Miracle-dz/UniFAR-FTUs huggingface dataset:
Mammary-Derived Growth Inhibitor Alters Traffic of EGFR and Induces a Novel Form of Cetuximab Resistance

Purpose: Only few predictive factors for the clinical activity of anti–epidermal growth factor receptor (EGFR) therapy are available. Mammary-derived growth inhibitor (MDGI) is a small cytosolic protein suggested to play a role in the differentiation of epithelial cells. Here, we have investigated the effect of MDGI expression on the EGFR signaling and cetuximab responsiveness of cancer cells. Experimental Design: MDGI mRNA expression was investigated in clinical breast and lung cancer samples and in nontransformed and malignant cell lines. The effect of ectopic expression of MDGI on EGFR, ErbB2, and integrin function and traffic was investigated in breast and lung cancer cell lines using multiple methods. The effect of anti-EGFR agents on these cells were tested by cell proliferation measurements and by assessing tumor growth of breast cancer cells in cetuximab treated and control athymic nude mice. Results: Here, we show that although MDGI is absent in cultured cell lines because of epigenetic silencing, MDGI mRNA is expressed in 40% of clinical breast carcinomas and 85% of lung cancers. Ectopic expression of MDGI rendered breast and lung cancer cells resistant to the anti-EGFR antibody cetuximab in vitro and in an orthotopic breast cancer xenograft model in vivo. When expressed in cancer cells, MDGI induces intracellular accumulation of EGFR, but not ErbB2, and the internalized receptor is phosphorylated and not degraded. Conclusions: MDGI-driven inherent desensitization of cancer cells is a novel molecular mechanism for resistance to the anti-EGFR antibody therapy, and MDGI may be a biomarker for responsiveness to anti-EGFR antibody therapy. (Clin Cancer Res 2009;15(21):6570–81)
Accessible numerical information: Cookery recipes as a special case

This paper presents a proposal to solve the need of making numerical information accessible. We use the domain of cooking recipes as a special case of study. For the numerical information in recipes to be accessible it is necessary to adapt the different mathematical forms used for the quantities of different ingredients. It is a first approach to automate the adaptation of the recipes for universal accessibility.
Anaerobic Cometabolic Conversion of Benzothiophene by a Sulfate-Reducing Enrichment Culture and in a Tar-Oil-Contaminated Aquifer

ABSTRACT Anaerobic cometabolic conversion of benzothiophene was studied with a sulfate-reducing enrichment culture growing with naphthalene as the sole source of carbon and energy. The sulfate-reducing bacteria were not able to grow with benzothiophene as the primary substrate. Metabolite analysis was performed with culture supernatants obtained by cometabolization experiments and revealed the formation of three isomeric carboxybenzothiophenes. Two isomers were identified as 2-carboxybenzothiophene and 5-carboxybenzothiophene. In some experiments, further reduced dihydrocarboxybenzothiophene was identified. No other products of benzothiophene degradation could be determined. In isotope-labeling experiments with a [13C]bicarbonate-buffered culture medium, carboxybenzothiophenes which were significantly enriched in the13C content of the carboxyl group were formed, indicating the addition of a C1 unit from bicarbonate to benzothiophene as the initial activation reaction. This finding was consistent with the results of earlier studies on anaerobic naphthalene degradation with the same culture, and we therefore propose that benzothiophene was cometabolically converted by the same enzyme system. Groundwater analyses of the tar-oil-contaminated aquifer from which the naphthalene-degrading enrichment culture was isolated exhibited the same carboxybenzothiophene isomers as the culture supernatants. In addition, the benzothiophene degradation products, in particular, dihydrocarboxybenzothiophene, were significantly enriched in the contaminated groundwater to concentrations almost the same as those of the parent compound, benzothiophene. The identification of identical metabolites of benzothiophene conversion in the sulfate-reducing enrichment culture and in the contaminated aquifer indicated that the same enzymatic reactions were responsible for the conversion of benzothiophene in situ.
Biomimetic Silica Microspheres in Biosensing

Lipid vesicles spontaneously fuse and assemble into a lipid bilayer on planar or spherical silica surfaces and other substrates. The supported lipid bilayers (SLBs) maintain characteristics of biological membranes, and are thus considered to be biomembrane mimetic systems that are stable because of the underlying substrate. Examples of their shared characteristics with biomembranes include lateral fluidity, barrier formation to ions and molecules, and their ability to incorporate membrane proteins into them. Biomimetic silica microspheres consisting of SLBs on solid or porous silica microspheres have been utilized for different biosensing applications. The advantages of such biomimetic microspheres for biosensing include their increased surface area to volume ratio which improves the detection limits of analytes, and their amenability for miniaturization, multiplexing and high throughput screening. This review presents examples and formats of using such biomimetic solid or porous silica microspheres in biosensing.
Fuzzy support vector machine based on feature- data huffman compression

More and more state variables in the case, the filter state variables, and identify operating modes means that the state collect and spend a lot of computing time, which lost control of real-time. And contains a lot of noise in electronic devices such as exceptions, these factors will influence the support vector machine to establish the optimal classification surface. High-frequency signal conversion equipment needs in the shortest possible time, alarm, address this requirement, we use a Huffman coding on the control signal compression, Then use a Kernel density estimation method, a structural form of fuzzy membership function, the membership function applied to the fuzzy support vector machines for fault diagnosis, This method can eliminate the characteristics of the impact of noise and outliers, through training support vector machines, we can get fault diagnosis model to realize the failure of electronic equipment, diagnostic classification. The method is applied to high-frequency signal conversion equipment for fault diagnosis, the results show that the compression algorithm used to retain equipment operation to the maximum extent, while greatly reducing the information processing time, Fuzzy support vector functions highlight the different characteristics of fault and correctly diagnose the fault type and effective, this method of fault diagnosis of electronic devices to provide a new way.
Observational Learning in the Jumping Spider Phidippus audax

Observational learning is a complex form of learning most frequently studied in social vertebrates. However, evidence for social learning exists in several invertebrate species Evidence of invertebrate observational learning also exists, though to a lesser extent. This study addresses observational learning in a jumping spider (Phidippus audax) through video playback. My results suggest that while observational learning is occurring, the exact mechanism in use remains unclear. Spiders that saw a conspecific satiate itself on a prey item readily attacked a similar prey item when exposed to a live prey item. However, spiders exposed to a conspecific rejecting or in the absence of a prey item were much more cautious when exposed to a live prey item. While virtually all spiders did eventually attack, a significant increase was found in the latency to the attack. No other groups demonstrated deviated from the behavior of the control group. My data suggest that observational learning may be present in a more diverse array of taxa than is held in traditional views of social learning, including highly asocial invertebrates such as spiders.
Comparison of Piezoresistive Monofilament Polymer Sensors

The development of flexible polymer monofilament fiber strain sensors have many applications in both wearable computing (clothing, gloves, etc.) and robotics design (large deformation control). For example, a high-stretch monofilament sensor could be integrated into robotic arm design, easily stretching over joints or along curved surfaces. As a monofilament, the sensor can be woven into or integrated with textiles for position or physiological monitoring, computer interface control, etc. Commercially available conductive polymer monofilament sensors were tested alongside monofilaments produced from carbon black (CB) mixed with a thermo-plastic elastomer (TPE) and extruded in different diameters. It was found that signal strength, drift, and precision characteristics were better with a 0.3 mm diameter CB/TPE monofilament than thick (∼2 mm diameter) based on the same material or commercial monofilaments based on natural rubber or silicone elastomer (SE) matrices.
Robust array interpolation using second-order cone programming

We study Friedlander's (1993) array interpolation technique, whose main shortcoming in multisource scenarios is that it does not provide sufficient robustness against sources arriving outside specified interpolation sectors. In this letter, we develop a new robust interpolation approach by minimizing the interpolation error inside the sectors of interest while setting multiple "stopband" constraints outside these sectors to prevent performance degradation effects caused by out-of-sector sources. Computationally efficient convex formulations of the robust interpolation matrix design problem using second-order cone programming are derived.
Weighted residuals for very deep networks

Deep residual networks have recently shown appealing performance on many challenging computer vision tasks. However, the original residual structure still has some defects making it difficult to converge on very deep networks. In this paper, we introduce a weighted residual network to address the incompatibility between ReLU and element-wise addition and the deep network initialization problem. The weighted residual network is able to learn to combine residuals from different layers effectively and efficiently. The proposed models enjoy a consistent improvement over accuracy and convergence with increasing depths from 100+ layers to 1000+ layers. Besides, the weighted residual networks have little more computation and GPU memory burden than the original residual networks. The networks are optimized by projected stochastic gradient descent. Experiments on CIFAR-10 have shown that our algorithm has a faster convergence speed than the original residual networks and reaches a high accuracy at 95.3% with a 1192-layer model. Experiments on CIFAR-100 and ImageNet-1k have also verified the effectiveness of our proposed design.
Neuromuscular adaptation to early post-operative, high-intensity, short resistance training of non-operated lower extremity in elderly patients: a randomized controlled trial.

OBJECTIVE To evaluate the short-term effects of high-intensity, task-specific vs regular rehabilitation programme on neuromuscular activity and functional changes in elderly inpatients. DESIGN Single-blind, randomized controlled trial. PATIENTS Sixty-two patients, aged 74.1 (standard deviation (SD) 6.9) years, 12.8 (SD 3.6) days after operation of one lower limb. METHODS Allocation to either high- or regular-intensity strength training of non-operated limb, of 3 weeks duration. OUTCOME MEASUREMENTS ratio of integrated electromyography per weight lifted (mm vastus medialis and lateralis), isometric electromyography (EMG), maximal weight lifted expressed as 1 repetition maximum, torque in Nm and isometric maximal force of uninvolved leg, timed "up and go" test (TUG) and Western Ontario and McMaster Universities (WOMAC) questionnaire. RESULTS Both programmes resulted in significant improvements in: integrated EMG/weight lifted ratio, isometric EMG, 1 repetition maximum, torque and isometric force, TUG performance and WOMAC items (all p < 0.0001, Wilcoxon signed-rank test). Significant differences were found between the high- and regular-intensity groups for integrated EMG/weight lifted (p = 0.026), 1 repetition maximum and torque (p = 0.0014, Wilcoxon rank-sum test). CONCLUSION High-intensity, short-period, early postoperative muscle training has a superior effect on maximal weight lifted by the knee extensors and integrated EMG/weight lifted compared with regular raining. These findings are in accordance with more rapid neuronal adaptation by a high intensity rehabilitation programme.
Identifying Biological Echoes in Radar Scans Using Machine Learning

Radar ornithology using data from the NEXRAD weather radar system has given scientists new tools for studying bird migration in the United States. Unfortunately, the process of identifying echoes from birds in radar data still largely requires that trained technicians spend hours manually scouring radar scans. This paper provides some background for understanding biological echoes in NEXRAD data and then describes our initial investigations of the use of machine learning techniques to help automate the process of echo classification. Doppler data allows researchers to look at bird migration by examining large clusters of birds that could be observed approaching and descending at stopover points where they would rest until they began the next leg of their journey. One of the NEXRAD system's greatest strengths has also been a significant obstacle for researchers studying bird migration: with 154 radar stations across the United States, each often producing hundreds of volume scans per day, the amount of data to sort through is staggering. The real problem is that classifying birds in radar scans currently requires a skilled technician who has been trained in visually identifying the tell-tale signs that distinguish biological echoes from non-biological echoes. Consequently, the task of plotting a specific migration over any significant amount of space and time quickly becomes a difficult and resource intensive problem. We have begun by using a K-nearest neighbour classifier, a naive Bayes classifier, and a neural network to classify the echoes. Early validation results using tenfold cross-validation procedures are hopeful and indicate that machine learning techniques could be well suited for this task. Accuracy rates have exceeded 98 percent. Although these early results are encouraging, it is important to keep in mind that each of the training sweeps used in these experiments was selected by an expert because it could be considered a prototypical example of one particular echo type dominating a sweep. Intuitively, these sweeps are the easiest to classify, which may explain the results. The real test will be to apply these methods to more complex data, including ambiguous data as well as mixed sweeps containing both types of echoes. Our next efforts will concentrate on acquiring and experimenting with such data. Our eventual goal is to use machine learning methods to map bird migration pathways.
References made and citations received by scientific articles

This article studies massive evidence about references made and citations received after a 5‐year citation window by 3.7 million articles published in 1998 to 2002 in 22 scientific fields. We find that the distributions of references made and citations received share a number of basic features across sciences. Reference distributions are rather skewed to the right while citation distributions are even more highly skewed: The mean is about 20 percentage points to the right of the median, and articles with a remarkable or an outstanding number of citations represent about 9% of the total. Moreover, the existence of a power law representing the upper tail of citation distributions cannot be rejected in 17 fields whose articles represent 74.7% of the total. Contrary to the evidence in other contexts, the value of the scale parameter is above 3.5 in 13 of the 17 cases. Finally, power laws are typically small, but capture a considerable proportion of the total citations received. © 2011 Wiley Periodicals, Inc.
The tension of cosmological magnetic fields as a contribution to dark energy

We propose that cosmological magnetic fields generated in regions of finite spatial dimensions may manifest themselves in the global dynamics of the Universe as “dark energy”. We test our model in the context of spatially flat cosmological models by assuming that the Universe contains non-relativistic matter ρm ∝ α −3 ,d ark energyρQ ∝ α −3(1+w) , and an extra fluid with ρB ∝ α n−3 that corresponds to the magnetic field. We place constraints on the main cosmological parameters of our model by combining the recent supernovae type Ia data and the differential ages of passively evolving galaxies. In particular, we find that the model which best reproduces the observational data when Ωm = 0.26 is one with ΩB � 0.03, n � 7.68, ΩQ � 0.71 and w �− 0.8.
A Generalized Reduction Construct for Deterministic OpenMP

In parallel programming, a reduction is an operation that combines values across threads into a single result, and can be designed and implemented so as to enforce determinism, not only on the result, but also on the intermediate values and evaluation sequence. These features make the reduction an attractive feature for a language-based approach to deterministic parallelism, especially one that, like OpenMP, provides annotations to parallelize legacy serial code. Hence reductions are a planned feature of the Deterministic OpenMP (DOMP) project. To enable DOMP to help programmers eliminate nondeterministic code wherever possible, we propose a generalized reduction that supports arbitrary data types and user-defined operations—a generalization rich in challenges for language design and implementation.
Towards Milli-Second IGP Convergence

It is possible for link-state routing protocols to converge in link propagation time scales, that is, in tens of milliseconds. However, current deployments of ISIS[6][1], a link-state routing protocol, are not anywhere near this point. In this paper, we present some analyses of ISIS convergence by showing its behavior upon link/router failures and repairs, and its scaling properties to large networks, both in terms of number of nodes and links. We then explore changes needed in the ISIS specification and implementations to reach IGP convergence in milliseconds. Our results are based on experimentation done with ISIS, but some of the findings may apply to OSPF as well.
A CGS-MSM Parallel Genetic Algorithm Based on Multi-agent

In order to solve problem of the speed of convergence and early-maturing of the serial genetic algorithm, people begin to study the parallel genetic algorithm. However most researcher devoted to study the single way parallel genetic algorithm (PGA), while solving some combination optimizing problem, have made good result, but as to the more complicated combination optimizing problem, all have certain deficiencies. This paper propose a coarse grain size-master slaver model PGA (CGS-MSM PGA), which is make up of several coarse grain size PGA (CGS-PGA) that is combine of several master-slaver model parallel genetic algorithm (MSM-PGA), and every sub-algorithm is one agent. This algorithm has improved the speed and precision of calculation greatly through the good communication coordination among many agents, utilized the advantages of CGS-PGA and MSM-PGA, overcome the shortcoming of their two.
Development and Characterization of aStaphylococcus aureus Nasal Colonization Model in Mice

ABSTRACT Staphylococcus aureus nasal carriage is a risk factor for infection in humans, particularly in the hospital environment. Attenuation of carriage has proven effective in reducing the prevalence of infection in some high-risk groups. To study staphylococcal factors that influence nasal colonization, a mouse model of S. aureus nasal colonization was developed. Mice were inoculated intranasally with S. aureus Reynolds, and nasal carriage was evaluated by quantitating cultures of the nasal tissues from mice sacrificed at various time points after inoculation. The majority of mice inoculated with 108 CFU of S. aureusmaintained nasal carriage for at least 20 days. Nasal colonization rates were similar for inbred (BALB/c and C57BL/6) and outbred (ICR) mice. Colonization was not affected by mouse passage of strain Reynolds. Lower inoculum doses (<107 CFU) resulted in reduced colonization after 7 days. However, mice given streptomycin in their drinking water developed long-term carriage of S. aureus, and they were colonized with inocula as low as 105 CFU. Nasal colonization was also established with two other S. aureus strains (one strain each of human and murine origins). S. aureus recovered from the nares of experimentally colonized mice expressed high levels of capsule, and the ability of a capsule-defective mutant to persist in the nares was reduced in comparison to that of the parent strain. This nasal colonization model should prove useful for studies of factors that mediate S. aureus colonization and for assessment of targets for antimicrobial intervention or vaccine development.
A Novel Measure of the Need for Moral Cognition

Abstract This research developed the first measure of the need for moral cognition, or the tendency to seek out, talk about, reflect on, or otherwise engage with ethical or moral issues. Interpretative phenomenological analysis was conducted on transcripts from interviews with members of the target population and focus groups with content experts. Pilot items were administered to a large online sample; 2 latent factors were identified. Scores were assessed for test–retest reliability, and convergent-, discriminant-, and criterion-validity. Collectively, these studies demonstrate that the Need for Moral Cognition scale is a valid and reliable measure suitable for use in psychological research.
STAT3-iNOS Signaling Mediates EGFRvIII-Induced Glial Proliferation and Transformation

Malignant gliomas, including glioblastoma multiforme, constitute the most common and aggressive primary brain tumors in adults. The transcription factor signal transducer and activator of transcription 3 (STAT3) plays an essential role in glioblastoma pathogenesis downstream of the major oncogenic protein epidermal growth factor receptor variant III (EGFRvIII). However, the critical gene targets of STAT3 that mediate EGFRvIII-induced glial transformation have remained unknown. Here, we identify inducible nitric oxide synthase (iNOS) as a novel target gene of STAT3 in EGFRvIII-expressing mouse astrocytes. Endogenous STAT3 occupies the endogenous iNOS promoter and stimulates iNOS transcription in EGFRvIII-expressing astrocytes. STAT3 does not appear to control iNOS transcription in astrocytes deficient in the major glioblastoma tumor suppressor protein phosphatase and tensin homolog (PTEN), suggesting that STAT3 regulates iNOS transcription specifically in EGFRvIII-expressing astrocytes. Importantly, inhibition of iNOS by distinct approaches, including knockdown by RNA interference, reduces cell population growth and invasiveness of EGFRvIII-expressing astrocytes. In addition, upon iNOS knockdown or administration of a small-molecule inhibitor of iNOS, EGFRvIII-expressing astrocytes form smaller tumors in vivo. These findings suggest that inhibition of iNOS may have potential therapeutic value for EGFRvIII-activated brain tumors.
Lip reading from scale-space measurements

Systems that attempt to recover the spoken word from image sequences usually require complicated models of the mouth and its motions. Here we describe a new approach based on a fast mathematical morphology transform called the sieve. We form statistics of scale measurements in one and two dimensions and these are used as a feature vector for standard Hidden Markov Models (HMMs).