Miracle-dz/UniFAR-FTUs huggingface dataset:
Prospect for anti-HER2 receptor therapy in breast cancer.

The carcinogenesis process is characterized, in part, by the dysfunction of cellular communication pathways, such as the one involving HER2. HER2 is a member of the EGF receptor family, which participates in cell growth and proliferation. HER2 may be overexpressed in 15 to 30% of breast cancer cases and is associated with poor prognosis, shortened overall survival and shorter time to disease progression. Furthermore, an increasing number of studies have demonstrated the relevance of HER2 serum concentrations (sHER2, extracellular domain released into blood by proteolysis) as a predictive marker of resistance to chemotherapy in HER2-overexpressing metastatic breast cancer. The determination of HER2 overexpression/ amplification in the diagnosis of relapse of breast cancer is currently a routine procedure. Immunohistochemistry (IHC) and fluorescent in situ hybridization (FISH) techniques, which are used to detect HER2 expression in the tumor, are improving constantly, and other parallel techniques such as chromogenic in situ hybridization (CISH) are starting to emerge. sHER2 concentrations can be measured using ELISA techniques, which can be automated. All of these procedures still need to be standardized. The discovery of a monoclonal antibody (4D5) that can inhibit the growth and proliferation of cells overexpressing HER2 led to the development of trastuzumab. Like 4D5, trastuzumab recognizes an epitope on the extracellular domain of HER2. Moreover, trastuzumab is also able to stimulate antibody-dependent cellular toxicity (ADCC). It is administered alone or in combination (with navelbine, taxol, carboplatin.) in patients with metastatic breast cancer overexpressing HER2. Other active antibodies have since been discovered, as well as other specific molecules, such as tyrosine kinase inhibitors which will undoubtedly find a place in the therapeutic arsenal used in breast cancer, especially to avoid the emergence of resistance to treatment.
Uterine Lipoleiomyosarcoma: Report of 2 Cases and Review of the Literature

Uterine lipoleiomyosarcoma is a rare entity with only 6 case reports in the Pubmed database at the time of writing this article. We report 2 additional cases of uterine lipoleiomyosarcoma, characterized on microscopy by coexistence of leiomyosarcomatous and liposarcomatous components, with focal intermingling, without an intervening lipoleiomyomatous area. The liposarcomatous component in both of our cases had the morphology of myxoid liposarcoma. Both cases underwent postoperative chemotherapy. One of our cases had recurrence in the pelvis with microscopic features of myxoid liposarcoma. This patient died with multiple metastases 4.5 yr after hysterectomy, with the metastatic lesions being liposarcomas, without an evident leiomyosarcomatous component. Although lacking a treatment protocol because of the rarity of such cases, postoperative adjuvant therapy is mandatory.
Life without glutamate: the epigenetic effects of glutamate deletion

Li et al. (2013) studied ThVGdKO mice produced by genetic elimination of the genes for both vesicular transporters Vglut-1 and Vglut-2. In these mice thalamocortical glutamate neurotransmission in somatosensory cortex (but not in visual or auditory cortex) is silenced or knocked-out, thereby disabling the release of glutamate from synaptic vesicles. This resulted in a failure to construct barrel cortex columns, and induced severe defects in both cortical layer 4 lamination and in the morphology of spiny stellate cells. The authors explain this finding as follows: “We favor a model in which stellate neurons modulate activity and direct the local migration of neurons into barrels, modify gene expression, and influence cell morphologic development. ” Li et al. (2013) present an hypothesis to explain their results: see also the preview of this paper by Barth and Kuhlman (2013). They suggest that it is possible that, by binding to its NMDA or metabotropic receptors, glutamate would stimulate second messenger chains that terminate by activating the appropriate transcription factors. We accept their hypothesis as a partial explanation, but we suggest that there may be other factors involved that they have not considered. To explain these more fully we add two additional hypotheses. These hypotheses are not competitive, either with each other or with the original hypothesis of Li et al. (2013), but constitute additions.
Study of energy efficiency in industry

This paper presents the study of energy efficiency in industry based upon information measurements, audits and technological processes. The economic operators analysed in this study are companies with primary activity in metalworking, food and bakery industry. The products of these companies belong to categories like: bread and specialities of bread, industrial scales, bearings and meat processing.
Cue Selection and Category Learning : A Systematic Comparison of Three Theories 1

We evaluate three approaches to tasks involving categorisation with probabilistic cues (the Bayesian approach, the associationist approach, and the hypothesis testing approach) by comparing the behaviour of three classes of cognitive model with that of human participants on a simulated medical diagnosis task. The task yields dependent measures relating to both categorisation accuracy and cue selection. A systematic exploration of the effects of processing biases within the models reveals that all three approaches are able to account for the effects in the human data, provided that appropriate performance factors and processing biases are incorporated. The discussion focuses on the methodology used to evaluate the approaches and on the role of performance factors and processing biases within the various models.
CCD characterization for a range of color cameras

CCD cameras are widely used for remote sensing and image processing applications. However, most cameras are produced to create nice images, not to do accurate measurements. Post processing operations such as gamma adjustment and automatic gain control are incorporated in the camera. When a (CCD) camera is used as measurement instrument, it is important to have an accurate model of the imaging process and its noise. This model needs to be verified for the camera at hand and the model parameters should be estimated. Often however, a model is assumed and optimal performance is claimed without verification of the model. In this paper, we analyze a range of different types of cameras. We verify a general model of a CDD camera including post processing using experiments. It is shown that for a typical consumer webcam, the general model does not hold. The parameters of the model are estimated. It is shown that for most cameras the model can even be simplified under normal operating conditions by neglecting the dark current. We further show that the amount of additive noise is exceeded by the amount of multiplicative noise at intensity values larger than 10-30% of the intensity range
Audio Music Similarity and Retrieval: Evaluation Power and Stability

In this paper we analyze the reliability of the res ults in the evaluation of Audio Music Similarity and Retrieval systems. We focus on the power and stability of the evaluati on, that is, how often a significant difference is found bet ween systems and how often these significant differences ar e incorrect. We study the effect of using different effect iveness measures with different sets of relevance judgments , for varying number of queries and alternative statistic al procedures. Different measures are shown to behave simil arly overall, though some are much more sensitive and st able than others. The use of different statistical proce dures does improve the reliability of the results, and it allo ws using as little as half the number of queries currently used in MIREX evaluations while still offering very similar relia bility levels. We also conclude that experimenters can be very confident that if a significant difference is found between t wo systems, the difference is indeed real.
Uninsured ? Fewer services , less readmission , more death . A study of Maryland ’ s hospitals

Uninsured individuals receive fewer health care services for at least three reasons: higher prices, responsibility for the entire bill, and potential provider reductions for concern of nonpayment. This study isolates differences in service levels between insured and uninsured individuals that are attributed to different effective prices; the uninsured pay the bill without a contribution from an insurance company. I capitalize on Maryland’s highly regulated health care system, where prices are set by the state, are uniform across all patients, and hospitals are compensated for free care and bad debt, to isolate the difference in quantity demanded by the uninsured. While the Oregon study compares Medicaid individuals and their low-income uninsured counterparts, this paper considers income variation among the uninsured, quantifies the difference in demand in an environment with uniform prices, and evaluates health outcomes in light of these reductions. A Blinder-Oaxaca decomposition estimates uninsured individuals receive 8.4% fewer services after accounting for differences in characteristics. Compared to insured patients, the uninsured are 74% as likely to be readmitted. This difference in service level and readmission rates is larger for patients residing in low-income zip codes and smaller in wealthy zip codes. This suggests that income is a substantial constraint for uninsured patients, and as this constraint relaxes, more services are demanded. For illnesses with a high risk of mortality, both services demanded and readmission rates are not statistically different for insured and uninsured individuals. Mortality rates are similar for insured and uninsured individuals living in the bottom 75% of average income by zip code. Differences in the top 25% seem to be attributable to small numbers of uninsured individuals. While this paper analyses Maryland, it provides insight into demand for insured individuals with high-deductible plans country wide. Prior to meeting their deductible, these insured patients face similar conditions to uninsured patients in Maryland — they have access to negotiated rates but are solely paying the bill.
Torque Analysis of an Axial Flux Permanent Magnet Synchronous Machine by Using Analytical Magnetic Field Calculation

This paper presents a torque analysis of an axial flux permanent magnet synchronous machine (AFPMSM) based on analytical analysis of magnetic field. The proposed method for torque analysis requires less computational time and is more flexible than finite element method (FEM). This is important advantage, especially for the early stages of the design process. Torque measurements on prototype AFPMSM confirm the validity of the torque analysis method.
Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloudflow: a Data-aware Programming Model for Cloud Workflow Applications on Modern Hpc Systems

• CloudFlow programming model is designed for cloud workflow on modern HPC systems. • CloudFlow is not only data-aware, but also shared-data-aware. • The programming model supports multiple Map and Reduce functions. • Theoretical analysis proves the correctness and uniqueness of each desired output. • Results show the speedup of CloudFlow exceeds 4X compared to traditional MapReduce. a b s t r a c t Traditional High-Performance Computing (HPC) based big-data applications are usually constrained by having to move large amount of data to compute facilities for real-time processing purpose. Modern HPC systems, represented by High-Throughput Computing (HTC) and Many-Task Computing (MTC) platforms, on the other hand, intend to achieve the long-held dream of moving compute to data instead. This kind of data-aware scheduling, typically represented by Hadoop MapReduce, has been successfully implemented in its Map Phase, whereby each Map Task is sent out to the compute node where the corresponding input data chunk is located. However, Hadoop MapReduce limits itself to a one-map-to-one-reduce framework, leading to difficulties for handling complex logics, such as pipelines or workflows. Meanwhile, it lacks built-in support and optimization when the input datasets are shared among multiple applications and/or jobs. The performance can be improved significantly when the knowledge of the shared and frequently accessed data is taken into scheduling decisions. To enhance the capability of managing workflow in modern HPC system, this paper presents CloudFlow, a Hadoop MapReduce based programming model for cloud workflow applications. CloudFlow is built on top of MapReduce, which is proposed not only being data aware, but also shared-data aware. It identifies the most frequently shared data, from both task-level and job-level, replicates them to each compute node for data locality purposes. It also supports user-defined multiple Map-and Reduce functions, allowing users to orchestrate the required data-flow logic. Mathematically, we prove the correctness of the whole scheduling framework by performing theoretical analysis. Further more, experimental evaluation also shows that the execution runtime speedup exceeds 4X compared to traditional MapReduce implementation with a manageable time overhead.
Design Tradeoffs for SSD Performance

Solid-state disks (SSDs) have the potential to revolutionize the storage system landscape. However, there is little published work about their internal organization or the design choices that SSD manufacturers face in pursuit of optimal performance. This paper presents a taxonomy of such design choices and analyzes the likely performance of various configurations using a trace-driven simulator and workload traces extracted from real systems. We find that SSD performance and lifetime is highly workload-sensitive, and that complex systems problems that normally appear higher in the storage stack, or even in distributed systems, are relevant to device firmware.
Stanniocalcin-1 Reduces Tumor Size in Human Hepatocellular Carcinoma

Growing evidence has revealed high expression levels of stanniocalcin-1 (STC1) in different types of human cancers. Numerous experimental studies using cancer cell lines demonstrated the involvement of STC1 in inflammatory and apoptotic processes; however the role of STC1 in carcinogenesis remains elusive. Hepatocellular carcinoma (HCC) an exemplified model of inflammation-related cancer, represents a paradigm of studying the association between STC1 and tumor development. Therefore, we conducted a statistical analysis on the expression levels of STC1 using clinicopathological data from 216 HCC patients. We found that STC1 was upregulated in the tumor tissues and its expression levels was positively correlated with the levels of interleukin (IL)-6 and IL-8. Intriguingly tumors with greater expression levels of STC1 (tumor/normal ≥ 2) were significantly smaller than the lower level (tumor/normal<2) samples (p = 0.008). A pharmacological approach was implemented to reveal the functional correlation between STC1 and the ILs in the HCC cell-lines. IL-6 and IL-8 treatment of Hep3B cells induced STC1 expression. Lentiviral-based STC1 overexpression in Hep3B and MHCC-97L cells however showed inhibitory action on the pro-migratory effects of IL-6 and IL-8 and reduced size of tumor spheroids. The inhibitory effect of STC1 on tumor growth was confirmed in vivo using the stable STC1-overexpressing 97L cells on a mouse xenograft model. Genetic analysis of the xenografts derived from the STC1-overexpressing 97L cells, showed upregulation of the pro-apoptotic genes interleukin-12 and NOD-like receptor family, pyrin domain-containing 3. Collectively, the anti-inflammatory and pro-apoptotic functions of STC1 were suggested to relate its inhibitory effect on the growth of HCC cells. This study supports the notion that STC1 may be a potential therapeutic target for inflammatory tumors in HCC patients.
Mixed Impulse Fuzzy Filter Based on MAD, ROAD, and Genetic Algorithms

In this paper, we propose a genetic fuzzy image filtering based on rank-ordered absolute differences (ROAD) and median of the absolute deviations from the median (MAD). The proposed method consists of three components, including fuzzy noise detection system, fuzzy switching scheme filtering, and fuzzy parameters optimization using genetic algorithms (GA) to perform efficient and effective noise removal. Our idea is to utilize MAD and ROAD as measures of noise probability of a pixel. Fuzzy inference system is used to justify the degree of which a pixel can be categorized as noisy. Based on the fuzzy inference result, the fuzzy switching scheme that adopts median filter as the main estimator is applied to the filtering. The GA training aims to find the best parameters for the fuzzy sets in the fuzzy noise detection. By the experimental results, the proposed method has successfully removed mixed impulse noise in low to medium probabilities, while keeping the uncorrupted pixels less affected by the median filtering. It also surpasses the other methods, either classical or soft computing-based approaches to impulse noise removal, in MAE and PSNR evaluations.
Nurse home visits with or without alert buttons versus usual care in the frail elderly: a randomized controlled trial

Objective To assess whether an intervention based on nurse home visits including alert buttons (NV+AB) is effective in reducing frailty compared to nurse home visits alone (NV-only) and usual care (control group) for older adults. Design Unblinded, randomized, controlled trial. Setting Insured population covered by the Mexican Social Security Institute living in the city of Ensenada, Baja California, Mexico. Participants Patients were aged over 60 years with a frailty index score higher than 0.14. Intervention After screening and informed consent, participants were allocated randomly to the control, NV+AB, or NV-only groups. Measurements The primary outcome was the frailty score 9 months later. Quality of life, depression, comorbidities, health status, and health service utilization were also considered. Results The framing sample included 819 patients. Of those, 591 were not located because they did not have a landline/telephone (341 patients), they had died (107), they were ill (50), or they were not currently living in the city (28). A screening interview was applied to 228 participants, and 57 had a score ≤0.14, 171 had ≥0.14, and 16 refused to complete the baseline questionnaire. A home visit was scheduled for 155 patients. However, 22 did not complete the baseline questionnaire. The final 133 subjects were randomized into the NV+AB (n = 45), NV-only (n = 44), and control (n = 44) groups. There were no statistically significant differences in the baseline characteristics of the groups. The mean age overall was 76.3 years (standard deviation 4.7) and 45% were men. At the baseline, 61.65% were classified as frail. At end of follow-up the adjusted prevalence of frailty in NV+AB group was 23.3% versus 58.3% in the control group. Conclusion: An intervention based on NV+AB seems to have a positive effect on frailty scores.
Biofouling of inlet pipes affects water quality in running seawater aquaria and compromises sponge cell proliferation

Marine organism are often kept, cultured, and experimented on in running seawater aquaria. However, surprisingly little attention is given to the nutrient composition of the water flowing through these systems, which is generally assumed to equal in situ conditions, but may change due to the presence of biofouling organisms. Significantly lower bacterial abundances and higher inorganic nitrogen species (nitrate, nitrite, and ammonium) were measured in aquarium water when biofouling organisms were present within a 7-year old inlet pipe feeding a tropical reef running seawater aquaria system, compared with aquarium water fed by a new, biofouling-free inlet pipe. These water quality changes are indicative of the feeding activity and waste production of the suspension- and filter-feeding communities found in the old pipe, which included sponges, bivalves, barnacles, and ascidians. To illustrate the physiological consequences of these water quality changes on a model organism kept in the aquaria system, we investigated the influence of the presence and absence of the biofouling community on the functioning of the filter-feeding sponge Halisarca caerulea, by determining its choanocyte (filter cell) proliferation rates. We found a 34% increase in choanocyte proliferation rates following the replacement of the inlet pipe (i.e., removal of the biofouling community). This indicates that the physiological functioning of the sponge was compromised due to suboptimal food conditions within the aquarium resulting from the presence of the biofouling organisms in the inlet pipe. This study has implications for the husbandry and performance of experiments with marine organisms in running seawater aquaria systems. Inlet pipes should be checked regularly, and replaced if necessary, in order to avoid excessive biofouling and to approach in situ water quality.
Instruction-Set Customization for Real-Time Embedded Systems

Application-specific customization of the instruction set helps embedded processors achieve significant performance and power efficiency. In this paper, we explore customization in the context of multi-tasking real-time embedded systems. We propose efficient algorithms to select the optimal set of custom instructions for a task set under two popular real-time scheduling policies. Our algorithms minimize the processor utilization through customization while satisfying the task deadlines and the constraint on silicon area. Experimental evaluation with various task sets shows that appropriate customization can achieve significant reduction in the processor utilization and the energy consumption
A Near-Duplicate Detection Algorithm to Facilitate Document Clustering

Web Ming faces huge problems due to Duplicate and Near Duplicate Web pages. Detecting Near Duplicates is very difficult in large collection of data like ”internet”. The presence of these web pages plays an important role in the performance degradation while integrating data from heterogeneous sources. These pages either increase the index storage space or increase the serving costs. Detecting these pages has many potential applications for example may indicate plagiarism or copyright infringement. This paper concerns detecting, and optionally removing duplicate and near duplicate documents which are used to perform clustering of documents . We demonstrated our approach in web news articles domain. The experimental results show that our algorithm outperforms in terms of similarity measures. The near duplicate and duplicate document identification has resulted reduced memory in repositories.
A classifier based on distance between test samples and average patterns of categorical nearest neighbors

The recognition rate of the typical nonparametric method "k-nearest neighbor rule (kNN)" is degraded when the dimensionality of feature vectors is large. Another nonparametric method "linear subspace methods" cannot represent the local distribution of patterns, so recognition rates decrease when pattern distribution is not normal distribution. This paper presents a classifier that outputs the class of a test sample by measuring the distance between the test sample and the average patterns, which are calculated using nearest neighbors belonging to individual categories. A kernel method can be applied to this classifier for improving its recognition rates. The performance of those methods is verified by experiments with handwritten digit patterns and two class artificial ones.