Miracle-dz/UniFAR-FTUs huggingface dataset:
Teleseismic response of a uniform dipping crust (part I of a series on crustal equalization of seismic arrays)

abstract Most seismic theory is confined to a consideration of crustal structures that can be abstracted as plane parallel elastic layers. For such configurations, the response of each element of a seismic array will be similar to any other except for a time delay. Signal enhancement of an array located on such an idealized structure can be accomplished by suitable time delays of the individual traces followed by superposition. On the other hand, if the crustal structure is anything but a plane parallel configuration, the signals received by the individual elements will not be identical to one another, but will include distortion effects characteristic of the local geometry. As a result, the records of seismic arrays located on realistic crustal configurations will have to be equalized to some standard reference if optimum signal processing is to be achieved. In this paper we introduce a ray procedure for the calculation of theoretical seismograms for the teleseismic response of an array of stations located above a uniform dipping crust (wedge-shaped). In terms of this mathematical model, we demonstrate the signal distortion effects of the geometry and discuss equalization techniques that will permit a superior recovery of the desired signal.
Deep learning-assisted ultrasonic diagnosis of cervical lymph node metastasis of thyroid cancer: a retrospective study of 3059 patients

Objective This study aimed to develop a deep learning system to identify and differentiate the metastatic cervical lymph nodes (CLNs) of thyroid cancer. Methods From January 2014 to December 2020, 3059 consecutive patients with suspected with metastatic CLNs of thyroid cancer were retrospectively enrolled in this study. All CLNs were confirmed by fine needle aspiration. The patients were randomly divided into the training (1228 benign and 1284 metastatic CLNs) and test (307 benign and 240 metastatic CLNs) groups. Grayscale ultrasonic images were used to develop and test the performance of the Y-Net deep learning model. We used the Y-Net network model to segment and differentiate the lymph nodes. The Dice coefficient was used to evaluate the segmentation efficiency. Sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) were used to evaluate the classification efficiency. Results In the test set, the median Dice coefficient was 0.832. The sensitivity, specificity, accuracy, PPV, and NPV were 57.25%, 87.08%, 72.03%, 81.87%, and 66.67%, respectively. We also used the Y-Net classified branch to evaluate the classification efficiency of the LNs ultrasonic images. The classification branch model had sensitivity, specificity, accuracy, PPV, and NPV of 84.78%, 80.23%, 82.45%, 79.35%, and 85.61%, respectively. For the original ultrasonic reports, the sensitivity, specificity, accuracy, PPV, and NPV were 95.14%, 34.3%, 64.66%, 59.02%, 87.71%, respectively. The Y-Net model yielded better accuracy than the original ultrasonic reports. Conclusion The Y-Net model can be useful in assisting sonographers to improve the accuracy of the classification of ultrasound images of metastatic CLNs.
BK virus-associated hemorrhagic cystitis in pediatric stem cell transplantation: a case report and scoping review

Introduction BK virus-associated hemorrhagic cystitis (BK-HC) is a debilitating and poorly understood complication of hematopoietic stem cell transplantation (SCT). Hematuria, dysuria, and other symptoms associated with BK-HC are common in the immediate post-SCT period, making BK-HC difficult to distinguish from other conditions presenting with these symptoms. Despite published criteria for diagnosis, the degree to which these criteria are consistently applied to either clinical diagnosis or to studies informing BK-HC management is unclear. We present a case of BK-HC in a pediatric SCT recipient, and discuss the challenges associated with treatment in the absence of rigorous data to inform clinical management. Methods We reviewed all cases of BK viruria at our center in patients undergoing SCT between January 2015 and December 2019. We then performed a scoping review of publications in PubMed addressing BK-HC, specifically focusing on how BK-HC was defined. Publications using the keywords “BK polyomavirus” and “hemorrhagic cystitis” were included if they involved a clinical study of SCT recipients and a full-text article was available in English. Case reports were excluded. Analysis focused on whether BK-HC was explicitly defined and whether the definition incorporated elements of diagnostic criteria published by European Conference on Infections in Leukemia (ECIL). Results A total of 30 studies published between January 2018 and 30 June 2021 met criteria for review, including 4 clinical trials, 7 prospective observational studies, and 19 retrospective observational studies. Fifteen of these studies included pediatric patients (7 pediatric only, 8 combined adult and pediatric). Of the 30 publications, 19 included a definition of either BK-HC or BK cystitis, with only five using ECIL criteria, all of which were observational studies. Multiple interventions are described for treatment of BK-HC, including cidofovir, leflunomide, quinolones, hyperbaric oxygen, keratinocyte growth factor, and BK-specific cytotoxic T lymphocytes. However, evidence to support efficacy for any of these interventions is lacking. Discussion Although BK-HC is a well-known complication of SCT, evidence to support available treatment options is limited. Well-controlled studies that incorporate clear diagnostic criteria are needed to better define the risk factors, natural history, and ideal interventions.
Prognostic stratification of sepsis through DNA damage response based RiskScore system: insights from single-cell RNA-sequencing and transcriptomic profiling

Background A novel risk scoring system, predicated on DNA damage response (DDR), was developed to enhance prognostic predictions and potentially inform the creation of more effective therapeutic protocols for sepsis. Methods To thoroughly delineate the expression profiles of DDR markers within the context of sepsis, an analytical approach utilizing single-cell RNA-sequencing (scRNA-seq) was implemented. Our study utilized single-cell analysis techniques alongside weighted gene co-expression network analysis (WGCNA) to pinpoint the genes that exhibit the most substantial associations with DNA damage response (DDR). Through Cox proportional hazards LASSO regression, we distinguished DDR-associated genes and established a risk model, enabling the stratification of patients into high- and low-risk groups. Subsequently, we carried out an analysis to determine our model’s predictive accuracy regarding patient survival. Moreover, we examined the distinct biological characteristics, various signal transduction routes, and immune system responses in sepsis patients, considering different risk categories and outcomes related to survival. Lastly, we conducted experimental validation of the identified genes through in vivo and in vitro assays, employing RT-PCR, ELISA, and flow cytometry. Results Both single-cell RNA sequencing (scRNA-seq) and bulk transcriptomic analyses have demonstrated a strong correlation between DNA damage response (DDR) levels and sepsis prognosis. Specific cell subtypes, including monocytes, megakaryocytes, CD4+ T cells, and neutrophils, have shown elevated DDR activity. Cells with increased DDR scores exhibited more robust and numerous interactions with other cell populations. The weighted gene co-expression network analysis (WGCNA) and single-cell analyses revealed 71 DDR-associated genes. We developed a four-gene risk scoring system using ARL4C, CD247, RPL7, and RPL31, identified through univariate COX, LASSO COX regression, and log-rank (Mantel-Cox) tests. Nomograms, calibration plots, and decision curve analyses (DCA) regarding these specific genes have provided significant clinical benefits for individuals diagnosed with sepsis. The study suggested that individuals categorized as lower-risk demonstrated enhanced infiltration of immune cells, upregulated expression of immune regulators, and a more prolific presence of immune-associated functionalities and pathways. RT-qPCR analyses on a sepsis rat model revealed differential gene expression predominantly in the four targeted genes. Furthermore, ARL4C knockdown in sepsis model in vivo and vitro caused increased inflammatory response and a worse prognosis. Conclusion The delineated DDR expression landscape offers insights into sepsis pathogenesis, whilst our riskScore model, based on a robust four-gene signature, could underpin personalized sepsis treatment strategies.
Cortical Thickness Index and Canal Calcar Ratio: A Comparison of Proximal Femoral Fractures and Non-Fractured Femora in Octogenarians to Centenarians

Background: The cortical thickness index (CTI) is a measure of bone quality and it correlates with the risk of proximal femoral fractures. The purpose of this study was to investigate the CTI in femoral neck, trochanteric fractures and non-fractured femora in geriatric patients and to determine whether there is a correlation between the CTI and the presence of a fracture. Methods: One hundred and fifty patients (fifty femoral neck- (FNFx), fifty trochanteric fractures (TFx) and fifty non-fractured (NFx)) with a mean age of 91 (range 80–104) years were included. Hip radiographs (antero-posterior (ap), lateral) were evaluated retrospectively. Measurements on the proximal femoral inner and outer cortices, including CTI and Dorr’s canal calcar ratio (CCR), were assessed for inter-observer reliability (ICC), differences of each fracture and correlation of parameters. Results: The mean ap CTI on the affected side was 0.43, 0.45 and 0.55 for FNFx, TFx and NFx, respectively. There was a significant difference of the ap CTI and CCR comparing the injured and healthy side for both fracture cohorts (p < 0.001). Patients with FNFx or TFx had significantly lower CTI on both sides compared to the NFx group (p < 0.05). There was no difference for CTI (p = 0.527) or CCR (p = 0.291) when comparing both sides in the NFx group. The mean inter-observer reliability was good to excellent (ICC 0.88). Conclusions: In proximal femoral fractures, the CTI and CCR are reduced compared with those in non-fractured femora. Both parameters are reliable and show a good correlation in geriatric patients. Therefore, especially for geriatric patients, the CTI and CCR may help to predict fracture risk and consult patients in daily practice.
Sediment dynamics on intertidal mudflats: A study based on in situ measurements and numerical modelling

Tidal flats provide essential ecosystem services (e.g. coastal protection and function as habitats sustaining coastal food webs). They are also under pressure due to climate change and human interventions. To investigate the sediment dynamic processes on tidal flats for a better understanding of the morphological development, in situ measurements were carried out on three tidal flats in the Yangtze Estuary (China) and in the Westerschelde Estuary (the Netherlands). The results show that erosion and deposition stages alternate due to the competition between hydrodynamic forces and bed strength, and due to variability of suspended sediment concentration. A bed-level change (BLC) model has been developed based on this understanding. Sediment dynamic processes on tidal flats are governed by tidal forcing exhibiting neap-spring variation, as well as by random wind events. The impacts from wind events were investigated by an integrated approach based on in situ measurements and the BLC model. This integrated approach also helped to reveal the spatial variability of bed erosional characteristics of tidal flats under influence of diatoms. This work not only improves the prediction of morphological changes of tidal flats, but also has implications for coastal engineering and for studies of coastal ecology and the coastal environment.
Psychological Distress Affects Performance during Exercise-Based Cardiac Rehabilitation

Background: It is known that psychosocial distress affects the morbidity and mortality of patients with cardiovascular disease of every age. The aim of this study was to produce novel information on how psychological distress can influence cardiovascular performance in patients after cardiac surgery undergoing multidisciplinary cardiac rehabilitation. Methods: Patients (n = 57) admitted after cardiac surgery for valvular or coronary disease underwent, within 5 days of admission, the Symptom Checklist-90-Revised (SCL-90-R) self-report questionnaire to measure psychiatric symptoms and the 12-item General Health Questionnaire (GHQ-12) to assess the level of psychological distress. The Positive Symptom Distress Index (PSDI) was measured to indicate the amplitude of symptom distress. Cardiovascular performance was assessed by a 6 min walking test (6MWT) at admission and discharge, and oxygen consumption (VO2 max) was derived. Results: Within the SCL-90-R score, somatic symptoms (47.4%), depressive and anxiety symptoms (36.8% and 33.3%, respectively), symptoms of phobic anxiety (21.1%), and psychoticism (24.6%) were over-represented. As for the GHQ-12, 75.4% of the sample reported an abnormally negative perception of their health status. An inverse correlation was shown between the variation in 6MWT and SCL depression (p = 0.048), PSDI (p = 0.022), and the GHQ-12 (p = 0.040). Similarly, an inverse correlation was shown between the variation in the VO2 max, GHQ-12 (p = 0.041), and the PSDI (p = 0.023). Conclusions: Post-cardiac surgery cardiac rehabilitation was associated with increased symptoms of psychological discomfort, as compared with the general population. The amplitude of psychological distress, depression, and hostility are associated with limited improvement in performance. These data strengthen the need for psychological support during cardiac rehabilitation programs.
A comparison of statistical and geometric reconstruction techniques : guidelines for correcting fossil hominin crania

The study of human evolution centres, to a large extent, around the study of fossil morphology, including the comparison and interpretation of these remains within the context of what is known about morphological variation within living species. However, many fossils suffer from environmentally caused damage (taphonomic distortion) which hinders any such interpretation: fossil material may be broken and fragmented while the weight and motion of overlaying sediments can cause their plastic distortion. To date, a number of studies have focused on the reconstruction of such taphonomically damaged specimens. These studies have used myriad approaches to reconstruction, including thin plate spline methods, mirroring, and regression-based approaches. The efficacy of these techniques remains to be demonstrated, and it is not clear how different parameters (e.g., sample sizes, landmark density, etc.) might effect their accuracy. In order to partly address this issue, this thesis examines three techniques used in the virtual reconstruction of fossil remains by statistical or geometrical means: mean substitution, thin plate spline warping (TPS), and multiple linear regression. These methods are compared by reconstructing the same sample of individuals using each technique. Samples drawn from Homo sapiens, Pan troglodytes, Gorilla gorilla, and various hominin fossils are reconstructed by iteratively removing then estimating the landmarks. The testing determines the methods' behaviour in relation to the extant of landmark loss (i.e., amount of damage), reference sample sizes (this being the data used to guide the reconstructions), and the species of the population from which the reference samples are drawn (which may be different to the species of the damaged fossil). Given a large enough reference sample, the regression-based method is shown to produce the most accurate reconstructions. Various parameters effect this: when using small reference samples drawn from a population of the same species as the damaged specimen, thin plate splines is the better method, but only as long as there is little damage. As the damage becomes severe (missing 30% of the landmarks, or more), mean substitution should be used instead: thin plate splines are shown to have a rapid error growth in relation to the amount of damage. When the species of the damaged specimen is unknown, or it is the only known individual of its species, the smallest reconstruction errors are obtained with a regression-based approach using a large reference sample drawn from a living species. Testing shows that reference sample size (combined with the use of multiple linear regression) is more important than morphological similarity between the reference individuals and the damaged specimen. The main contribution of this work are recommendations to the researcher on which of the three methods to use, based on the amount of damage, number of reference individuals, and species of the reference individuals.
Forecasting suspended sediment load using regularized neural network: Case study of the Isser River (Algeria)

Abstract In the management of water resources in different hydro-systems it is important to evaluate and predict the sediment load in rivers. It is difficult to obtain an effective and fast estimation of sediment load by artificial neural network without avoiding over-fitting of the training data. The present paper comprises the comparison of a multi-layer perception network once with non-regularized network and the other with regularized network using the Early Stopping technique to estimate and forecast suspended sediment load in the Isser River, upstream of Beni Amran reservoir, northern Algeria. The study was carried out on daily sediment discharge and water discharge data of 30 years (1971–2001). The results of the Back Propagation based models were evaluated in terms of the coefficient of determination (R2) and the root mean square error (RMSE). Results of the comparison indicate that the regularizing ANN using the Early Stopping technique to avoid over-fitting performs better than non-regularized networks, and show that the overtraining in the back propagation occurs because of the complexity of the data introduced to the network.
Analysis of the lightning‐attractive radius for wind turbines considering the developing process of positive attachment leader

In this paper, we have presented the upward leader propagation model, considering the transition of stream leader process by the finite element method and analyzing the inception and subsequent physical processes of upward leader and the attractive radius for large wind turbines. For validating our model, the comparison of simulated results with the optically high‐speed video observation shows that the model can predict an accepted result of upward leader from a 163 m tall tower, the simulated upward leader velocity and length before final jump are 2.3 × 105 m/s and 187.67 m presented by Warner (2010), which are very similar to the observed results of 2.8 × 105 m/s and 184 m, respectively. At the same time, we find that the assumed constant speed ratio of downward/upward leader is improper and cannot accurately predict the attractive radius by lightning strike. Also, the simulated results are compared with the widely used EGM (electro geometric model), and it is found that the EGM has an obvious underestimation of attractive radius more than 50%.
Observation of suction in a landslide

The field observation of suction in the landslide is very important for studying the criterion of the landslide instability induced by heavy rainfall in terms of the theory of unsaturated soil mechanics. It is essential to build a description of suction variation with depth in a landslide. For this purpose an observation well was constructed and observation data in almost half a year have been obtained. The analysis of these data gives us following ideas:Suction variation with depth in a landslide is controlled by the landslide structures and its material composition, and it is different from that described by some other authors.The variation of suction with rainfall in a landslide has a correspondence with primary rainfall at upper part of unsaturated zone, but the situation is quite different at middle and lower part of the unsaturated zone.
Accelerated Sparse-Coding-Inspired Feedback Neural Architecture Search for Hyperspectral Image Classification

Hyperspectral images (HSIs) have spectral variability, which leads to spectral dependence in adjacent and nonadjacent regions, and this dependence is essential for the classification of regions with mixed pixels. Current neural architecture search (NAS) methods have achieved significant advantages in HSI classification, but these methods cannot capture spectral dependence in nonadjacent regions because only use feedforward connections. Meanwhile, the cost of the search process in NAS is proportional to the scale of the search space, which limits the expansion of the search space. To address these issues, we propose a sparse-coding-inspired feedback NAS (SCIF-NAS) method for HSI classification. First, we view HSI samples as sequences and introduce a feedback mechanism in NAS to model the spectral dependence of nonadjacent regions to mitigate the effects of spectral variation. Second, we design several feedforward operations according to the characteristics of HSI, to form the search space together with feedback operations. Meanwhile, a sparse-coding-inspired NAS accelerated strategy is introduced to alleviate the search time burden caused by the expansion of search space. Third, we integrate center loss with cross-entropy loss to construct a hybrid loss function that helps to obtain a better classification boundary. Finally, we conduct experiments on three popular HSI benchmarks, which show that SCIF-NAS outperforms the state-of-the-art methods in HSI classification.
Howling Detection and Gain Control for Speech Reinforcement in a Noisy Car Cabin Environment

In-car speech communication is particularly challenging due to environmental noise. The speaker's microphone also acquires car and road noises, resulting in a low signal-to-noise ratio and persistent frequency-howls that do not decrease, which degrade the system's output sound quality. In this paper, we address the problem of howling control for in-room speech reinforcement systems under high environmental noise levels, specifically in a car cabin. We control the acoustic feedback via a gain control algorithm based on howling detection. Two detection methods are proposed to detect non-decreasing underdamped frequency-howls. A single-channel optimal Feedback Wiener gain is derived to enhance the desired near-end speech signal, followed by another Wiener filter that is based on the signal magnitude relative to the estimated noise power spectral density. Adjusting the howling energy threshold is proposed to deal with false-detection artifacts arising as the environment's environmental noise level rises. The performance improvements of the howling-detection-based gain control algorithm following the proposed adjustments are evaluated in clean and noisy environments. As detection of non-decreasing underdamped frequency-howls is feasible, it was found that it becomes unnecessary when the environmental noise is successfully reduced via the Wiener filter.