Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulating the flow of semidilute polymer solutions

There are a number of contexts involving polymer solutions, such as in the spinning of nano-fibres or in inkjet printing, where in order to achieve the most optimal outcome, the concentration of polymers must not be too dilute or too concentrated, but somewhere in between. While a lot is known about dilute and concentrated solutions and melts, not much is known about the vast regime of concentrations that lie in between, the so-called semidilute concentration regime. The reason much is known about dilute and concentrated solutions is because in either case, their behaviour can be understood by understanding the behaviour of single molecules. In the dilute case, this is obvious since there are few molecules interacting with each other. In the concentrated case, by treating all the molecules that surround a particular molecule as obstacles that constrain its motion, the entire problem is reduced to understanding the motion of a polymer in a tube. These approximations, however, are not valid in semidilute solutions and the problem of having to account for all the many-body interactions that arise in this regime must be addressed. The onset of the semidilute regime occurs at surprisingly low concentrations because even though the monomer concentration is very low, their being strung together into polymers that are extended objects in space gives rise to the early emergence of interactions. The broad aim of this thesis is to develop a predictive understanding of the flow behaviour of semidilute polymer solutions. In order to achieve this, firstly, an optimised mesoscopic multi-particle Brownian dynamics (BD) simulation algorithm is developed that is capable of accurately capturing both excluded volume and hydrodynamic interactions, and which can predict rheological properties of semidilute polymer solutions in shear, extensional and mixed flows. Secondly, the multi-particle BD algorithm is used to solve a number of different physical problems involving flowing semidilute polymer solutions, as discussed in greater detail below.
The value of early renal biopsy in systemic lupus erythematosus patients presenting with renal involvement.

BACKGROUND The goal of this study is to determine the value of early renal biopsy as a therapeutic guide in systemic lupus erythematosus (SLE) patients presenting with renal involvement. METHODS We retrospectively analyzed renal biopsies findings in SLE patients between January 2000 and December 2009 encountered at a medical center in Taiwan. An additional criterion for inclusion in this study was kidney biopsy done within 3 months of the first detection of sign(s) of renal disease. RESULTS There were 131 patients enrolled in this study. In patients presenting with acute renal failure, 91% of patients had proliferative lupus nephritis (Class IV, mixed Class V+III) and 9% had non-proliferative lupus nephropathy (pure Class V). In patients presenting with nephrotic range proteinuria, proliferative lupus nephritis (Class III, IV, mixed Class V+III) and non-proliferative lupus nephropathy (Class II, pure Class V) accounted for 55% and 36% of patients, respectively; and 9% had non-lupus nephropathy. In this group, except that elevated anti-double-stranded DNA antibody levels were more common in proliferative lupus nephritis (p = 0.043), no clinical findings could predict the renal morphology. In patients presenting with subnephrotic proteinuria, 49% of patients had proliferative lupus nephritis (Class III, IV, mixed Class V+III) and 51% had non-proliferative lupus nephropathy (Class II, pure Class V), and decreased C4 levels were more common in patients with proliferative lupus nephritis (p = 0.031). In patients presenting with isolated hematuria, all were not active forms of nephropathy. Immunosuppressive therapy was intensified because of biopsy findings in 29% of patients presenting with acute renal failure, 43% with nephrotic range proteinuria, and 53% with sub-nephrotic proteinuria. CONCLUSIONS Our data suggested that similar clinical renal manifestations may be observed despite very different classes of lupus nephritis. Clinicians tended to wait for histological identification of severe lupus nephritis before initiating potential harmful treatment with aggressive immunosuppressive therapy. Therefore, in SLE patients with clinical sign(s) of renal disease, early renal biopsy may be helpful in planning treatment.
Binary Volumetric Octree Representation for Image-Based Rendering

A Binary Volumetric Octree (BVO) is a volume array with binary opacity voxels, represented as octree. The BVO structure allows to stores not the complete object volume, but only the surface voxels. The new approach to Image Based Rendering (IBR), using this representation, makes it possible to use single data structure for fast, occlusion-compatible hierarchical warping, splatting-based rendering, and easy level-of-detail selection. This representation allows approximation of multiple depth images with guaranteed solution of the main problem of IBR methods gaps filling (with minor conditions imposed on the originals depth images). The rendering process can be either completely CPUbased, or use hardware assisted texture mapping.
Decentralized Adaptive Pinning Control for Cluster Synchronization of Complex Dynamical Networks

In this brief, we investigate pinning control for cluster synchronization of undirected complex dynamical networks using a decentralized adaptive strategy. Unlike most existing pinning-control algorithms with or without an adaptive strategy, which require global information of the underlying network such as the eigenvalues of the coupling matrix of the whole network or a centralized adaptive control scheme, we propose a novel decentralized adaptive pinning-control scheme for cluster synchronization of undirected networks using a local adaptive strategy on both coupling strengths and feedback gains. By introducing this local adaptive strategy on each node, we show that the network can synchronize using weak coupling strengths and small feedback gains. Finally, we present some simulations to verify and illustrate the theoretical results.
A Maximum Entropy Classifier for Cross-Lingual Pronoun Prediction

We present a maximum entropy classifier for cross-lingual pronoun prediction. The features are based on local source- and target-side contexts and antecedent information obtained by a co-reference resolution system. With only a small set of feature types our best performing system achieves an accuracy of 72.31%. According to the shared task’s official macroaveraged F1-score at 57.07%, we are among the top systems, at position three out of 14. Feature ablation results show the important role of target-side information in general and of the resolved targetside antecedent in particular for predicting the correct classes.
An Analytical Model for the Performance Analysis of Concurrent Transmission in IEEE 802.15.4

Interference is a serious cause of performance degradation for IEEE802.15. 4 devices. The effect of concurrent transmissions in IEEE 802.15. 4 has been generally investigated by means of simulation or experimental activities. In this paper, a mathematical framework for the derivation of chip, symbol and packet error probability of a typical IEEE 802.15. 4 receiver in the presence of interference is proposed. Both non-coherent and coherent demodulation schemes are considered by our model under the assumption of the absence of thermal noise. Simulation results are also added to assess the validity of the mathematical framework when the effect of thermal noise cannot be neglected. Numerical results show that the proposed analysis is in agreement with the measurement results on the literature under realistic working conditions.
An integrative approach for causal gene identification and gene regulatory pathway inference

MOTIVATION Gene expression variation can often be linked to certain chromosomal regions and are tightly associated with phenotypic variation such as disease conditions. Inferring the causal genes for the expression variation is of great importance but rather challenging as the linked region generally contains multiple genes. Even when a single candidate gene is proposed, the underlying biological mechanism by which the regulation is enforced remains unknown. Novel approaches are needed to both infer the causal genes and generate hypothesis on the underlying regulatory mechanisms. RESULTS We propose a new approach which aims at achieving the above objectives by integrating genotype information, gene expression, protein-protein interaction, protein phosphorylation, and transcription factor (TF)-DNA binding information. A network based stochastic algorithm is designed to infer the causal genes and identify the underlying regulatory pathways. We first quantitatively verified our method by a test using data generated by yeast knock-out experiments. Over 40% of inferred causal genes are correct, which is significantly better than 10% by random guess. We then applied our method to a recent genome-wide expression variation study in yeast. We show that our method can correctly identify the causal genes and effectively output experimentally verified pathways. New potential gene regulatory pathways are generated and presented as a global network. AVAILABILITY Source code is available upon request.
Stock Price Prediction and Trend Prediction Using Neural Networks

In this paper, I analyzed feed forward network using back propagation learning method with early stopping and radial basis neural network to predict the trend of stock price (i.e. classification) and to predict the stock price (i.e. value prediction). Fundamental data or Technical indicators were not used in this research as basic objective of this research was to determine the usability of artificial neural networks in predicting the future prices based on past prices alone.
Stem Cell Proliferation in the Skin: α-Catenin Takes Over the Hippo Pathway

α-Catenin inhibits the transcriptional coactivator Yap to limit epidermal stem cell proliferation. Epithelial tissues in the human body undergo constant renewal. For example, the skin is regenerated continuously through the periodic proliferation of normally quiescent stem cells in the basal compartment of the skin. Proper balance between stem cell quiescence and proliferation is maintained over the lifetime of the organism to preserve pools of stem cells required to maintain and repair tissues. However, mechanisms controlling the rate of stem cell renewal are poorly understood. Additionally, whether deregulation of these mechanisms within epidermal stem cells leads to skin cancer is not known. The adherens junction component α-catenin has been identified as a regulator of epidermal stem cell proliferation and as a suppressor of skin cancer through its inhibition of Yap, a transcriptional effector of the Hippo growth control pathway. Understanding the pathways that regulate the proliferation of stem cells in the skin holds promise for reversing the aging process and tumor development.
Development of a Genomic Metric That Can Be Rapidly Used to Predict Clinical Outcome in Severely Injured Trauma Patients*

Objective:Many patients have complicated recoveries following severe trauma due to the development of organ injury. Physiological and anatomical prognosticators have had limited success in predicting clinical trajectories. We report on the development and retrospective validation of a simple genomic composite score that can be rapidly used to predict clinical outcomes. Design:Retrospective cohort study. Setting:Multi-institutional level 1 trauma centers. Patients:Data were collected from 167 severely traumatized (injury severity score >15) adult (18–55 yr) patients. Methods:Microarray-derived genomic data obtained from 167 severely traumatized patients over 28 days were assessed for differences in messenger RNA abundance among individuals with different clinical trajectories. Once a set of genes was identified based on differences in expression over the entire study period, messenger RNA abundance from these subjects obtained in the first 24 hours was analyzed in a blinded fashion using a rapid multiplex platform, and genomic data reduced to a single metric. Results:From the existing genomic dataset, we identified 63 genes whose leukocyte expression differed between an uncomplicated and complicated clinical outcome over 28 days. Using a multiplex approach that can quantitate messenger RNA abundance in less than 12 hours, we reassessed total messenger RNA abundance from the first 24 hours after trauma and reduced the genomic data to a single composite score using the difference from reference. This composite score showed good discriminatory capacity to distinguish patients with a complicated outcome (area under a receiver–operator curve, 0.811; p <0.001). This was significantly better than the predictive power of either Acute Physiology and Chronic Health Evaluation II or new injury severity score scoring systems. Conclusions:A rapid genomic composite score obtained in the first 24 hours after trauma can retrospectively identify trauma patients who are likely to develop complicated clinical trajectories. A novel platform is described in which this genomic score can be obtained within 12 hours of blood collection, making it available for clinical decision making.
A neural-network virtual-reality mobility aid for the severely visually impaired

Many people who are registered as blind nevertheless retain some residual vision and are said to have "low vision". Conditions resulting in such low vision include cataracts, diabetic retinopathy, age-related maculopathy, and retinal detachment. In recent years an increasing amount of research has attempted to apply principles from computer vision to the requirements of the low vision subject. A variety of conventional image-processing techniques have been used to enhance the visual appearance of a scene, and devices from the field of virtual reality such as head-mounted displays have been investigated as an aid to low vision. However, a fundamental limitation with conventional image-processing techniques is that they are applied to an entire image with no knowledge of scene content, resulting in unwanted emphasis of noise and unimportant detail. Our aim is to produce a portable system comprising a processing unit with head-mounted camera and display which will allow a person with low vision to be self-sufficient and mobile in a typical urban environment. Our approach differs from previous research in that it uses a neural network object classifier to allow images to be enhanced in a way which considers the identity of objects in the scene. Primarily, our system transforms an original image into a classified image in which the types of objects in the scene are identified by an object outline filled with a particular high saturation colour according to the object type, chosen by the user. By classification our system allows the user to identify important objects in a scene simply by their colour, requiring no perception of shape or high spatial frequencies, and minimal contrast sensitivity. The resultant images are very simple and uncluttered and we expect that users would adapt quickly to use of the system. Results obtained to date suggest that the system is capable of providing registered-blind users with useful visual information. We are now working on improving the speed and classification accuracy of the system, and investigating the applicability of our techniques to specific conditions.
Targeted actinium-225 in vivo generators for therapy of ovarian cancer.

Advanced ovarian cancer is largely incurable, but initially it is frequently confined to the i.p. space. We explored i.p. radioimmunotherapy in a mouse model of human ovarian cancer. Use of a targeted actinium-225 ((225)Ac) in vivo generator of alpha particles exploits the extreme, selective cytotoxicity of alpha particles, while providing a feasible half-life to enable delivery to tumor. (225)Ac chelated with 2-(p-isothiocyanatobenzyl)-1,4,7,10-tetraazacyclododecane-1,4,7,10 tetraacetic acid was conjugated to trastuzumab, an anti-HER-2/neu antibody. The radioimmunoconjugate was tested for immunoreactivity, internalization, and cytotoxicity using a human ovarian carcinoma cell line, SKOV3. (225)Ac-labeled trastuzumab retained immunoreactivity (50-90%), rapidly internalized into cells (50% at 2 h), and had an ED(50) of 1.3 nCi/ml after 4 days of incubation in vitro. i.p. administered (225)Ac- or (111)In-labeled trastuzumab behaved similarly with high tumor uptake [56-60% injected dose per gram (% ID/g) at 4 h, which increased to 65-70% ID/g at 24 h]. Tumor uptake was 3-5-fold higher than liver and spleen, the normal organs with the highest uptake. i.v. administration of (111)In-labeled trastuzumab produced slightly higher normal organ uptake compared with i.p.-administered (111)In-labeled trastuzumab. However, tumor uptake was low, 5%-26% ID/g. Therapy was examined with native trastuzumab and 220, 330, and 450 nCi of (225)Ac-labeled trastuzumab or (225)Ac-labeled control antibody at different dosing schedules. Therapy was initiated 9 days after tumor seeding. Groups of control mice and those administered native trastuzumab had median survivals of 33 and 37 or 44 days, respectively. Median survival was 52-126 days with (225)Ac-labeled trastuzumab at various doses and schedules, and 48-64 days for (225)Ac-labeled control the same schedules. Deaths from toxicity occurred with the highest activity levels. In conclusion, i.p. administration with a (225)Ac-labeled internalizing anti-HER-2/neu antibody can extend survival significantly in a nude mouse model of human ovarian cancer at levels that produce no apparent gross toxicity.
Tracking plant preference for higher‐quality mycorrhizal symbionts under varying CO 2 conditions over multiple generations

Abstract The symbiosis between plants and root‐colonizing arbuscular mycorrhizal (AM) fungi is one of the most ecologically important examples of interspecific cooperation in the world. AM fungi provide benefits to plants; in return plants allocate carbon resources to fungi, preferentially allocating more resources to higher‐quality fungi. However, preferential allocations from plants to symbionts may vary with environmental context, particularly when resource availability affects the relative value of symbiotic services. We ask how differences in atmospheric CO 2‐levels influence root colonization dynamics between AMF species that differ in their quality as symbiotic partners. We find that with increasing CO 2‐conditions and over multiple plant generations, the more beneficial fungal species is able to achieve a relatively higher abundance. This suggests that increasing atmospheric carbon supply enables plants to more effectively allocate carbon to higher‐quality mutualists, and over time helps reduce lower‐quality AM abundance. Our results illustrate how environmental context may affect the extent to which organisms structure interactions with their mutualistic partners and have potential implications for mutualism stability and persistence under global change.
Wireless LXI Bus Clock Synchronization and Triggering Design

In this paper, a wireless local area network (WLAN) extension for instrumentation (LXI) [WLXI] bus combining the technology of WLAN and LXI bus is proposed. Several different kinds of methods are designed to implement clock synchronization and triggering. For clock synchronization, a method using the IEEE 1588 protocol based on extended Kalman filter (EKF) is introduced as emphasis. For this method, the wireless IEEE 1588 protocol is used as a communication protocol of clock synchronization, and EKF is used to resolve the clock's nonlinear shifting. The EKF improves the precision of clock synchronization, thus, high-precision clock synchronization for WLXI is realized. For triggering, a method based on the direct-sequence (DS) ultrawideband (UWB) is introduced and emphasized. For this method, DS spread-spectrum (DSSS) UWB is used as a modulation mode, combined with “lagging delay” wireless triggering to realize high-precision synchronization triggering for WLXI.
THE EFFECTS OF LIABILITY OF FOREIGNNESS ON PERFORMANCE : AN EMPIRICAL STUDY OF FOREIGN BANKS IN THE U

This paper is an attempt to answer research questions about the performance of foreign banks’ subsidiaries in the U.S. banking environment. The primary focus is to investigate the effects of liability of foreignness on these subsidiaries’ loans portfolio, efficiency, and overall performance by proposing and testing a partial mediating effect of the risky loans portfolio on the relationship between LOF and performance. It is an attempt to bridge the gap between strategy and finance research by conducting an empirical study that incorporates financial measures and methods used in current banking and finance literature. Other research questions, including reasons for entering the U.S. market and types of initial entry modes, are also examined in the present study. Effects of the U.S. banking regulations are also discussed. Factors, such as country of origin in terms of the countries’ status (developed vs. developing), and other cultural factors influencing sustainability of these foreign banks in the U.S., are considered. The hierarchical regression analyses of the relationships between the LOF and these foreign banks’ riskiness of loans portfolio, efficiency, and performance are performed. Suggestions for future research are presented and limitations of the current study are also
A DIGITAL FORENSIC INVESTIGATIVE MODEL FOR BUSINESS ORGANISATIONS

When a digital incident occurs there are generally three courses of actions that are taken, generally dependant on the type of organisation within which the incident occurs, or which is responding the event. In the case of law enforcement the priority is to secure the crime scene, followed by the identification of evidentiary sources which should be dispatched to a specialist laboratory for analysis. In the case of an incident military (or similar critical infrastructures) infrastructure the primary goal becomes one of risk identification and elimination, followed by recovery and possible offensive measures. Where financial impact is caused by an incident, and revenue earning potential is adversely affected, as in the case of most commercial organisations), root cause analysis, and system remediation is of primary concern, with in-depth analysis of the how and why left until systems have been restored. Traditional investigative models follow the general process of: identify the incident, secure the scene and/or evidence, analyse the evidence, generate a report on the findings and present the outcome. This approach is more suited towards law enforcement than to the business world. The business environment lends itself to an approach similar to that of the military, namely to be able to identify the incident, patch the necessary system(s) and continue earning revenue. The only addition is that the business is more likely to want to press charges in a court of law than launch a counter offensive. In the generic investigative model, there is little leeway for a business’s incident responders to satisfy the need to return the systems to operational status as quickly as possible whilst preserving the necessary evidence and has to be able to mount a successful prosecution. These two goals can be mutually exclusive as a thorough investigation needs time and during this time the business will loose revenue by not having its system live. The model presented in this paper builds on the traditional investigative model as prepared by the Digital Forensic Research Workshop (DFRWS) and provides a mechanism to conduct the two potentially mutually exclusive processes in parallel.