Miracle-dz/UniFAR-FTUs huggingface dataset:
Phosphoinositide 3-Kinase (PI3K) Pathway Alterations Are Associated with Histologic Subtypes and Are Predictive of Sensitivity to PI3K Inhibitors in Lung Cancer Preclinical Models

Purpose: Class 1 phosphatidylinositol 3-kinase (PI3K) plays a major role in cell proliferation and survival in a wide variety of human cancers. Here, we investigated biomarker strategies for PI3K pathway inhibitors in non–small-cell lung cancer (NSCLC). Experimental Design: Molecular profiling for candidate PI3K predictive biomarkers was conducted on a collection of NSCLC tumor samples. Assays included comparative genomic hybridization, reverse-transcription polymerase chain reaction gene expression, mutation detection for PIK3CA and other oncogenes, PTEN immunohistochemistry, and FISH for PIK3CA copy number. In addition, a panel of NSCLC cell lines characterized for alterations in the PI3K pathway was screened with PI3K and dual PI3K/mTOR inhibitors to assess the preclinical predictive value of candidate biomarkers. Results: PIK3CA amplification was detected in 37% of squamous tumors and 5% of adenocarcinomas, whereas PIK3CA mutations were found in 9% of squamous and 0% of adenocarcinomas. Total loss of PTEN immunostaining was found in 21% of squamous tumors and 4% of adenocarcinomas. Cell lines harboring pathway alterations (receptor tyrosine kinase activation, PI3K mutation or amplification, and PTEN loss) were exquisitely sensitive to the PI3K inhibitor GDC-0941. A dual PI3K/mTOR inhibitor had broader activity across the cell line panel and in tumor xenografts. The combination of GDC-0941 with paclitaxel, erlotinib, or a mitogen-activated protein–extracellular signal-regulated kinase inhibitor had greater effects on cell viability than PI3K inhibition alone. Conclusions: Candidate biomarkers for PI3K inhibitors have predictive value in preclinical models and show histology-specific alterations in primary tumors, suggesting that distinct biomarker strategies may be required in squamous compared with nonsquamous NSCLC patient populations. Clin Cancer Res; 18(24); 6771–83. ©2012 AACR.
Cellulose Synthase (CesA) Genes in the Green Alga Mesotaenium caldariorum

ABSTRACT Cellulose, a microfibrillar polysaccharide consisting of bundles of β-1,4-glucan chains, is a major component of plant and most algal cell walls and is also synthesized by some prokaryotes. Seed plants and bacteria differ in the structures of their membrane terminal complexes that make cellulose and, in turn, control the dimensions of the microfibrils produced. They also differ in the domain structures of their CesA gene products (the catalytic subunit of cellulose synthase), which have been localized to terminal complexes and appear to help maintain terminal complex structure. Terminal complex structures in algae range from rosettes (plant-like) to linear forms (bacterium-like). Thus, algal CesA genes may reveal domains that control terminal complex assembly and microfibril structure. The CesA genes from the alga Mesotaenium caldariorum, a member of the order Zygnematales, which have rosette terminal complexes, are remarkably similar to seed plant CesAs, with deduced amino acid sequence identities of up to 59%. In addition to the putative transmembrane helices and the D-D-D-QXXRW motif shared by all known CesA gene products, M. caldariorum and seed plant CesAs share a region conserved among plants, an N-terminal zinc-binding domain, and a variable or class-specific region. This indicates that the domains that characterize seed plant CesAs arose prior to the evolution of land plants and may play a role in maintaining the structures of rosette terminal complexes. The CesA genes identified in M. caldariorum are the first reported for any eukaryotic alga and will provide a basis for analyzing the CesA genes of algae with different types of terminal complexes.
Nerve lesions associated with limb-lengthening.

BACKGROUND Nerve injury is one of the most serious complications associated with limb-lengthening. We examined the risk, assessment, and treatment of nerve lesions associated with limb-lengthening. METHODS We retrospectively studied the records on 814 limb-lengthening procedures. Nerve lesions were defined by clinical signs and symptoms of motor function impairment, sensory alterations, referred pain in the distribution of an affected nerve, and/or positive results of quantitative sensory testing with use of a pressure specified sensory device. RESULTS Seventy-six (9.3%) of the limbs had a nerve lesion. Eighty-four percent of the nerve lesions occurred during gradual distraction, and 16% occurred immediately following surgery. The pressure specified sensory device showed 100% sensitivity and 86% specificity in the detection of nerve injuries. The patients in whom the lesion was diagnosed with this method, or with this method as well as with nerve conduction studies, had significantly faster recovery than did those diagnosed on the basis of clinical symptoms or nerve conduction studies alone (p = 0.02). Patients undergoing double-level tibial lengthening and those with skeletal dysplasia were at higher risk for nerve lesions (77% and 48%, respectively). Nerve decompression was performed in fifty-three cases (70%). The time between the diagnosis and the surgical decompression was strongly associated with the time to recovery (p = 0.0005). Complete clinical recovery was achieved in seventy-four of the seventy-six cases. CONCLUSIONS Early detection based on signs and symptoms or testing with a pressure specified sensory device improves the prognosis for nerve injury that occurs during limb-lengthening. Of the methods that we used to identify neurologic compromise, testing with the pressure specified sensory device was the most sensitive. Aggressive early treatment (slowing the rate of lengthening and/or performing decompression) allows continued lengthening without incurring permanent nerve injury. When indicated, decompression of the affected nerve should be performed as soon as possible, thereby improving the chances of and shortening the time to complete recovery.
The relationship between anti-Mullerian hormone, androgen and insulin resistance on the number of antral follicles in women with polycystic ovary syndrome.

BACKGROUND Anti-Müllerian hormone (AMH) is a biomarker that predicts the number of antral follicles and is involved in follicle arrest for women with polycystic ovary syndrome (PCOS). We investigated the association between the characteristic hyperandrogenemia, insulin resistance (IR), AMH, and the morphology and size of ovaries for women with PCOS. METHODS A total of 99 Taiwanese women with PCOS who were willing to undergo vaginal ultrasonography were enrolled in this cross-sectional study. RESULTS The number of antral follicles and the ovarian volume showed a significant correlation with AMH, total testosterone and the free androgen index, but not with age, body mass index (BMI) or the homeostasis model assessment of insulin resistance (HOMA-IR). AMH had a significant negative association with both BMI and HOMA-IR. Multiple stepwise regression analysis demonstrated that AMH, BMI and total testosterone were independently related to the number of antral follicles. AMH and total testosterone were the main determinants for ovarian volume in a stepwise regression model. CONCLUSIONS Our results suggest that not only the AMH level, but also obesity, IR and elevated androgen levels may relate to the development of the large size of antral follicle pool and ovarian volume in women with PCOS. Obesity and IR may enhance the follicular excess through the dysregulation of AMH or through the pathway of hyperandrogenemia. These findings might partly explain why adequate body weight management and improvement in IR can improve the ovulatory function for women with PCOS.
MTCache : Mid-Tier Database Caching for SQL Server

MTCache is a prototype mid-tier database caching solution for SQL Server that transparently offloads part of the query workload from a backend server to front-end servers. The goal is to improve system throughput and scalability but without requiring application changes. This paper outlines the architecture of MTCache and highlights several of its key features: modeling of data as materialized views, integration with query optimization, and support for queries with explicit data currency and consistency requirements.
Pheomelanin in fish?

1 Department of Molecular Biology, Max Planck Institute for Developmental Biology, T€ ubingen, Germany 2 Guest Group Evolutionary Genomics, Max Planck Institute for Evolutionary Biology, Pl€ on, Germany 3 L€ ubeck Institute of Experimental Dermatology, University of L€ ubeck, L€ ubeck, Germany 4 Department of Physiological Chemistry, Biocenter, University of W€ urzburg, W€ urzburg, Germany 5 Comprehensive Cancer Center Mainfranken, University Clinic W€ urzburg, W€ urzburg, Germany
Modeling trajectories of mental health: challenges and opportunities

More than two thirds of mental health problems have their onset during childhood or adolescence. Identifying children at risk for mental illness later in life and predicting the type of illness is not easy. We set out to develop a platform to define subtypes of childhood social-emotional development using longitudinal, multifactorial trait-based measures. Subtypes discovered through this study could ultimately advance psychiatric knowledge of the early behavioural signs of mental illness. To this extent we have examined two types of models: latent class mixture models and GP-based models. Our findings indicate that while GP models come close in accuracy of predicting future trajectories, LCMMs predict the trajectories as well in a fraction of the time. Unfortunately, neither of the models are currently accurate enough to lead to immediate clinical impact. The available data related to the development of childhood mental health is often sparse with only a few time points measured and require novel methods with improved efficiency and accuracy.
The Ras protein superfamily: Evolutionary tree and role of conserved amino acids

The Ras superfamily is a fascinating example of functional diversification in the context of a preserved structural framework and a prototypic GTP binding site. Thanks to the availability of complete genome sequences of species representing important evolutionary branch points, we have analyzed the composition and organization of this superfamily at a greater level than was previously possible. Phylogenetic analysis of gene families at the organism and sequence level revealed complex relationships between the evolution of this protein superfamily sequence and the acquisition of distinct cellular functions. Together with advances in computational methods and structural studies, the sequence information has helped to identify features important for the recognition of molecular partners and the functional specialization of different members of the Ras superfamily.
The Kuramoto model: A simple paradigm for synchronization phenomena

Synchronization phenomena in large populations of interacting elements are the subject of intense research efforts in physical, biological, chemical, and social systems. A successful approach to the problem of synchronization consists of modeling each member of the population as a phase oscillator. In this review, synchronization is analyzed in one of the most representative models of coupled phase oscillators, the Kuramoto model. A rigorous mathematical treatment, specific numerical methods, and many variations and extensions of the original model that have appeared in the last few years are presented. Relevant applications of the model in different contexts are also included.
Faster and simpler width-independent parallel algorithms for positive semidefinite programming

This paper studies the problem of finding a (1+ε)-approximate solution to positive semidefinite programs. These are semidefinite programs in which all matrices in the constraints and objective are positive semidefinite and all scalars are non-negative. At FOCS'11, Jain and Yao gave an NC algorithm that requires O(t 1/ε13 log13 m log n) iterations on input n constraint matrices of dimension m-by-m, where each iteration performs at least Ω(mω) work since it involves computing the spectral decomposition. We present a simpler NC parallel algorithm that on input with n constraint matrices, requires O(1/ε4 log4 n log(1/ε)) iterations, each of which involves only simple matrix operations and computing the trace of the product of a matrix exponential and a positive semidefinite matrix. Further, given a positive SDP in a factorized form, the total work of our algorithm is nearly-linear in the number of non-zero entries in the factorization. Our algorithm can be viewed as a generalization of Young's algorithm and analysis techniques for positive linear programs (Young, FOCS'01) to the semidefinite programming setting.
Generation of a 5.5-Mb BAC/PAC contig of pig chromosome 6q1.2 and its integration with existing RH, genetic and comparative maps

We generated a sequence-ready BAC/PAC contig spanning approximately 5.5 Mb on porcine chromosome 6q1.2, which represents a very gene-rich genome region. STS content mapping was used as the main strategy for the assembly of the contig and a total of 6 microsatellite markers, 53 gene-related STS and 116 STS corresponding to BAC and PAC end sequences were analyzed. The contig comprises 316 BAC and PAC clones covering the region between the genes GPI and LIPE. The correct contig assembly was verified by RH-mapping of STS markers and comparative mapping of BAC/PAC end sequences using BLAST searches. The use of microsatellite primer pairs allowed the integration of the physical maps with the genetic map of this region. Comparative mapping of the porcine BAC/PAC contig with respect to the gene-rich region on the human chromosome 19q13.1 map revealed a completely conserved gene order of this segment, however, physical distances differ somewhat between HSA19q13.1 and SSC6q1.2. Three major differences in DNA content between human and pig are found in two large intergenic regions and in one region of a clustered gene family, respectively. While there is a complete conservation of gene order between pig and human, the comparative analysis with respect to the rodent species mouse and rat shows one breakpoint where a genome segment is inverted.
Learning large-scale fuzzy cognitive maps using a hybrid of memetic algorithm and neural network

Fuzzy cognitive maps (FCMs) are cognition fuzzy influence graphs, which are based on fuzzy logic and neural network. In this paper, we propose a novel method combining Memetic Algorithms (MAs) and Neural Networks (NNs) to learn large-scale FCMs, which is labeled as MA-NN-FCM. In MA-NN-FCM, MAs are used to determine the regulatory connections in the network from multiple observed response sequences and NNs are used to calculate the interactions between concepts. In the experiments, the performance of MA-NN-FCM is validated on synthetic data with different number of nodes. The experimental results demonstrate the efficiency of our method, and show MA-NN-FCM can construct FCMs with high accuracy without expert knowledge. The performance of MA-NN-FCM is better than that of other FCM learning algorithms, such as ant colony optimization, non-linear Hebbian learning, and real-coded genetic algorithm.
Adaptive Navigation Control for Swarms of Autonomous Mobile Robots

Deploying a large number of resource-constrained mobile robots performing a common group task may offer many advantages in efficiency, costs per system, and fault-tolerance (Sahin, 2005). Therefore, robot swarms are expected to perform missions in a wide variety of applications such as environment and habitat monitoring, exploration, odor localization, medical service, and search-and-rescue. In order to perform the above-mentioned tasks successfully, one of the most important concerns is how to enable swarms of simple robots to autonomously navigate toward a specified destination in the presence of obstacles and dead-end passageways as seen in Fig. 1. From the standpoint of the decentralized coordination, the motions of individual robots need to be controlled to support coordinated collective behavior. We address the coordinated navigation of a swarm of mobile robots through a cluttered environment without hitting obstacles and being trapped in dead-end passageways. Our study is motivated by the observation that schools of fish exhibit emergent group behavior. For instance, when schools of fish are faced with obstacles, they can split themselves into a plurality of smaller groups to avoid collision and then merge into a single group after passing around the obstacles (Wilson, 1976). It is also worth noting that a group of fish facing a dead end can get out of the area. Based on the observation of schooling behavior in fish, this work aims to present a novel adaptive group behavior, enabling large-scale robot swarms with limited sensing capabilities to navigate toward a goal that is visible only to a limited number of robots. In particular, the coordinated navigation is achieved without using any leader, identifiers, common coordinate system, and explicit communication. Under such a minimal robot model, the adaptive navigation scheme exploits the geometric local interaction which allows three neighboring robots to form an equilateral triangle. Specifically, the proposed algorithm allows robot swarms to 1) navigate while maintaining equilateral triangular lattices, 2) split themselves into multiple groups while maintaining a uniform distance of each other, 3) merge into a single group while maintaining a uniform distance of each other,
A versatile discrete-time approach for modeling switch-mode controllers

This paper presents a universal method for modeling the frequency response of comparators in switch- mode controllers. As the main non-linearity in most switch- mode controllers, understanding the comparator is the key to understanding the system. Based on discrete-time modeling, the proposed method is demonstrated to allow very precise predictions of comparator frequency response in a variety of control schemes. In the presented work, the modeling method is exemplified for the standard PWM and two different self-oscillating (a.k.a. sliding mode) control schemes. The proposed method is believed by the authors to be the first method that is able to handle these fundamentally different control schemes within a single modeling framework. Experimentally measured output impedance and comparator magnitude responses are compared to the model results. Great accuracy is achieved from DC to frequencies far beyond the switching frequency.
Daisaku Ikeda’s Curriculum of Soka Education: Creating Value Through Dialogue, Global Citizenship, and “Human Education” in the Mentor–Disciple Relationship

Abstract This essay review focuses on Daisaku Ikeda (b. 1928) and his curriculum of Soka, or value‐creating, education present in two works: Choose Life: A Dialogue (Toynbee & Ikeda, 1976) and Thoughts on Education for Global Citizenship (Ikeda, 1996b). In reviewing these works, the authors trace the biographical roots of Ikeda’s educational philosophy to his encounter with Josei Toda (1900–1958) and to the overwhelming concerns he has grappled with since childhood about the forces that ravaged his youth and family life; the authors also examine Ikeda’s concept of value‐creating education relative to value‐creating pedagogy theorized by Tsunesaburo Makiguchi (1981–1988) and suggest that Ikeda’s curriculum of Soka education is comprised of three key principles that also serve as its processes and goals—dialogue, global citizenship, and “human education” in the mentor–disciple relationship. The authors conclude with the implications of Ikeda’s curriculum of Soka education and of the two reviewed works.
Beginning Early Childhood Education Teachers’ Struggles in Remote Areas

The purpose of this study was to investigate pre-service teachers’ concerns about working in remote areas before they enter teaching profession, and their real life experiences when they start to teach in remote areas in Turkey. Sixteen pre-service teachers were interviewed immediately after their graduation. Then, six teachers started to teach in remote areas. Two more interviews were conducted with six beginning teachers when they finish first and second semester they taught. The phenomenological analysis developed by Moustakas (1994) was used in this study. Findings of this study revealed that pre-service teachers were reluctant to work in remote areas due to social, economical, cultural, and safety problems. When they start to teach in those regions they realized that actual situation is far worse than their initial expectations. They especially struggled to teach children coming from low socioeconomic status and children who did not speak Turkish. Involving low SES parents into their children’s education and lack of social life in a small and closed society were also challenging for beginning teachers.
A Multilayer Improved RBM Network Based Image Compression Method in Wireless Sensor Networks

The processing capacity and power of nodes in a Wireless Sensor Network (WSN) are limited. And most image compression algorithms in WSN are subject to random image content changes or have low image qualities after the images are decoded. Therefore, an image compression method based on multilayer Restricted Boltzmann Machine (RBM) network is proposed in this paper. The alternative iteration algorithm is also applied in RBM to optimize the training process. The proposed image compression method is compared with a region of interest (ROI) compression method in simulations. Under the same compression ratio, the qualities of reconstructed images are better than that of ROI. When the number of hidden units in top RBM layer is 8, the peak signal-to-noise ratio (PSNR) of the multilayer RBM network compression method is 74.2141, and it is much higher than that of ROI which is 60.2093. The multilayer RBM based image compression method has better compression performance and can effectively reduce the energy consumption during image transmission in WSN.
A simplified skid-steering model for torque and power analysis of tracked small unmanned ground vehicles

The ability to predict the torque and power consumption of a tracked robot, a.k. a. , small unmanned ground vehicle (SUGV) is important for its design. Accurate torque and power consumption prediction enables motion planning that does not exceed the torque and power limitations of the propulsion system or the terrain. Due to the fact that skid steering may consume a large percentage of the propulsion power of tracked SUGVs, it must be included in any accurate power analysis. Modeling of skid steering of tracks is difficult on soft soils because of the track-soil interaction and the distributed nature of shear stress along the contact area. This study begins with a general theory of skid steering track-soil interaction at steady state. A fast yet accurate 2-D simplified model is then developed, which is solved with pre-calculated skid steering resistance coefficient maps. Subsequently a quadratic equation for internal power consumption is obtained experimentally. The simplified model is verified using the experimental data obtained from an iRobot Packbot driving on dry sand.
Address-block extraction by Bayesian rule

A method for extracting a recipient address-block from a mail image has been developed. The method is composed of two steps: nomination of address-block candidates and evaluation of these candidates by using the Bayesian rule according to each of address-block type. Accordingly, the proposed method can cope with various types of address-blocks. The effectiveness of the method was confirmed in several address extraction experiments. These experiments show that the top-five extraction results include one correct address-block in 94% of total number of printed-mail cases and 89% in of handwritten-mail cases.