Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatio‐temporal graph convolutional network for diagnosis and treatment response prediction of major depressive disorder from functional connectivity

The pathophysiology of major depressive disorder (MDD) has been explored to be highly associated with the dysfunctional integration of brain networks. It is therefore imperative to explore neuroimaging biomarkers to aid diagnosis and treatment. In this study, we developed a spatiotemporal graph convolutional network (STGCN) framework to learn discriminative features from functional connectivity for automatic diagnosis and treatment response prediction of MDD. Briefly, dynamic functional networks were first obtained from the resting‐state fMRI with the sliding temporal window method. Secondly, a novel STGCN approach was proposed by introducing the modules of spatial graph attention convolution (SGAC) and temporal fusion. A novel SGAC was proposed to improve the feature learning ability and special anatomy prior guided pooling was developed to enable the feature dimension reduction. A temporal fusion module was proposed to capture the dynamic features of functional connectivity between adjacent sliding windows. Finally, the STGCN proposed approach was utilized to the tasks of diagnosis and antidepressant treatment response prediction for MDD. Performances of the framework were comprehensively examined with large cohorts of clinical data, which demonstrated its effectiveness in classifying MDD patients and predicting the treatment response. The sound performance suggests the potential of the STGCN for the clinical use in diagnosis and treatment prediction.
Shaping efficiency of WaveOne Gold and One Curve in simulated J-shaped root canals

One Curve (OC) is a single-file system that works in a continuous rotation movement. It has several tip diameters (0.25, 0.35 and 0.40) and tapers (4% and 6%). It is manufactured from C-Wire by using proprietary heat treatment in 2017. This alloy has two main steps: an initial electropolishing and a subsequent heat-treatment which provide its advanced properties (5). It reduces treatment time and allows irrigation more time during root canal therapy. It is a hyper-flexible instrument through its control memory technology (6). One G (OG) is a glide path file that is a compliment for One Curve system. It has 0.14 mm tip diameter and 3% constant taper. It has an asymmetrical crosssection with a variable pitch between each cutting edge, which limits its screwing effect. According to the manufacturer claims, it respects and maintains original root canal anatomy and could be used in thin and curved root canals safely (7).
ABCNet v2: Adaptive Bezier-Curve Network for Real-Time End-to-End Text Spotting

End-to-end text-spotting, which aims to integrate detection and recognition in a unified framework, has attracted increasing attention due to its simplicity of the two complimentary tasks. It remains an open problem especially when processing arbitrarily-shaped text instances. Previous methods can be roughly categorized into two groups: character-based and segmentation-based, which often require character-level annotations and/or complex post-processing due to the unstructured output. Here, we tackle end-to-end text spotting by presenting Adaptive Bezier Curve Network v2 (ABCNet v2). Our main contributions are four-fold: 1) For the first time, we adaptively fit arbitrarily-shaped text by a parameterized Bezier curve, which, compared with segmentation-based methods, can not only provide structured output but also controllable representation. 2) We design a novel BezierAlign layer for extracting accurate convolution features of a text instance of arbitrary shapes, significantly improving the precision of recognition over previous methods. 3) Different from previous methods, which often suffer from complex post-processing and sensitive hyper-parameters, our ABCNet v2 maintains a simple pipeline with the only post-processing non-maximum suppression (NMS). 4) As the performance of text recognition closely depends on feature alignment, ABCNet v2 further adopts a simple yet effective coordinate convolution to encode the position of the convolutional filters, which leads to a considerable improvement with negligible computation overhead. Comprehensive experiments conducted on various bilingual (English and Chinese) benchmark datasets demonstrate that ABCNet v2 can achieve state-of-the-art performance while maintaining very high efficiency. More importantly, as there is little work on quantization of text spotting models, we quantize our models to improve the inference time of the proposed ABCNet v2. This can be valuable for real-time applications. Code and model are available at: https://git.io/AdelaiDet.
A 32 × 32-Pixel Flash LiDAR Sensor With Noise Filtering for High-Background Noise Applications

This article introduces a pulsed laser direct time-of-flight (dTOF) flash light detection and ranging (LiDAR) sensor fabricated in 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> HV CMOS technology. The chip includes <inline-formula> <tex-math notation="LaTeX">$32\times 32$ </tex-math></inline-formula> macro pixels and 1024 time-to-digital converters (TDCs). A noise filtering circuit with different threshold (<inline-formula> <tex-math notation="LaTeX">$\text{N}_{\mathrm {th}}$ </tex-math></inline-formula>) configuration is adopted in each macro pixel [formed by four single-photon avalanche diodes (SPADs)], which can suppress strong background light (BG) induced pile-up. To verify the imaging function and effectiveness of the noise filtering circuit, two systems are implemented (System1 for indoor imaging measurement and System2 for outdoor distance measurement). With the help of the noise filtering circuit and a reasonable signal-to-background noise ratio (SBR), the maximum detection range outdoors with reasonable accuracy can be greatly extended (from 12m @ N<sub>th</sub> = 1 of System2 to more than 20m @ N<sub>th</sub> = 2 of System2 under 70klux of background noise). The counter in the noise filtering circuit can be reused to get intensity information. A robust 13-bit TDC with a reliable reset is introduced. Thanks to a dedicated START/STOP logic and a Schmitt trigger, large TDC quantization errors can be avoided. It achieves a 200ps resolution (LSB) and exhibits an INL<sub>p-p</sub> of 3.55LSB and a DNL<sub>p-p</sub> of 0.53LSB. The maximum inter-frame rate can reach 270kfps with 16 IOs operating at speed of 500MHz. Combining 9k inter-frames to get one frame, a frame rate of 30 is achieved for an indoor 3-D imaging. For outdoor measurement, more laser pulses should be accumulated.
Association of Baseline Urinary Metabolic Biomarkers with ADPKD Severity in TAME-PKD Clinical Trial Participants

Visual Abstract Key Points Urine excretion of two key glycolytic enzymes correlated with autosomal dominant polycystic kidney disease (ADPKD) severity (height-adjusted total kidney volume and eGFR) at baseline in the TAME-PKD study population. These findings are the first to provide evidence in human urine samples that upregulated glycolytic flux is a feature of ADPKD severity. Future analyses will test whether metformin affects ADPKD disease progression and urinary metabolic biomarkers in patients during the study. Background Recent work suggests that dysregulated cellular metabolism may play a key role in the pathogenesis of autosomal dominant polycystic kidney disease (ADPKD). The TAME-PKD clinical trial is testing the safety, tolerability, and efficacy of metformin, a regulator of cell metabolism, in patients with ADPKD. This study investigates the cross-sectional association of urinary metabolic biomarkers with ADPKD severity among TAME-PKD trial participants at baseline. Methods Concentrations of total protein, targeted metabolites (lactate, pyruvate, succinate, and cAMP), and key glycolytic enzymes (pyruvate kinase M2 [PKM2], lactate dehydrogenase A [LDHA], and pyruvate dehydrogenase kinase 1 [PDK1]) were measured by ELISA, enzymatic assays, and immunoblotting in baseline urine specimens of 95 TAME-PKD participants. These analytes, normalized by urinary creatinine or osmolality to estimate excretion, were correlated with patients’ baseline height-adjusted total kidney volumes (htTKVs) by MRI and eGFR. Additional analyses were performed, adjusting for participants’ age and sex, using multivariable linear regression. Results Greater htTKV correlated with lower eGFR (r=−0.39; P=0.0001). Urinary protein excretion modestly correlated with eGFR (negatively) and htTKV (positively). Urinary cAMP normalized to creatinine positively correlated with eGFR. Among glycolytic enzymes, PKM2 and LDHA excretion positively correlated with htTKV, whereas PKM2 excretion negatively correlated with eGFR. These associations remained significant after adjustments for age and sex. Moreover, in adjusted models, succinate excretion was positively associated with eGFR, and protein excretion was more strongly associated with both eGFR and htTKV in patients <43 years old. Conclusions Proteinuria correlated with ADPKD severity, and urinary excretion of PKM2 and LDHA correlated with ADPKD severity at baseline in the TAME-PKD study population. These findings are the first to provide evidence in human urine samples that upregulated glycolytic flux is a feature of ADPKD severity. Future analysis may reveal if metformin treatment affects both disease progression and the various urinary metabolic biomarkers in patients throughout the study.
Stressful Life Events Are Related to More Negative Interpretations, but Not Under Acute Stress

Studies have identified deleterious effects of stress on multiple cognitive processes such as memory and attention. Little is known about the impact of stress on interpretation. We investigated how an induced acute stress and more long-term stress related to life events were associated with interpretations of ambiguous stimuli. Fifty participants answered a questionnaire indexing the number of stressful life events. A median split was used to compare those reporting few or more events. Half of participants performed an arithmetic task that induced acute stress; they were compared to a control group performing a less stressful task. We measured the interpretation of ambiguous visual stimuli, which participants had to judge as “negative” or “positive”. We found a significant interaction between the number of stressful life events and the induced acute stress on the proportion of positive interpretations. In the control group, participants reporting more stressful events produced less positive interpretations than those reporting few events. In the induced stress condition, no significant difference was found. Life events tend to influence interpretation in the absence of an acute stressor, which seems to be more influent in the short term.
The Behavioral and Psychological Symptoms of Dementia in Down Syndrome Scale (BPSD-DS II): Optimization and Further Validation1

Background: People with Down syndrome (DS) are at high risk to develop Alzheimer’s disease dementia (AD). Behavioral and psychological symptoms of dementia (BPSD) are common and may also serve as early signals for dementia. However, comprehensive evaluation scales for BPSD, adapted to DS, are lacking. Therefore, we previously developed the BPSD-DS scale to identify behavioral changes between the last six months and pre-existing life-long characteristic behavior. Objective: To optimize and further study the scale (discriminative ability and reliability) in a large representative DS study population. Methods: Optimization was based on item irrelevance and clinical experiences obtained in the initial study. Using the shortened and refined BPSD-DS II, informant interviews were conducted to evaluate 524 individuals with DS grouped according to dementia status: no dementia (DS, N = 292), questionable dementia (DS + Q, N = 119), and clinically diagnosed dementia (DS + AD, N = 113). Results: Comparing item change scores between groups revealed prominent changes in frequency and severity for anxious, sleep-related, irritable, restless/stereotypic, apathetic, depressive, and eating/drinking behavior. For most items, the proportion of individuals displaying an increased frequency was highest in DS + AD, intermediate in DS + Q, and lowest in DS. For various items within sections about anxious, sleep-related, irritable, apathetic, and depressive behaviors, the proportion of individuals showing an increased frequency was already substantial in DS + Q, suggesting that these changes may serve as early signals of AD in DS. Reliability data were promising. Conclusion: The optimized scale yields largely similar results as obtained with the initial version. Systematically evaluating BPSD in DS may increase understanding of changes among caregivers and (timely) adaptation of care/treatment.
Deep Learning-Quantified Calcium Scores for Automatic Cardiovascular Mortality Prediction at Lung Screening Low-Dose CT.

Purpose To examine the prognostic value of location-specific arterial calcification quantities at lung screening low-dose CT for the prediction of cardiovascular disease (CVD) mortality. Materials and Methods This retrospective study included 5564 participants who underwent low-dose CT from the National Lung Screening Trial between August 2002 and April 2004, who were followed until December 2009. A deep learning network was trained to quantify six types of vascular calcification: thoracic aorta calcification (TAC); aortic and mitral valve calcification; and coronary artery calcification (CAC) of the left main, the left anterior descending, and the right coronary artery. TAC and CAC were determined in six evenly distributed slabs spatially aligned among chest CT images. CVD mortality prediction was performed with multivariable logistic regression using least absolute shrinkage and selection operator. The methods were compared with semiautomatic baseline prediction using self-reported participant characteristics, such as age, history of smoking, and history of illness. Statistical significance between the prediction models was tested using the nonparametric DeLong test. Results The prediction model was trained with data from 4451 participants (median age, 61 years; 37.9% women) and then tested on data from 1113 participants (median age, 61 years; 37.9% women). The prediction model using calcium scores achieved a C statistic of 0.74 (95% CI: 0.69, 0.79), and it outperformed the baseline model using only participant characteristics (C statistic, 0.69; P = .049). Best results were obtained when combining all variables (C statistic, 0.76; P < .001). Conclusion Five-year CVD mortality prediction using automatically extracted image-based features is feasible at lung screening low-dose CT. © RSNA, 2021.
Measuring rater bias in diagnostic tests with ordinal ratings

Diagnostic tests are frequently reliant upon the interpretation of images by skilled raters. In many clinical settings, however, the variability observed between experts' ratings plays a detrimental role in the degree of confidence in these interpretations, leading to uncertainty in the diagnostic process. For example, in breast cancer testing, radiologists interpret mammographic images, while breast biopsy results are examined by pathologists. Each of these procedures involves elements of subjectivity. We propose here a flexible two‐stage Bayesian latent variable model to investigate how the skills of individual raters impact the diagnostic accuracy of image‐related testing in large‐scale medical testing studies. A strength of the proposed model is that the true disease status of a patient within a reasonable time frame may or may not be known. In these studies, many raters each contribute classifications on a large sample of patients using a defined ordinal grading scale, leading to a complex correlation structure between ratings. Our modeling approach considers the different sources of variability contributed by experts and patients while accounting for correlations present between ratings and patients, in contrast to currently available methods. We propose a novel measure of a rater's ability (magnifier) that, in contrast to conventional measures of sensitivity and specificity, is robust to the underlying prevalence of disease in the population, providing an alternative measure of diagnostic accuracy across patient populations. Extensive simulation studies demonstrate lower bias in estimation of parameters and measures of accuracy, and illustrate outperformance of the proposed model when compared with existing models. Receiver operator characteristic curves are derived to assess the diagnostic accuracy of individual experts and their overall performance. Our proposed modeling approach is applied to a large breast imaging study for known disease status and a uterine cancer dataset for unknown disease status.
Isolated tricuspid valve surgery: first outcomes report according to a novel clinical and functional staging of tricuspid regurgitation.

OBJECTIVES The goal of this study was to assess the applicability of a novel classification of patients with tricuspid regurgitation based on 5 stages and to evaluate outcomes following isolated surgical treatment. METHODS All patients treated with isolated tricuspid valve repair or tricuspid valve replacement (TVR) from March 1997 to January 2020 at a single institution were retrospectively reviewed. Patients were divided according to a novel clinical-functional classification, based on the degree of regurgitation together with symptoms, right ventricular size and function and medical therapy. A total of 195 patients were treated; however, 23/195 were excluded due to lack of sufficient preoperative data. RESULTS A total of 172 patients were considered; of these, 129 (75%) underwent TVR and 43 (25%) had tricuspid valve repair. The distribution of patients showed that 46.5% of patients who underwent tricuspid valve repair were in stage 2, whereas 51.9% who underwent TVR were in stage 3. TVR patients were in more advanced stages of the disease, with dilated right ventricles, more pronounced symptoms and development of organ damage. Hospital mortality was 5.8%, in particular 0% in stages 2 and 3 and 15.3% in stages 4 and 5 (P < 0.001). Both intensive care unit and hospital stays were significantly longer in more advanced stages (P < 0.001). Patients in stages 4 and 5 developed more postoperative complications, such as acute kidney injury (3.7-10% in stages 2 and 3 vs 44-100% in stages 4 and 5; P < 0.001) and low cardiac output syndrome (15-50% in stages 2 and 3 vs 71-100% in stages 4 and 5; P < 0.001). CONCLUSIONS Patients in more advanced stages had higher hospital mortality and longer hospitalizations. Timely referral is associated with lower mortality, short postoperative course and mostly valve repair.
Prediction of treatment responses to neoadjuvant chemotherapy in breast cancer using contrast-enhanced ultrasound.

Background Elucidation the efficacy of neoadjuvant chemotherapy (NAC) in breast cancer is important for informing therapeutic decisions. This study aimed at evaluating the potential value of contrast-enhanced ultrasound (CEUS) parameters in predicting breast cancer responses to NAC. Methods We performed CEUS examinations before and after two cycles of NAC. Quantitative CEUS parameters [maximum intensity (IMAX), rise time (RT), time to peak (TTP), and mean transit time (mTT)], tumor diameter, and their changes were measured and compared to histopathological responses, according to the Miller-Payne Grading (MPG) system (score 1, 2, or 3: minor response; score 4 or 5: good response). Prediction models for good response were developed by multiple logistic regression analysis and internally validated through bootstrap analysis. The receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. Results A total of 143 patients were enrolled in this study among whom 98 (68.5%) achieved a good response and while 45 (31.5%) exhibited a minor response. Several imaging variables including diameter, IMAX, changes in diameter (Δdiameter), IMAX (ΔIMAX) and TTP (ΔTTP) were found to be significantly associated with good therapeutic responses (P<0.05). The areas under the curve (AUC) increased from 0.748 to 0.841 in the multivariate model that combined CEUS parameters and molecular subtypes with a sensitivity and specificity of 0.786, 0.745, respectively. Tumor molecular subtype was the primary predictor of primary endpoint. Conclusions CEUS is a potential tool for predicting responses to NAC in locally advanced breast cancer patients. Compared to the other molecular subtypes, triple negative and HER2+/ER- subtypes are more likely to exhibit a good response to NAC.
Vascular Control of Kidney Epithelial Transporters.

A major pathway in hypertension pathogenesis involves direct activation of Ang II (AT1) receptors in the kidney, stimulating sodium reabsorption. AT1 receptors in tubular epithelia control expression and stimulation of sodium transporters and channels. Recently, we found reduced blood pressure and enhanced natriuresis in mice with cell-specific deletion of AT1 receptors in smooth muscle (SMKOs). While impaired vasoconstriction and preserved renal blood flow might contribute to exaggerated urinary sodium excretion in SMKOs, we considered whether alterations in sodium transporter expression might also play a role and therefore carried out a proteomic analysis of key sodium transporters and associated proteins. Here we show that levels of Na+-K+-2Cl- cotransporter isoform 2 (NKCC2) and Na+/H+ exchanger isoform 3 (NHE3) are reduced at baseline in SMKOs, accompanied by attenuated natriuretic and diuretic responses to furosemide. During Ang II hypertension, we find widespread remodeling of transporter expression in wild-type mice with significant increases in levels of total NCC, phosphorylated-NCCps71, and NKCC2-P, along with the cleaved, activated forms of the a- and g-ENaC. However, the increases in a- and g-ENaC with Ang II were substantially attenuated in SMKOs. This was accompanied by reduced natriuretic response to amiloride. Thus, enhanced urinary sodium excretion observed after cell-specific deletion of AT1 receptors from smooth muscle cells is associated with altered sodium transporter abundance across epithelia in multiple nephron segments. These findings suggest a system of vascular-epithelial cross-talk in the kidney, modulating expression of sodium transporters and contributing to regulation of pressure-natriuresis.
Morphological and Molecular Data Reveal Two New Species of Viannaia (Nematoda: Viannaiidae), Parasitizing Opossums (Mammalia: Didelphidae) in Mexico

ABSTRACT Two new species of Viannaia from the intestine of the North American opossums, Didelphis virginiana (Virginia opossum), and Philander opossum (gray four-eyed opossum), are described based on morphological and molecular data, through an integrative taxonomic approach. Maximum likelihood and Bayesian inference analyses for each dataset and the concatenated dataset were performed using a mitochondrial cytochrome c oxidase subunit 1 (COI) gene, and the nuclear ribosomal internal transcribed spacer region (ITS1–5.8S–ITS2). The phylogenetic analyses revealed 2 new species that occur in Mexico, one from the western state of Colima and another from the southern state of Chiapas. Our phylogenetic trees for both molecular markers and concatenated datasets yielded similar topologies with high bootstrap values and posterior probabilities. Viannaia is recovered as a monophyletic group, but the family Viannaiidae appears as non-monophyletic, due to the position of Travassostrongylus scheibelorum, similar to previous studies. Finally, the morphology of Viannaia and Hoineffia is discussed.