Miracle-dz/UniFAR-FTUs huggingface dataset:
Experimental analysis of multiple factors on hydraulic fracturing in coalbed methane reservoirs

Hydraulic fracturing can improve the permeability of coalbed methane (CBM) reservoirs effectively, which is of great significance to the commercial production of CBM. However, the efficiency of hydraulic fracturing is affected by multiple factors. The mechanism of fracture initiation, morphology and propagation in CBM reservoirs is not clear and need to be further explored. Hydraulic fracturing experiment is an accurate tool to explore these mechanisms. The quantity of experimental coal rock is large and processing method is complex, so specimen made of similar materials was applied to replace coal rock. The true triaxial hydraulic fracturing experimental apparatus, 3D scanning device for coal rock section were applied to carry out hydraulic fracturing experiment. The results show that the initiation pressure is inversely proportional to the horizontal stress difference (Δσ) and positively related to fracturing fluid injection rate. When vertical stress (σv) is constant, the initiation pressure and fracture width decrease with the increasing of Δσ. Natural fractures can be connected by main fracture when propagates perpendicular to the direction of minimum horizontal stress (σh), then secondary fractures and fracture network form in CBM reservoirs. When two stresses of crustal stress are close and far different from the third one, the fracture morphology and propagation become complex. Influenced by perforations and filtration of fracturing fluid in specimen, fracturing fluid flows to downward easily after comparing horizontal well fracturing with vertical well fracturing. Fracture width increases with the decreasing of elastic modulus, the intensity of fracture is positively related with the elastic modulus of coal rock. The research results can provide theoretical basis and technical support for the efficient development of CBM.
Null tDCS Effects in a Sustained Attention Task: The Modulating Role of Learning

The purpose of this study was to investigate sustained attention through modulation of the fronto-cerebral network with transcranial direct current stimulation (tDCS) in adults with attention-deficit/hyperactivity disorder (ADHD) and control participants. Thirty-seven participants (21 with ADHD) underwent three separate sessions (baseline, active tDCS, and sham) and performed the MOXO Continuous Performance Test (CPT). We applied double anodal stimulation of 1.8 mA tDCS for 20 min over the left and right dorsolateral prefrontal cortex (DLPFC), with the cathode over the cerebellum. Baseline session revealed significant differences between ADHD and control participants in the MOXO-CPT attention and hyperactivity scores, validating the MOXO as a diagnostic tool. However, there were no tDCS effects in most MOXO-CPT measures, except hyperactivity, due to a significant learning effect. We conclude that learning and repetition effects in cognitive tasks need to be considered when designing within-subjects tDCS experiments, as there are natural improvements between sessions that conceal potential stimulation effects.
Disruption of p16 and Activation of Kras in Pancreas Increase Ductal Adenocarcinoma Formation and Metastasis in vivo

Inactivation of tumor suppressor gene p16/INK4A and oncogenic activation of KRAS occur in almost all pancreatic cancers. To better understand the roles of p16 in pancreatic tumorigenesis, we created a conditional p16 knockout mouse line (p16flox/flox), in which p16 is specifically disrupted in a tissue-specific manner without affecting p19/ARF expression. p16flox/flox; LSL-KrasG12D; Pdx1-Cre mice developed the full spectrum of pancreatic intraepithelial neoplasia (mPanIN) lesions, pancreatic ductal adenocarcinoma (PDA), and metastases were observed in all the mice. Here we report a mouse model that simulates human pancreatic tumorigenesis at both genetic and histologic levels and is ideal for studies of metastasis. During the progression from primary tumors to metastases, the wild-type allele of Kras was progressively lost (loss of heterozygosity at Kras or LOH at Kras) in p16flox/flox; LSL- KrasG12D; Pdx1-Cre mice. These observations suggest a role for Kras beyond tumor initiation. In vitro assays performed with cancer cell lines derived from primary pancreatic tumors of these mice showed that cancer cells with LOH at Kras exhibited more aggressive phenotypes than those retained the wild-type Kras allele, indicating that LOH at Kras can provide cancer cells functional growth advantages and promote metastasis. Increased LOH at KRAS was also observed in progression of human pancreatic primary tumors to metastases, again supporting a role for the KRAS gene in cancer metastasis. This finding has potential translational implications- future KRAS target therapies may need to consider targeting oncogenic KRAS specifically without inhibiting wild-type KRAS function.
A machine learning approach for gesture recognition with a lensless smart sensor system

Hand motion tracking traditionally requires highly complex and expensive systems in terms of energy and computational demands. A low-power, low-cost system could lead to a revolution in this field as it would not require complex hardware while representing an infrastructure-less ultra-miniature (∼ 100μm — [1]) solution. The present paper exploits the Multiple Point Tracking algorithm developed at the Tyndall National Institute as the basic algorithm to perform a series of gesture recognition tasks. The hardware relies upon the combination of a stereoscopic vision of two novel Lensless Smart Sensors (LSS) combined with IR filters and five hand-held LEDs to track. Tracking common gestures generates a six-gestures dataset, which is then employed to train three Machine Learning models: k-Nearest Neighbors, Support Vector Machine and Random Forest. An offline analysis highlights how different LEDs' positions on the hand affect the classification accuracy. The comparison shows how the Random Forest outperforms the other two models with a classification accuracy of 90–91 %.
Gait-Based Person Recognition Using Arbitrary View Transformation Model

Gait recognition is a useful biometric trait for person authentication because it is usable even with low image resolution. One challenge is robustness to a view change (cross-view matching); view transformation models (VTMs) have been proposed to solve this. The VTMs work well if the target views are the same as their discrete training views. However, the gait traits are observed from an arbitrary view in a real situation. Thus, the target views may not coincide with discrete training views, resulting in recognition accuracy degradation. We propose an arbitrary VTM (AVTM) that accurately matches a pair of gait traits from an arbitrary view. To realize an AVTM, we first construct 3D gait volume sequences of training subjects, disjoint from the test subjects in the target scene. We then generate 2D gait silhouette sequences of the training subjects by projecting the 3D gait volume sequences onto the same views as the target views, and train the AVTM with gait features extracted from the 2D sequences. In addition, we extend our AVTM by incorporating a part-dependent view selection scheme (AVTM_PdVS), which divides the gait feature into several parts, and sets part-dependent destination views for transformation. Because appropriate destination views may differ for different body parts, the part-dependent destination view selection can suppress transformation errors, leading to increased recognition accuracy. Experiments using data sets collected in different settings show that the AVTM improves the accuracy of cross-view matching and that the AVTM_PdVS further improves the accuracy in many cases, in particular, verification scenarios.
Evaluation of an automatic non-invasive quantitative measurement method of the regional cerebral blood flow using 99 m Tc-ECD brain uptake ratio

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Normative data for the HADS from a large non-clinical sample.

OBJECTIVES To provide normative data for the Hospital Anxiety Depression Scale (HADS). DESIGN Repeated measures and correlational. METHODS The HADS was administered to a non-clinical sample, broadly representative of the general adult UK population (N = 1792) in terms of the distributions of age, gender and occupational status. Correlational analysis was used to determine the influence of demographic variables on HADS scores. RESULTS Demographic variables had only very modest influences on HADS scores. The reliability of the HADS is acceptable; the Anxiety and Depression scales are moderately correlated (.53). Tables to convert raw scores to percentiles are presented for females and males. CONCLUSIONS The present normative data allow clinicians to assess the rarity of a given HADS score, and thus provide a useful supplement to existing cut-off scores.
The DIVA Accelerometer and VIG Gyro: Two Quartz Inertial MEMS for Guidance and Navigation Applications

ONERA, the French National Establishment for Aerospace Research, in partnership with DGA (the French MoD Procurement Agency), has been developing for a few years a new generation of inertial sensors, working within a vibrating mode. Those vibrating sensors, manufactured with micro-machining technologies, are particularly well suited for low cost and miniaturized inertial measurement units, to be used in e.g. stabilization, guidance or inertial navigation applications. Presently, ONERA is developing two monolithic quartz sensors, a Vibrating Beam Accelerometer (VBA) “DIVA” (Differential Inertial Vibrating Accelerometer) and a Coriolis Vibrating Gyro (CVG) “VIG” (Vibration Integrated Gyro). The main originality of the inertial sensors developed at ONERA concerns the high level of insulation of the useful vibrations. This paper describes the concepts of the DIVA and the VIG sensors. Specific problems as the locking zone in VBA and the mechanical coupling between the two useful vibrations in CVG are discussed. The manufacturing process is described and principal performances of the DIVA and VIG sensors are given: a few 100 µg for a measurement range of +- 100 g for the DIVA accelerometer and an output noise of 0.01 degree/s/SquareRoot(Hz) in a 100 Hz bandwidth with a measurement range of +- 1000 degrees/s for the VIG gyro.
A stream-wise blind selected mapping technique for low-PAPR multi-user MIMO transmission

Peak-to-average power ratio (PAPR) of transmit waveform becomes higher when transmit filtering and/or pre-coding are employed. In this paper, we introduce a PAPR reduction scheme called selected mapping without side information transmission (called blind SLM) for both orthogonal frequency division multiplexing (OFDM) and single-carrier (SC) signals. Firstly, we review the proposed blind SLM techniques, considering both single-input single output (SISO) and space-time block coded transmit diversity (STBC-TD). Then, a blind SLM technique for multi-user multiple-input multiple-output (MU-MIMO) transmission with minimum mean-square error (MMSE) filtering and singular value decomposition (SVD), called MMSE-SVD, is introduced. To realize a simple data detection without side-information in MU-MIMO, we recommend that the phase rotation sequence multiplication should be applied to transmit data streams before applying the transmit filtering (called stream-wise SLM). At the receiver, the phase rotation sequence estimation is done for each users data streams after applying the receive filtering. Simulation results confirm that the stream-wise blind SLM for MMSE-SVD can reduce the PAPR of transmit waveforms without degrading bit-error rate (BER).
Predicting the impact of diet and enzymopathies on human small intestinal epithelial cells

Small intestinal epithelial cells (sIECs) have a significant share in whole body metabolism as they perform enzymatic digestion and absorption of nutrients. Furthermore, the diet plays a key role in a number of complex diseases including obesity and diabetes. The impact of diet and altered genetic backgrounds on human metabolism may be studied by using computational modeling. A metabolic reconstruction of human sIECs was manually assembled using the literature. The resulting sIEC model was subjected to two different diets to obtain condition-specific metabolic models. Fifty defined metabolic tasks evaluated the functionalities of these models, along with the respective secretion profiles, which distinguished between impacts of different dietary regimes. Under the average American diet, the sIEC model resulted in higher secretion flux for metabolites implicated in metabolic syndrome. In addition, enzymopathies were analyzed in the context of the sIEC metabolism. Computed results were compared with reported gastrointestinal (GI) pathologies and biochemical defects as well as with biomarker patterns used in their diagnosis. Based on our simulations, we propose that (i) sIEC metabolism is perturbed by numerous enzymopathies, which can be used to study cellular adaptive mechanisms specific for such disorders, and in the identification of novel co-morbidities, (ii) porphyrias are associated with both heme synthesis and degradation and (iii) disturbed intestinal gamma-aminobutyric acid synthesis may be linked to neurological manifestations of various enzymopathies. Taken together, the sIEC model represents a comprehensive, biochemically accurate platform for studying the function of sIEC and their role in whole body metabolism.
PolyUnpack : Automating the Hidden-Code Extraction of

Modern malware often hide the malicious portion of their program code by making it appear as data at compiletime and transforming it back into executable code at runtime. This obfuscation technique poses obstacles to researchers who want to understand the malicious behavior of new or unknown malware and to practitioners who want to create models of detection and methods of recovery. In this paper we propose a technique for automating the process of extracting the hidden-code bodies of this class of malware. Our approach is based on the observation that sequences of packed or hidden code in a malware instance can be made self-identifying when its runtime execution is checked against its static code model. In deriving our technique, we formally define the unpack-executing behavior that such malware exhibits and devise an algorithm for identifying and extracting its hidden-code. We also provide details of the implementation and evaluation of our extraction technique; the results from our experiments on several thousand malware binaries show our approach can be used to significantly reduce the time required to analyze such malware, and to improve the performance of malware detection tools.
Accurate content-based video copy detection with efficient feature indexing

We describe an accurate content-based copy detection system that uses both local and global visual features to ensure robustness. Our system advances state-of-the-art techniques in four key directions. (1) Multiple-codebook-based product quantization: conventional product quantization methods encode feature vectors using a single codebook, resulting in large quantization error. We propose a novel codebook generation method for an arbitrary number of codebooks. (2) Handling of temporal burstiness: for a stationary scene, once a query feature matches incorrectly, the match continues in successive frames, resulting in a high false-alarm rate. We present a temporal-burstiness-aware scoring method that reduces the impact from similar features, thereby reducing false alarms. (3) Densely sampled SIFT descriptors: conventional global features suffer from a lack of distinctiveness and invariance to non-photometric transformations. Our densely sampled global SIFT features are more discriminative and robust against logo or pattern insertions. (4) Bigram- and multiple-assignment-based indexing for global features: we extract two SIFT descriptors from each location, which makes them more distinctive. To improve recall, we propose multiple assignments on both the query and reference sides. Performance evaluation on the TRECVID 2009 dataset indicates that both local and global approaches outperform conventional schemes. Furthermore, the integration of these two approaches achieves a three-fold reduction in the error rate when compared with the best performance reported in the TRECVID 2009 workshop.
Design and Technology Challenges for Body Proximate Display Ecosystems

Body proximate display environments can be formed by combining multiple hand-held, head-mounted, wrist-worn or other displays. Wearable displays such as smartwatches and smartglasses have the potential to extend the interaction capabilities of mobile users beyond a single display. However, the display ecosystem formed by multiple personal displays on and around users' bodies is not well understood, yet. Within this paper, we investigate the design and technology challenges that could inhibit the creation and the use of interactive display ecosystems.
Clarithromycin Decreases IL-6 Concentration in Serum and BAL Fluid in Patients with Cryptogenic Organizing Pneumonia.

BACKGROUND Inflammatory cytokines are involved in the development of cryptogenic organizing pneumonia (COP). It has been shown that macrolides inhibit cytokine production in the alveolar macrophages of COP patients. OBJECTIVES The aim of the study was to assess the concentrations of interleukin 1β (IL-1β), IL-6, IL-8 and transforming growth factor β (TGF-β) in serum and in bronchoalveolar lavage fluid (BAL-f) in COP patients treated with clarithromycin (CAM). MATERIAL AND METHODS The study involved 26 patients (18 women and 8 men, mean age 56.46 ± 8.83 years) with biopsy-proven COP. After being treated with CAM, a complete recovery was achieved in 22 patients, while four patients did not respond to the treatment. The ELISA method was used to measure the serum and BAL-f concentrations of IL-1β, IL-6, IL-8 and TGF-β. RESULTS Before treatment, the serum IL-1β1, IL-6, IL-8 and TGF-β1 concentrations were similar in responders and non-responders. Significant decreases in serum concentrations of IL-6 (8.98 ± 13.26 pg/mL vs. 3.1 ± 6.95 pg/mL; p = 0.005), IL-8 (20.14 ± 25.72 pg/mL vs. 10.14 ± 6.8 pg/mL; p = 0.007) and TGF-β1 (37.89 ± 12.49 ng/mL vs. 26.49 ± 12.45 ng/mL; p = 0.001) were found after treatment, as well as a significant decrease in the BAL-f concentration of IL-6 (30.56 ± 56.78 pg/mL vs. 4.53 ± 5.84 pg/mL; p = 0.036). Clarithromycin treatment resulted in a significantly lower mean value of serum IL-6 responders than non-responders. CONCLUSIONS In COP patients, response to clarithromycin treatment was associated with decreases in serum concentrations of IL-6, IL-8 and TGF-β, and of rations, and of the BAL-f concentration of IL-6.
Endothelium-Derived Hyperpolarizing Factor and Vascular Function

Endothelial function refers to a multitude of physiological processes that maintain healthy homeostasis of the vascular wall. Exposure of the endothelium to cardiac risk factors results in endothelial dysfunction and is associated with an alteration in the balance of vasoactive substances produced by endothelial cells. These include a reduction in nitric oxide (NO), an increase in generation of potential vasoconstrictor substances and a potential compensatory increase in other mediators of vasodilation. The latter has been surmised from data demonstrating persistent endothelium-dependent vasodilatation despite complete inhibition of NO and prostaglandins. This remaining non-NO, non-prostaglandin mediated endothelium-dependent vasodilator response has been attributed to endothelium-derived hyperpolarizing factor/s (EDHF). Endothelial hyperpolarization is likely due to several factors that appear to be site and species specific. Experimental studies suggest that the contribution of the EDHFs increase as the vessel size decreases, with a predominance of EDHF activity in the resistance vessels, and a compensatory up-regulation of hyperpolarization in states characterized by reduced NO availability. Since endothelial dysfunction is a precursor for atherosclerosis development and its magnitude is a reflection of future risk, then the mechanisms underlying endothelial dysfunction need to be fully understood, so that adequate therapeutic interventions can be designed.
Examining the contraceptive decisions of young, HIV-infected women: A qualitative study

ABSTRACT This study qualitatively examined factors that influenced contraceptive choices in a sample of young, HIV-infected women. Individual qualitative interviews were conducted among 30 vertically and horizontally HIV-infected women (n = 26 African American) from the ages of 14 to 24 years (Mean age = 20.9 years). We recruited sample groups with the following characteristics: (a) current contraceptive/condom use with ≥1 child (n = 11); (b) current contraceptive/condom use with no children (n = 12); and (c) no current contraceptive/condom use with no children (n = 7). A semi-structured interview guide was used to ask participants about factors influencing past and current contraceptive choices. Individual interviews were digitally recorded and transcribed verbatim; analyses to identify core themes were informed by the Grounded Theoretical approach. Young, HIV-infected women did not identify their HIV serostatus or disease-related concerns as influential in their contraceptive decisions. However, they reported that recommendations from health-care providers and input from family and friends influenced their contraceptive choices. They also considered a particular method’s advantages (e.g., menstrual cycle improvements) and disadvantages (e.g., increased pill burden) when selecting a method. Findings suggested that HIV-infected young women’s contraceptive decisions were influenced by factors other than those related to their infection.
A Computational Model for Discriminating Music Performers Efstathios Stamatatos

In this study, a computational model that aims at the automatic discrimination of different human music performers playing the same piece is presented. The proposed model is based on the note level and does not require any deep (e.g., structural or harmonic, etc.) analysis. A set of measures that attempts to capture both the style of the author and the style of the piece is introduced. The presented approach has been applied to a database of piano sonatas by W.A. Mozart performed by both a French and a Viennese pianist with very encouraging preliminary results.