KSTAR: An algorithm to predict patient-specific kinase activities from phosphoproteomic data

Kinase inhibitors as targeted therapies have played an important role in improving cancer outcomes. However, there are still considerable challenges, such as resistance, non-response, patient stratification, polypharmacology, and identifying combination therapy where understanding a tumor kinase activity profile could be transformative. Here, we develop a graph- and statistics-based algorithm, called KSTAR, to convert phosphoproteomic measurements of cells and tissues into a kinase activity score that is generalizable and useful for clinical pipelines, requiring no quantification of the phosphorylation sites. In this work, we demonstrate that KSTAR reliably captures expected kinase activity differences across different tissues and stimulation contexts, allows for the direct comparison of samples from independent experiments, and is robust across a wide range of dataset sizes. Finally, we apply KSTAR to clinical breast cancer phosphoproteomic data and find that there is potential for kinase activity inference from KSTAR to complement the current clinical diagnosis of HER2 status in breast cancer patients

Modeling Multimodal Cues in a Deep Learning-based Framework for Emotion Recognition in the Wild

In this paper, we propose a multimodal deep learning architecture for emotion recognition in video regarding our participation to the audio-video based sub-challenge of the Emotion Recognition in the Wild 2017 challenge. Our model combines cues from multiple video modalities, including static facial features, motion patterns related to the evolution of the human expression over time, and audio information. Specifically, it is composed of three sub-networks trained separately: the first and second ones extract static visual features and dynamic patterns through 2D and 3D Convolutional Neural Networks (CNN), while the third one consists in a pretrained audio network which is used to extract useful deep acoustic signals from video. In the audio branch, we also apply Long Short Term Memory (LSTM) networks in order to capture the temporal evolution of the audio features. To identify and exploit possible relationships among different modalities, we propose a fusion network that merges cues from the different modalities in one representation. The proposed architecture outperforms the challenge baselines (38.81% and 40.47%): we achieve an accuracy of 50.39% and 49.92% respectively on the validation and the testing data

Normative Minor Childhood Stress and Risk of Later Adult Psychopathology in Saudi Arabia

Chronic minor childhood stress in the form of corporal punishment predicts adult psychopathology in the United States but has not been demonstrated in a country where corporal punishment is normative. We tested whether adult psychopathology was predicted by recalled frequency of childhood corporal punishment and recalled controllability of punishment in Saudi Arabia. Two hundred and fifty nine Saudi men with substance addictions (who for cultural reasons were at risk for depression) completed a survey measuring: demographic variables, frequency of beating and controllability of punishment as a child, depression and borderline personality disorder symptoms (BPD). Beating frequency and punishment control were uncorrelated and unrelated to patients’ or parents’ education. 92 men (36%) had major depression (PHQ-9 ≥15). Compared to those never beaten, those experiencing infrequent beating (once or twice a year) were significantly more likely to have major depression and higher BPD symptoms, after controlling for demographic variables. Those experiencing frequent beating (monthly or more frequent) were more likely to have major depression and higher BDP symptoms compared to those never beaten, but only when perceived control was low. Perceived punishment control was not significantly related to outcome for those who never had or had infrequent corporal punishment. These results provide evidence in a culture where corporal punishment is normative that corporal punishment, even when infrequent, predisposes to adult psychopathology and that uncontrollability increases the pathogenic effect of frequent corporal punishment. These results support the hypothesis that frequent minor stressors in childhood act as kindling factor for later depression

Fermionic coherent states for pseudo-Hermitian two-level systems

We introduce creation and annihilation operators of pseudo-Hermitian fermions for two-level systems described by pseudo-Hermitian Hamiltonian with real eigenvalues. This allows the generalization of the fermionic coherent states approach to such systems. Pseudo-fermionic coherent states are constructed as eigenstates of two pseudo-fermion annihilation operators. These coherent states form a bi-normal and bi-overcomplete system, and their evolution governed by the pseudo-Hermitian Hamiltonian is temporally stable. In terms of the introduced pseudo-fermion operators the two-level system' Hamiltonian takes a factorized form similar to that of a harmonic oscillator.Comment: 13 pages (Latex, article class), no figures; v2: some amendments in section 2, seven new refs adde

A genetic algorithm for shortest path with real constraints in computer networks

The shortest path problem has many different versions. In this manuscript, we proposed a muti-constrained optimization method to find the shortest path in a computer network. In general, a genetic algorithm is one of the common heuristic algorithms. In this paper, we employed the genetic algorithm to find the solution of the shortest path multi-constrained problem. The proposed algorithm finds the best route for network packets with minimum total cost, delay, and hop count constrained with limited bandwidth. The new algorithm was implemented on four different capacity networks with random network parameters, the results showed that the shortest path under constraints can be found in a reasonable time. The experimental results showed that the algorithm always found the shortest path with minimal constraints

Lepton Polarization Asymmetry in B l l(bar) decays in R-parity violating Minimal Supersymmetric Standard Model

We study the implication of R-parity violating Rp Minimal Supersymmetric Standard Model (MSSM) model in lepton polarization asymmetry ALP in B l l(bar) decays . The analysis show that the ALP is significant in a certain phenomenological parametric region of Yukawa couplings. We have also placed indirect bounds on Lambda' lambda couplings as obtained from B t t(bar).Comment: 6 pages, 4 figures Changes of notation in Eq(8-11,17-19),Eq.20 adde