Miracle-dz/UniFAR-FTUs huggingface dataset:
An ensemble of three-dimensional deep neural network models for multi-attribute scoring and classification of pulmonary nodules

Lung cancer is the uncontrolled growth of cells that originates in the lung parenchyma or cells that line the air passages. These cells divide rapidly to form malicious tumors. This paper proposes a multi-task ensemble of three dimensional (3D) deep neural network (DNN) based model, namely: pre-trained EfficientNetB0, BiGRU-based SEResNext101, and the proposed LungNet. The ensemble model performs binary classification and regression tasks to accurately classify the benign and malignant pulmonary nodules. This study also explores the attribute importance and proposes a domain knowledge-based regularization technique. The proposed model is evaluated on the public benchmark LIDC-IDRI dataset. Through a comparative study, it was shown that when coefficients generated by the random forest (RF) are used in the loss function, the proposed ensemble model offers a better prediction capability of the accuracy of 96.4% compared to the state-of-the-art methods. In addition, the receiver operating characteristic curves show that the proposed ensemble model has better performance than the base learners. Thus, the proposed CAD-based model can efficiently detect malignant pulmonary nodules.
Risk of malignancy and diagnostic accuracy of fine-needle aspiration biopsy in thyroid nodules with diameters greater than 4 centimeters

ABSTRACT Objective: The risk of malignancy and diagnostic accuracy of fine-needle aspiration biopsy (FNAB) of thyroid nodules (TN) with diameters ≥ 3-4 cm remains controversial. However, some groups have indicated surgical treatment in these patients regardless of the FNAB results. We aimed to evaluate the diagnostic accuracy of the FNAB in systematically resected ≥4 cm TN and if the risk of malignancy is higher in these patients. Subjects and methods: We retrospectively evaluated 138 patients (142 nodules) with TN with diameters ≥4 cm who underwent thyroidectomy. Results: The FNAB results were nondiagnostic/unsatisfactory (ND/UNS) in 2.1% of the cases and benign in 51.4%. They indicated atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS) in 23.9% of cases, follicular neoplasia/suspicious for a follicular neoplasm (FN/SFN) in 9.2%, suspicion of malignancy (SUS) in 8.5%, and malignant in 4.9%. The histopathological analysis after thyroidectomy revealed a thyroid cancer rate of 100% in the FNABs classified as malignant, 33.3% in SUS cases, 7.7% in FN/SFN, 17.6% in AUS/FLUS, and 4.1% in benign FNABs. None of the ND/UNS FNABs were malignant. The global malignancy diagnosis was 14.8% (n = 21). However, the rate of false negatives for FNAB was low (4.1%). Conclusion: We showed that the risk of malignancy in nodules with diameters ≥4 cm was higher compared to the risk of thyroid cancer in TN in general. However, we found a low rate of false-negative cytological results; therefore, our data do not justify the orientation of routine resection for these larger nodules.
Age differences in neural distinctiveness during memory encoding, retrieval, and reinstatement

Abstract Robust evidence points to mnemonic deficits in older adults related to dedifferentiated, i.e. less distinct, neural responses during memory encoding. However, less is known about retrieval-related dedifferentiation and its role in age-related memory decline. In this study, younger and older adults were scanned both while incidentally learning face and house stimuli and while completing a surprise recognition memory test. Using pattern similarity searchlight analyses, we looked for indicators of neural dedifferentiation during encoding, retrieval, and encoding–retrieval reinstatement. Our findings revealed age-related reductions in neural distinctiveness during all memory phases in visual processing regions. Interindividual differences in retrieval- and reinstatement-related distinctiveness were strongly associated with distinctiveness during memory encoding. Both item- and category-level distinctiveness predicted trial-wise mnemonic outcomes. We further demonstrated that the degree of neural distinctiveness during encoding tracked interindividual variability in memory performance better than both retrieval- and reinstatement-related distinctiveness. All in all, we contribute to meager existing evidence for age-related neural dedifferentiation during memory retrieval. We show that neural distinctiveness during retrieval is likely tied to recapitulation of encoding-related perceptual and mnemonic processes.
Clinical application of novel integrated suctioning semi-rigid ureteroscopic lithotripsy

Abstract Introduction Urinary calculi are frequently encountered in urology. Traditionally, the lack of a perfect water injection and drainage system means the observation field is affected during ureteroscopy. Here, we explored the effect and clinical value of a new integrated suctioning semi-rigid ureteroscopic lithotripsy (URSL) for treating ureteral calculi. Material and methods A total of 180 patients were successfully enrolled in this study (60 in each group). Group A included patients who underwent a traditional semi-rigid URSL, group B included patients who underwent a suctioning semi-rigid URSL with a sheath being connected to a vacuum device, and group C included patients who underwent a new type of suctioning integrated rigid URSL with a novel designed ureteroscope. Results In total, 164 cases of URSL were completed in one stage. Compared with group A, group C had a higher stone-clearance rate at 30 days postoperatively, shorter operation time, and fewer hospitalization days (p < .05); compared with group B, group C had a higher one-stage operation success rate, shorter operation time, and fewer hospitalization days (p < .05). Conclusions Comparatively, the new suctioning integrated semi-rigid URSL is advantageous for treating upper urinary calculi, considering the reduced operation time, length of hospital stay, and low invasiveness.
Diagnostic accuracy of hand-held colposcope (Gynocular) in comparison with standard colposcope in patients with abnormal cervical cytology or visual inspection with acetic acid positivity: a cross over randomized controlled study

Objective To compare the diagnostic accuracy of a handheld colposcope (Gynocular) versus a standard colposcope in women with abnormal cervical cytology or visual Inspection with acetic acid positivity. Methods This crossover randomized clinical trial was conducted in Pondicherry, India, and included 230 women who were referred for colposcopy. Swede scores were calculated using both colposcopes, and a cervical biopsy was performed from the most visually abnormal areas. Swede scores were compared with the histopathological diagnosis, which was used as the reference standard. The level of agreement between the two colposcopes was calculated using Kappa (κ) statistics. Results The level of agreement of Swede scores between the standard and Gynocular colposcopes was 62.56%, and the κ statistic was 0.43 (P<0.001). Cervical intraepithelial neoplasia (CIN) 2+ (CIN 2, CIN 3, CIN 3+) was diagnosed in 40 (17.4%) women. There were no significant differences between the two colposcopes in terms of sensitivity, specificity, or predictive value for detecting CIN 2+ lesions. Conclusion The diagnostic accuracy of Gynocular colposcopy was similar to that of standard colposcopy for detecting CIN 2+ lesions. Gynocular colposcopes showed a good level of agreement with standard colposcopes when the Swede score was used.
Numerical Modeling of a Coastal Trapped Disturbance. Part I: Comparison with Observations

Abstract The coastal trapped disturbance (CTD) of 15–17 May 1985 represents an example of a strong mesoscale trapped event along the west coast of North America with abrupt transitions in many basic meteorological parameters. In this study, a comparison between observations and a numerical simulation of this event using the Regional Atmospheric Modeling System (RAMS) is presented. The model is shown to realistically reproduce CTD characteristics such as the coastal transition from northerly to southerly flow, as a mesoscale coastal ridge of higher pressure with associated drops in marine-layer temperature propagates northward along the west coast of North America. Simulated sea level pressure and temperature fields near the surface match well with observations, especially at the synoptic scale. The model realistically simulates mesoscale sea level pressure and 6-h pressure changes during the event. The modeled hourly time evolution of sea level pressure and the southerly transitions at a series of coastal.
Oral postbiotics derived from Lactobacillus sp. in treatment of atopic dermatitis: a meta-analysis.

INTRODUCTION The use of postbiotics, which are defined as dead microorganisms and/or their components that provide health benefits to the target host, has been shown to reduce the severity of atopic dermatitis (AD) in several studies. METHODS A systematic literature review was conducted in Pubmed, the Cochrane Library, Science Direct, Clinicaltrials.gov, and Google Scholar, covering the period from January 2012 to July 2022 in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. AD patients of all ages that received oral postbiotics or placebo as treatment were the focus of the study. The main study outcome was the scoring of atopic dermatitis (SCORAD) and other measures, such as extension area, disease intensity, and adverse events. The final data were pooled using a fixed-effect model. RESULTS A meta-analysis of three studies found that, compared to placebo, SCORAD was lower in subjects that were given oral postbiotics from Lactobacillus sp. (mean difference: -2.90, 95% confidence interval [CI; -4.21, -1.59], p < 0.00001). From the comparison of two studies, the differences in disease extension (mean difference: -2.40, 95% CI [-7.67, 2.81], p = 0.37) and intensity (mean difference: -0.27, 95% CI [-0.84, 0.30], p = 0.36) were not significant. CONCLUSIONS The administration of oral postbiotics from Lactobacillus sp. has the potential to alleviate the severity of AD as indicated by a reduction in SCORAD scores.
Effect of insulin resistance on left ventricular remodelling in essential hypertensives: a cross-sectional study.

BACKGROUND In clinical practice, left ventricular hypertrophy (LVH) is defined by physical findings and electrocardiographic criteria, which are useful but imperfect tools, echocardiographic criteria and cardiac magnetic resonance imaging. In echocardiography, LVH is defined not by left ventricular wall thicknesses but by left ventricular mass. The latter is calculated according to Devereux's formula, and is increased by insulin resistance/hyperinsulinaemia. It is however unclear whether insulin resistance, hyperinsulinaemia, or both, is actually causative and what their collective or individual influence is on the components of Devereux's formula and parameters of left ventricular diastolic function. This study evaluated the associations of the homeostatic model assessment for insulin resistance (HOMAIR) and fasting plasma insulin levels with components of Devereux's formula and parameters of left ventricular diastolic function. METHODS Relevant clinical data were collected from 220 hypertensive patients recruited between January and December 2019. The associations of components of Devereux's formula and parameters of diastolic function with insulin resistance were tested using binary ordinal, conditional and classical logistic regression models. RESULTS Thirty-two (14.5%) patients (43.9 ± 9.1 years), 99 (45%) patients (52.4 ± 8.7 years) and 89 (40.5%) patients (53.1 ± 9.8 years) had normal left ventricular geometry, concentric left ventricular remodelling and concentric left ventricular hypertrophy, respectively. In multivariable adjusted analysis, 46.8% of variation in interventricular septum diameter (R² = 0.468; overall p = 0.001) and 30.9% of E-wave deceleration time (R² = 0.309; overall p = 0.003) were explained by insulin level and HOMAIR, 30.1% of variation in left ventricular end-diastolic diameter (R² = 0.301; p = 0.013) by HOMAIR alone, and 46.3% of posterior wall thickness (R² = 0.463; p = 0.002) and 29.4% of relative wall thickness (R² = 0.294; p = 0.007) by insulin level alone. CONCLUSIONS Insulin resistance and hyperinsulinaemia did not have the same influence on the components of Devereux's formula. Insulin resistance appeared to act on left ventricular end-diastolic diameter, while hyperinsulinaemia affected the posterior wall thickness. Both abnormalities acted on the interventricular septum and contributed to diastolic dysfunction via the E-wave deceleration time.
Data-glove-driven vocal tract configuration methods for vowel synthesis

This paper describes data-glove-driven vocal tract configuration methods. Unlike direct mapping from hand gestures to sounds using a data glove, intuitive manipulation of the data glove was applied to configure the vocal tract shape. Two manipulation methods were proposed and then evaluated in terms of the vocal tract area function, resulting formant frequencies and ease of manipulation. It was revealed that although both methods were capable of producing the resulting formant frequencies with reasonable accuracy for steady vowel production, the method with three fingers enabled users to easily configure the vocal tract shape. Moreover, the effect of training in manipulating the data glove to configure the vocal tract shape for continuous vowels was evaluated in terms of their sound spectrograms and the distribution of the resulting formant frequencies. An experiment to evaluate the effectiveness of training proved that beginners were able to produce continuous vowels within about three training sessions.
Constitutive Modeling of Mechanical Behaviors of Carbon-Based CNTs and GSs, and Their Sensing Applications as Nanomechanical Resonators: A Review

Carbon-based nanomaterials, including carbon nanotubes (CNTs) and graphene sheets (GSs), have garnered considerable research attention owing to their unique mechanical, physical, and chemical properties compared with traditional materials. Nanosensors are sensing devices with sensing elements made of nanomaterials or nanostructures. CNT- and GS-based nanomaterials have been proved to be very sensitive nanosensing elements, being used to detect tiny mass and force. In this study, we review the developments in the analytical modeling of mechanical behavior of CNTs and GSs, and their potential applications as next-generation nanosensing elements. Subsequently, we discuss the contributions of various simulation studies on theoretical models, calculation methods, and mechanical performance analyses. In particular, this review intends to provide a theoretical framework for a comprehensive understanding of the mechanical properties and potential applications of CNTs/GSs nanomaterials as demonstrated by modeling and simulation methods. According to analytical modeling, nonlocal continuum mechanics pose small-scale structural effects in nanomaterials. Thus, we overviewed a few representative studies on the mechanical behavior of nanomaterials to inspire the future development of nanomaterial-based sensors or devices. In summary, nanomaterials, such as CNTs and GSs, can be effectively utilized for ultrahigh-sensitivity measurements at a nanolevel resolution compared to traditional materials.
Urinary collagen peptides: Source of markers for bone metabolic processes in kidney transplant recipients

Introduction: Kidney transplant recipients (KTRs) are at an increased risk of fractures. Total urinary hydroxyproline excretion served as marker for bone resorption (BR) but was replaced by β‐CrossLaps (CTX), a C‐terminal collagen α‐1(I) chain (COL1A1) telopeptide. We investigated the low‐molecular‐weight urinary proteome for peptides associated with changes in bone metabolism after kidney transplantation. Methods: Clinical and laboratory data including serum levels of CTX in 96 KTR from two nephrology centers were correlated with signal intensities of urinary peptides identified by capillary electrophoresis mass spectrometry. Results: Eighty‐two urinary peptides were significantly correlated with serum CTX levels. COL1A1 was the predominant peptide source. Oral bisphosphonates were administered for decreased bone density in an independent group of 11 KTR and their effect was evaluated on the aforementioned peptides. Study of the peptides cleavage sites revealed a signature of Cathepsin K and MMP9. Seventeen of these peptides were significantly associated with bisphosphonate treatment, all showing a marked reduction in their excretion levels compared to baseline. Discussion: This study provides strong evidence for the presence of collagen peptides in the urine of KTR that are associated with BR and that are sensitive to bisphosphonate treatment. Their assessment might become a valuable tool to monitor bone status in KTR.
Modelling the rejection probability of a quality test consisting of multiple measurements

Quality control is an essential part of manufacturing, and the different properties of the products can be tested with standardized methods. If the decision of qualification is based on only one test specimen representing a batch of products, the testing procedure is quite straightforward. However, when the measured property has a high variability within the product, as usual, several test specimens are needed for the quality verification. When a quality property is predicted, the response value of the model that most effectively finds the critical observations should naturally be selected. In this thesis, it has been shown that LIB-transformation (Larger Is Better) is a suitable method for multiple test samples, because it effectively recognizes especially the situations where one of the measurements is very low. The main contribution of this thesis is to show how to model quality of phenomena that consist of several measurement samples for each observation. The process contains several steps, beginning from the selection of the model type. Prediction of the exceedance probability provides more information for the decision making than that of the mean. Especially with the selected application, where the quality property has no optimal value, but the interest is in adequately high value, this approach is more natural. With industrial applications, the assumption of constant variance should be analysed critically. In this thesis, it is shown that exceedance probability modelling can benefit from the use of an additional variance model together with a mean model in prediction. The distribution shape modelling improves the model further, when the response variable may not be Gaussian. As the proposed methods are fundamentally different, the model selection criteria have to be chosen with caution. Different methods for model selection were considered and commented, and EPS (Exceedance Probability Score) was chosen, because it is most suitable for probability predictors. This thesis demonstrates that especially a process with high diversity in its production and more challenging distribution shape gains from the deviation modelling, and the results can be improved further with the distribution shape modelling.
Modules Whose Endomorphism Rings are Unit-Regular

Unit-regular rings have been extensively studied in the literature. In this article, we introduce the notion of unit endoregular modules which generalizes that of unit-regular rings. A module is called unit endoregular if its endomorphism ring is unit-regular. It is proved that a ring R is a right V-ring if and only if every finitely cogenerated right R-module is unit endoregular. Some characterizations of unit endoregular modules are provided so that several known results on unit-regular rings are generalized. We also provide some properties of unit endoregular modules. For instance, any module which is mutually subisomorphic to a unit endoregular module M is isomorphic to M.
Adaptive Region-Specific Loss for Improved Medical Image Segmentation

Defining the loss function is an important part of neural network design and critically determines the success of deep learning modeling. A significant shortcoming of the conventional loss functions is that they weight all regions in the input image volume equally, despite the fact that the system is known to be heterogeneous (i.e., some regions can achieve high prediction performance more easily than others). Here, we introduce a region-specific loss to lift the implicit assumption of homogeneous weighting for better learning. We divide the entire volume into multiple sub-regions, each with an individualized loss constructed for optimal local performance. Effectively, this scheme imposes higher weightings on the sub-regions that are more difficult to segment, and vice versa. Furthermore, the regional false positive and false negative errors are computed for each input image during a training step and the regional penalty is adjusted accordingly to enhance the overall accuracy of the prediction. Using different public and in-house medical image datasets, we demonstrate that the proposed regionally adaptive loss paradigm outperforms conventional methods in the multi-organ segmentations, without any modification to the neural network architecture or additional data preparation.