Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficacy and safety of sorafenib for advanced non-small cell lung cancer: a meta-analysis of randomized controlled trials.

BACKGROUND Many clinical trials have been conducted to evaluate sorafenib for the treatment of advanced NSCLC, but the results for efficacy have been inconsistent. The aim of this study was to evaluate the efficacy and safety of sorafenib in patients with advanced NSCLC in more detail by meta-analysis. METHODS This meta-analysis of randomized controlled trials (RCTs) was performed after searching PubMed, EMBASE, ASCO Abstracts, ESMO Abstracts, and the proceedings of major conferences for relevant clinical trials. Two reviewers independently assessed the quality of the trials. Outcomes analysis were disease control rate (DCR), progression- free survival (PFS), overall survival (OS) with 95% confidence intervals (CI) and major toxicity. Subgroup analysis was conducted according to sorafenib monotherapy, in combination with chemotherapy or EGFR-TKI to investigate the preferred therapy strategy. RESULTS Results reported from 6 RCTs involving 2,748 patients were included in the analysis. Compared to sorafenib-free group, SBT was not associated with higher DCR (RR 1.31 (0.96-1.79), p=0.09), PFS (HR 0.82 (0.66-1.02), p=0.07) and OS (HR 1.01 (0.92-1.12), p=0.77). In terms of subgroup results, sorafenib monotherapy was associated with significant superior DCR and longer PFS, but failed to show advantage with regard to OS. Grade 3 or greater sorafenib-related adverse events included fatigue, hypertension, diarrhea, oral mucositis, rash and HFSR. CONCLUSIONS SBT was revealed to yield no improvement in DCR, PFS and OS. However, sorafenib as monotherapy showed some activity in NSCLC. Further evaluation may be considered in subsets of patients who may benefit from this treatment. Sorafenib combined inhibition therapy should be limited unless the choice of platinum-doublet regimen, administration sequence or identification of predictive biomarkers are considered to receive better anti-tumor activity and prevention of resistance mechanisms.
Real-time Rendering of Translucent Volumetric Surfaces

We present a real-time technique for rendering translucent features on opaque volumetric surfaces. It relies on modern reprogrammable graphics hardware to compute the complicated visual phenomena seen in translucent materials. The proposed technique is based on relief mapping techniques, and is therefore capable of interacting with the underlying opaque surface accurately. A hard self-shadowing technique used for shadowing opaque relief mapped surfaces is extended to suit the simulation of translucent features. Also, light absorption and planar local reflection methods are suggested as improvements to the base technique. The proposed rendering technique proves feasible for real-time applications, and is capable of simulating small-scale translucent features, such as drops of liquid, on opaque surfaces in a very realistic manner.
Measurement of Colloidal Stability in Solutions of Simple, Nonadsorbing Polyelectrolytes

The stability of a solution of charged polystyrene particles in the presence of nonadsorbing polyelectrolyte macromolecules is measured using optical light scattering. The particles were negatively charged polystyrene latex spheres (0.5-1 &mgr;m diameter) while the macromolecules were simulated using negatively charged colloidal silica spheres (5-7 nm diameter). Because of the electrostatic repulsion between the particles, the solution is found to be stable against primary flocculation (irreversible flocculation into a primary energy minima). However, because of long-range attractive depletion forces, reversible secondary flocculation of the particles occurs into a local potential energy minimum. As observed with uncharged macromolecules, the polyelectrolyte first induces flocculation at a critical flocculation concentration (v *), but later restabilizes the system at a critical restabilization concentration (v **). These critical concentrations are found to decrease with decreasing macromolecule size and increasing particle size. The restabilized solutions are found to remain suspended for periods greater than 20 days. Comparison of the measured flocculation and restabilization results to predictions made using a recently developed force-balance model show qualitative agreement.
Neglected Parasitic Infections in the United States: Toxoplasmosis

Toxoplasma gondii is a leading cause of severe foodborne illness in the United States. Population-based studies have found T. gondii infection to be more prevalent in racial/ethnic minority and socioeconomically disadvantaged groups. Soil contaminated with cat feces, undercooked meat, and congenital transmission are the principal sources of infection. Toxoplasmosis-associated illnesses include congenital neurologic and ocular disease; acquired illness in immunocompetent persons, most notably ocular disease; and encephalitis or disseminated disease in immunosuppressed persons. The association of T. gondii infection with risk for mental illness is intriguing and requires further research. Reduction of T. gondii in meat, improvements in hygiene and food preparation practices, and reduction of environmental contamination can prevent toxoplasmosis, but more research is needed on how to implement these measures. In addition, screening and treatment may help prevent toxoplasmosis or reduce the severity of disease in some settings.
Adaptive broadcast transmission in distributed two-way relaying networks

In this paper we consider adaptive, distributed two-way relaying networks using physical-layer network coding (PLNC). In the multiple-access (MA) phase, two sources transmit simultaneously to multiple relays. Depending on the decoding success at the relays, adaptive transmission schemes are investigated to avoid error propagation in the broadcast (BC) phase employing distributed orthogonal space-time block codes (D-OSTBCs). Recently, adaptive schemes have been proposed, where only relays with correct estimates of the network coded message participate in the BC transmission. In this work, we extend the analysis by incorporating also the case, that some relays are able to detect only one source message and propose a corresponding modified adaptive transmission scheme. For performance evaluations we resort to a semi-analytical method in order to examine the outage behavior of the presented schemes. As demonstrated by link-level simulations, the proposed adaptive scheme outperforms the traditional scheme significantly, especially for asymmetric network topology.
Toxicology rewrites its history and rethinks its future: Giving equal focus to both harmful and beneficial effects

This paper assesses how medicine adopted the threshold dose–response to evaluate health effects of drugs and chemicals throughout the 20th century to the present. Homeopathy first adopted the biphasic dose–response, making it an explanatory principle. Medicine used its influence to discredit the biphasic dose–response model to harm homeopathy and to promote its alternative, the threshold dose–response. However, it failed to validate the capacity of its model to make accurate predictions in the low‐dose zone. Recent attempts to validate the threshold dose–response indicate that it poorly predicts responses below the threshold. The long marginalized biphasic/hormetic dose–response model made accurate predictions in these validation studies. The failure to accept the possibility of the hormetic‐biphasic dose–response during toxicology's dose–response concept formative period, while adopting the threshold model, and later the linear no‐threshold model for carcinogens, led toxicology to adopt a hazard assessment process that involved testing only a few very high doses. This created the framework that toxicology was a discipline that only studied harmful responses, ignoring the possibility of benefit at low doses by the induction of adaptive mechanisms. Toxicology needs to assess the entire dose–response continuum, incorporating both harmful and beneficial effects into the risk assessment process. Environ. Toxicol. Chem. 2011;30:2658–2673. © 2011 SETAC
Learning block importance models for web pages

Previous work shows that a web page can be partitioned into multiple segments or blocks, and often the importance of those blocks in a page is not equivalent. Also, it has been proven that differentiating noisy or unimportant blocks from pages can facilitate web mining, search and accessibility. However, no uniform approach and model has been presented to measure the importance of different segments in web pages. Through a user study, we found that people do have a consistent view about the importance of blocks in web pages. In this paper, we investigate how to find a model to automatically assign importance values to blocks in a web page. We define the block importance estimation as a learning problem. First, we use a vision-based page segmentation algorithm to partition a web page into semantic blocks with a hierarchical structure. Then spatial features (such as position and size) and content features (such as the number of images and links) are extracted to construct a feature vector for each block. Based on these features, learning algorithms are used to train a model to assign importance to different segments in the web page. In our experiments, the best model can achieve the performance with Micro-F1 79% and Micro-Accuracy 85.9%, which is quite close to a person's view.
Compressing Deep Convolutional Networks using Vector Quantization

Deep convolutional neural networks (CNN) has become the most promising method for object recognition, repeatedly demonstrating record breaking results for image classification and object detection in recent years. However, a very deep CNN generally involves many layers with millions of parameters, making the storage of the network model to be extremely large. This prohibits the usage of deep CNNs on resource limited hardware, especially cell phones or other embedded devices. In this paper, we tackle this model storage issue by investigating information theoretical vector quantization methods for compressing the parameters of CNNs. In particular, we have found in terms of compressing the most storage demanding dense connected layers, vector quantization methods have a clear gain over existing matrix factorization methods. Simply applying k-means clustering to the weights or conducting product quantization can lead to a very good balance between model size and recognition accuracy. For the 1000-category classification task in the ImageNet challenge, we are able to achieve 16-24 times compression of the network with only 1% loss of classification accuracy using the state-of-the-art CNN.
Understanding the mechanisms of food intake and obesity

Rapid progress is being made in understanding the brain-processing and related psychology of the sensory propertiesof food, and how the satiety (fullness) signals producedduring and after eating regulate appetite. While brain mech-anisms control appetite, a number of sensory and environ-mental factors contribute to overstimulation of the sensorysystems, producing sensory reward signals that are strongerthan can always be controlled easily by satiety signals (1,2). This knowledge makes it seem very likely that, whilesatiety signals have not changed in the last 30 years,changes in the same period in the sensory side of the controlprocess mean that they are now being overridden, contrib-uting to the increasing incidence of obesity. One implica-tion of this work is that, if factors on the sensory side ofthe appetite control process could be regulated, it wouldopen avenues to address obesity prevention and control.From the neuroscience and psychology perspective, thereare, however, many other mechanisms that interact withthe brain and contribute to obesity.
Cluster-based similarity aggregation for ontology matching

Cluster-based similarity aggregation (CSA) is an automatic similarity aggregating system for ontology matching. The system have two main part. The first is calculation and combination of different similarity measures. The second is extracting alignment. The system first calculates five different basic measures to create five similarity matrixes, i.e, string-based similarity measure, WordNet-based similarity measure. Furthermore, it exploits the advantage of each measure through a weight estimation process. These similarity matrixes are combined into a final similarity matrix. After that, the pre-alignment is extracted from this matrix. Finally, to increase the accuracy of the system, the pruning process is applied.
Duration Abnormality Detection in Sequences of Human Activity

Activity duration is an essential element in the accurate modelling of human behaviour. The application of a standard hidden Markov Model (HMM) for the detection of abnormality in sequences of human activity can create a situation in which highly unusual duration less than or greater than the duration normally observed can fail to be detected. In this paper 1 , we show how the application of the explicit state duration HMM can alleviate this problem, enabling us to distinguish between sequences of activity in which the order of observations is identical but the duration of activities is different and to identify the presence of abnormal activity duration. Experimental results highlight the improvement over the standard HMM for both abnormality detection and classification in certain anomalous situations.
Automatic Processing of Foreign Language Documents

Experiments conducted over the last few years with the SMART document retrieval system have shown that fully automatic text processing methods using relatively simple English language analysis tools are as effective for document indexing, classification, search, and retrieval as the more elaborate manual methods normally used. The present study describes an extension of the SMART procedures to German language materials. A multilingual thesaurus is used for the analysis of documents and search requests, and tools are provided which make it possible to process English documents against German queries, and vice versa. The methods are evaluated and it is shown that the effectiveness of the mixed language processing is approximately equivalent to that of the standard process operating within a single language only.
Arctigenin Treatment Protects against Brain Damage through an Anti-Inflammatory and Anti-Apoptotic Mechanism after Needle Insertion

Convection enhanced delivery (CED) infuses drugs directly into brain tissue. Needle insertion is required and results in a stab wound injury (SWI). Subsequent secondary injury involves the release of inflammatory and apoptotic cytokines, which have dramatic consequences on the integrity of damaged tissue, leading to the evolution of a pericontusional-damaged area minutes to days after in the initial injury. The present study investigated the capacity for arctigenin (ARC) to prevent secondary brain injury and the determination of the underlying mechanism of action in a mouse model of SWI that mimics the process of CED. After CED, mice received a gavage of ARC from 30 min to 14 days. Neurological severity scores (NSS) and wound closure degree were assessed after the injury. Histological analysis and immunocytochemistry were used to evaluated the extent of brain damage and neuroinflammation. Terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) was used to detect universal apoptosis. Enzyme-linked immunosorbent assays (ELISA) was used to test the inflammatory cytokines (tumor necrosis factor (TNF)-α, interleukin (IL)-6 and IL-10) and lactate dehydrogenase (LDH) content. Gene levels of inflammation (TNF-α, IL-6, and IL-10) and apoptosis (Caspase-3, Bax and Bcl-2) were detected by reverse transcription-polymerase chain reaction (RT-PCR). Using these, we analyzed ARC’s efficacy and mechanism of action. Results: ARC treatment improved neurological function by reducing brain water content and hematoma and accelerating wound closure relative to untreated mice. ARC treatment reduced the levels of TNF-α and IL-6 and the number of allograft inflammatory factor (IBA)- and myeloperoxidase (MPO)-positive cells and increased the levels of IL-10. ARC-treated mice had fewer TUNEL+ apoptotic neurons and activated caspase-3-positive neurons surrounding the lesion than controls, indicating increased neuronal survival. Conclusions: ARC treatment confers neuroprotection of brain tissue through anti-inflammatory and anti-apoptotic effects in a mouse model of SWI. These results suggest a new strategy for promoting neuronal survival and function after CED to improve long-term patient outcome.
Nonresponse in Time: A Time Series Analysis of the Finnish Labour Force Survey

The relation between the response mechanism in the Finnish Labour Force Survey (LFS) and the indicators for both social and political changes as well as organisational aspects are examined in this study using time series models. I show that besides the changes in the social climate, organisational decisions can have substantial effects on survey participation. The findings are discussed and the implications are shown to be organisation specific.
Can LDN data provide corroborating evidence for a Lightning event at a specific geographic location?

In many cases of lightning attachment, the exact timing of the event is unknown (property damage, loss of life etc.) making comparison with LDN data difficult. A methodology for matching data from a Lightning Detection Network (LDN) with to a possible location of attachment using only the reported positions of strokes is proposed. A case study of lightning attachment to a tall structure (Brixton Tower, Johannesburg, South Africa) is used to evaluate of the methodology. Photographs of the lightning attachments are time-correlated with data from the South African Lightning Detection Network (SALDN) and these results are compared with the results of the methodology. It is found that the methodology returns 40% false positive results and further evidence is needed to distinguish the time-correlated matches from the false positive results. The median confidence ellipses provided for each stroke are also investigated but it appears that this does not provide the evidence needed to do this. It is concluded that LDN data can provide corroborating evidence of lightning attachment to a location but cannot be used in isolation.
A Molecular Phylogenomic Analysis of the ILR1-Like Family of IAA Amidohydrolase Genes

The ILR1-like family of hydrolase genes was initially isolated in Arabidopsis thaliana and is thought to help regulate levels of free indole-3-acetic-acid.We have investigated how this family has evolved in dicotyledon, monocotyledon and gymnosperm species by employing the GenBank and TIGR databases to retrieve orthologous genes. The relationships among these sequences were assessed employing phylogenomic analyses to examine molecular evolution and phylogeny. The members of the ILR1-like family analysed were ILL1, ILL2, ILL3, ILL6, ILR1 and IAR3. Present evidence suggests that IAR3 has undergone the least evolution and is most conserved. This conclusion is based on IAR3 having the largest number of total interspecific orthologues, orthologous species and unique orthologues. Although less conserved than IAR3, DNA and protein sequence analyses of ILL1 and ILR1 suggest high conservation. Based on this conservation, IAR3, ILL1 and ILR1 may have had major roles in the physiological evolution of ‘higher’ plants. ILL3 is least conserved, with the fewest orthologous species and orthologues. The monocotyledonous orthologues for most family-members examined have evolved into two separate molecular clades from dicotyledons, indicating active evolutionary change. The monocotyledon clades are: (a) those possessing a putative endoplasmic reticulum localizing signal; and (b) those that are putative cytoplasmic hydrolases. IAR3, ILL1 and ILL6 are all highly orthologous to a gene in the gymnosperm Pinus taeda, indicating an ancient enzymatic activity. No orthologues could be detected in Chlamydomonas, moss and fern databases.
Flow cytometric method for quantifying viable Mycoplasma agassizii, an agent of upper respiratory tract disease in the desert tortoise (Gopherus agassizii)

Aims: Mycoplasma agassizii can cause upper respiratory tract disease in the threatened desert tortoise of the Southwestern United States. Two technical challenges have impeded critical microbiological studies of this microorganism: (i) its small size limits the use of light microscopy for cell counting and (ii) its extremely slow growth in broth and agar cultures impedes colony counting. Our aim was to develop a rapid and sensitive flow cytometric method using a vital fluorescent dye to enumerate viable M. agassizii cells.
Model Selection in Ultrasonic Measurements on Trabecular Bone

Previous work from our laboratory showed that the widely reported decrease in phase velocity with frequency (negative dispersion) for ultrasonic waves propagating through trabecular bone can arise from the interference of two compressional waves, each of which exhibits a positive dispersion. Previous simulations suggest that Bayesian probability theory can be employed to recover the material properties linked to these two interfering waves, even when the waves overlap sufficiently that visual inspection cannot distinguish two modes. In the present study, Bayesian probability theory is applied first to simulated data and then to representative experimental bone data to determine whether one or two compressional wave modes are present. Model selection is implemented by evaluating the posterior probability for each model. The calculation is implemented by defining a model indicator and then using Markov chain Monte Carlo with simulated annealing to draw samples from the joint posterior probability for the ul.
A Survey On : Distributed Monitoring for Scalable Hosting Infrastructures

Large scale distributed system has become the fundamental platforms for many real-world production systems. However, Automatic and continuous management of such distributed computing infrastructures is a challenging task since solution has to achieve both scalability and high precision while monitoring a large number of intra-node and inter-node attributes (e.g., CPU usage, free memory, free disk, inter-node network delay). In this paper we focus on specific domain that is fine-grained, scalable, failure resilient distributed system monitoring framework based on video coding techniques. This approach models the distributed system metrics as an image and uses both intra-image and inter-image encoding to compress monitoring traffic. In this article, we highlight the importance of fine grain monitoring and scalability challenge of fine-grained distributed system monitoring . We identify the state-of-the-art of scalability issues related to fine grained monitoring and discuss the various solutions proposed that are Centralized, Decentralize Monitoring, and Compression and correlation Based Approach.