Miracle-dz/UniFAR-FTUs huggingface dataset:
Tankyrase Inhibition Causes Reversible Intestinal Toxicity in Mice with a Therapeutic Index < 1

Activated Wnt/β-catenin signaling is frequently associated with colorectal cancer. Wnt inhibitors, including tankyrase inhibitors, are being explored as potential anticancer agents. Wnt signaling is also critical for intestinal tissue homeostasis, and Wnt inhibitors have been shown to cause intestinal toxicity in mice by affecting intestinal stem cells. This study sought to characterize the intestinal toxicity of tankyrase inhibitors, including reversibility, and to assess their therapeutic index. Novel tankyrase inhibitor G-631 caused dose-dependent intestinal toxicity with a therapeutic index < 1 after 14 days of dosing in mice. At a tolerated subtherapeutic dose level, the intestinal toxicity was composed of enteritis characterized by villus blunting, epithelial degeneration, and inflammation, which fully reversed after 14 days of recovery. Doubled exposure showed weak antitumor activity in a xenograft colorectal cancer model but also caused more severe intestinal toxicity characterized by multifocal-regionally extensive necrotizing and ulcerative enteritis leading to morbidity or moribundity in some animals. This toxicity was only partially reversed after 14 days of recovery, with evidence of crypt and villus regeneration, mildly blunted villi, and/or scarring in association with chronic inflammation of the submucosa. Therefore, the clinical utility of tankyrase inhibitors is likely limited by the on-target intestinal toxicity and a therapeutic index < 1 in mice.
Functional integration of the posterior superior temporal sulcus correlates with facial expression recognition

Face perception is essential for daily and social activities. Neuroimaging studies have revealed a distributed face network (FN) consisting of multiple regions that exhibit preferential responses to invariant or changeable facial information. However, our understanding about how these regions work collaboratively to facilitate facial information processing is limited. Here, we focused on changeable facial information processing, and investigated how the functional integration of the FN is related to the performance of facial expression recognition. To do so, we first defined the FN as voxels that responded more strongly to faces than objects, and then used a voxel‐based global brain connectivity method based on resting‐state fMRI to characterize the within‐network connectivity (WNC) of each voxel in the FN. By relating the WNC and performance in the “Reading the Mind in the Eyes” Test across participants, we found that individuals with stronger WNC in the right posterior superior temporal sulcus (rpSTS) were better at recognizing facial expressions. Further, the resting‐state functional connectivity (FC) between the rpSTS and right occipital face area (rOFA), early visual cortex (EVC), and bilateral STS were positively correlated with the ability of facial expression recognition, and the FCs of EVC‐pSTS and OFA‐pSTS contributed independently to facial expression recognition. In short, our study highlights the behavioral significance of intrinsic functional integration of the FN in facial expression processing, and provides evidence for the hub‐like role of the rpSTS for facial expression recognition. Hum Brain Mapp 37:1930–1940, 2016. © 2016 Wiley Periodicals, Inc.
Genetic testing in an ethnically diverse cohort of high-risk women: a comparative analysis of BRCA1 and BRCA2 mutations in American families of European and African ancestry.

CONTEXT Ten years after BRCA1 and BRCA2 were first identified as major breast cancer susceptibility genes, the spectrum of mutations and modifiers of risk among many ethnic minorities remain undefined. OBJECTIVES To characterize the clinical predictors, spectrum, and frequency of BRCA1 and BRCA2 mutations in an ethnically diverse high-risk clinic population and to evaluate the performance of the BRCAPRO statistical model in predicting the likelihood of a mutation. DESIGN, SETTING, AND PARTICIPANTS Comparative analysis of families (white, Ashkenazi Jewish, African American, Hispanic, Asian) with 2 or more cases of breast and/or ovarian cancer among first- and second-degree relatives. Families were identified at US sites between February 1992 and May 2003; in each family, the individual with the highest probability of being a mutation carrier was tested. MAIN OUTCOME MEASURES Frequency of BRCA1 and BRCA2 mutations and area under the receiver operating characteristic curve for the BRCAPRO model. RESULTS The mutation spectrum was vastly different between families of African and European ancestry. Compared with non-Hispanic, non-Jewish whites, African Americans had a lower rate of deleterious BRCA1 and BRCA2 mutations but a higher rate of sequence variations (27.9% vs 46.2% and 44.2% vs 11.5%; P<.001 for overall comparison). Deleterious mutations in BRCA1 and BRCA2 were highest for Ashkenazi Jewish families (69.0%). Early age at diagnosis of breast cancer and number of first- and second-degree relatives with breast and ovarian cancer were significantly associated with an increased likelihood of carrying a BRCA1 or BRCA2 mutation. In discriminating between mutation carriers, BRCAPRO performed as well in African American families as it did in white and Jewish families, with an area under the curve of 0.77 (95% confidence interval, 0.61-0.88) for African American families and 0.70 (95% confidence interval, 0.60-0.79) for white and Jewish families combined. CONCLUSIONS These data support the use of BRCAPRO and genetic testing for BRCA1 and BRCA2 mutations in the management of high-risk African American families. Irrespective of ancestry, early age at diagnosis and a family history of breast and ovarian cancer are the most powerful predictors of mutation status and should be used to guide clinical decision making.
Delay–Interleaved Cooperative Relay Networks

In this letter, we propose a novel system design for implementing distributed space-time codes (DSTCs) in order to improve the performance of the codes. The proposed method called delay and interleaved cooperative relay networks manages to force transmitted signals to experience different channel fading coefficients as much as possible, which increases the coding gain or even, in some cases, the diversity order of the codes. The only degradation to the conventional system is a small delay that is negligible in many communication applications. There are no extra computational complexities burdened to the relays, and only a simple permutation is performed at the relays and the destination. Simulation results also confirm the performance superiority of this innovative method over the conventional DSTC system.
Effect of Polyhydroxyl Compounds on the Thermal
Stability and Structure of Laccase

In this study, glucose, inositol, glycerol, and sorbitol have been chosen as chemical modifiers to enhance laccase stability. The regularity of laccase activity variations was studied at different temperatures. Low concentrations of four polyhydroxyl compounds can improve the thermal stability of laccase at the whole temperature gradient (20~60oC). The effect of glycerol is best in the all additives, and the relative enzyme activity of laccase increased 1.35 times by glycerol, in the concentration of 0.6 mol/L. The conformation of modified laccase is characterized by UV, fluorescence and circular dichroism. The result shows that the polyhydroxy compounds can change the conformation of laccase by increasing the hydrogen bonds between the laccase molecules and prevent the adverse change due to the increase of temperature in the secondary structure of laccase. Therefore, low concentrations of polyhydroxy compounds could be served as an additive to improve the thermal stability of the laccase.
Lattice Boltzmann Algorithms for Fluid Turbulence

Lattice Boltzmann algorihms are a mesoscopic representation of nonlinear continuum physics (like Navier-Stokes, magnetohydro dynamics (MHD), Gross- Pitaevskii equations) which are ideal for parallel supercomputers because they transform the difficult nonlinear convective macroscopic derivatives into purely local moments of distribution functions. The macroscopic nonlinearities are recovered by relaxation distribution functions in the collision operator whose dependence on the macroscopic velocity is algebraically nonlinear and thus purely local. Unlike standard computational fluid dynamics codes, there is no loss in parallelization in handling arbitrary geometric boundaries, e.g., using bounce-back rules from kinetic theory. By encoding detailed balance into the collision operator through the introduction of discrete H-function, the lattice Boltzmann algorithm can be made unconditionally stable for arbitrary high Reynolds numbers. It is shown that this approach is a special case of a quantum lattice Boltzmann algorithm that entangles local qubits through unitary collision operators and which is ideally parallelized on quantum computer architectures. Here we consider turbulence simulations using 2,048 PEs on a 1,6003-spatial grid. A connection is found between the rate of change of enstrophy and the onset of laminar-to- turbulent flows.
On the Two-level Hybrid Clustering Algorithm

In this paper, we design the hybrid clustering algorithms, which involve two level clustering. At each of the levels, users can select the k-means, hierarchical or SOM clustering techniques. Unlike the existing cluster analysis techniques, the hybrid clustering approach developed here represents the original data set using a smaller set of prototype vectors (cluster means), which allows efficient use of a clustering algorithm to divide the prototype into groups at the first level. Since the clustering at the first level provides data abstraction first, it reduces the number of samples for the second level clustering. The reduction of the number of samples, hence, the reduction of computational cost is especially important when hierarchical clustering is used in the second stage. The prototypes clustered at the first level are local averages of the data and therefore less sensitive to random variations than the original data. The empirical evaluation of the two-level hybrid clustering algorithms is made at four data sets
Controlled serial grinding for high‐resolution three‐dimensional reconstruction

This work reports an improved preparation procedure for acquiring SEM images for three‐dimensional (3D) reconstruction. The images are acquired consecutively after serial grinding and polishing. Ugelstad beads are used as landmarks for registration purposes and for determining the thickness of the abraded sections. An estimation of the bead sizes necessary for suitable thickness quantification at the corresponding spatial resolution and uncertainty is given. Shape‐based interpolation is used for filling the gaps between the cross‐sections. An indication of the distance between cross‐sections necessary for good interpolation is also given. An example of a 3D reconstructed paper volume is presented. The method is suitable for preparation of fibre and paper as well as other materials.
Using Gaussian Mixtures for Hindi Speech Recognition System

The goal of automatic speech recognition (ASR) system is to accurately and efficiently convert a speech signal into a text message independent of the device, speaker or the environment. In general the speech signal is captured and pre-processed at front-end for feature extraction and evaluated at back-end using the Gaussian mixture hidden Markov model. In this statistical approach since the evaluation of Gaussian likelihoods dominate the total computational load, the appropriate selection of Gaussian mixtures is very important depending upon the amount of training data. As the small databases are available to train the Indian languages ASR system, the higher range of Gaussian mixtures (i.e. 64 and above), normally used for European languages, cannot be applied for them. This paper reviews the statistical framework and presents an iterative procedure to select an optimum number of Gaussian mixtures that exhibits maximum accuracy in the context of Hindi speech recognition system.
The business model development of e-money services in Indonesia

In order to create “less cash society”, since 2014 Bank Indonesia has launched Non-Cash National Movement (Gerakan Nasional Non-Tunai) to increase people's awareness in using non-cash payment instruments. Currently, only around one-third Indonesian own bank account which was relatively low compared to cellular phone penetration that reached 123.1% in 2014. Based on this fact, Telkomsel launched its mobile money, T-Cash Tap, so that customers can do financial transactions using mobile phones. However, in its implementation, mobile money in Indonesia still has some issues, such as awareness, regulations, and low rate of industry attractiveness. The research was conducted to understand the business environment of T-Cash in Indonesia and to develop a business model for T-Cash Tap. Qualitative approach is used in this research through secondary data which taken from relevant reports, journals, and articles regarding mobile money. To support the qualitative data, a survey was conducted using questionnaires sent by mail to 130 Telkomsel's subscribers consist of 79 male and 51 female. Following the survey result analysis, a business model is proposed to be implemented by Telkomsel in aim to increase its mobile money usage.
Effective ROI extraction methods for hybrid medical image compression

Medical images do contain important and unimportant spatial regions. Compression methods which are capable of reconstructing the image with high quality are required to compress the medical images. For these images, only a portion of it is useful for diagnosis hence a region based coding techniques are significant for compressing and transmission. Extracting a significant region is of great demand since a slighter mistake may leads to wrong diagnosis. This paper is focused on investigating multiple image processing algorithms for medical images. All the images may not contain the same region of interest, so different approaches are supposed to apply for different images. In these three types of medical images were considered like magnetic resonance (MR) brain images, computer tomography (CT) abdomen images and X-ray lung images. In this paper three automatic region of interest extraction algorithms were proposed for different types of images.
Hydrostatic versus increased permeability pulmonary edema: diagnosis based on radiographic criteria in critically ill patients.

To evaluate radiographic criteria recently proposed for determining causes of pulmonary edema, the authors studied 45 patients with severe pulmonary edema. Hydrostatic and increased permeability edemas were distinguished by means of the ratio of pulmonary edema fluid protein to plasma protein concentration and clinical and hemodynamic data. Chest radiographs were classified as showing hydrostatic, increased permeability, or mixed edema by three independent readers without knowledge of the clinical diagnosis. Overall, 87% of patients with hydrostatic edema but only 60% of patients with increased permeability edema were correctly identified. A patchy, peripheral distribution of edema was the single most discriminating criterion and was relatively specific for increased permeability edema, occurring in 13% of patients with hydrostatic and 50% with increased permeability edema (P less than .05). Some features considered more typical of hydrostatic edema were commonly found in patients with increased permeability, including a widened vascular pedicle (56%), pleural effusions (36%), peribronchial cuffs (72%), and septal lines (40%). The authors conclude that chest radiography is limited in the differentiation of type of pulmonary edema in severe cases.
Quantum chemical geometry optimizations in proteins using crystallographic raw data

A method is developed for the combination of quantum chemical geometry optimizations and crystallographic structure refinement. The method is implemented by integrating the quantum chemical software Turbomole with the crystallographic software Crystallography and NMR System (CNS), using three small procedures transferring information between the two programs. The program (COMQUM‐X)is used to study the binding of the inhibitor N‐methylmesoporphyrin to ferrochelatase, and we show that the method behaves properly and leads to an improvement of the structure of the inhibitor. It allows us to directly quantify in energy terms how much the protein distort the structure of the bound inhibitor compared to the optimum vacuum structure (4–6 kJ/mol). The approach improves the standard combined quantum chemical and molecular mechanics (QC/MM) approach by guaranteeing that the final structure is in accordance with experimental data (the reflections) and avoiding the risk of propagating errors in the crystal coordinates. The program can also be seen as an improvement of standard crystallographic refinement, providing an accurate empirical potential function for any group of interest. The results can be directly interpreted in standard crystallographic terms (e.g., R factors or electron density maps). The method can be used to interpret crystal structures (e.g., the protonation status of metal‐bound water molecules) and even to locally improve them. © 2002 Wiley Periodicals, Inc. J Comput Chem 23: 1058–1070, 2002
Heat shock factor-1 and nuclear factor-kappaB are systemically activated in human acute pancreatitis.

CONTEXT Nuclear factor-kappa B (NF-kappaB) is a transcription factor for a wide range of proinflammatory mediators while heat shock factor-1 (HSF-1) transcribes stress proteins that protect against cellular damage. Both are attractive therapeutic targets, undergoing investigation in other acute inflammatory conditions, such as sepsis. OBJECTIVE To evaluate the role of the transcription factors NF-kappaB and HSF-1 in human acute pancreatitis and their relationship to cytokine/chemokine production, disease severity and outcome. PATIENTS Twenty-four patients with acute pancreatitis and 12 healthy controls. MAIN OUTCOME MEASURES Peripheral blood mononuclear cells were isolated. NF-kappaB and HSF-1 were measured by electrophoretic mobility shift assay. Soluble tumor necrosis factor (TNF) receptor II and interleukin-8 were measured by ELISA. Acute physiology scores (APS), APACHE II scores and final Atlanta designations of severity were also determined. RESULTS Systemic NF-kappaB activation occurs in acute pancreatitis compared to healthy controls (P=0.004). However, there was no significant difference between those with mild and severe disease (P=0.685). Systemic activation of HSF-1 was observed in acute pancreatitis compared to healthy controls although this did not reach statistical significance (P=0.053). Activation, however, was greatest in those who had a final Atlanta designation of mild pancreatitis compared to those who had a severe attack of acute pancreatitis (P=0.036). Furthermore, HSF-1 was inversely correlated with acute physiology score (APS; r=-0.49, P=0.019) and APACHE II score (r=-0.47, P=0.026). CONCLUSIONS Both NF-kappaB and HSF-1 are systemically activated in human acute pancreatitis. HSF-1 activation may protect against severity of pancreatitis.
Reconstruction of Synthetic Aperture Radar Raw Data under Analog-To-Digital Converter Saturation Distortion for Large Dynamic Range Scenes

Digital storage and transmission are common processes in modern synthetic aperture radar systems; thus, analog-to-digital converters are indispensable. Such processes can lead to two types of error: quantization (or granular) error and saturation (or clipping) error, which cause sampling noise, and radiometric and harmonic distortions in final images. Traditionally, reasonable choices of the gain and the number of quantization bits by the analog-to-digital converter based on the echo distribution can effectively reduce these errors. However, establishing the gain control repository of a synthetic aperture radar mission is a long process. In addition, if the dynamic range of the backscattering coefficient is extremely large or if unexpected strong targets appear in a scene, then harmonics occur in the echo, which turns the variable gain amplifier into chaos based on statistic and, inevitably, results in saturation in the raw data. Once raw data saturation occurs, the SAR system can conventionally adjust only the analog-to-digital converter in the next observation, thus reducing timeliness. Power loss compensation based on a statistical model and saturation (clipping) factor on a large-scale could compensate for the energy loss in images; however, detail interference, such as harmonic distortion, cannot be effectively suppressed, which will lead to false targets in the focused data. To address this particular problem, a novel anti-saturation method for large dynamic range scenes is proposed in this paper. The log-normal distribution is used in this article to describe dynamic range scenes with strong isolated targets, which mainly cause receiver saturation. Using the statistical distribution of complex scenes as a priori information, a maximum a posteriori estimation algorithm is proposed to simultaneously compensate for the saturated values in the raw data and retain the non-saturated values. Thus, the details of the weak background are well preserved, and the isolated strong targets with sparsity are reconstructed perfectly. With Monte Carlo simulation, the proposed method can improve the radiometric accuracy by 5 to 10 dB and effectively suppress the energy of false targets. Based on TerraSAR-X, ALOS-2, and Radarsat-1 synthetic aperture radar data, the effectiveness and robustness of the proposed method are also verified by simulations.
End-to-end Active Object Tracking via Reinforcement Learning

We study active object tracking, where a tracker takes as input the visual observation (i.e., frame sequence) and produces the camera control signal (e.g., move forward, turn left, etc.). Conventional methods tackle the tracking and the camera control separately, which is challenging to tune jointly. It also incurs many human efforts for labeling and many expensive trial-and-errors in realworld. To address these issues, we propose, in this paper, an end-to-end solution via deep reinforcement learning, where a ConvNet-LSTM function approximator is adopted for the direct frame-toaction prediction. We further propose an environment augmentation technique and a customized reward function, which are crucial for a successful training. The tracker trained in simulators (ViZDoom, Unreal Engine) shows good generalization in the case of unseen object moving path, unseen object appearance, unseen background, and distracting object. It can restore tracking when occasionally losing the target. With the experiments over the VOT dataset, we also find that the tracking ability, obtained solely from simulators, can potentially transfer to real-world scenarios.
Performance improvement of haptic collision detection using subdivision surface and sphere clustering

Haptics applications such as surgery simulations require collision detections that are more precise than others. An efficient collision detection method based on the clustering of bounding spheres was proposed in our prior study. This paper analyzes and compares the applied effects of the five most common subdivision surface methods on some 3D models for haptic collision detection. The five methods are Butterfly, Catmull-Clark, Mid-point, Loop, and LS3 (Least Squares Subdivision Surface). After performing a number of experiments, we have concluded that LS3 method is the most appropriate for haptic simulations. The more we applied surface subdivision, the more the collision detection results became precise. However, it is observed that the performance becomes better until a certain threshold and degrades afterward. In order to reduce the performance degradation, we adopted our prior work, which was the fast and precise collision detection method based on adaptive clustering. As a result, we obtained a notable improvement of the speed of collision detection.