Come On. I Need An Answer. A Mixed-Methods Study Of Barriers And Disparities In Diagnostic Odysseys

Background: The boom of next generation DNA sequencing over the past decade has improved our ability to provide accurate genetic diagnoses for children with previously undiagnosed diseases, in turn leading to important advances in management and prognostication. Even given this progress, two areas of ongoing need are the accurate definition of further novel genetic diseases and to make genetic expertise and diagnostics widely available to children and families who have frequently endured grueling diagnostic odysseys. The Pediatric Genomics Discovery Program (PGDP) at Yale is an advanced genomics program focusing on both these areas, enrolling over 700 patients since its inception and eventually providing approximately one-third with new genetic diagnoses. Despite this success, we questioned whether the PGDP was achieving its full potential for impact by reaching a broad, representative participant population. Hypothesis: Current PGDP participant demographics are not representative of the racial/ethnic and socioeconomic diversity in the community of patients with potentially undiagnosed genetic diseases, which may relate to systemic barriers along the diagnostic odyssey. Methods: We created a questionnaire and in-depth interview process for existing PGDP participants to evaluate barriers to diagnostic care, then analyzed transcripts for themes. We analyzed demographic characteristics and referral routes of the PGDP cohort to find factors related to recruitment. We developed a screening tool based on diagnostic codes and queried the Yale New Haven Health System (YNHHS) electronic health record (EHR) to identify inpatient children between 2017-2022 with potentially undiagnosed genetic conditions, estimate their prevalence, and compare their characteristics with those already enrolled in PGDP. Then, we manually reviewed patient charts further narrow patients down to those who likely had undiagnosed genetic diseases. We used Pearson chi-square for categorical data, a multinomial regression model for predictors of enrollment, and Kruskal-Wallis one-way analysis of variance with pairwise comparisons with Bonferroni correction for multiple comparisons. Results: Survey results noted 1) Not knowing the PGDP existed (42%) and 2) Not knowing if they qualified for PGDP (36%) as the most common barriers to participant enrollment. Qualitative interviews identified three overarching themes related to the search for a unifying medical diagnosis for patients and families: 1) Challenges along the diagnostic odyssey (largely barriers in the healthcare system), 2) Tools to navigate the uncertainty (particularly parent serving as a care-captain) and 3) Perceptions of the PGDP (having reservations about participating vs desire for a diagnosis). In the PGDP cohort analysis, being directly identified by a PGDP-affiliated physician was associated with the highest representation of URM (52%) compared to referrals through Yale Genetics (27%) or Other Referrals (16%), and a significantly greater URM representation compared to both the national pediatric population (p=0.008) and to a peer genetics program (

What-is and How-to for Fairness in Machine Learning: A Survey, Reflection, and Perspective

Algorithmic fairness has attracted increasing attention in the machine learning community. Various definitions are proposed in the literature, but the differences and connections among them are not clearly addressed. In this paper, we review and reflect on various fairness notions previously proposed in machine learning literature, and make an attempt to draw connections to arguments in moral and political philosophy, especially theories of justice. We also consider fairness inquiries from a dynamic perspective, and further consider the long-term impact that is induced by current prediction and decision. In light of the differences in the characterized fairness, we present a flowchart that encompasses implicit assumptions and expected outcomes of different types of fairness inquiries on the data generating process, on the predicted outcome, and on the induced impact, respectively. This paper demonstrates the importance of matching the mission (which kind of fairness one would like to enforce) and the means (which spectrum of fairness analysis is of interest, what is the appropriate analyzing scheme) to fulfill the intended purpose

Enhancing Super-Resolution Networks through Realistic Thick-Slice CT Simulation

This study aims to develop and evaluate an innovative simulation algorithm for generating thick-slice CT images that closely resemble actual images in the AAPM-Mayo's 2016 Low Dose CT Grand Challenge dataset. The proposed method was evaluated using Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE) metrics, with the hypothesis that our simulation would produce images more congruent with their real counterparts. Our proposed method demonstrated substantial enhancements in terms of both PSNR and RMSE over other simulation methods. The highest PSNR values were obtained with the proposed method, yielding 49.7369 $\pm$ 2.5223 and 48.5801 $\pm$ 7.3271 for D45 and B30 reconstruction kernels, respectively. The proposed method also registered the lowest RMSE with values of 0.0068 $\pm$ 0.0020 and 0.0108 $\pm$ 0.0099 for D45 and B30, respectively, indicating a distribution more closely aligned with the authentic thick-slice image. Further validation of the proposed simulation algorithm was conducted using the TCIA LDCT-and-Projection-data dataset. The generated images were then leveraged to train four distinct super-resolution (SR) models, which were subsequently evaluated using the real thick-slice images from the 2016 Low Dose CT Grand Challenge dataset. When trained with data produced by our novel algorithm, all four SR models exhibited enhanced performance.Comment: 11 pages, 4 figure

Diseño del paisaje del refugio contra inundaciones de Wanzhou

Wanzhou is located in Chongqing. There are general floods every other or two years. The interval between super floods is getting shorter and shorter, and each super flood causes a loss of up to 10 million euros. The design of this flood shelter uses the principle of sponge city, strengthens the rainwater collection and drainage system in the urban area, and sets up new tributaries away from the urban area. The purpose is to improve the flood situation and study the restoration of the riverside landscape

Human Treelike Tubular Structure Segmentation: A Comprehensive Review and Future Perspectives

Various structures in human physiology follow a treelike morphology, which often expresses complexity at very fine scales. Examples of such structures are intrathoracic airways, retinal blood vessels, and hepatic blood vessels. Large collections of 2D and 3D images have been made available by medical imaging modalities such as magnetic resonance imaging (MRI), computed tomography (CT), Optical coherence tomography (OCT) and ultrasound in which the spatial arrangement can be observed. Segmentation of these structures in medical imaging is of great importance since the analysis of the structure provides insights into disease diagnosis, treatment planning, and prognosis. Manually labelling extensive data by radiologists is often time-consuming and error-prone. As a result, automated or semi-automated computational models have become a popular research field of medical imaging in the past two decades, and many have been developed to date. In this survey, we aim to provide a comprehensive review of currently publicly available datasets, segmentation algorithms, and evaluation metrics. In addition, current challenges and future research directions are discussed.Comment: 30 pages, 19 figures, submitted to CBM journa

Tier Balancing: Towards Dynamic Fairness over Underlying Causal Factors

The pursuit of long-term fairness involves the interplay between decision-making and the underlying data generating process. In this paper, through causal modeling with a directed acyclic graph (DAG) on the decision-distribution interplay, we investigate the possibility of achieving long-term fairness from a dynamic perspective. We propose Tier Balancing, a technically more challenging but more natural notion to achieve in the context of long-term, dynamic fairness analysis. Different from previous fairness notions that are defined purely on observed variables, our notion goes one step further, capturing behind-the-scenes situation changes on the unobserved latent causal factors that directly carry out the influence from the current decision to the future data distribution. Under the specified dynamics, we prove that in general one cannot achieve the long-term fairness goal only through one-step interventions. Furthermore, in the effort of approaching long-term fairness, we consider the mission of "getting closer to" the long-term fairness goal and present possibility and impossibility results accordingly

Wpływ ekologicznych działań badawczo-rozwojowych na emisje SO2 w Chinach – dane z panelowego modelu progowego

Previous studies on the effectiveness of improving sustainable development have acknowledged the importance of domestic research and development (R&D) activities. However, these studies remain general and ambiguous because they assume that all R&D activities are related to energy-saving and sustainable development. The corresponding empirical evidence is scabrous and ambiguous. In this paper, we focus on the effect of green innovation R&D activities on SO2 emission which is an important greenhouse gas affect global climate change and eco-civilization. Considering that there is heterogeneity exists in the innovation activities, the R&D activities are divided into three performers with two purposes. The empirical results based on a Chinese inter-provincial dataset of 2000-2016 suggest that the green innovation R&D activities are crucial for the reduction of the SO2 emission. However, the innovation R&D activities of different purposes and performers show statistically differentiated effects on SO2 emission. The major positive effect of green innovation R&D activities on SO2 emissions reduction is mainly from enterprises and utility-type of R&D activities. A further study based on the panel threshold also indicates that effects of green innovation R&D activities on SO2 emissions are nonlinear, depending on the technology absorptive ability.Dotychczasowe badania nad zrównoważonym rozwojem potwierdziły znaczenie krajowych działań badawczo-rozwojowych (B + R). Jednak badania te pozostają ogólne i niejednoznaczne, ponieważ zakładają, że wszystkie działania B + R są związane z energooszczędnością i zrównoważonym rozwojem. Odpowiednie dowody empiryczne są niejednoznaczne. W artykule skupiamy się na wpływie działań badawczo-rozwojowych związanych z zielonymi innowacjami na emisję SO2, który jest ważnym gazem cieplarnianym, wpływającym na globalne zmiany klimatyczne. Biorąc pod uwagę, że istnieje heterogeniczność działań innowacyjnych, działalność B + R wskazano 3 aktorów z 2 celami. Wyniki empiryczne oparte na chińskim międzyprowincjalnym zbiorze danych z lat 2000-2016 sugerują, że działania badawczo-rozwojowe związane z zielonymi innowacjami są kluczowe dla redukcji emisji SO2. Jednak innowacyjne działania o różnych celach i różnych wykonawcach wykazują statystycznie zróżnicowany wpływ na emisję SO2. Główny pozytywny wpływ działań B + R w zakresie zielonych innowacji na redukcję emisji SO2 wynika głównie z działalności przedsiębiorstw i działalności B + R o charakterze użytkowym. Dalsze badanie oparte na panelu wskazuje również, że wpływ działań badawczo-rozwojowych związanych z zielonymi innowacjami na emisje SO2 jest nieliniowy, w zależności od zdolności absorpcyjnej technologii

Spiking Neural Network for Ultra-low-latency and High-accurate Object Detection

Spiking Neural Networks (SNNs) have garnered widespread interest for their energy efficiency and brain-inspired event-driven properties. While recent methods like Spiking-YOLO have expanded the SNNs to more challenging object detection tasks, they often suffer from high latency and low detection accuracy, making them difficult to deploy on latency sensitive mobile platforms. Furthermore, the conversion method from Artificial Neural Networks (ANNs) to SNNs is hard to maintain the complete structure of the ANNs, resulting in poor feature representation and high conversion errors. To address these challenges, we propose two methods: timesteps compression and spike-time-dependent integrated (STDI) coding. The former reduces the timesteps required in ANN-SNN conversion by compressing information, while the latter sets a time-varying threshold to expand the information holding capacity. We also present a SNN-based ultra-low latency and high accurate object detection model (SUHD) that achieves state-of-the-art performance on nontrivial datasets like PASCAL VOC and MS COCO, with about remarkable 750x fewer timesteps and 30% mean average precision (mAP) improvement, compared to the Spiking-YOLO on MS COCO datasets. To the best of our knowledge, SUHD is the deepest spike-based object detection model to date that achieves ultra low timesteps to complete the lossless conversion.Comment: 14 pages, 10 figure

Prenatal diagnosis and molecular cytogenetic characterization of hereditary complex chromosomal rearrangements in a Chinese family

Objectives: To report a family with an extremely rare and previously undescribed complex chromosomal rearrangement (CCR). To explore the molecular cytogenetic mechanism of ‘octaradial chromosome’. Material and methods: G-banding karyotype analysis was performed on all the members of the family. Chromosomal microarray analysis(CMA) was performed on the five members of the family. Results: This case presented with a karyotypically balanced CCR (46,XX,t(2;4;11;5)(p21;q34;q21;p15)). The familial CCR was stably transmitted across three generations. Conclusions: We report an extremely rare and previously undescribed complex chromosomal arrangement that is transmitted across three generations. The clinical outcome of this CCR is complex. Careful characterization of all the breakpoint regions is required for prenatal diagnosis and genetic counseling