Miracle-dz/UniFAR-FTUs huggingface dataset:
Interleukin 2 gene therapy of colorectal carcinoma with autologous irradiated tumor cells and genetically engineered fibroblasts: a Phase I study.

The purpose of this study was to determine the safety, toxicity, and antitumor immune response following S.C. immunizations with a mixture of irradiated, autologous tumor cells and autologous fibroblasts that were genetically modified to express the gene for interleukin 2 (IL-2) in patients with colorectal carcinoma. Ten patients were treated with a fixed dose of tumor cells (10(7)) and escalating doses of fibroblasts secreting IL-2 (per 24 h): 100 units (three patients), 200 units (three patients), 400 units (three patients), and 800 units (one patient). Pre- and posttreatment peripheral blood mononuclear cells were evaluated for evidence of antitumor immune responses. Fatigue and/or flu-like symptoms were experienced by seven patients and delayed-type hypersensitivity-like skin reactions were observed at the sites of the second or subsequent vaccinations in five patients. Low frequencies of tumor cytotoxic T-cell precursors (range, 1/190,000-1/1,320,000 peripheral blood mononuclear cells) were detected prior to therapy in four of seven patients. There was a 5-fold increase following treatment in the frequency of tumor cytotoxic T-cell precursors in two of six evaluable patients. Some patients with colorectal cancer have low frequencies of tumor cytotoxic T-cell precursors that may be increased by this well-tolerated form of IL-2 gene therapy, which warrants continued clinical evaluation.
Intelligent Data Placement Mechanism for Replicas Distribution in Cloud Storage Systems

The Hadoop Distributed File System (HDFS) is a distributed storage system that stores large volumes of data reliably and provide access to the data by the applications at high bandwidth. HDFS provides high reliability and availability by replicating data, typically three copies, and distribute these replicas across multiple data nodes. The placement of data replicas is one of the key issues that affect the performance of HDFS. In the current HDFS replica placement policy the replicas of data blocks cannot be evenly distribute across cluster nodes, so the current HDFS has to rely on load balancing utility to balance replica distributions which results in more time and resources consuming. These challenges drive the need for intelligent methods that solve the data placement problem to achieve high performance without the need for load balancing utility. In this paper, we propose an intelligent policy for data placement in cloud storage systems addressing the above challenges.
Subspace estimation and decomposition for hybrid analog-digital millimetre-wave MIMO systems

In this work, we address the problem of channel estimation and precoding / combining for the so-called hybrid millimeter wave (mmWave) MIMO architecture. Our proposed channel estimation scheme exploits channel reciprocity in TDD MIMO systems, by using echoing, thereby allowing us to implement Krylov subspace methods in a fully distributed way. The latter results in estimating the right (resp. left) singular subspace of the channel at the transmitter (resp. receiver). Moreover, we also tackle the problem of subspace decomposition whereby the estimated right (resp. left) singular subspaces are approximated by a cascade of analog and digital precoder (resp. combiner), using an iterative method. Finally we compare our scheme with an equivalent fully digital case and conclude that a relatively similar performance can be achieved, however, with a drastically reduced number of RF chains - 4 ~ 8 times less (i.e., massive savings in cost and power consumption).
Description of novel members of the family Sphingomonadaceae: Aquisediminimonas profunda gen. nov., sp. nov., and Aquisediminimonas sediminicola sp. nov., isolated from freshwater sediment.

Two Gram-stain-negative bacterial strains, DS48-3T and CH68-4T, were isolated from freshwater sediment taken from the Daechung Reservoir, Republic of Korea. Cells of strains DS48-3T and CH68-4T were aerobic, non-motile, non-spore-forming and rod-shaped. Strain DS48-3T was isolated from a sediment surface sample at a depth of 48 m from the Daechung Reservoir and was most closely related to the genus Sphingopyxis according to 16S rRNA gene sequence analysis (94.5-95.9 % similarity). Strain CH68-4T was isolated from the very bottom of a 67-cm-long sediment core collected from Daechung Reservoir at a water depth of 17 m and was most closely related to the genus Sphingopyxis (16S rRNA gene sequence similarity of 93.7-95.0 %). Phylogenetic analysis based on 16S rRNA gene sequencing indicated that the two strains formed a separate lineage within the order Sphingomonadales showing similarity values below 95.9 % with their closest phylogenetic neighbours, and sharing 97.3 % similarity with each other. The combined genotypic and phenotypic data showed that strains DS48-3T and CH68-4T could be distinguished from all genera within the family Sphingomonadaceae and represented two distinct species of a novel genus, Aquisediminimonas profunda gen. nov., sp. nov. (type strain DS48-3T=KCTC 52068T=CCTCC AB 2018061T) and Aquisediminimonas sediminicola sp. nov. (type strain CH68-4T=KCTC 62205T=CCTCC AB 2018062T).
Sampling variability of the Kunst-Mackenbach relative index of inequality

Study objective: To derive methods of calculating confidence limits for the relative index of inequality, defined by Kunst and Mackenbach as a measure of the influence of socioeconomic status on an adverse health index, such as mortality rate. The methods may be used for a health outcome recorded on a continuous scale, as a Poisson count or as a binomial variable. Results and Conclusion: The confidence limits depend on the sampling variability of both the mean mortality rate and the slope of the regression line of mortality on the socioeconomic status scale variable. The best method for a continuous health outcome is based on Fieller’s theorem but a good approximation is obtained by substituting the confidence limits for the slope of the regression line into the formula for the calculation of the index, or by using the variance of the logarithmic transform of the index. The last method is the most appropriate for the construction of significance tests comparing indices. The mortality rates may show statistically significant departure from linearity, while not suggesting that a linear relation is inappropriate, and the main decision is whether to base the confidence limits on the conventional standard error of the slope derived from the regression analysis or whether to use the standard deviation of the estimates of mortality rates.
Present and Future of Allogeneic Natural Killer Cell Therapy

Natural killer (NK) cells are innate lymphocytes that are capable of eliminating tumor cells and are therefore used for cancer therapy. Although many early investigators used autologous NK cells, including lymphokine-activated killer cells, the clinical efficacies were not satisfactory. Meanwhile, human leukocyte antigen (HLA)-haploidentical hematopoietic stem cell transplantation revealed the antitumor effect of allogeneic NK cells, and HLA-haploidentical, killer cell immunoglobulin-like receptor ligand-mismatched allogeneic NK cells are currently used for many protocols requiring NK cells. Moreover, allogeneic NK cells from non-HLA-related healthy donors have been recently used in cancer therapy. The use of allogeneic NK cells from non-HLA-related healthy donors allows the selection of donor NK cells with higher flexibility and to prepare expanded, cryopreserved NK cells for instant administration without delay for ex vivo expansion. In cancer therapy with allogeneic NK cells, optimal matching of donors and recipients is important to maximize the efficacy of the therapy. In this review, we summarize the present state of allogeneic NK cell therapy and its future directions.
Study on performance-centric offload strategies for LTE networks

Currently, cellular networks are overloaded with mobile data traffic due to the rapid growth of mobile broadband subscriptions and the increasing popularity of applications for smartphones. One possible solution to alleviate this problem is the offloading of mobile data traffic from the primary access technology to the WiFi infrastructure to gain extra capacity and improve the overall network performance. As the strategy what and when to offload data is non-trivial, it is of vital importance to develop novel algorithms to guide this process. This paper addresses solutions for WiFi offloading in Long Term Evolution (LTE) cellular networks when performance needs exceed the capability of the LTE access. It then compares the performance of each access technology using different network performance metrics. In detail, an optimized Signal-to-noise ratio (SNR)-threshold based handover solution and extension to the 3rd Generation Partnership Project (3GPP) standard for Access Network Discovery and Selection Function (ANDSF) framework for WiFi offloading is proposed. Our simulation results have shown that ANDSF discovery can be used to control the amount of offloading.
Snake robot shape sensing using micro-inertial sensors

Real-time shape sensing and state acquisition is important for closed-loop control of hyper-redundant snake robots in minimally invasive surgery. Due to the miniaturized size of such minimally invasive surgery robots, it is not feasible to use existing angular sensors involving rotary encoders. With recent advances of the MEMS technology, micro inertial sensors have shown their potential for robot state estimation. Previous studies have demonstrated that accurate joint angles can be estimated for one degree-of-freedom (DoF) joints. However, higher DoF joints of the robot can impose a number of challenges to the current joint angle estimation methods. This paper presents a micro-sensing platform and shape reconstruction algorithm for minimally invasive surgery snake robot with two DoF joints. The method incorporates both gravitational and gyroscopic sensing for calculating the rotation difference between any consecutive robot segments. The gyroscope measurements are first used as the input to predict the rotation difference by direct orientation integration. The orientation difference is then derived from the consecutive acceleration vectors to update the prediction through a complementary filter. To demonstrate the performance of our proposed approach, a robot prototype with two universal joints was fabricated. Detailed experimental results have demonstrated that high accuracy can be achieved by using the proposed method for joint angle estimation.
Environmentally Robust Motion Detection for Video Surveillance

Most video surveillance systems require to manually set a motion detection sensitivity level to generate motion alarms. The performance of motion detection algorithms, embedded in closed circuit television (CCTV) camera and digital video recorder (DVR), usually depends upon the preselected motion sensitivity level, which is expected to work in all environmental conditions. Due to the preselected sensitivity level, false alarms and detection failures usually exist in video surveillance systems. The proposed motion detection model based upon variational energy provides a robust detection method at various illumination changes and noise levels of image sequences without tuning any parameter manually. We analyze the structure mathematically and demonstrate the effectiveness of the proposed model with numerous experiments in various environmental conditions. Due to the compact structure and efficiency of the proposed model, it could be implemented in a small embedded system.
Finding Statistically Significant Attribute Interactions

In many data exploration tasks it is meaningful to identify groups of attribute interactions that are specific to a variable of interest. For instance, in a dataset where the attributes are medical markers and the variable of interest (class variable) is binary indicating presence/absence of disease, we would like to know which medical markers interact with respect to the binary class label. These interactions are useful in several practical applications, for example, to gain insight into the structure of the data, in feature selection, and in data anonymisation. We present a novel method, based on statistical significance testing, that can be used to verify if the data set has been created by a given factorised class-conditional joint distribution, where the distribution is parametrised by a partition of its attributes. Furthermore, we provide a method, named ASTRID, for automatically finding a partition of attributes describing the distribution that has generated the data. State-of-the-art classifiers are utilised to capture the interactions present in the data by systematically breaking attribute interactions and observing the effect of this breaking on classifier performance. We empirically demonstrate the utility of the proposed method with examples using real and synthetic data.
Return of spontaneous Circulation Is Not Affected by Different Chest Compression Rates Superimposed with Sustained Inflations during Cardiopulmonary Resuscitation in Newborn Piglets

Objective Recently, sustained inflations (SI) during chest compression (CC) have been suggested as an alternative to the current approach during neonatal resuscitation. However, the optimal rate of CC during SI has not yet been established. Our aim was to determine whether different CC rates during SI reduce time to return of spontaneous circulation (ROSC) and improve hemodynamic recovery in newborn piglets with asphyxia-induced bradycardia. Intervention and measurements Term newborn piglets were anesthetized, intubated, instrumented and exposed to 45-min normocapnic hypoxia followed by asphyxia. Resuscitation was initiated when heart rate decreased to 25% of baseline. Piglets were randomized into three groups: CC superimposed by SI at a rate of 90 CC per minute (SI+CC 90, n = 8), CC superimposed by SI at a rate of 120 CC per minute (SI+CC 120, n = 8), or a sham group (n = 6). Cardiac function, carotid blood flow, cerebral oxygenation and respiratory parameters were continuously recorded throughout the experiment. Main results Both treatment groups had similar time of ROSC, survival rates, hemodynamic and respiratory parameters during cardiopulmonary resuscitation. The hemodynamic recovery in the subsequent 4h was similar in both groups and was only slightly lower than sham-operated piglets at the end of experiment. Conclusion Newborn piglets resuscitated by SI+CC 120 did not show a significant advantage in ROSC, survival, and hemodynamic recovery as compared to those piglets resuscitated by SI+CC 90.
The probabilistic nature of synaptic transmission at a mammalian excitatory central synapse

The synaptic connection between single group I afferents and dorsal spinocerebellar tract (DSCT) neurons in the cat spinal cord has been studied in an attempt to gain insight into the mechanisms of excitatory synaptic transmission in the mammalian CNS. Fluctuations in the amplitude of single group I fiber EPSPs in DSCT neurons were examined using a numerical deconvolution procedure to reduce the effects of contaminating noise. In general, it was found that single fiber EPSPs fluctuate in peak amplitude between discrete levels separated by equal or quantal increments. Many previous studies have proposed simple binomial statistics as a general model of quantal synaptic transmission. In the present study we show that simple binomial statistics do not describe the fluctuations in amplitude of single group I fiber EPSPs in DSCT neurons. It is suggested that nonuniformities in the probability of transmitter release from release site to release site explain the failure of the binomial model to describe the EPSP fluctuation pattern at this synapse. Nonuniform quantal transmission is proposed as a more adequate description of excitatory synaptic transmission in the mammalian CNS.
A Local-Global Graph Approach for Facial Expression Recognition

In this article, we present a local global graph (LGG) method for recognizing facial expressions from static images irrespective of different illumination conditions, shadows and cluttered backgrounds. First, a neural color constancy based skin detection procedure to detect skin in complex real world images is presented. Second, the LGG method for detecting faces and facial expressions with a maximum confidence from skin segmented images is presented. The LGG approach presented here emulates the human visual perception for face and expression detection. In general, humans first extract the most important facial features such as eyes, nose, mouth, etc. and then inter-relate them for face and facial expression representations. The LG Graph embeds both the local information (the shape of facial feature is stored within the local graph at each node) and the global information (the topology of the face). Facial expression recognition from the detected face images is obtained by comparing the LG Expression Graphs with the existing the LG expression models present in the LGG database. Experimental results on the AR database and real-world images suggest the robustness of the proposed approach for facial expression recognition
One-class LS-SVM with zero leave-one-out error

This paper extends the closed form calculation of the leave-one-out (LOO) error for least-squares support vector machines (LS-SVMs) from the two-class to the one-class case. Furthermore, it proposes a new algorithm for determining the hyperparameters of a one-class LS-SVM with Gaussian kernels which exploits the efficient LOO error calculation. The standard deviations are selected by prior knowledge while the regularization parameter is optimized in order to obtain a tight decision boundary under the constraint of a zero LOO error.
Semi-tied full deviation matrices for laplacian density models

The Philips speech recognition system uses mixtures of Laplacian densities with diagonal deviations to model acoustic feature vectors. Such an approach neglects the correlations between different feature components that typically exist in the acoustic vectors. This paper extends the conventional Laplacian approach to model the between-feature interdependencies explicitly. These extensions either lead to a full deviation matrix model or to an integrated feature space transformation similar to the semi-tied covariances for Gaussian densities. Both methods can be efﬁciently implemented by exploiting a strong tying of the feature transformations and the deviation matrices, respec-tively. The novel approach is evaluated on two different digit string recognition tasks.
On Adapting Pixel-based Classification to Unsupervised Texture Segmentation

An inherent problem of unsupervised texture segmentation is the absence of previous knowledge regarding the texture patterns present in the images to be segmented. A new efficient methodology for unsupervised image segmentation based on texture is proposed. It takes advantage of a supervised pixel-based texture classifier trained with feature vectors associated with a set of texture patterns initially extracted through a clustering algorithm. Therefore, the final segmentation is achieved by classifying each image pixel into one of the patterns obtained after the previous clustering process. Multi-sized evaluation windows following a top-down approach are applied during pixel classification in order to improve accuracy. The proposed technique has been experimentally validated on MeasTex, VisTex and Brodatz compositions, as well as on complex ground and aerial outdoor images. Comparisons with state-of the-art unsupervised texture segmenters are also provided.
Artificial intelligence based task mapping and pipelined scheduling for checkpointing on real time systems with imperfect fault detection

Fault-tolerance is emerging as one of the important optimization objectives for designs in deep submicron technology nodes. This paper proposes a technique of application mapping and scheduling with checkpointing on a multiprocessor system to maximize the reliability considering transient faults. The proposed model incorporates checkpoints with imperfect fault detection probability, and pipelined execution and cyclic dependency associated with multimedia applications. This is solved using an Artificial Intelligence technique known as Particle Swarm Optimization to determine the number of checkpoints of every task of the application that maximizes the confidence of the output. The proposed approach is validated experimentally with synthetic and real-life application graphs. Results demonstrate the proposed technique improves the probability of correct result by an average 15% with imperfect fault detection. Additionally, even with 100% fault detection, the proposed technique is able to achieve better results (25% higher confidence) as compared to the existing fault-tolerant techniques.
Facet discovery for structured web search: a query-log mining approach

In recent years, there has been a strong trend of incorporating results from structured data sources into keyword-based web search systems such as Bing or Amazon. When presenting structured data, facets are a powerful tool for navigating, refining, and grouping the results. For a given structured data source, a fundamental problem in supporting faceted search is finding an ordered selection of attributes and values that will populate the facets. This creates two sets of challenges. First, because of the limited screen real-estate, it is important that the top facets best match the anticipated user intent. Second, the huge scale of available data to such engines demands an automated unsupervised solution. In this paper, we model the user faceted-search behavior using the intersection of web query-logs with existing structured data. Since web queries are formulated as free-text queries, a challenge in our approach is the inherent ambiguity in mapping keywords to the different possible attributes of a given entity type. We present an automated solution that elicits user preferences on attributes and values, employing different disambiguation techniques ranging from simple keyword matching, to more sophisticated probabilistic models. We demonstrate experimentally the scalability of our solution by running it on over a thousand categories of diverse entity types and measure the facet quality with a real-user study.
Issues Arising in Applying Machine Learning to Oil Slick Detection

Applications in image processing and remote sensing raise questions that have so far received only marginal attention from the machine learning community. And yet, each of our issues, we believe, represents a rich research topic, in addition to being of practical importance. The task of the presented case study is to expose some of them in the context where we encountered them, and to encourage discussion that might spawn further research.