Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Comprehensive Rehabilitation on Pulmonary Function in Patients Recovering from COVID-19

The aim of this study is to evaluate the effect of inpatient rehabilitation on pulmonary function in patients recovering from COVID-19, a multifaceted disease caused by the SARS-CoV-2 virus. This aspect of recovery is crucial, as pneumonia associated with this disease can cause lung-function abnormalities with varying degrees of hypoxemia. This study involved 150 patients qualified for inpatient rehabilitation following SARS-CoV-2 infection. Functional assessment of the lungs was performed by spirometry. The mean age of patients was 64.66 (±11.93) years, and the mean body mass index (BMI) was 29.16 (±5.68). The tests showed a statistically significant improvement in spirometric parameters. The rehabilitation program based on aerobic, strength, and endurance exercises improved lung-function parameters in the long term. The improvement in spirometric parameters in patients after COVID-19 may be related to body mass index (BMI).
Characterizing Dietary Advanced Glycation End-Product (dAGE) Exposure and the Relationship to Colorectal Adenoma Recurrence: A Secondary Analysis

Limited studies have evaluated the association between dietary advanced glycation end-product AGE (dAGEs) intake and cancer risk; however, no studies have addressed adenoma risk or recurrence. The objective of this study was to determine an association between dietary AGEs and adenoma recurrence. A secondary analysis was conducted using an existing dataset from a pooled sample of participants in two adenoma prevention trials. Participants completed a baseline Arizona Food Frequency Questionnaire (AFFQ) to estimate AGE exposure. NƐ- carboxymethyl-lysine (CML)-AGE values were assigned to quantify foods in the AFFQ using a published AGE database, and participants’ exposure was evaluated as a CML-AGE (kU/1000 kcal) intake. Regression models were run to determine the relationship between CML-AGE intake and adenoma recurrence. The sample included 1976 adults with a mean age of 67.2 y ± 7.34. The average CML-AGE intake was 5251.1 ± 1633.1 (kU/1000 kcal), ranging between 4960 and 17032.4 (kU/1000 kcal). A higher intake of CML-AGE had no significant association with the odds of adenoma recurrence [OR(95% CI) = 1.02 (0.71,1.48)] compared to participants with a lower intake. In this sample, CML-AGE intake was not associated with adenoma recurrence. Future research is needed and should be expanded to examine the intake of different types of dAGEs with consideration for the direct measurement of AGE.
Quality of life in parents of autistic children: A transcultural perspective

Introduction The concepts of health, illness, and disability as well as the perceptions of autism and quality of life (QoL) vary greatly across cultures and across time. This study sought to explore the interplay of culture on QoL and impact on parents caring for autistic children. Methods We used a transcultural dataset from seven countries (Australia, Hungary, Malaysia, Romania, Singapore, Spain, and the United Kingdom) with participating parents/carers reporting on the Quality of Life in Autism (QoLA) questionnaire. The QoLA questionnaire is a validated measure of QoL for parents of autistic children, with Part A subscale measuring parental QoL and part B subscale assessing the parental impact of the child’s autism spectrum disorder (ASD) symptoms or features. We used the Quade’s ranked analysis of covariance to determine significant differences between the countries in relation to QoLA Part A and Part B scores while adjusting for baseline differences using covariates such as parents’ gender, child’s age, and gender. Additionally, a post-hoc analysis with Bonferroni correction was also conducted to examine multiple pairwise comparisons. Results and conclusion We found that while the effect of features of ASD (Part B subscale) stayed strongly comparable between cultures, the self-reported parental QoL was most likely determined by different aspects of culture in different countries. It is concluded that while the ASD symptoms or features appear to affect parents in the same way across different countries, the parental QoL may be a culturally informed construct.
Toward fiber-based high-frequency 3D ultrasound imaging

We present a fiber-based optical detection system for high-frequency 3D ultrasound and photoacoustic imaging. Optically probing the surface of a thin polymer Fabry-Perot etalon defines the acoustic array geometry and element size. We have previously demonstrated wide bandwidth signal detection (>40 MHz) and element size on the order of 15 &mgr;m. By integrating an etalon into a photoacoustic imaging system, high-resolution 3D images were obtained. However, the previous system is limited for clinical applications because the etalon is rigidly attached to a free-space optical scanning system. To move etalon detector technology toward a practical clinical device, we designed a remote-probe system based on a fiber bundle. A fiber bundle, composed of 160,000 individual light guides of 8-&mgr;m diameter, delivers the optical probe to the etalon. Light coupled into a single guide creates an active element on the etalon surface. We successfully measured the ultrasound signals from 10 MHz and 50 MHz ultrasound transducers using a laser tunable around 1550 nm. With further progress on reducing the size of the etalon, it will be possible to build a practical device for in vivo high-frequency 3D ultrasound and photoacoustic imaging, especially for intravascular and endoscopic applications.
Histopathological image-based breast cancer detection employing 3D-convolutional neural network feature extraction and Stochastic Diffusion Kernel Recursive Neural Networks classification

ABSTRACT Among the various cancer categories, breast cancer diagnoses are commonly found in many women, and automatic classification of breast cancer images is a crucial task using computer-aided analysis. This paper proposed a method for the detection of histopathological image-based breast cancer and the main motive of the technique is to employ feature extraction and classification based on DL techniques. The input image is filtered using edge smoothening for noise removal and image resizing on the pre-processing method and used 3D-Convolutional Neural network (3D_CNN) for extracting the features of the image process. Extracted deep features have been given as input for the classification process using Stochastic Diffusion Kernel Recursive Neural Networks (SDKRNN). The suggested model combines the sturdiness of convolutional and kernel blocks and demonstrates performance stability when compared to existing techniques. The experimental results are assessed using a variety of performance metrics and compared to current breast cancer detection methodologies. The result demonstrated the experimental evaluation analyzed for various datasets in terms of accuracy, precision, F-1 score, specificity, and AUC of 98%, 93.8%, 89%, 80%, and 70% and it determined the outperforms in the detection of breast cancer.
Ship Contour Extraction From SAR Images Based on Faster R-CNN and Chan–Vese Model

Compared with most ship detection methods for synthetic aperture radar (SAR) images, ship contour extraction can provide more of the detailed shape and edge information of an observed ship and play a significant role in sea surface monitoring and marine transportation. In this study, a joint ship contour extraction method (faster region convolutional neural network (R-CNN), fast nonlocal mean (FNLM) filter and Chan–Vese model (FFCV) method) was proposed to obtain detailed ship information from SAR images, including ship detection in complex scenes and contour extraction in target slices. First, Faster R-CNN was employed to slice ships from large-scene SAR images. Then, FNLM filtering was applied to denoise and enhance the structural information of the target slices. Finally, an optimized Chan–Vese model was proposed in this article, which can not only accurately extract the contour of the observed ship but also reduce the computation time of the model. The SAR ship detection dataset (SSDD) was selected and finely relabeled to evaluate the contour extraction performance. An evaluation index $R_{N}$ , including quantitative value and offset direction, was developed to evaluate the extraction accuracy of the target contour from the SAR images. Compared with the Mask R-CNN network, the average contour extraction accuracy index $R_{N}$ of the proposed FFCV method reached −0.002 on all the images in the SSDD dataset, and its results were closer to the real ship contours while maintaining the applicability to complex scenes.
External validation of a convolutional neural network for the automatic segmentation of intraprostatic tumor lesions on 68Ga-PSMA PET images

Introduction State of the art artificial intelligence (AI) models have the potential to become a “one-stop shop” to improve diagnosis and prognosis in several oncological settings. The external validation of AI models on independent cohorts is essential to evaluate their generalization ability, hence their potential utility in clinical practice. In this study we tested on a large, separate cohort a recently proposed state-of-the-art convolutional neural network for the automatic segmentation of intraprostatic cancer lesions on PSMA PET images. Methods Eighty-five biopsy proven prostate cancer patients who underwent 68Ga PSMA PET for staging purposes were enrolled in this study. Images were acquired with either fully hybrid PET/MRI (N = 46) or PET/CT (N = 39); all participants showed at least one intraprostatic pathological finding on PET images that was independently segmented by two Nuclear Medicine physicians. The trained model was available at https://gitlab.com/dejankostyszyn/prostate-gtv-segmentation and data processing has been done in agreement with the reference work. Results When compared to the manual contouring, the AI model yielded a median dice score = 0.74, therefore showing a moderately good performance. Results were robust to the modality used to acquire images (PET/CT or PET/MRI) and to the ground truth labels (no significant difference between the model’s performance when compared to reader 1 or reader 2 manual contouring). Discussion In conclusion, this AI model could be used to automatically segment intraprostatic cancer lesions for research purposes, as instance to define the volume of interest for radiomics or deep learning analysis. However, more robust performance is needed for the generation of AI-based decision support technologies to be proposed in clinical practice.
When the disposition effect proves to be rational: Experimental evidence from professional traders

The disposition effect is a behavioural finance anomaly that has been observed in many populations including non-professional investors as well as professional investors and has been linked to reduced trading performance. However, the majority of studies to date have looked at the disposition effect in the context of non-mean reverting markets. We conducted a within-subject experiment with n = 193 professional traders, to examine how the tendency towards the disposition effect varies across decision-making for mean reverting securities and non-mean reverting securities. In addition, we consider whether a simple informational intervention that makes the disposition effect salient can alter decision-making. Overall, we find that prior to the intervention the traders exhibit the disposition effect in the direction that aligns with profit maximisation goals suggesting that they are acting rational. For decisions on mean reverting securities the traders tend to make decisions in the direction of the disposition effect, which is rational given their mean reverting properties. We also find that the informational intervention is effective in changing the level of the disposition effect observed and decision-making, regardless of whether traders are considering decisions over mean reverting or non-mean reverting securities. Further, we provide evidence that our simple informational intervention improves trader returns when making decisions on non-mean reverting securities. In contrast, it has a negative impact when utilised for mean reverting securities. Our study highlights the power of simple interventions to make disproportionately large changes to decision-making regardless of whether they are in our best interests, and their beneficial role only when the context is right.
Elemental Abundance Analyses with DAO Spectrograms. XXXIV. A Three-Dimensional Graphical Examination of the Elemental Abundances of the Mercury-Manganese and Metallic-Line Stars

Detailed analyses of high-dispersion, high signal-to-noise spectra enable astronomers to infer many stellar properties. We study nonmagnetic normal and chemically peculiar B, A, and F stars to understand the details of their optical region abundances via graphical techniques using two kinds of figures for 32 elements. By characterizing the anomalies of the mercury-manganese (HgMn) and the metallic-line (Am) stars, we provide major theoretical tests. We confirmed the known Hg dichotomy between HgMn stars, which are greatly overabundant, and the Am stars with normal abundances. Further P, Ga, Xe, Pt, and Au values were only overabundant for some HgMn stars, and lines of the rare earth elements, such as Sm and Eu, were seen only in some Am and normal stars. These observations might be due in some cases to changes in the major ionization state of atoms in the relevant stellar atmosphere. That some HgMn stars with large Ga overabundances have positions close in the H-R diagram to HgMn stars that lack Ga II lines in the optical region suggests a dichotomy similar to Hg with a boundary close to, but not identical, to that for Hg. The spread of the abundance anomalies for a given element tends to be smaller among the Am stars than among the HgMn stars. Star-to-star differences are superimposed upon abundance trends.
Forecast of severe typhoon formation based on unbalanced data

As extreme weather events, severe typhoons have a great impact on people's daily production and social stability. Therefore, many scholars have used different methods to predict the formation of severe typhoons in order to mitigate the impact of severe typhoons. However, the severe typhoon in reality is a small probability event and the number of severe typhoon samples used for prediction is much smaller than the number of ordinary typhoon samples. Existing models rarely consider the impact of unbalanced data on model training and prediction, which makes it difficult to apply the model to the actual severe typhoon prediction. Therefore, we propose a severe typhoon formation prediction model based on unbalanced data. The model uses a convolutional neural network to obtain features from the severe typhoon environmental field and an LSTM model to implement the prediction of severe typhoon formation. A customized loss function designed in this paper is used to add different classification weights for normal typhoon samples and severe typhoon samples, so that the model improves the prediction of unbalanced severe typhoon formation. The experiments show that the severe typhoon formation prediction model based on unbalanced data outperforms the traditional machine learning model for unbalanced severe typhoon formation prediction.
Optimization of the Danish National Electronic Prescribing System to Improve Patient Safety: Development of a User-Friendly Prototype of the Digital Platform Shared Medication Record

This study uses a participatory design to develop a user-friendly prototype of the current Danish digital platform, Shared Medication Record (SMR), to improve patient safety and minimize medication errors for patients with multimorbidity. A fundamental challenge for medication prescribing is the lack of access to an accurate medication list, which impairs effective communication between healthcare professionals and increases the risk of medication errors. We used a participatory design to identify the major problems with the existing SMR and develop a prototype for a redesigned SMR that addresses these problems. We argue that this prototype will improve communication between healthcare providers, promote patient involvement in their own care, and ultimately reduce medication errors related to the SMR. Moreover, we argue that the participatory design with its emphasis on user involvement and design iterations is a strong approach when designing IT solutions for complex problems in healthcare.
Towards a comprehensive evaluation of V-I-S sub-pixel fractions and land surface temperature for urban land-use classification in the USA

Remote-sensing image classification based on the vegetation–impervious surface–soil (V-I-S) model and land-surface temperature (LST) has proved to be more efficient in characterizing the urban landscape than conventional spectral-based classification. However, current literature emphasizes discussion of the classifier's accuracy improvement achieved by the input of V-I-S fractions and LST over conventional spectral-based classification while ignoring the stability evaluation. Hence, this study proposes an evaluation framework for exploring the superiority of the input features and the stability of classifiers by integrating statistical randomization techniques and a kappa-error diagram. The evaluation framework was applied to case studies for demonstrating the superiority of V-I-S fractions and LST in the context of urban land-use classification with five different types of classifiers, including the maximum likelihood classifier (MLC), the tree classifier, the Bagging classifier, the random forest (RF) and the support vector machine (SVM). It followed that the use of V-I-S fractions and LST (1) could alleviate the ‘salt and pepper’ effect; (2) is preferred by tree and tree-based ensembles for branch splitting; (3) could produce classification trees with less complexity; (4) could benefit the stability of classifiers in addition to the accuracy improvement; and (5) could allow histograms following nearly normal distribution in its feature space, which boosts the performance of MLC. It is shown that MLC becomes comparable with modern classifiers when trained with V-I-S fractions and LST combination. Because of its adequacy and simplicity, MLC is recommended for urban land-use classification when V-I-S fractions and LST are used as the only input features. However, replacing them with, or including, the band reflectance might degrade MLC. A direct use of spectral band reflectance is not recommended for any of the classification approaches being considered in this study, except for SVM, which is the most robust classifier as it has a consistently high performance for all the input feature combinations. We recommend using tree-based ensemble classifiers or SVM when V-I-S fractions and LST as well as the band reflectance are all used in the classification. The proposed evaluation framework can also be applied to the assessment of input features and classifiers in other remote-sensing classification endeavours.
Comprehensive analysis of the internal losses in 2.0μm(AlGaIn)(AsSb) quantum-well diode lasers

We have fabricated and characterized high-power 2.0 μm-wavelength (AlGaIn)(AsSb) quantum-well diode lasers emitting a power of 1.7 W in continuous-wave operation and over 9 W in pulsed operation at 300 K heat sink temperature. For potential further improvement of laser performance, the different contribution to the internal losses αi has been analyzed in detail for the present laser structure. Consistent results have been obtained for a series of samples, for which different design parameters were varied systematically: As expected, the losses in the cladding layers are dominated by free carrier absorption in the p-doped cladding. The cross section for free-hole absorption in Al0.84Ga0. 16As0.06Sb0. 94 is determined to σP=4.6×10−17 cm2, which is comparable to values reported in the literature for (AlGaIn)(AsP)-based lasers emitting at 1.5 μm. The losses in the active region were found to increase linearly with increasing number of quantum wells at a rate of 1.5 cm−1 per quantum well, whereas the losses in t.
Evaluating parental experiences in using technology for remote learning to teach students with special needs during the COVID-19 pandemic

Background The 2020–2021 school year shut down because of the COVID-19 pandemic brought an unprecedented burden on parents, especially those with special needs children. Parents with children with special needs were left to assist their children with remote learning at home using technology for the first time. These students with special needs were used to face-to-face and one-to-one classroom learning by skilled educators but are now left to be educated by their parents. Objective This study explored parents’ experiences assisting their special needs children with remote learning for the first time, using technology at home during the COVID-19 pandemic. Method A transcendental (descriptive) phenomenology was undertaken to explore the experiences of nine participants, recruited from two school divisions in Manitoba, Canada, on their child’s education and the challenges they experienced during remote learning from home. A purposive sampling technique was used, and data were collected through telephone interviews. Results Eight out of nine parents reported a negative experience with remote learning. Four major themes emerged after the data analysis: participants’ fear and anxiety during remote learning, difficulty maintaining routines during remote learning, students’ behavioral issues and mental health changes during remote learning, and lack of home support during remote learning. Furthermore, results indicated that integrating technology in remote learning for students with special needs was ineffective. Conclusion This study suggests poor communication between parents and teachers, and parents’ desire to be involved in planning remote learning for students with special needs during the COVID-19 pandemic lockdown was not met. This study also suggests that schools failed to meet students’ IEPs during remote learning. Furthermore, this study highlights that remote learning for special-needs students is inappropriate without educational assistance.
Impact of Terrain Altitude and Cloud Height on Ozone Remote Sensing from Satellite

AbstractTerrain and cloud height heavily impact ozone information despite ozone being concentrated in the stratosphere. The ozone weighting function (OWF) provides important information towards understanding the capabilities and limitations of a given channel. The factors that impact the OWF can be analyzed using radiative transfer theory and modeling. At the 9.6-μm infrared spectral region, both the OWF values and peaks are related to the surface temperature, terrain altitude, and cloud height. Warmer surface temperatures, lower terrain altitude, or lower cloud levels will give larger weighting function values, and the peak of the weighting function slightly increases with the increase in surface temperature, terrain altitude, or cloud height. For longer UV bands such as 306 and 318 nm, OWF shows smaller values for higher terrains, while showing larger values when clouds are present. However, in the shorter UV bands such as 274 and 288 nm, OWF has almost no relationship with the surface and clouds. There.