Miracle-dz/UniFAR-FTUs huggingface dataset:
[Clinical outcome of ISOBAR TTL dynamic stabilization with pars bone grafting for treatment of lumbar spondylolysis].

Objective To evaluate the effectiveness of ISOBAR TTL dynamic stabilization with pars bone grafting for the treatment of lumbar spondylolysis. Methods A retrospective analysis was made the clinical data of 26 patients with lumbar spondylolysis who received ISOBAR TTL dynamic stabilization with pars bone grafting between September 2009 and March 2014. There were 14 males and 12 females, with a mean age of 31 years (range, 19-47 years). The disease duration ranged from 9 to 60 months (mean, 16 months). Preoperative lumbar anteroposterior and lateral X-ray films, CT three-dimensional reconstruction, and MRI scans were performed to identify the location of pars defect. The involved levels were L 4 in 9 cases and L 5 in 17 cases. There were 10 cases of pure spondylolysis and 16 cases of spondylolysis accompanied with I degree slipping. The clinical outcome was assessed by visual analogue scale (VAS) and Oswestry disability index (ODI) scores at preoperation and 1 week, 3 months, 6 months after operation, and at last follow-up. The lumbar CT three-dimensional reconstruction was obtained at 6 months after operation to evaluate bone fusion of the pars. The adjacent segment degeneration was assessed by the University of California at Los Angeles (UCLA) grading scale at preoperation and last follow-up. Results All incisions healed by first intention; no infection, cerebrospinal fluid leakage, or pain at iliac crest donor site occurred. Twenty-six patients were followed up 2-5 years (mean, 36.5 months). No secondary spondylolisthesis, internal fixation loosening and breakage were found during follow-up. The postoperative VAS and ODI scores showed significant differences ( P<0.05) when compared with preoperative scores, and the scores at 3 months, 6 months, and last follow-up were significantly better than that at 1 week ( P<0.05), but no significant difference was shown between the other time points after operation ( P>0.05). The CT three-dimensional reconstruction indicated satisfactory union at the pars in 23 cases (88.5%) at 6 months after operation. Of the 3 nonunion patients, good union was obtained in 1 patient at 1 year after operation; nonunion was observed in 2 patients at last follow-up, but low back pain was obviously alleviated. According to the UCLA grading scale, 20 cases were rated as grade I and 6 cases as grade II at last follow-up, which was the same as preoperative grade and indicated no adjacent segment degeneration. Conclusion It is safe and effective to use the ISOBAR TTL dynamic stabilization with pars bone grafting technique to treat lumbar spondylolysis with or without degree I slipping. The fusion rate is satisfactory, and adjacent segment degeneration can be slowed down after lumbar fusion surgery.
In human alloreactive CD 4 + T-cells , dichloroacetate inhibits aerobic glycolysis , induces apoptosis and favors differentiation towards the regulatory T-cell subset instead of effector T-cell subsets

Although kidney transplantation is the best therapy for end-stage renal disease, rejection remains a concern, and currently available immunosuppressive agents contribute to morbidity and mortality. Thus, novel immunosuppressive drugs are required. Dichloroacetate (DCA) is already used in the treatment of congenital lactic acidosis and characterized by limited toxicity. As DCA inhibits aerobic glycolysis, which is a prerequisite for CD4+ T-cell proliferation and differentiation into effector T-cells, its possible immunosuppressive role in mixed lymphocyte reaction (MLR), a model of alloreactivity, was investigated. Glucose and lactate concentrations were measured in the supernatants, and cell proliferation was assessed immunoenzymatically. CD4+ T-cells were then isolated from the MLRs and the expression of cleaved caspase-3, various enzymes involved in glycolysis, and the signature transcription factors of CD4+ T-cell subsets were evaluated by western blotting. In MLRs, DCA decreased glucose consumption and aerobic glycolysis, while it exerted a negligible effect on cell proliferation. In CD4+ T-cells, DCA induced apoptosis, and decreased the expression of glucose trasporter-1, hexokinase II, lactate dehydrogenase-A and phosphorylated pyruvate dehydrogenase, while it increased total pyruvate dehydrogenase. In addition, DCA increased the expression of transcription factor forkhead box P3, whereas it decreased the expression of T-box transcription factor TBX21, trans‐acting T‐cell‐specific transcription factor GATA-3 and retinoic acid receptor related orphan receptor-γt. In conclusion, in alloreactive CD4+ T-cells, DCA inhibits aerobic glycolysis, induces apoptosis and favors differentiation towards the regulatory T-cell subset. These characteristics render it a promising immunosuppressive agent in the field of transplantation.
Citation Data-set for Machine Learning Citation Styles and Entity Extraction from Citation Strings

Citation parsing is fundamental for search engines within academia and the protection of intellectual property. Meticulous extraction is further needed when evaluating the similarity of documents and calculating their citation impact. Citation parsing involves the identification and dissection of citation strings into their bibliography components such as "Author", "Volume","Title", etc. This meta-data can be difficult to acquire accurately due to the thousands of different styles and noise that can be applied to a bibliography to create the citation string. Many approaches exist already to accomplish accurate parsing of citation strings. This dissertation describes the creation of a large data-set which can be used to aid in the training of these approaches which have limited data.It also describes the investigation into if the downfall of these approaches to citation parsing and in particular the machine learning based approaches is because of the lack of size associated to the data used to train them.
Compact low cost 5G NR n78 band pass filter with silicon IPD technology

A low cost and compact 1608 size Silicon integrated passive device (IPD) band pass filter design for the new 5G New Radio (NR) n78 band is discussed in this paper. Top coupled filter topology with transmission zeroes is selected to give low insertion loss (IL) < 1.8 dB in passband 3.3 ∼ 3.8 GHz, with >30dB attenuation at 2.7 GHz and 4.9 GHz. Therefore, this design can effectively filter out adjacent ISM, 5G NR n41, and WLAN bands. Model correlation is verified with Silicon IPD wafer measurements. Variation study and Flip Chip die level performance is also discussed.
Comprehensive model for software fault prediction

Software Fault prediction (SFP) is an important task in the fields of software engineering to develop a cost effective software. Most of the software fault prediction is performed on same project date i.e., training and testing with same projects fault data. In case of unavailability of fault training data which is possible for the new project, data from the similar types/category of other projects can be used to train the model for the prediction. The software projects has been categorized into three categories by Boehm. The project within a certain group will be having good similarities with other projects within the group. So it is more suitable to train using the projects from same group. In this work we proposed to develop a model with similar category of data to predict the fault of another project belongs to same category. On basis of KLOC we have taken five organic software projects and performed various cross project and within project experiments. To generate a comprehensive generalized model for organic software's fault prediction, we have modeled various rule based to learner. Various rule-based learners used for comparison are JRip, CART, Conjunctive Rule, C4.5, NNge, OneR, Ridor, PART, and decision table-Naive Bayes hybrid classifier (DTNB).
Whole Brain Susceptibility Mapping Using Harmonic Incompatibility Removal

Quantitative susceptibility mapping (QSM) aims to visualize the three dimensional susceptibility distribution by solving the field-to-source inverse problem using the phase data in magnetic resonance signal. However, the inverse problem is ill-posed since the Fourier transform of integral kernel has zeroes in the frequency domain. Although numerous regularization based models have been proposed to overcome this problem, the incompatibility in the field data has not received enough attention, which leads to deterioration of the recovery. In this paper, we show that the data acquisition process of QSM inherently generates a harmonic incompatibility in the measured local field. Based on such discovery, we propose a novel regularization based susceptibility reconstruction model with an additional sparsity based regularization term on the harmonic incompatibility. Numerical experiments show that the proposed method achieves better performance than the existing approaches.
Early anomaly detection in wind turbine bolts breaking problem — Methodology and application

Early anomaly detection plays an important role in many fields, such as fraud detection in financial data, a signal indicating machine unhealthy status, etc. It leads to fault diagnostics and even prognostics according to data analytics need. In this paper, we apply an early anomaly detection model in wind turbine bolts breaking problem with good detection result, which can be used to substitute prognostic model in a solid manner. Wind turbine bolts breaking is a common problem in wind turbine, which may cause serious impact but hard to detect, especially when no extra sensor on total 50 bolts for each blade. To solve this problem, a novel process is designed to answer following questions: (1) whether there are parameters whose behavior is influenced by the bolts breaking, (2) can we detect such parameters reflecting the breaking problem, and (3) find out how the impacts happen and how far the impacts grow by comparing different number of blots are broken. We develop a conditional detection model, which is able to detect only 2 bolts breaking by automatically extracting key features with 92% accuracy.
Generating bipartite networks with a prescribed joint degree distribution

We describe a class of new algorithms to construct bipartite networks that preserves a prescribed degree and joint-degree (degree-degree) distribution of the nodes. Bipartite networks are graphs that can represent real-world interactions between two disjoint sets, such as actor-movie networks, author-article networks, co-occurrence networks, and heterosexual partnership networks. Often there is a strong correlation between the degree of a node and the degrees of the neighbors of that node that must be preserved when generating a network that reflects the structure of the underling system. Our bipartite 2K (B2K) algorithms generate an ensemble of networks that preserve prescribed degree sequences for the two disjoint set of nodes in the bipartite network, and the joint-degree distribution that is the distribution of the degrees of all neighbors of nodes with the same degree. We illustrate the effectiveness of the algorithms on a romance network using the NetworkX software environment to compare other properties of a target network that are not directly enforced by the B2K algorithms. We observe that when average degree of nodes is low, as is the case for romance and heterosexual partnership networks, then the B2K networks tend to preserve additional properties, such as the cluster coefficients, than algorithms that do not preserve the joint-degree distribution of the original network.
Theoretically Efficient Parallel Graph Algorithms Can Be Fast and Scalable

There has been significant recent interest in parallel graph processing due to the need to quickly analyze the large graphs available today. Many graph codes have been designed for distributed memory or external memory. However, today even the largest publicly-available real-world graph (the Hyperlink Web graph with over 3.5 billion vertices and 128 billion edges) can fit in the memory of a single commodity multicore server. Nevertheless, most experimental work in the literature report results on much smaller graphs, and the ones for the Hyperlink graph use distributed or external memory. Therefore, it is natural to ask whether we can efficiently solve a broad class of graph problems on this graph in memory. This paper shows that theoretically-efficient parallel graph algorithms can scale to the largest publicly-available graphs using a single machine with a terabyte of RAM, processing them in minutes. We give implementations of theoretically-efficient parallel algorithms for 13 important graph problems. We also present the optimizations and techniques that we used in our implementations, which were crucial in enabling us to process these large graphs quickly. We show that the running times of our implementations outperform existing state-of-the-art implementations on the largest real-world graphs. For many of the problems that we consider, this is the first time they have been solved on graphs at this scale. We provide a publicly-available benchmark suite containing our implementations.
On the Relationship between the Theory of Cointegration and the Theory of Phase Synchronization

The theory of cointegration has been a leading theory in econometrics with powerful applications to macroeconomics during the last decades. On the other hand the theory of phase synchronization for weakly coupled complex oscillators has been one of the leading theories in physics for many years with many applications to different areas of science. For example, in neuroscience phase synchronization is regarded as essential for functional coupling of different brain regions. In an abstract sense both theories describe the dynamic fluctuation around some equilibrium. In this paper, we point out that there exists a very close connection between both theories. Apart from phase jumps, a stochastic version of the Kuramoto equations can be approximated by a cointegrated system of difference equations. As one consequence, the rich theory on statistical inference for cointegrated systems can immediately be applied for statistical inference on phase synchronization based on empirical data. This includes tests for phase synchronization, tests for unidirectional coupling and the identification of the equilibrium from data including phase shifts. We study two examples on a unidirectionally coupled Rossler-Lorenz system and on electrochemical oscillators. The methods from cointegration may also be used to investigate phase synchronization in complex networks. Conversely, there are many interesting results on phase synchronization which may inspire new research on cointegration.
Evidence for Social Learning in a Family Living Lizard

Social learning is widespread among family living species, particularly mammals and birds with relatively high levels of social complexity and overt social interaction. However, the occurrence of social learning has never been documented in lizards with kin-based sociality, which have less obvious social interactions. We tested for social learning in Australian tree skinks (Egernia striolata), a species that commonly lives in family groups in the wild, using a two-step foraging task. Lizards were randomly allocated to either a social learning treatment or a control group and presented first with an instrumental task requiring the displacement of a lid, followed by an association task, consisting of two dishes with different colored lids. Prior to each task, lizards in the social learning treatment observed a trained demonstrator extract a food reward while the control also viewed a conspecific, but in the absence of the foraging task. The social learning treatment and control group solved the instrumental task at similar rates, but in the association task lizards in the social learning treatment made fewer errors and reached our learning criterion sooner. To the best of our knowledge, we present the first evidence for social learning in a lizard with kin-based sociality.
Using Diffusion and Structural MRI for the Automated Segmentation of Multiple Sclerosis Lesions

This work proposes to use scalar features calculated from diffusion MR data alongside structural MR intensities in the automated segmentation of Multiple Sclerosis (MS) lesions. We acquired and processed multi-contrast MR data from 7 MS patients, used random forests to segment lesions, and evaluated our method via DICE scores, achieving scores over 0.65. Finally, we made use of the random forest framework to assess the discriminative power of the estimated features. We show that diffusion features estimated from the diffusion tensor are as discriminative as T1 and T2 intensities for the classification task.
SAGE algorithm based MAP channel estimation for multi-cell massive MIMO systems

This paper represents an efficient space-alternating generalized expectation-maximization (SAGE) algorithm based maximum a posteriori (MAP) channel estimation method for multi-cell massive multiple input multiple output (MIMO) systems. MAP channel estimation method requires conjugate transpose of a t × k pilot matrix where t is the number of pilot symbols per user and k is the number of single antenna users. Conjugate transpose of the large-size matrix increases computational complexity. The proposed method estimates the channel iteratively and converges to the same mean square error (MSE) performance of the MAP estimator with the increasing number of iterations. Consequently, the proposed method with low-rank approximation avoids conjugate transpose of the large-size matrix and hence reduces the computational complexity significantly.
Glucose Metabolic Brain Network Differences between Chinese Patients with Lewy Body Dementia and Healthy Control

Dementia with Lewy bodies (DLB) is the second most common degenerative dementia of the central nervous system. The technique 18F-fluorodeoxyglucose positron emission tomography (18F FDG PET) was used to investigate brain metabolism patterns in DLB patients. Conventional statistical methods did not consider intern metabolism transforming connections between various brain regions; therefore, most physicians do not understand the underlying neuropathology of DLB patients. In this study, 18F FDG-PET images and graph-theoretical methods were used to investigate alterations in whole-brain intrinsic functional connectivity in a Chinese DLB group and healthy control (HC) group. This experimental study was performed on 22 DLB patients and 22 HC subjects in Huashan Hospital, Shanghai, China. Experimental results indicate that compared with the HC group, the DLB group has severely impaired small-world network. Compared to those of the HC group, the clustering coefficients of the DLB group were higher and characteristic path lengths were longer, and in terms of global efficiencies, those of the DLB group was also lower. Moreover, four significantly altered regions were observed in the DLB group: Inferior frontal gyrus, opercular part (IFG.R), olfactory cortex (OLF.R), hippocampus (HIP.R), and fusiform gyrus (FFG.L). Amongst them, in the DLB group, betweenness centrality became strong in OLF.R, HIP.R, and FFG.L, whereas betweenness centrality became weaker in IFG.R. Finally, IFGoperc.R was selected as a seed and a voxel-wise correlation analysis was performed. Compared to the HC group, the DLB group showed several regions of strengthened connection with IFGoperc.R; these regions were located in the prefrontal cortex and regions of weakened connection were located in the occipital cortex. The results of this paper may help physicians to better understand and characterize DLB patients.
OpenCL-Darknet: An OpenCL Implementation for Object Detection

Object detection is a technology that deals with recognizing classes of objects and their location. It is used in many different areas, such as in face-detecting digital cameras, surveillance tools, or self-driving cars. These days, deep learning-based object detection approaches have achieved significantly better performance than the classic feature-based algorithms. Darknet [1] is a deep learning-based object detection framework, which is well known for its fast speed and simple structure. Unfortunately, like many other frameworks, Darknet only supports NVIDIA CUDA [2] for accelerating its calculations. For this reason, a user has only limited options for graphic card selection. OpenCL" (open computing language) [3] is an open standard for cross-platform, parallel programming of heterogeneous systems. It is available not only for CPUs, GPUs (graphics processing units), but also for DSPs (digital signal processors), FPGAs (field-programmable gate arrays) and other hardware accelerators. In this paper, we present the OpenCL-Darknet, which transforms the CUDA-based Darknet into an open standard OpenCL backend. Our goal was to implement a deep learning-based object detection framework that will be available for the general accelerator hardware and to achieve competitive performance compared to the original CUDA version. We evaluated the OpenCL-Darknet in AMD R7-integraged APU (accelerated processing unit) with OpenCL 2.0 and AMD Radeon RX560 with OpenCL 1.2 using a VOC 2007 dataset [4]. We also compared its performance with the original Darknet for NVIDIA GTX 1050 with CUDA 8.0 and cuDNN 6.0.
Pectoral Morphology in Doliodus: Bridging the ‘Acanthodian’-Chondrichthyan Divide

ABSTRACT Doliodus problematicus (NBMG 10127), from the Lower Devonian of New Brunswick, Canada (approx. 397–400 Mya) is the earliest sharklike jawed vertebrate (gnathostome) in which the pectoral girdle and fins are well preserved. Its pectoral endoskeleton included sharklike expanded paired coracoids, but Doliodus also possessed an “acanthodian-like” array of dermal spines, described here for the first time. Doliodus provides the strongest anatomical evidence to date that chondrichthyans arose from “acanthodian” fishes by exhibiting an anatomical mosaic of “acanthodian” and sharklike features.
Collaborative SQL-injections detection system with machine learning

Data mining and information extraction from data is a field that has gained relevance in recent years thanks to techniques based on artificial intelligence and use of machine and deep learning. The main aim of the present work is the development of a tool based on a previous behaviour study of security audit tools (oriented to SQL pentesting) with the purpose of creating testing sets capable of performing an accurate detection of a SQL attack. The study is based on the information collected through the generated web server logs in a pentesting laboratory environment. Then, making use of the common extracted patterns from the logs, each attack vector has been classified in risk levels (dangerous attack, normal attack, non-attack, etc.). Finally, a training with the generated data was performed in order to obtain a classifier system that has a variable performance between 97 and 99 percent in positive attack detection. The training data is shared to other servers in order to create a distributed network capable of deciding if a query is an attack or is a real petition and inform to connected clients in order to block the petitions from the attacker's IP.