Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of an Expert Knowledge-Platform

Considering the growing interest towards next generation sequencing (NGS) and data analysis, and the substantial challenges associated to fully exploiting these technologies and data without the proper experience, an expert knowledge-based user-friendly analytical tool was developed to allow non-bioinformatics experts to process NGS genomic data, automatically prioritise genomic variants and make their own annotations. This tool was developed using a user-centred methodology, where an iterative process was followed until a useful product was developed. This tool allows the users to set-up the pre-processing pipeline, filter the obtained data, annotate it using external and local databases (DBs) and help on deciding which variants are more relevant for each study, taking advantage of its customised expert-based scoring system. The end users involved in the project concluded that CRIBOMICS was easy to learn, use and interact with, reducing the analysis time and possible errors of variant prioritisation for genetic diagnosis.
Assessing costs and benefits of current climate negotiations

Last December 2015 the Parties under the UNFCCC succeeded in reaching the so-called Paris Agreement (COP21), which represents a timid step towards a burden sharing involving all countries in emission mitigation actions. However, given the heterogeneity of countries and their relative differences in vulnerability, compensating schemes for the most vulnerable countries are required to reach a successful agreement. This paper addresses the debate about the Green Climate Fund (GCF), the main compensating measure under a global climate regime, regarding the allocation mechanisms that are still under discussion. A dynamic climate-economy computable general equilibrium model (GDynE) is developed by considering both a monetary evaluation of climate change damage costs and two alternative criteria in the GCF resources allocation. Results show that, despite the high costs associated to the implementation of mitigation actions, developing countries would face even higher costs in case of inaction. Furthermore, the preference of a country for an allocation method is strongly influenced by its characteristics and needs. Consequently, our main policy advise is to design a well structured country-specific GCF in order to maximize country participation in a global agreement. six life-supporting sectors: food, water, health, ecosystem service, human habitat, and infrastructure. 36 indicators (two per component in each sector) contribute to the measure of vulnerability, obtained as a simple mean of the sector scores, which are the average scores of component indicators. Readiness measures the ability of a country’s private and public sectors to absorb investment resources and successfully apply them to reduce climate change vulnerability. Readiness includes indicators for three components (social, economic and governance indicators) not weighted equally (Economic Readiness is 50% of the readiness score while governance and social readiness are 25%).
Analysis of high capacity short reach optical links

Over the last few years, the global Internet traffic has grown exponentially due to the advent of the social networks, high definition streaming, online gaming, high performance computing and cloud services. The network is saturating, facing a challenge to provide enough capacity to such ever-demanding bandwidth expensive applications. Fiber optic communications is the only technology capable of dealing such high demands due to its advantages over the traditional electrical transmission technology. The short haul transmissions currently rely on direct detection due to low cost, low power and low complexity as compared to the coherent detection schemes. In order to increase the bit rate, several advance modulation formats are under investigation for short reach transmissions. Such links mostly use intensity modulation direct detection (IMDD) schemes providing a simple system when compared with the coherent receivers. In this thesis the performance of Multilevel Pulse Amplitude Modulation (MPAM) is studied using IMDD, providing good spectral efficiency as well as able to deal with the limited electronic devices bandwidth. MPAM can address the typical optical channel without the need to go with more complex and higher power modulation schemes. It provides a trade off between sensitivity and the complexity. So a simple communication system using MPAM is implemented using an external modulated laser transmitted over a distance of 2 km. In order to reduce the cost, single laser and single receiver technique is being adopted. The performance of the MPAM system in a bandwidth limited scenarios is studied with a possibility to use equalization techniques to improve the sensitivity. The utility of Forward Error Correction codes is also studied to improve the performance without increasing the latency. By increasing the number of bits per symbol, the system becomes more sensitive to the impairments. Moreover, the components and the connectors in the transmission system also introduces multipath interference (MPI) that is a key limitation to the use of advance modulation formats. Hence a detailed study is carried out to investigate the MPI effects. At the end, a novel idea based on reflective Mach-Zehnder modulator (MZM) is presented that reuses the modulated wavelength eliminating the need for a laser. As a consequent, the cost and power consumption specifically targeted for the optical interconnect environment is reduced. In a nutshell, the thesis provides an overview of the direct detection system targeted to the short optical links. It includes the studies related to the optical transmission systems and provides an insight of the available advance modulation formats and the detection schemes. Finally, the simulations and laboratory results are provided showing that adoption of MPAM is a viable solution that should be employed in high capacity short reach optical links.
A Hybrid Intelligent System for Company Financial Risk Detection Based on Tree-Based Model and Deep Neural Network

At present, the process of security issuance in China has changed from examination and approval system to approval system. With the increasing stock that enters the capital market through the IPO, establishing an effective financial risk monitoring and control system of listed companies has great practical significance and application value. In the perspective of big data, this paper constructs a new practical financial early warning integration model. A total of 32,283 financial statements of 3,025 listed companies are collected from 2000 to 2017. First, this paper collected and built a financial crisis prediction database: a total of 32,283 financial statements of 3,025 listed companies occurred from year 2000 to 2017 and corresponding financial indicators including 6 primary indicators and 25 secondary indicators are collected. Next, the financial crisis model based on financial indicators is proposed and trained by the classic machine learning method such as random forest and gradient boosting decision tree. And then, through natural language processing and deep learning technology, this paper also builds a financial crisis prediction model based on financial statement text. Finally, an ensemble model is proposed and its performances are evaluated; the results indicate that the proposed ensemble model performed the best and significantly outperforms other benchmark methods for financial crisis prediction and identification, indicating that it can be employed as an intelligent identification system to enhance identification accuracy for early warning and identification of financial crisis of listed companies in China stock marketing.
Tunnel lining water leakage image Segmentation based on improved BlendMask

Water leakage of tunnel is a common disease in all kinds of existing tunnels. Automatic, timely, and accurate detection of water leakages is of great significance to the safe operation and maintenance for all kinds of tunnels. Due to the complicated background of tunnel surfaces, it is difficult for traditional tunnel defect detection algorithms to detect such defects quickly and accurately. To address these problems, this article presents an improved BlendMask image segmentation model with top-down and bottom-up architecture which accurately extract tunnel’s water leakage area from tunnel lining images. The proposed method is validated by an experimental study, and the results are compared with those obtained by the tunnel defect segmentation methods including Mask R-CNN and DeepCrack and other deep learning methods including CondInst, SoloV2, fully convolutional network and UNet. The Recall, Precision, F1-score, Dice and mean intersection over union (mIoU) for the proposed method are superior than those by the other methods above with respect to on test images. The subjective analysis on predicted results by different methods also shows the presented method can perform well.
Left ventricular dysfunction with preserved ejection fraction: the most common left ventricular disorder in chronic kidney disease patients

ABSTRACT Chronic kidney disease (CKD) is a risk factor for premature cardiovascular disease. As kidney function declines, the presence of left ventricular abnormalities increases such that by the time kidney replacement therapy is required with dialysis or kidney transplantation, more than two-thirds of patients have left ventricular hypertrophy. Historically, much research in nephrology has focussed on the structural and functional aspects of cardiac disease in CKD, particularly using echocardiography to describe these abnormalities. There is a need to translate knowledge around these imaging findings to clinical outcomes such as unplanned hospital admission with heart failure and premature cardiovascular death. Left ventricular hypertrophy and cardiac fibrosis, which are common in CKD, predispose to the clinical syndrome of heart failure with preserved left ventricular ejection fraction (HFpEF). There is a bidirectional relationship between CKD and HFpEF, whereby CKD is a risk factor for HFpEF and CKD impacts outcomes for patients with HFpEF. There have been major improvements in outcomes for patients with heart failure and reduced left ventricular ejection fraction as a result of several large randomized controlled trials. Finding therapy for HFpEF has been more elusive, although recent data suggest that sodium-glucose cotransporter 2 inhibition offers a novel evidence-based class of therapy that improves outcomes in HFpEF. These observations have emerged as this class of drugs has also become the standard of care for many patients with proteinuric CKD, suggesting that there is now hope for addressing the combination of HFpEF and CKD in parallel. In this review we summarize the epidemiology, pathophysiology, diagnostic strategies and treatment of HFpEF with a focus on patients with CKD.
Data proxies, the cognitive layer, and application locality : enablers of cloud-connected vehicles and next-generation internet of things

Intelligent and Connected Vehicles reduce cost, improve safety, and enhance comfort relative to isolated vehicles. This ability for cars to sense, infer, and act facilitates data-driven improvements in occupant experience and vehicle design. This thesis explores informed individual vehicle improvements and proposes a secure and efficient architecture supporting connected vehicle applications. Applying On-Board Diagnostic and smartphone data, I built a suite of prognostic applications. Engine coolant temperature data supports inference of oil viscosity and remaining life. A linear SVM using Fourier, Wavelet, and Mel Cepstrum audio features provides 99% accurate engine misfire detection. PCA-transformed Fourier acceleration features and GPS data inform decision trees attaining 91% wheel imbalance and 80% tire pressure and tread depth classification accuracy. These applications demonstrate the ability for local vehicle and peripheral device data to proactively improve individual vehicle reliability and performance. Connectivity facilitates crowdsourced data to further improve current vehicles and future designs. Exploring vehicular connectivity, I consider data timeliness, availability and bandwidth cost in the context of an efficiency-improving idle time predictor. This predictor uses contextual information to eliminate short idle shutoffs in Automatic Engine Start/Stop systems, minimizing driver annoyance and improving compliance. These applications reveal an opportunity to address excess resource consumption and system insecurity in Connected Vehicles and other constrained devices. I introduce a secure and efficient model-based Internet of Things (IoT) architecture consisting of a “Data Proxy” utilizing a Cloud-run estimator to mirror an object with limited sensor input. The use of digital duplicates abstracts physical from digital objects, allowing the use of a mediating “Cognitive Layer” consisting of firewall and supervisory elements. These “Cognitive” elements apply the system model to monitor system evolution and simulate the impact of commands against known and learned limits.
Biology and Model Predictions of the Dynamics and Heterogeneity of Chromatin-Nuclear Lamina Interactions

Associations of chromatin with the nuclear lamina, at the nuclear periphery, help shape the genome in 3 dimensions. The genomic landscape of lamina-associated domains (LADs) is well characterized, but much remains unknown on the physical and mechanistic properties of chromatin conformation at the nuclear lamina. Computational models of chromatin folding at, and interactions with, a surface representing the nuclear lamina are emerging in attempts to characterize these properties and predict chromatin behavior at the lamina in health and disease. Here, we highlight the heterogeneous nature of the nuclear lamina and LADs, outline the main 3-dimensional chromatin structural modeling methods, review applications of modeling chromatin-lamina interactions and discuss biological insights inferred from these models in normal and disease states. Lastly, we address perspectives on future developments in modeling chromatin interactions with the nuclear lamina.
Exogenous Melatonin Ameliorates the Negative Effect of Osmotic Stress in Human and Bovine Ovarian Stromal Cells

Ovarian tissue cryopreservation transplantation (OTCT) is the most flexible option to preserve fertility in women and children with cancer. However, OTCT is associated with follicle loss and an accompanying short lifespan of the grafts. Cryopreservation-induced damage could be due to cryoprotective agent (CPA) toxicity and osmotic shock. Therefore, one way to avoid this damage is to maintain the cell volume within osmotic tolerance limits (OTLs). Here, we aimed to determine, for the first time, the OTLs of ovarian stromal cells (OSCs) and their relationship with reactive oxygen species (ROS) and mitochondrial respiratory chain activity (MRCA) of OSCs. We evaluated the effect of an optimal dose of melatonin on OTLs, viability, MRCA, ROS and total antioxidant capacity (TAC) of both human and bovine OSCs in plated and suspended cells. The OTLs of OSCs were between 200 and 375 mOsm/kg in bovine and between 150 and 500 mOsm/kg in human. Melatonin expands OTLs of OSCs. Furthermore, melatonin significantly reduced ROS and improved TAC, MRCA and viability. Due to the narrow osmotic window of OSCs, it is important to optimize the current protocols of OTCT to maintain enough alive stromal cells, which are necessary for follicle development and graft longevity. The addition of melatonin is a promising strategy for improved cryopreservation media.
Adaptive passivity-based control of an autonomous underwater vehicle

An Autonomous Underwater Vehicle (AUV) is a nonlinear nonholonomic dynamical system. Tracking control of AUVs in the presence of disturbances and uncertainties is a challenging problem. In this paper, an adaptive passivity-based algorithm is proposed for tracking control of an AUV. Also, a disturbance estimator has been used to attenuate the effects of unknown disturbances. A proper adaptive rule is designed to estimate unknown parameters and disturbances. The stability of the proposed method is analyzed using passivity properties and the Lyapunov theory. Obtained comparative results show the advantages of the proposed Adaptive Computed Torque Method ACTM plus disturbance estimator.
Glucose metabolism inhibitor PFK-015 combined with immune checkpoint inhibitor is an effective treatment regimen in cancer

ABSTRACT Metabolic inhibition via PFKFB3 inhibition has demonstrated considerable tumor inhibitory effects in various studies; however, PFKFB3 inhibition did not show satisfactory tumor inhibition when used in clinical trials. PFKFB3 is a crucial metabolic enzyme that is highly upregulated in cancer cells and directly affects tumor glycolysis. Here, we showed that PFKFB3 inhibition suppresses tumors in vitro and in vivo in immune-deficient xenografts. However, this inhibition induces the upregulation of PD-L1 levels, which inactivated cocultured T-cells in vitro, compromises anti-tumor immunity in vivo, and reduced anti-tumor efficacy in an immune-competent mouse model. Functionally, PD-1 mAb treatment enhances the efficacy of PFKFB3 inhibition in immunocompetent and hu-PBMC NOG mouse models. Mechanistically, PFKFB3 inhibition increases phosphorylation of PFKFB3 at residue Ser461, which increases interaction with HIF-1α, and their colocalization into the nucleus, where HIF-1α transcriptionally upregulate PD-L1 expression and causes subsequent tumor immune evasion. Higher phos-PFKFB3 correlated with higher PD-L1 expression, lower CD8 and GRZMB levels, and shorter survival time in ESCC patients.
Prediction of Disease Case Severity Level To Determine INA CBGs Rate

Indonesian Case-Based Groups (INA CBGs) is case-mix payment system using software grouper application. INA CBGs consisting of four digits code where the last digits indicating the severity level of disease cases. Severity level influence by secondary diagnosis (complications and co-morbidity) related to resource intensity level. It is medical resources used to treat a hospitalized patient. Objectives of this research is developing decision support system to predict severity level of disease cases and illustrate INA CBGs rate by using data mining decision tree classification model. Primary diagnosis (DU), first secondary diagnosis (DS 1), and second secondary diagnosis (DS 2) are attributes that used as input of severity level. The training process using C4.5 algorithm and the rules will represent in the IF-THEN form. Credibility of the system analyzed through testing process and confusion matrix present the results. Outcome of this research shows that first secondary diagnosis influence significant to form severity level predicting rules from new disease cases and INA CBGs rate illustration.
SYSTEMATIC USABILITY INSPECTION APPROACH FOR SONIFICATION APPLICATIONS

In previously reported research, most sonification designers have needed to develop at least a working prototype for user testing. The results are interpreted and analysed to look fo r possible problems and solutions to further improve the desig n. This paper introduces a new systematic usability inspection ap proach for the design of sonification applications design before they go to the initial development phase. This process gives an al ternative for designers to evaluate their design, detect possible problems and improve the design before they start developing it. It uses two of our models - the Sonification Application (SA) model and the User Interpretation Construction (UIC) model. In th is paper we discuss the steps of this process, which include pr eparing inspection materials, implementing inspection and managing the results.
The Effect of Packaging on Consumer Choice Of Washing Detergents in Nairobi

vi LIST OF ABBREVIATIONS . xi CHAPTER ONE . 1 1.0 INTRODUCTION. 1 1.1 Background of the Problem . 1 1.2 Statement of the Problem . 7 1.3 Purpose of the Study . 8 1.4 Research Questions . 8 1.5 Importance of the Study . 8 1.6 Scope of the Study . 9 1.7 Definition of Terms. 9 1.8 Chapter Summary . 10 CHAPTER TWO . 11 2.0 LITERATURE REVIEW . 11 2.
Assessment of Pipeline Integrity after Trawling Impact by Investigating Dent with Gouge

Mechanical damage from trawling gear interference amongst others cause a significant amount of damage to pipelines. The present study has done a review of current standards and recommended practise for assessment methods for unconstrained plain dent with gouge. A test method was developed to investigate stress concentration factor and fatigue life of the combined damage with plain dent and gouge. Three different indenter shapes was created and indented four dents on two welded round precision tubes (E235+CR1) with a diameter/thickness ratio of 51. Three of them also contained a gouge. After denting, strain gauges was applied. The pipe samples was then pressurized to evaluate stress concentration factor (SCF). A numerical model was created according to the experiment. The numerical model was consistent with the indention process, showing good results when comparing numerical and experimental results. The 3D acquisition tool Autodesk 123D Catch proved to be a good and efficient method to scan the pipe samples in order to create a three dimensional (3D) model. Numerical model predicted rerounding and dent depth at a satisfactory manner. Furthermore when comparing the strain gauges with numerical work with regards to SCF the model did not produce valid results. The strain gauges was located were the highest values for strain was expected to be, however it was shown that there might be locations with higher strains and the initially assumptions was mistaken. Therefore the numerical model needs improvement when investigating SCF and fatigue life.
Molecular characterization and morphology of Philonema onchorhynchi (Nematoda: Philometridae), a parasite infecting the Nile perch (Lates niloticus) in Egypt with some of its histopathological effects.

The present study aimed to detect the helminth parasites from the Nile Perch Lates niloticus Linnaeus, 1758 collected from Manzala lake in Damietta province, Egypt. Twenty-six (34.7%) of 75 fish samples were found infected with one species of nematode Philonema onchorhynchi Kuitunen-Ekbaum, 1933 (Family: Philometridae). The mean intensity was (0.51±0.79) and relative density (0.50) with no significant difference ( 𝑃 >0.05) in the infection between male and female hosts. Partial fragments of the first internal transcribed spacer (ITS-I) region and small subunit ribosomal RNA (SSU rRNA) 28S gene of the nematode were amplified, sequenced, and available in GenBank under accession number MZ766122. Molecular data confirmed the identification of the species as P. onchorhynchi belonging to the family Philometridae. The obtained nematode was described using light and scanning electron microscopy which revealed it is characterized by inconspicuous small and large papillae distributed on the cephalic region surrounding the mouth opening with no true lips and four pseudolapia. The histopathology of the intestine of the infected hosts was also investigated.
Zigzag nanosheets transistor introduction: a path for nanosheet FET architecture scalability

This paper presents a new transistor design that enables scalability of a nanosheet field effect transistor (FET) architecture. By introducing an innovative patterning design for the nanosheets (a zigzag shape), the contacted poly pitch (CPP) can be reduced to a 30nm size. Semiconductor virtual fabrication was used to create virtual prototypes of this new device architecture and evaluate different process and design assumptions.