Miracle-dz/UniFAR-FTUs huggingface dataset:
The Antitumor Efficacy of IL2/IL21-Cultured Polyfunctional Neu-Specific T Cells Is TNFα/IL17 Dependent

Purpose: Infusion of HER2-specific T cells, derived from vaccine-primed patients and expanded with IL2/IL12, has induced tumor regression in a minority of patients with metastatic treatment-refractory HER2+ breast cancer. We questioned whether alteration of cytokine growth factors used to culture vaccine–primed T cells could improve antitumor activity. Experimental Design: Using the TgMMTV-neu murine mammary tumor model, we cultured T cells derived from mice immunized with a previously defined neu class II peptide, p98-114 (neu p98), and evaluated different cytokine combinations for expansion. Results: Infusion of neu p98-specific T-cell lines derived from all cytokine conditions evaluated resulted in significant antitumor activity compared with infused naïve splenocytes (P < 0.05). T cells cultured with IL2/IL21 could uniquely mediate complete regression of spontaneous mammary tumors. IL2/IL21 cultured neu-specific T cells demonstrated a different cytokine secretion pattern as compared with other cultured T cells; secreting high levels of TNFα and IL17 (P < 0.05). Moreover, tumor-infiltrating CD8+ cells were significantly increased after the infusion of IL2/IL21 cultured T cells as compared with tumors treated with T cells expanded under other cytokine conditions (P < 0.001). The antitumor effect of the infusion of IL2/IL21 cultured cells was mediated by CD8 T cells. Depletion of TNFα or IL17, but not IFNγ, abrogated the tumor growth inhibition induced by the IL2/IL21 T cells and markedly decreased the influx of CD8 into tumors. Finally, IL2/IL21-cultured human antigen specific T cells also displayed a similar polyfunctional Th1/Th17 phenotype. Conclusions: Expansion of HER2 vaccine–primed T cells with IL2/IL21 may have the potential to effectively mediate tumor regression when used in adoptive transfer. Clin Cancer Res; 22(9); 2207–16. ©2015 AACR.
Credit Default Swap Spreads and Systemic Financial Risk

This paper presents a novel method to measure the joint default risk of large financial institutions (systemic default risk) using information in bond and credit default swap (CDS) prices. Bond prices reflect individual default probabilities of the issuers. CDS contracts, which insure against such defaults, pay o only as long as the seller of protection itself is solvent. Therefore, CDS prices contain information about the probability of joint default of both the bond issuer and the protection seller. If we consider the entire set of CDS contracts written by each financial institution against the default of each other institution we can learn about all pairwise default probabilities across the financial network. This information, however, is not sucient to completely characterize the joint distribution function of defaults of these banks. In this paper, I show how this information can be optimally aggregated to construct bounds on the probability of systemic default events. This method enables me to measure systemic default risk without making any assumptions about the joint distribution function. Two main results emerge from the empirical application of this method to the recent financial crisis. First, I show that an increase in systemic risk in large global banks did not occur until after Bear Stearns’ collapse in March 2008. Second, some of the large observed spikes in CDS spreads and bond yield spreads during this period (for example, following Lehman Brothers’ default) correspond to spikes in idiosyncratic default risk rather than systemic risk.
On stability of fixed points of limit models of univariate marginal distribution algorithm and factorized distribution algorithm

Aims to study the advantages of using higher order statistics in estimation distribution of algorithms (EDAs). We study two EDAs with two-tournament selection for discrete optimization problems. One is the univariate marginal distribution algorithm (UMDA) using only first-order statistics and the other is the factorized distribution algorithm (FDA) using higher order statistics. We introduce the heuristic functions and the limit models of these two algorithms and analyze stability of these limit models. It is shown that the limit model of UMDA can be trapped at any local optimal solution for some initial probability models. However, degenerate probability density functions (pdfs) at some local optimal solutions are unstable in the limit model of FDA. In particular, the degenerate pdf at the global optimal solution is the unique asymptotically stable point in the limit model of FDA for the optimization of an additively decomposable function. Our results suggest that using higher order statistics could improve the chance of finding the global optimal solution.
NaV1.5 Na+ channels allosterically regulate the NHE-1 exchanger and promote the activity of breast cancer cell invadopodia

Summary The degradation of the extracellular matrix by cancer cells represents an essential step in metastatic progression and this is performed by cancer cell structures called invadopodia. NaV1.5 (also known as SCN5A) Na+ channels are overexpressed in breast cancer tumours and are associated with metastatic occurrence. It has been previously shown that NaV1.5 activity enhances breast cancer cell invasiveness through perimembrane acidification and subsequent degradation of the extracellular matrix by cysteine cathepsins. Here, we show that NaV1.5 colocalises with Na+/H+ exchanger type 1 (NHE-1) and caveolin-1 at the sites of matrix remodelling in invadopodia of MDA-MB-231 breast cancer cells. NHE-1, NaV1.5 and caveolin-1 co-immunoprecipitated, which indicates a close association between these proteins. We found that the expression of NaV1.5 was responsible for the allosteric modulation of NHE-1, rendering it more active at the intracellular pH range of 6.4–7; thus, it potentially extrudes more protons into the extracellular space. Furthermore, NaV1.5 expression increased Src kinase activity and the phosphorylation (Y421) of the actin-nucleation-promoting factor cortactin, modified F-actin polymerisation and promoted the acquisition of an invasive morphology in these cells. Taken together, our study suggests that NaV1.5 is a central regulator of invadopodia formation and activity in breast cancer cells.
Components of therapy as mechanisms of change in cognitive therapy for people at risk of psychosis: Analysis of the EDIE-2 trial

Background Research suggests that the way in which cognitive therapy is delivered is an important factor in determining outcomes. We test the hypotheses in which the development of a shared problem list, use of case formulation, homework tasks and active intervention strategies will act as process variables. Method Presence of these components during therapy is taken from therapist notes. The direct and indirect effect of the intervention is estimated by an instrumental variable analysis. Results A significant decrease in the symptom score for case formulation (coefficient =–23, 95% CI –44 to –1.7, P = 0.036) and homework (coefficient =–0.26, 95% CI –0.51 to –0.001, P = 0.049) is found. Improvement with the inclusion of active change strategies is of borderline significance (coefficient =–0.23, 95% CI –0.47 to 0.005, P = 0.056). Conclusions There is a greater treatment effect if formulation and homework are involved in therapy. However, high correlation between components means that these may be indicators of overall treatment fidelity.
Deep Computation Model for Unsupervised Feature Learning on Big Data

Deep learning has been successfully applied to feature learning in speech recognition, image classification and language processing. However, current deep learning models work in the vector space, resulting in the failure to learn features for big data since a vector cannot model the highly non-linear distribution of big data, especially heterogeneous data. This paper proposes a deep computation model for feature learning on big data, which uses a tensor to model the complex correlations of heterogeneous data. To fully learn the underlying data distribution, the proposed model uses the tensor distance as the average sum-of-squares error term of the reconstruction error in the output layer. To train the parameters of the proposed model, the paper designs a high-order back-propagation algorithm (HBP) by extending the conventional back-propagation algorithm from the vector space to the high-order tensor space. To evaluate the performance of the proposed model, we carried out the experiments on four representative datasets by comparison with stacking auto-encoders and multimodal deep learning models. Experimental results clearly demonstrate that the proposed model is efficient to perform feature learning when evaluated using the STL-10, CUAVE, SANE and INEX datasets.
Cluster assignment for high-performance embedded VLIW processors

Clustering is an effective method to increase the available parallelism in VLIW datapaths without incurring severe penalties associated with a large number of register file ports. Efficient utilization of a clustered datapath requires careful binding/assignment of operations to clusters. The article proposes a binding algorithm that effectively explores trade-offs between in-cluster operation serialization and delays associated with data transfers between clusters. Extensive experimental evidence is provided showing that the algorithm generates high quality solutions for representative kernels, with up to 33% improvement over a state-of-the-art binding algorithm.
Characterization of Guinier-Preston Zones by High-Angle Annular Detector Dark-Field Scanning Transmission Electron Microscopy

Age-hardened alloys are everywhere: in aeroplanes, aluminum sashes, electrical connections for computer devices, and so on. The materials that have made possible the very existence of these human inventions are manufactured based on a single materials-science principle known as ageing. Here, supersaturated impurity atoms that dissolved in an otherwise pure metal gather around inside the metal as time goes by, and precipitates, sometimes in the form of a spherical cluster, sometimes in the form of a disc aligned in parallel to a crystallographic plane of the metal matrix. These crystallographically coherent precipitations are collectively known as Guinier-Preston (GP) zones after the pioneering scientists who revealed the structure of the zones in the Al-Cu system by X-ray diffraction technique [1, 2]. Since these precipitate phases are highly coherent with the matrix, it is very difficult to identify their structure unambiguously even with high-resolution transmission electron microscopy (HRTEM). For example, in the Al-Cu system, the Cu atoms precipitate in parallel to the {100} planes of the Al matrix, and thus the phase contrast is highly sensitive to a number of experimental conditions, including the thickness of the specimen and defocus of the objective lens. This can be evidenced by a number of simulation works that have been reported so far in order to elucidate the structure [3-7]. High-angle annular detector darkfield scanning transmission electron microscopy (HAADF-STEM), on the other Toyohiko J. Konno , Masahiro Kawasaki and Kenji Hiraga
1 EVA : Extraction , Visualization and Analysis of the Telecommunications and Media Ownership Network

We present EVA, a prototype system for extracting, visualizing, and analyzing corporate ownership information as a social network. Using probabilistic information retrieval and extraction techniques, we automatically extract ownership relationships from heterogeneous sources of online text, including corporate annual reports (10-Ks) filed with the U.S. Securities and Exchange Commission (SEC). A browser-based visualization interface allows users to query the relationship database and explore large networks of companies. Applying the system and methodology to the telecommunications and media industries, we construct an ownership network with 6,726 relationships among 8,343
Detecting Fraud in Financial Payments

Recent studies have shown the potential of automatised fraud detection techniques [1]. Therefore, we employed machine learning methods in order to develop and train a model for real-time detection of fraudulent transactions so that these may be acted upon instantaneously. Using a dataset containing various features, including temporal, financial, and geographical attributes of several hundred thousand transactions provided by Adyen, a global payments firm, we train a single-layer neural network using fraud oversampling and a focal loss-function. The latter penalizes uncertain predictions in order to accommodate the dataset’s relative sparsity of fraudulent transactions. Despite this sparsity, we manage to achieve an overall test accuracy of 83% and a fraud detection test accuracy of 88%. *Head of Growth at Adyen
Journal of the Experimental Analysis of Behavior Human Obser Ving: Maintained by Nega Tive Informative Stimuli Only If Correlated with Improvement in Response Efficiency

Two experiments investigated the effect of observing responses that enabled college students to emit more efficient distributions of reinforced responses. In Experiment 1, the gains of response efficiency enabled by observing were minimized through use of identical low-effort response requirements in two alternating variable-interval schedules. These comprised a mixed schedule of reinforcement; they differed in the number of money-backed points per reinforcer. In each of three choices between two stimuli that varied in their correlation with the variable-interval schedules, the results showed that subjects preferred stimuli that were correlated with the larger average amount of reinforcement. This is consistent with a conditioned-reinforcement hypothesis. Negative informative stimuli-that is, stimuli correlated with the smaller of two rewards-did not maintain as much observing as stimuli that were uncorrelated with amount of reward. In Experiment 2, savings in effort made possible by producing S-were varied within subjects by alternately removing and reinstating the response-reinforcement contingency in a mixed variable-interval/extinction schedule of reinforcement. Preference for an uncorrelated stimulus compared to a negative informative stimulus (S-) decreased for each of six subjects , and usually reversed when observing permitted a more efficient temporal distribution of the responses required for reinforcement; in this case, the responses were pulls on a relatively high-effort plunger. When observing the S-could not improve response efficiency , subjects again chose the control stimulus. All of these results were inconsistent with the uncertainty-reduction hypothesis. Observing responses are those that may produce stimuli correlated with the components of a mixed schedule of reinforcement. In a mixed schedule of reinforcement, the schedules of reinforcement that operate independently in each component all share the same stimulus. Other than identifying the component currently operative, by effectively converting the mixed to a multiple schedule, observing responses have no effect on primary reinforcement (Wyckoff, 1952). A large number of experiments investigating why the contingent stimuli reinforce observing have produced results consistent This hypothesis proposes that the stimuli are reinforcing because of their positive correlation with primary reinforcement. Although extensive support has been found in several studies
Combining energy and power based safety metrics in controller design for domestic robots

This paper presents a general passivity based interaction controller design approach that utilizes a combined energy and power based safety norms to assert safety of domestic robots. Since these robots are expected to co-habit the same environment with a human user, analysing and ensuring their safety is an important requirement. Safety analysis of domestic robots determine whether a robot achieves a desired safety level according to some quantitative safety metrics. When it comes to controller design for human friendly robots, it often involves introducing compliance and ensuring asymptotic stability using impedance control technique and passivity theories. The controller proposed in this work also uses a passive design that extends the standard impedance control scheme with energy and power based safety metrics to ensure that safety requirements defined in these norms are achieved by domestic robots. The effectiveness of the proposed guideline is illustrated with simulation and experimental results.
CCAAT/Enhancer Binding Protein (cid:1) , But Not Steroidogenic Factor-1, Modulates the Phthalate-Induced Dysregulation of Rat Fetal Testicular Steroidogenesis

Prolonged in utero exposure of fetal male rats to dibutyl phthalate (DBP) can result in a feminized phenotype characterized by malformed epididymides, hypospadias, cryp- torchidism, and retained thoracic nipples, among others. These symptoms likely result, in part, from decreased expres- sion of steroidogenic enzymes and, therefore, reduced testosterone biosynthesis. However, the molecular mechanisms in- volved in these changes in gene expression profiles are unknown. To understand these mechanisms in rats, in vivo DNase footprinting was adapted to provide a semiquantita-tive map of changes in DNA-protein interactions in the pro- moter region of steroidogenic genes, including steroidogenic acute regulatory, scavenger receptor B-1, cytochrome P450 side chain cleavage, and cytochrome P450 17A1, that are down-regulated after an in utero DBP exposure. Regions with altered DNase protection were coordinated with a specific DNA binding protein event by EMSA, and binding activity confirmed with chromatin immunoprecipitation. Results demonstratedalteredDNaseprotectionatregionsmappingtoCCAAT/enhancerbindingprotein (cid:1) (c/ebp (cid:1) ) and steroidogenic factor-1 (SF-1). Chromatin immunoprecipitation con- firmed declines in DNA-protein interactions of c/ebp (cid:1) in DBP treated animals, whereas SF-1 was reduced in both diethyl phthalate (nontoxic) and DBP (toxic) treatments. These results suggest that inhibition of c/ebp (cid:1) , and not SF-1, is critical in DBP induced inhibition of steroidogenic genes. In addition, these observations suggest a pathway redundancy in the reg- ulation of steroidogenesis in fetal testis. In conclusion, this study presents a snapshot of changes in the structure of tran- scriptional machinery and proposes a mechanism of action resulting from DBP exposure. ( Endocrinology 148: 5851–5864, 2007)
Methotrexate Is a JAK/STAT Pathway Inhibitor

Background The JAK/STAT pathway transduces signals from multiple cytokines and controls haematopoiesis, immunity and inflammation. In addition, pathological activation is seen in multiple malignancies including the myeloproliferative neoplasms (MPNs). Given this, drug development efforts have targeted the pathway with JAK inhibitors such as ruxolitinib. Although effective, high costs and side effects have limited its adoption. Thus, a need for effective low cost treatments remains. Methods & Findings We used the low-complexity Drosophila melanogaster pathway to screen for small molecules that modulate JAK/STAT signalling. This screen identified methotrexate and the closely related aminopterin as potent suppressors of STAT activation. We show that methotrexate suppresses human JAK/STAT signalling without affecting other phosphorylation-dependent pathways. Furthermore, methotrexate significantly reduces STAT5 phosphorylation in cells expressing JAK2 V617F, a mutation associated with most human MPNs. Methotrexate acts independently of dihydrofolate reductase (DHFR) and is comparable to the JAK1/2 inhibitor ruxolitinib. However, cells treated with methotrexate still retain their ability to respond to physiological levels of the ligand erythropoietin. Conclusions Aminopterin and methotrexate represent the first chemotherapy agents developed and act as competitive inhibitors of DHFR. Methotrexate is also widely used at low doses to treat inflammatory and immune-mediated conditions including rheumatoid arthritis. In this low-dose regime, folate supplements are given to mitigate side effects by bypassing the biochemical requirement for DHFR. Although independent of DHFR, the mechanism-of-action underlying the low-dose effects of methotrexate is unknown. Given that multiple pro-inflammatory cytokines signal through the pathway, we suggest that suppression of the JAK/STAT pathway is likely to be the principal anti-inflammatory and immunosuppressive mechanism-of-action of low-dose methotrexate. In addition, we suggest that patients with JAK/STAT-associated haematological malignancies may benefit from low-dose methotrexate treatments. While the JAK1/2 inhibitor ruxolitinib is effective, a £43,200 annual cost precludes widespread adoption. With an annual methotrexate cost of around £32, our findings represent an important development with significant future potential.
Genome Scale Prediction of Two-Component Signal Transducers from the Knowledgeof Regulatory Interactions

Predicting gene functions from the whole genome sequence is an important problem in a postgenome era. We are developing a function predicting system from the whole genome sequence utilizing the functionally well annotated genome as a reference organism for the knowledge of biologically well known pathways. The databases of gene catalogs and pathways are compiled under the KEGG project. In this paper we show an instance for identifying functions of genes involved in the two-component signal transduction system.
Transmembrane helices predicted at 95% accuracy

We describe a neural network system that predicts the locations of transmembrane helices in integral membrane proteins. By using evolutionary information as input to the network system, the method significantly improved on a previously published neural network prediction method that had been based on single sequence information. The input data were derived from multiple alignments for each position in a window of 13 adjacent residues: amino acid frequency, conservation weights, number of insertions and deletions, and position of the window with respect to the ends of the protein chain. Additional input was the amino acid composition and length of the whole protein. A rigorous cross‐validation test on 69 proteins with experimentally determined locations of transmembrane segments yielded an overall two‐state per‐residue accuracy of 95%. About 94% of all segments were predicted correctly. When applied to known globular proteins as a negative control, the network system incorrectly predicted fewer than 5% of globular proteins as having transmembrane helices. The method was applied to all 269 open reading frames from the complete yeast VIII chromosome. For 59 of these, at least two transmembrane helices were predicted. Thus, the prediction is that about one‐fourth of all proteins from yeast VIII contain one transmembrane helix, and some 20%, more than one.
Volume Rendering with Advanced GPU Scheduling Strategies

Modern GPUs are powerful enough to enable interactive display of high-quality volume data even despite the fact that many volume rendering methods do not present a natural fit for current GPU hardware. However, there still is a vast amount of computational power that remains unused due to the inefficient use of the available hardware. In this work, we demonstrate how advanced scheduling methods can be employed to implement volume rendering algorithms in a way that better utilizes the GPU by example of three different state-of-the-art volume rendering techniques.