standardized sarcopenia diagnosis in clinical practice by providing an automatic gait analysis system. Our model is recorded from geriatric patients for whole gait understanding. Experimental results demonstrate that our proposed model improves gait recognition performance compared to baseline methods. We believe, the quantitative evaluation provided by our method will assist the clinical diagnosis of sarcopenia and the experimental results on our gait datasets verify the feasibility and effectiveness of the proposed method.
The effect of the start and finish in the 50 m and 100 m freestyle performance in elite male swimmers

ABSTRACT The aim of this study was to: (1) verify differences between swimmers of the same competitive level in variables related to the start and finish (50 m and 100 m freestyle); (2) verify if starting and finish variables are responsible for faster race time, and which starting variables are responsible for the start performance in such events. For the 50 m and 100 m freestyle race at the junior European Championships 2019, 86 and 88 male swimmers were analysed, respectively. A set of starting and finishing variables were used for analysis. Both races (50 m: p < 0.001; 100 m: p < 0.001) presented a significant level effect for the final race time. The same trend was observed for the start and finish performances. For both races, hierarchical linear modelling retained the 15 m mark time and finish speed as predictors. The 50 m start retained the reaction time and underwater speed, and the 100 m start retained the reaction time and the water break distance. This indicates the underwater phase of the start is of substantial importance to improve the 15 m mark time. Coaches and swimmers are advised to enhance the start underwater phase, and finish segments to improve the swimmers’ performance.