Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of prognostic biomarkers for malignant melanoma using microarray datasets

Malignant melanoma is one of the most common types of cancer worldwide. Efforts have been made to elucidate the pathology of malignant melanoma. However, its molecular mechanisms remain unclear. Therefore, the microarray datasets GSE3189, GSE4570 and GSE4587 from the Gene Expression Omnibus database were used for the elucidation of candidate genes involved in the initiation and progression of melanoma. Assessment of the microarray datasets led to the identification of differentially expressed genes (DEGs), which were subsequently used for function enrichment analysis. These data were utilized in the construction of the protein-protein interaction network and module analysis was conducted using STRING and Cytoscape software. The results of these analyses led to the identification of a total of 182 DEGs, including 52 downregulated and 130 upregulated genes. The functions and pathways found to be enriched in the DEGs were GTPase activity, transcription from RNA polymerase II promoter, apoptotic processes, cell adhesion, membrane related pathways, calcium signaling cascade and the PI3K-Akt signaling pathway. The identified genes were demonstrated to belong to a set of 10 hub genes biologically involved in proliferation, apoptosis, cytokinesis, adhesion and migration. Survival analysis and Oncomine database analysis revealed that the calmodulin gene family, BAX and VEGFA genes, may be associated with the initiation, invasion or recurrence of melanoma. In conclusion, the DEGs and hub genes identified in the present study may be used to understand the molecular pathways involved in the initiation and progression of malignant melanoma. Furthermore, the present study may aid in the identification of possible targets for the diagnosis and treatment of melanoma.
Low Complexity Iterative Detection for a Large-Scale Distributed MIMO Prototyping System

In this paper, we study the low-complexity iterative soft-input soft-output (SISO) detection algorithm in a large-scale distributed multiple-input multiple-output (MIMO) system. The uplink interference suppression matrix is designed to decompose the received multi-user signal into independent single-user receptions. An improved minimum-mean-square-error iterative soft decision interference cancellation (MMSE-ISDIC) based on eigenvalue decomposition (EVD-MMSE-ISDIC) is given to perform low-complexity detection of the decomposed signals. Furthermore, two iteration schemes are given to improve receiving performance, which are iterative detection and decoding (IDD) scheme and iterative detection (ID) scheme. While IDD utilizes the external information generated by the decoder for iterative detection, the output information of the detector is directly exploited with ID. In particular, the performance of the schemes is evaluated in a 128×128 (16 remote antenna units (RAUs) and 16 users, each equipped with 8 antennas) large-scale distributed MIMO prototyping system, which is also a cell-free massive MIMO. The experimental results show that the proposed iterative receiver greatly outperforms the linear MMSE receiver, since it reduces the average number of error blocks of the system significantly.
The ability of calcaneal and multisite quantitative ultrasound variables in the identification of osteoporosis in women and men.

Objectives The aim of this study was to assess the ability of calcaneal and multisite quantitative ultrasound (QUS) parameters in the identification of osteoporosis in women and men. Patients and methods A total of 131 women (mean age 53.7±11.9 years; range, 21 to 79 years) and 109 men (mean age 57.8±13.7 years; range, 24 to 85 years) whose bone mineral density (BMD) at the spine and proximal femur was measured between January 2010 and January 2012, using dual-energy X-ray absorptiometry (DXA) were included. Acoustic bone properties were also examined using both a calcaneal and a multisite QUS. The receiver operating characteristic analysis with the calculation of areas under the curve (AUCs) to evaluate the ability of both QUS devices for the identification of osteoporosis. We also calculated a lower and an upper threshold at a specificity of 90% and at a sensitivity of 90%, respectively, for the identification of osteoporosis along with a threshold/cut-off value with the best compromise between sensitivity and specificity. Results All calcaneal QUS parameters showed significant AUCs within the range of 0.712 (for Broadband Ultrasound Attenuation [BUA]) and 0.764 (for Speed of Sound [SOS]) in women and ranging from 0.661 (for BUA) to 0.735 (for SOS) in men, while only radial SOS of the multisite QUS demonstrated a significant AUC value of 0.661 for identifying osteoporosis in women. A Quantitative Ultrasound Index T-score of -1.53 for women and -1.68 for men showed sensitivity and specificities around 70%. Conclusion Based on the results of this study, all calcaneal QUS parameters in both women and men and possibly radial SOS measurements of the multisite QUS in women may be helpful for the identification of osteoporosis.
Probability-weighted ensemble classifier using holoentropy-enabled decision tree for data stream classification

ABSTRACT Data stream mining is an interesting area that classifies the incoming data based on the available data points. The major issue faced by the stream mining mechanism is regarding the accuracy of classification, classification time, automatic classification, and the concept drift. In order to overcome these issues, this paper proposes a probability-weighted ensemble classifier that uses the decision tree for classifying the data stream. The holoentropy-enabled decision tree (HDT) uses the holoentropy parameter for performing the classification, and the concept drift calculation depends on the Centroid Deviation Distance (CSD) of the models. The accuracy of the classification is maintained through the frequent update in the classifier models based on the new data points. Experimentation is carried out using the three datasets, and the performance analysis is made in terms of the performance metrics, like the accuracy and the FAR. The comparative analysis of the proposed method with the existing methods proves that the proposed method attains the maximum accuracy and minimum FAR than the existing methods.
Hierarchical Task Analysis dalam pengembangan gagasan produk

Human, have been claimed to be central in a world perspective and bring an another investigating in design science. One of the process development in the design process is using Hierarchy Task Analysis (HTA) for idea development through task analysis. This method constituted from interdisciplinary science between industrial design and ergonomic. In the process, HTA has been adjusted and developed for the design process requirement. This condition affects the process analysis and format of the HTA. The paradigm shift, give more access for a designer to addresses idea development. Furthermore, Human activities, how they do it, using what and what kind of problems that can happen is going focused on user interaction with the product. All of these analysis and idea development is conducted using scheme and sketches. The result of ideas sketches to be able to work on next step process, for example, work on paper models or clay models. The result of ideas sketches to be able to continue work on next step process, for example, product existing analysis and work on modells. Research and developing HTA method for product design had the use of action research approach. This process engages lecturer and student as a user of HTA method. Through this approach, be able to found appropriateness HTA method which solves provided a problem. The result of HTA development has a benefit for a student as a one of planning and product development to arise a product based on ergonomic. Key words: product design, Hierarchy Task Analysis (HTA), ideation, design process, ideas sketches
Image Processing for Detection of Vehicles In Motion

Some problems that traffic managers face nowadays are traffic congestions in cities and highways, which are main contributors to the number of traffic-accidents and the increasing environmental impacts. Said congestions are caused, amongst other reasons, by the slow growth of the capacity of roads compared to the number of vehicles that travel in them. The development of new infrastructures is costly and upgrading existing ones causes traffic disruptions, leading to more problems. One solution to this problem is to improve the efficiency of the existing transportation systems. In order to do so, new and more efficient ways of gathering road usage data have to be developed. Automatic traffic surveillance for vehicle detection, classification and tracking using visionbased methods has attracted a great deal of interest from researchers in the past decade. Even though many solutions have been proposed, real-time fully automatic systems need to be further developed. Systems based on Convolutional Neural Networks provide solutions for many of the present-day problems in vehicle detection, classification and tracking, outperforming other techniques in Computer Vision. Thus, the objective of this dissertation is to propose a framework for vehicle detection, classification and tracking in traffic videos that can be used in a series of traffic applications, such as the system here presented. For vehicle detection and classification, we used the YOLOv3 model with pre-trained weights on the COCO dataset. In the next step, the results are fed into a multi-object tracking algorithm. The implemented tracker is based on the IOU tracker a track-by-detection approach where first an object detector is applied to a frame and second a tracker is used to associate the detections to tracks. Then a tracking zone is identified in the video image where the vehicles are tracked and counted. We validated our approach by testing our application for vehicle counting in videos captured by traffic cameras. The results obtained were compared with a previous study using unsupervised learning methods. Our approach showed significant improvements on the tasks of detection, classification and tracking especially when there were occlusions. YOLOv3 is a state-of-the-art object detection and classification system that can detect and classify objects in real time but it does not keep temporal information of the objects. We proposed a framework that introduces object tracking to the YOLOv3 model without worsening its performance. Our system achieves real-time detections, classification and tracking in videos which can be used in real applications. The domain of our framework is not limited to traffic systems and can be easily adapted to other applications such as people tracking. Categories: Computing Methodologies→Artificial Intelligence→Computer Vision→Computer Vision Problems→Object Detection.
Correlations between estimating GFR methods and oncological outcomes during cancer chemotherapy

Background: Glomerular filtration rate (GFR) measurements are critical in patients with cancer. A variety of methods are used to calculate the estimated GFR. The aim of this study is to investigate whether there is a correlation between these methods and oncologic outcomes according to the stage and treatment agents. Methods: A total of 153 patients were retrospectively recruited. All GFR measurement methods was determined in all patients, before the first cycle of chemotherapy and before the subsequent administrations. Results: In the study population 40% of whom received platinum-based chemotherapy. In this group, overall survival was statistically significant in patients with a CKD-EPI creatinine value of 65 or greater (p:0.023). When we separated the arms according to the stage, there was no relationship between CKD-EPI Cystatin C and progression-free survival in metastatic patients (p: 0.13). In the non-metastatic group, median DFS was 7 months and OS was 13.9 months in patients with CKD-EPI Cystatine C level above 45 (p:0.005). Conclusions: Both CKD-EPI creatinine and CKD-EPI cystatin C were significantly associated with overall survival and disease-free survival in patients receiving platinum-based chemotherapy. When assessed according to the stage, there was a general survival relationship with CKD-EPI cystatin C in the non-metastatic group and in the other groups there was no significant correlation with the estimated GFR measurements.
Solving Time Complexity Issue in Copy-Move Forgery Detection Thru Pre-processing Techniques

Copy-move forgery detection (CMFD) has become a popular an important research focus in digital image forensic. Copy-move forgery happens when a region in an image is copied and paste into the same image. Apart from the main problem of detection robustness and accuracy, CMFD is struggle with time complexity issue. One of the options to resolve this problem was by including pre-processing step in CMFD pipeline. This paper reviews on the importance of pre-processing step, and available techniques in reducing time complexity of copy-move forgery detection. An experiment using discrete wavelet transform (DWT) as a pre-processing technique was carried out to evaluate the performance of adopting pre-processing technique in CMFD pipeline. The experimental result has shown a significant reduction in processing time with some trade off to detection accuracy.
Validation and application of the International Classification of Functioning core set for spinal cord injury in the Turkish patients.

Objectives The aim of this study was to apply the Comprehensive International Classification of Functioning, Disability and Health (ICF)- Core Set for spinal cord injury (SCI) -early post-acute-situation in the Turkish SCI patients and to investigate its construct validity. Patients and methods One-hundred and twenty patients with SCI were included in this prospective and descriptive study. ICF data were primarily collected by conducting interviews with patients as well as from their acute medical management records, physical examination findings and laboratory measurements. The percentage of participants and the frequency of the problems encountered at each level of ICF category were reported. Furthermore, the construct validity was evaluated by calculating the Spearman correlation between the ICF categories and other generic and disease specific measures. Results The study illustrated that 55 of the 63 ICF categories of the Component Body Functions (CBF) and each and every category for the Component Body Structures (CBS) as well as the Component Activities and Participation (CAP) were reported as a problem among the Turkish patients with SCI. Furthermore, 24 ICF categories for the Component Environmental Factors (CEF) were determined as a facilitator while 6 ICF categories were identified as a barrier. The ICF-Core Set for SCI illustrated a high construct validity with some of the generic and disease-specific measures. Conclusion Our results identified the common problems, complications and special needs in a Turkish population with SCI. We suggest that the application of ICF-Core Set in our patients provided us with a unique capability to assess their every aspect of disability, health and functioning.
Epidemiology of Alcohol Use in Late Adolescence in Greece and Comorbidity with Depression and Other Common Mental Disorders

Background The aim of the current study was to examine the prevalence and associations of hazardous alcohol use with sociodemographic variables and its comorbidity with depression and other common mental disorders in a sample of Greek adolescents between 16 and 18 year old. Methods We recruited 2431 adolescents attending 25 senior high schools in Greece. We assessed depressive and anxiety disorders using the computerized version of a fully-structured psychiatric interview (the revised Clinical Interview Schedule / CIS-R). Alcohol use was assessed using questions taken from a previous WHO school survey. Results Approximately one-third of adolescents (overall: 30.7%, boys: 39.2%, girls: 21.9%, p < 0.001) consumed alcohol on a weekly basis. The experience of excessive consumption, leading to drunkenness at least two or more times in their lifetime, was reported by 15.39% of the adolescents (19.42% for the boys and 11.24% for the girls, p < 0.001). Frequent alcohol consumption and drunkenness were strongly associated with the presence of depression, all other anxiety disorders except panic disorder, current smoking, and lifetime cannabis use, lower school performance, bad or fair relationship with parents, and increased health services use. Conclusion Alcohol use is highly prevalent among Greek adolescents. Special attention for the development of more focused preventive strategies should be paid to adolescents suffering from depression or other common mental disorders.
Laboratory approbation of a new visualization form of hazardous objects control operator current psycho-emotional and functional state

The paper suggests a new form of visualization of the current psycho-emotional and functional state of operators for managing dangerous objects. Integral assessment of the current psycho-emotional and functional state of the operator is carried out on the basis of the registered bio-parameters analysis. These bio-parameters characterize the current functioning of its cardiovascular system, the respiratory system and the peripheral nervous system. Registration of these bio-parameters is carried out on the basis of passive remote non-contact technologies, typical of which are acoustic and optical ones. The proposed form of visualization allows the display of both the indicators reached within a certain time interval and their change in dynamics, which significantly expands the scope of its possible application. The considered form of visualization also makes it possible to display the trends of a possible change in the state of the operator in the short and long-term per-spectives. Experimental approbation of visualization technology in solving problems of optimization of educational and training sessions, as well as in developing methodological tools for assessing the level of acquired professional knowledge and in adapting educational process to groups of various specializations, confirmed its high efficiency.
Parsing Next Generation Sequencing Data in Parallel Environments for Downstream Genetic Variation Analysis

With the recent advances in next generation sequencing technology, analysis of prevalent DNA sequence variants from patients with a particular disease has become an important tool for understanding the associations between the disease and genetic mutations. A publicly accessible bioinformatics pipeline, called OncoMiner (http://oncominer.utep.edu), was implemented in 2016 to help biomedical researchers analyze large genomic datasets from patients with cancer. However, the current version of OncoMiner can only accept input files with a highly specific format for sequence variant description. In order to handle data from a broader range of sequencing platforms, a data preprocessing tool is necessary. We have therefore implemented the OncoMiner Preprocessing (OP) program for parsing data files in the popular FastQ and BAM formats to generate an OncoMiner input file. OP involves using the open source Bowtie2 and SAMtools software, followed by a python script we developed for genetic sequence variant identification. To preprocess very large datasets efficiently, the OP program has been parallelized on two local computers and the Blue Waters system at the National Center for Supercomputing Applications using a multiprocessing approach. Although reasonable parallelization efficiency has been obtained on the local computers, the OP program’s speedup on Blue Waters has been limited, possibly due to I/O issues and individual node memory constraints. Despite these, Blue Waters has provided the necessary resources to process 35 datasets from patients with acute myeloid leukemia and demonstrated significant correlation of OP runtimes with the BAM input size and chromosome diversity.
Spisevansker og vekstforstyrrelser blant barn med cerebral parese i Sør- og Nord-Trøndelag

Background. Growth retardation is common in children with cerebral palsy. This may in part be due to the cerebral injury, but insufficient nutrition may also play a role. The aim of the present study was to estimate the prevalence of feeding problems, growth retardation, underweight and overweight in children with cerebral palsy. Material and methods. Population-based study of children with cerebral palsy in two Norwegian counties. Information was obtained both from parents and from medical records. 154 children born between 1 January 1982 and 31 December 1996 were included in the study. Results. 30 % of the children had height below the 2.5th centile, 10 % had weight for height below the 2.5th centile, and 7 % were obese (weight above the 97.5th centile). 26 % of the children had oral motor dysfunction and 33 % were unable to self-feed. Although these problems were more prominent in children with severe forms of cerebral palsy, the results of the multivariate analyses suggested that lack of ability to self-feed was a significant independent risk factor for height growth retardation and underweight. Parents of 24 (15 %) children reported that the family’s quality of life was significantly impaired by the feeding problems of the child. Interpretation. Our results are consistent with previous hospital-based studies and suggest that assessment of nutrition and growth should receive particular attention in the rehabilitation of children with cerebral palsy, in particular if the child is unable to self-feed.
Sparsity Learning-Based Multiuser Detection in Grant-Free Massive-Device Multiple Access

In this paper, we study the multiuser detection (MUD) problem for a grant-free massive-device multiple access (MaDMA) system, where a large number of single-antenna user devices transmit sporadic data to a multi-antenna base station (BS). Specifically, we put forth two MUD schemes, termed random sparsity learning multiuser detection (RSL-MUD) and structured sparsity learning multiuser detection (SSL-MUD) for the time-slotted and non-time-slotted grant-free MaDMA systems, respectively. In RSL-MUD, active users generate and transmit data packets with random sparsity. In SSL-MUD, we introduce a sliding-window-based detection framework, and the user signals in each observation window naturally exhibit structured sparsity. We show that by exploiting the sparsity embedded in the user signals, we can recover the user activity state, the channel, and the user data in a single phase, without using pilot signals for channel estimation and/or active user identification. To this end, we develop a message-passing-based statistical inference framework for the BS to blindly detect the user data without any prior knowledge of the identities and the channel state information (CSI) of active users. The simulation results show that our RSL-MUD and SSL-MUD schemes significantly outperform their counterpart schemes in both reducing the transmission overhead and improving the error behavior of the system.