Miracle-dz/UniFAR-FTUs huggingface dataset:
Development and validation of a nomogram for early assessment the severity of acute pancreatitis

Abstract Background Acute pancreatitis is an acute inflammatory disorder of the pancreas, and severe acute pancreatitis is associated with high mortality. Early assessment the severity of AP has an important significance for improving clinical outcomes. Our object aimed to develop a nomogram with high simplicity and rapidity for predicting the severity of acute pancreatitis. Methods Patients admitted to the Hunan Provincial People’s Hospital within 72 h from onset of AP from January 2010 and December 2020 were enrolled to establish a nomogram. Independent predictors were determined using univariate and multivariate analysis and then assembled to construct a predicting nomogram. The performance of proposed nomogram was evaluated by Brier score and Harrell’s concordance index (C-index). Meanwhile, clinical data of AP patients from January 2021 to January 2022 were collected for external validation. Results Album (OR 0.891, 95%CI 0.867–0.917), calcium (OR 0.151, 95%CI 0.084–0.273), neutrophil to lymphocyte ratio (OR 1.055, 95%CI 1.023–1.088) and systemic inflammatory response syndrome (OR 6.292, 95%CI 4.459–8.879) were identified as independent factors of SAP after univariate and multivariate analysis (p < .05). A predictive nomogram was accordingly established using these four independent variables. The internally verified C-index was 0.796 (95% CI 0.773–0.818), Brier score was 0.138. The externally verified C index was 0.820 (95% CI 0.754–0.887). Conclusion A nomogram for predicting the severity of AP was well developed, it may be of great significance for clinicians to quickly assess the progress of AP and choose more-targeted strategies.
The Application of a Particle Filter to Urban Ground Target Localization, Tracking, and Intercept

Ground vehicle localization is a problem of significance in an urban setting given the recent global conflicts, security interests, and rapid growth of sensor networks. This task is often difficult due to the lack of information regarding a target vehicle’s position, velocity, and destination. Field operatives can provide binary measurements of a target’s presence in an area, and these measurements can be processed to obtain estimates of the target’s location. A particle filter is more suitable for this application than a Kalman filter due to its ability to handle non-Gaussian distributions and non-differentiable measurement models, however it is computationally expensive. Suppose there is a mobile ground vehicle of known description but unknown position, velocity, or destination that is to be found, tracked, and intercepted by an unmanned aerial vehicle. The vehicle is known to be in an urban environment, and full knowledge of that environment (roads, obstacles, intersection constraints, and speed limits) is available. There are numerous issues of interest within this problem. A particle filter in an urban environment was developed to locate, track, and intercept a ground vehicle given soft binary measurements (measurements from human sources). Two particular issues are studied in this work: the effect of a sophisticated particle dynamic model on target localization and tracking, and the development of a real time path planning routine in the particle filter framework to enable target interception. The contributions of this work are threefold. First, the importance and impact of an accurate particle time update on target localization and tracking is validated. Secondly, a thorough investigation into the effect of particle spatial resolution in the presence of imperfect measurements is made that will prove valuable for future particle filter applications. Finally, the path planning routine offers reduced computational expense when compared to existing
The Combination of a Buccal Trapezoidal Flap With a Palatal Connective Pedicle Flap for the Closure of Oro-Antral Fistulae

Abstract: Various surgical techniques have been developed for the closure of oro-antral fistulae (OAF), all of them presenting some limitations. Nineteen consecutive patients with a clinical diagnosis of OAF were enrolled in the present case series study. The technique being tested included the reflection of a buccal trapezoid flap with the OAF at its center. The lesion was then fully exposed and debrided. A palatal pedicle connective graft was split from the overlying mucosa and it was rotated over the lesion and under the buccal flap, thereby double-sealing the fistula. The superficial layer of the palatal flap was left at its primary position and sutured, thus ensuring primary intention healing of the palate. The patients were followed for 30 days. No patient showed residual oro-antral communication by the end of the follow-up period. The pain level was highest at the end of the first follow-up week, with a median visual analog scale of 4, which decreased to 3 in the second week, and to 0 in the fourth week. The same trend could be observed for Discomfort. The combination of a buccal flap with a split palatal connective tissue flap is an effective surgical technique with good predictability and high patient satisfaction levels.
Deciphering the heterogeneous niche in the tumor progression of hepatocellular carcinoma: a Spatial single-cell landscape and multi-omics atlas analysis

Hepatocellular carcinoma (HCC) is an invasive disease which is characteristic with highly heterogeneous molecular phenotype, rich blood supply, and unique immune niche, therefore it is of great significance to explore the tumor heterogeneous niche and clonal evolution progress of these malignant cells. Based on the advance in single-cell technology, spatial transcriptome technology, and Oxford nanopore technology, this study innovatively reconstructed and delineated the heterogeneity of the HCC tumor niche and its tumor progression pattern. Our results showed that the copy number variation (CNV) of cells in cancer lesions and liver cirrhosis lesions of the same patient is basically the same and is mainly regulated by transcription factors such as TP53, HOXA7, FOXN3, and PPARG, suggests that malignant cells of common origin gradually evolve into different lesions in a very rare numbers of different CNVs, which are mainly regulated by expression patterns and mediate the heterogeneity between the tumor and cirrhosis lesions. Angiogenesis-related genes (SREBF1, ZNF585A, and HOXB5) may mediate communication between HCC subpopulations and endothelial cells via exosomes, thereby contributing to the angiogenic niche before HCC metastasis. In addition, numerous CNVs were found in patients with early recurrent HCC, and these mutated genes is the potential niche genes for the early tumor recurrence. In summary, this study provides a general transcriptional landscape of the ecological structure of HCC, systematically maps the molecular, cellular, and spatial composition of different HCC cell niches, and provides a scientific and theoretical basis at the molecular and cellular levels for personalized and accurate treatment strategies for HCC.
Polymorphism in IFNAR contributes to glucocorticoid response and outcome in ARDS and COVID-19

The use of glucocorticoids has given contradictory results for treating acute respiratory distress syndrome (ARDS). Here we report a novel disease association of a SNP rs9984273, which is situated in the interferon alpha/beta receptor (IFNAR2) gene in an area corresponding to a binding motif of the glucocorticoid receptor (GR). The minor allele of SNP rs9984273 associates with higher IFNAR expression, lower IFN-gamma and IL-6 levels and less severe form of coronavirus diseases (COVID-19) according to the COVID-19 Host Genetics Initiative database, and better outcome in interferon (IFN) beta treated patients with ARDS. Thus, the distribution of this SNP within clinical study arms may explain the contradictory results of multiple ARDS studies and outcomes in COVID-19 concerning type I IFN signalling and glucocorticoids.
The microbiome in adult acute appendicitis

Abstract Acute appendicitis is a common acute surgical emergency; however, the pathogenesis of adult appendicitis remains poorly understood. The microbiome is increasingly thought to play a key role in inflammatory disease of the bowel and similarly, may play a role in appendicitis. This study aimed to characterise the microbiome of adult acute appendicitis in a prospective cohort. We recruited 60 adults with acute appendicitis and 20 healthy controls. Rectal swabs were taken from each patient. After DNA extraction, 16S rRNA amplicon sequencing was carried out for analysis of diversity and taxonomic abundance. Phylogenetic sequencing of the samples indicated that there is a difference between the microbial composition of those with acute appendicitis and healthy controls, with a statistically significant decrease in alpha diversity in rectal swabs of appendicitis patients compared to healthy controls. At the genus level, we saw an increased abundance of potential pathogens, for example, Parvimonas and Acinetobacter, and a decrease in commensal taxa such as Faecalibacterium, Blautia and Lachnospiraceae in appendicitis patients compared to healthy controls. There was a reduction in diversity and loss of commensals in the microbiome of those with acute appendicitis, which may play a role in the cascade leading to acute appendicitis or the result of this.
A multi-focus image fusion method based on nested U-Net

Multi-focus image fusion is a popular research direction of image fusion, however, because of the complexity of the image, it has always been difficult in scientific research to accurately judge the clear area, especially in the clear and fuzzy edge of the complex environment. To better determine the focus area of the source image and obtain a clear image, the improved U2-Net model is used to analyze the focus area, and the multi-scale feature extraction scheme is used to generate the decision map. At the same time, the algorithm uses the NYU-D2 depth image as the training dataset in this paper. To achieve a better training effect, the method of image segmentation, Graph Cut, is combined with manual adjustment to make the training dataset. The experimental results show that compared with several existing latest algorithms, this fusion method can obtain accurate decision diagrams and has better performance in visual perception and objective evaluation.
Jitter in Ring Oscillators

Jitter in ring oscillaton is theoretically described, and predictions are experimentally verified. A design procedure is developed in the context of time domain measures or oscillator Jitter in a plase-locked loop (PLL). A major contribution is the identification of a design figure of merit k , which is independent or the number of stages in the ring. This figure of merit is used to relate fundamental circuit-level noise sources (such as thermal and shot noise) to system-level jitter performance. The procedure is applied to a ring oscillator composed of bipolar differential pair delay stages. The theoretical predictions are tested on 155 and 622 MHz clock-recovery PLL's which have been fabricated in a dielectrically isolated, complementary bipolar process. The measured closed-loop Jitter is within 10% of the design procedure prediction.
Robust Deep Representation Learning for Road Crack Detection

Computer vision (CV) based inspection has recently attracted considerable attention and is progressively replacing traditional visual inspection which is subject to poor accuracy, high subjectivity and inefficiency. This paper, benefiting from hybrid structures of multichannel parallel convolutional neural networks (pCNNs), introduces a unique deep learning framework for road crack detection. Ideally, CNN-based frameworks require a relatively huge computing resources for accurate image analysis. However, the portability objective of this work necessitates the utilization of low power processing units. To that purpose, we propose robust deep representation learning for RoadCrackDetection (RoCDe) which uses a multichannel pCNNs. Bayesian optimization algorithm (BOA) was used to optimize the multichannel pCNNs training with the fewest possible neural network (NN) layers to achieve the maximum accuracy, improved efficiency and minimum processing time. The CV training was done using two distinct optimizers namely Adam and RELU on a sufficiently available datasets through image preprocessing and data augmentation. Experimental results show that, the proposed algorithm can achieve high accuracy around 95% in crack detection, which is good enough to replace human inspections normally conducted on-site. This is largely due to well-calibrated predictive uncertainty estimates (WPUE). Effectiveness of the proposed model is demonstrated and validated empirically via extensive experiments and rigorous evaluation on large scale real world datasets. Furthermore, the performance of hybrid CNNs is compared with state-of-the art NN models and the results provides remarkable difference in success level, proving the strength of multichannel pCNNs.
Improvement of Dense 3D Reconstruction System Based on Depth Camera

Aiming at solving the problems such as over-smoothing and holes in the traditional 3D reconstruction system, a dense 3D reconstruction system based on depth camera was designed. First a joint optimization method based on the global spherical harmonic illumination model was adopted to obtain an accurate truncated signed distance function model, and then an improved moving cubes algorithm to extract isosurfaces from the truncated signed distance function model. In order to verify its reliability, a performance test was performed by own-collected datasets. The results show that over-smoothing and holes in reconstructed surface were well solved. Compared with the original model, the reconstructed model error is about 3%.
Inverse Problems Using Neural Networks for Cracks Characterization in Materials

Inverse eddy current problem using Artificial Neural Networks (ANN) approach for the localization and the classification shape of defects is considered. The task of reconstructing the cracks and damage in the plate profile of an inspected specimen in order to estimate its material properties can be described in this research work. This is accomplished by inverting eddy current probe impedance measurements that are recorded as a function of probe position, excitation frequency or both. In eddy current nondestructive evaluation, this is widely recognized as a complex theoretical problem whose solution is likely to have a significant impact on the characterization of cracks in materials .
Towards Practical Oblivious Join

Many individuals and companies choose the public cloud as their data and IT infrastructure platform. But remote accesses over the data inevitably bring the issue of trust. Despite strong encryption schemes, adversaries can still learn sensitive information from encrypted data by observing data access patterns. Oblivious RAMs (ORAMs) are proposed to protect against access pattern attacks. However, directly deploying ORAM constructions in an encrypted database brings large computational overhead. In this work, we focus on oblivious joins over a cloud database. Existing studies in the literature are restricted to either primary-foreign key joins or binary equi-joins. Our major contribution is to support general binary and multiway equi-joins. We integrate B-tree indices into ORAMs for each input table and retrieve blocks through the indices in join processing. The key points are to address the security issue (i.e., leaking the number of accesses to any index) in the extended existing solutions and bound the total number of block accesses. Our index nested-loop join algorithm can also support some types of band joins obliviously. The effectiveness and efficiency of our algorithms are demonstrated through extensive evaluations over real-world datasets. Our method shows orders of magnitude speedup for oblivious multiway equi-joins in comparison with baseline algorithms.
This paper is included in the Proceedings of the 19th USENIX Symposium on Networked Systems Design and Implementation

We present data-parallel actors (DPA) , a programming model for building distributed query serving systems. Query serving systems are an important class of applications characterized by low-latency data-parallel queries and frequent bulk data updates; they include data analytics systems like Apache Druid, full-text search engines like ElasticSearch, and time series databases like InﬂuxDB. They are challenging to build because they run at scale and need complex distributed functionality like data replication, fault tolerance, and update consistency. DPA makes building these systems easier by allowing developers to construct them from purely single-node components while automatically providing these critical properties. In DPA, we view a query serving system as a collection of stateful actors, each encapsulating a partition of data. DPA provides parallel operators that enable consistent, atomic, and fault-tolerant parallel updates and queries over data stored in actors. We have used DPA to build a new query serving system, a simpliﬁed data warehouse based on the single-node database MonetDB, and enhance existing ones, such as Druid, Solr, and MongoDB, adding missing user-requested features such as load balancing and elasticity. We show that DPA can distribute a system in <1K lines of code (>10 × less than typical implementations in current systems) while achieving state-of-the-art performance and adding rich functionality.
Hapten sensitization to vaginal mucosa induces less recruitment of dendritic cells accompanying TGF‐β‐expressing CD206+ cells compared with skin

Contact hypersensitivity (CHS), a type of delayed‐type hypersensitivity, is induced by hapten exposure to the skin and mucosa. We previously reported that, in a murine model of CHS, the vaginal mucosa (VM) sensitization showed lower T‐cell responses as compared with the abdominal skin sensitization. To investigate mechanisms of impaired CHS by the VM sensitization, we compared migration of hapten‐captured dendritic cells (DCs) in the draining lymph nodes (dLNs) and recruitment of DCs at the sensitized local sites.
Public service provision for Syrian refugees in Turkey: challenges and prospects

ABSTRACT Refugee crises are among the most heartrending and vexatious humanitarian issues since the beginning of civilization. These crises are particularly evident in the case of Syria, where, since 2011, civil war and terrorism have led millions of people to seek refugee status in neighbouring countries, including Turkey. Since 2011, Turkey has pursued an open-door policy accompanied by a national temporary protection regime to protect more than four million Syrians fleeing the civil war. As a corollary, government institutions (the public service) in Turkey have the responsibility of providing essential services, including education and health. Against this backdrop, this study aims to examine and analyse the opportunities and challenges Turkey is currently facing in accommodating the Syrian refugees from the perspectives of Syrian migrants living in Turkey. This research employed the qualitative research approaches, which adopted in-depth interviews, focus group discussions and documentary reviews as the data collection instruments. The findings revealed that the Government of Turkey is being overstretched by the refugee crisis, and this has affected the quality of public service provisions. Consequently, this study is a rare attempt to examine the impact of the Syrian Refugee crisis on the public service provision of Turkey.
Improved Efficiency in Engine Cooling System by Repositioning of Turbo Inter Cooler

Turbochargers are an integral part of today’s modern diesel engines and are a major reason that they are able to produce more power. Unlike a super charger that is driven via a belt from the engine, a turbo takes the exhaust that the engine is producing and puts it to good use. As Turbochargers are driven by exhaust, heat is an unwelcome by product and something that wasn’t really taken into account in automobiles. Then those intercoolers started to come into play in turbocharged automobiles. The forced air produced by the turbocharger is routed through the intercooler where its temperature is reduced before reaching the engine. The use of intercoolers has made turbocharged vehicles far more reliable and, in the case of today’s heavy duty diesel trucks, is a very important component. The inlet air of an IC engine from turbocharger temperature is very much high (due to compression) means oxygen content is very much less. And also air with high temperature causes pre-ignition and detonation. So fuel combustion does not take place properly. Inter Cooling of inlet air is very much essential according to performance point of view. Turbo intercoolers are used for cooling the inlet air of an IC engine from turbo chargers. Moreover cooling of air makes it denser and contributes for better combustion and more power they are mounted close to the radiators for achieving lower air temperature. This arrangement affects the performance of both. So in this project an attempt will be made to increase the efficiency of the turbo intercooler arrangement through design modification and repositioning of intercooler by taking the TATA MARCOPOLO-Star Bus 909 as a reference.
Deep convolutional neural network for environmental sound classification via dilation

In the recent time, enviromental sound classification has received much popularity. This area of research comes under domain of non-speech audio classification. In this work, we have proposed a dilated Convolutional Neural Network approch to classify urban sound. We have carried out feature extraction, data augmentation techniques to carry out our experimental strategy smoothly. We also found out the activation maps of each layers of dilated convolution neural network. An increamental dilation rate has exploited Overall we achieved 84.16% of accuracy from the proposed dilated convolutional method. The gradual increaments of dilation rate has exploited the worse effect of grindding and has lowered down the computational cost. Also, overall classification performance, precision, recall,overall truth and kappa value have been obtained from our proposed method. We have considered 10 fold cross validation for the implementation of the dilated CNN model.
Low frequency noise characteristics

In this work we present detailed investigation of low frequency noise characteristics of BaTiO3-NiFe2O4 ceramic composites of different percentage of NiFe2O4. At the particular temperatures (that gets under the broad maximum of dielectric permittivity observed around 300 K) very intensive few separate peaks of random telegraph noise signal were observed. It shows that phase transition in the investigated materials goes through separate stages instead of the smeared out one. Characteristic times of the physical processes during the phase transition are close to one millisecond, and activation energy is around (0,34- 0,49) eV.