Miracle-dz/UniFAR-FTUs huggingface dataset:
Cell-type and Donor-specific Transcriptional Responses to Interferon-α

A sensitive, specific, reproducible, robust, and cost-effective customized cDNA array system based on established nylon membrane technology has been developed for convenient multisample expression profiling for several hundred genes of choice. The genes represented are easily adjusted (depending on the availability of corresponding cDNAs) and the method is accordingly readily applicable to a wide variety of systems. Here we have focused on the expression profiles for interferon-α2a, the most widely used interferon for the treatment of viral hepatitis and malignancies, in primary cells (peripheral blood mononuclear cells, T cells, and dendritic cells) and cell lines (Kit255, HT1080, HepG2, and HuH7). Of 150 genes studied, only six were consistently induced in all cell types and donors, whereas 74 genes were induced in at least one cell type. IRF-7 was identified as the only gene exclusively induced in the hematopoietic cells. No gene was exclusively induced in the nonhematopoietic cell lines. In T cells 12, and in dendritic cells, 25 genes were induced in all donors whereas 45 and 42 genes, respectively, were induced in at least one donor. The data suggest that signaling through IFN-α2 can be substantially modulated to yield significant cell-type and donor-specific qualitative and quantitative differences in gene expression in response to this cytokine under highly standardized conditions.
Androgen Receptor Function and Androgen Receptor–Targeted Therapies in Breast Cancer: A Review

Importance The androgen receptor (AR) pathway is emerging as a potential therapeutic target in breast cancer. To date, AR-targeted drugs have been approved only for treatment of prostate cancer; however, AR-targeted treatment for breast cancer is an area of active investigation. Through review of preclinical studies, retrospective clinical studies, and clinical trials, we examined the biology of AR and AR-related pathways, the potential for AR-targeted therapies in breast cancer, and potential biomarkers for AR-targeted treatments. Observations The rate of AR positivity in breast cancer is about 60% to 80%. Biologically, the AR pathway has cross-talk with several other key signaling pathways, including the PI3K/Akt/mTOR and MAPK pathways, and with other receptors, including estrogen receptor and human epidermal growth factor receptor-2. The value of AR positivity as a prognostic marker has not yet been defined. Androgen receptor–targeted therapies, including AR agonists, AR antagonists, and PI3K inhibitors, have shown promising results in clinical trials in patients with breast cancer, and combinations of AR-targeted therapies with other agents have been investigated for overcoming resistance to AR-targeted therapies. Biomarkers to stratify patients according to the likelihood of response to AR-targeted drugs are yet to be established. Potential biomarkers of response to AR inhibitors include AR phosphorylation and AR gene expression. Conclusions and Relevance Androgen receptor–targeted treatments for breast cancer are in development and have shown promising preliminary results. In-depth understanding of AR and AR-related signaling pathways would improve the treatment strategies for AR-positive breast cancer. Further preclinical and clinical studies of AR-targeted drugs alone and in combination with other drugs are justified and warranted to clarify the biology of AR and inform the development of AR-targeted therapies to improve survival outcome in patients with breast cancer.
Coordinate expression of OPN and associated receptors during monocyte/macrophage differentiation of HL‐60 cells

Promyelocytic leukemia HL‐60 cells promoted by PMA to differentiate along the monocyte pathway adhere to tissue culture plates. To explore the regulation of adhesion molecules in cells promoted to differentiate, the expression and secretion of osteopontin (OPN) and expression of associated cell surface receptors, CD44 and integrin subunits αv, β3, β1, were examined. Results were as follows: (1) PMA induced OPN mRNA and OPN secretion into media; (2) untreated cells expressed β1 and CD44 mRNA, and PMA induced αv and β3 mRNA and increased β1 and CD44 mRNA expression; (3) PMA increased levels of αv, β3, β1 and CD44 protein on the cell surface; and (4) retinoic acid, which promotes granulocytic differentiation of HL‐60 cells, did not affect OPN, αv, β3, β1, or CD44 mRNA or protein expression. These data suggest that induction of OPN and associated receptors may play a role during monocytic differentiation of HL‐60 cells. J. Cell. Physiol. 175:229–237, 1998. © 1998 Wiley‐Liss, Inc.
Differential Evolution Enhanced With Multiobjective Sorting-Based Mutation Operators

Differential evolution (DE) is a simple and powerful population-based evolutionary algorithm. The salient feature of DE lies in its mutation mechanism. Generally, the parents in the mutation operator of DE are randomly selected from the population. Hence, all vectors are equally likely to be selected as parents without selective pressure at all. Additionally, the diversity information is always ignored. In order to fully exploit the fitness and diversity information of the population, this paper presents a DE framework with multiobjective sorting-based mutation operator. In the proposed mutation operator, individuals in the current population are firstly sorted according to their fitness and diversity contribution by nondominated sorting. Then parents in the mutation operators are proportionally selected according to their rankings based on fitness and diversity, thus, the promising individuals with better fitness and diversity have more opportunity to be selected as parents. Since fitness and diversity information is simultaneously considered for parent selection, a good balance between exploration and exploitation can be achieved. The proposed operator is applied to original DE algorithms, as well as several advanced DE variants. Experimental results on 48 benchmark functions and 12 real-world application problems show that the proposed operator is an effective approach to enhance the performance of most DE algorithms studied.
Overcoming Terminology Barrier Using Web Resources for Cross-Language Medical Information Retrieval

A number of authoritative medical websites, such as PubMed and MedlinePlus, provide consumers with the most up-to-date health information. However, non-English speakers often encounter not only language barriers (from other languages to English) but also terminology barriers (from laypersons inverted exclamation mark| terms to professional medical terms) when retrieving information from these websites. Our previous work address language barriers by developing a multilingual medical thesaurus, Chinese-English MeSH, while this study presents an approach to overcome terminology barriers based on Web resources. Two techniques were utilized in our approach: monolingual concept mapping using approximate string matching and crosslingual concept mapping using Web resources. The evaluation shows that our approach can significantly improve the performance on MeSH concept mapping and cross-language medical information retrieval.
The grey area in wireless communications: A multiplatform experimental study

Experimental studies in wireless sensor networks reveal a region with random packet loss and highly unreliable links. Some studies refer to the region as the Grey Area. This area may affect upper layer protocol performance. Unlike previous studies, our paper presents an experimental study of link performance using three different hardware platforms: one for Radio Frequency Identification (RFID) and two for Wireless Sensor Networks (WSNs). Therefore, our study considers short and long distance (up to 400 meters) behavior. The study measures Received Signal Strength Indicator (RSSI) values and Packet Reception Ratio (PRR). Preliminary results demonstrate that the Grey Area appears in all platforms and it occupies a maximum 28.6% of the radio range. However, one WSN platform (XbeePro900) presents the smaller proportional Grey Area. Moreover, our results support the hypothesis that Grey Area is mainly due to SNR decrease because of attenuation, and previously proposed explanations such as multipath and shadowing may be negligible. Additionally, we verified that better radio technologies experience a smaller grey area.
A Practical Framework of Conversion Rate Prediction for Online Display Advertising

Cost-per-action (CPA), or cost-per-acquisition, has become the primary campaign performance objective in online advertising industry. As a result, accurate conversion rate (CVR) prediction is crucial for any real-time bidding (RTB) platform. However, CVR prediction is quite challenging due to several factors, including extremely sparse conversions, delayed feedback, attribution gaps between the platform and the third party, etc. In order to tackle these challenges, we proposed a practical framework that has been successfully deployed on Yahoo! BrightRoll, one of the largest RTB ad buying platforms. In this paper, we first show that over-prediction and the resulted over-bidding are fundamental challenges for CPA campaigns in a real RTB environment. We then propose a safe prediction framework with conversion attribution adjustment to handle over-predictions and to further alleviate over-bidding at different levels. At last, we illustrate both offline and online experimental results to demonstrate the effectiveness of the framework.
Development of an intelligent robotic system for the automation of a meat-processing task

A robotic system and specifically designed end-effector tool has been developed for the automation of a meat processing task. This tool replicates the operation of a human butcher, while providing improved aseptic conditions and an increase in the reliability, repeatability and quality of the process. The robotic system incorporates a neural network-based fault detection scheme, which uses a learning vector quantisation network to classify axial load signals. A proposed online adaptive learning algorithm will provide continuous optimisation of the robot cut-path, leading to the automation of the path tuning process. This will allow the system to respond to seasonal carcass variations automatically and maintain a consistently high level of performance.
Sentiment classification using Enhanced Contextual Valence Shifters

We have explored different methods of improving the accuracy of sentiment classification. The sentiment orientation of a document can be positive (+), negative (-), or neutral (0). We combine five dictionaries from [2, 3, 4, 5, 6] into the new one with 21137 entries. The new dictionary has many verbs, adverbs, phrases and idioms, that are not in five ones before. The paper shows that our proposed method based on the combination of Term-Counting method and Enhanced Contextual Valence Shifters method has improved the accuracy of sentiment classification. The combined method has accuracy 68.984% on the testing dataset, and 69.224% on the training dataset. All of these methods are implemented to classify the reviews based on our new dictionary and the Internet Movie data set.
Antidepressant Treatment of Depression: A Metaanalysis

Objective: To carry out a metaanalysis of antidepressant studies to calculate the effect sizes for antidepressant effect in depressive disorder. Method: A metaanalysis of all antidepressant studies that included an active comparison drug as well as placebo was used to calculate the effect size. Articles were selected from a MEDLINE search for the period January 1966 to June 1995. Forty-nine studies were included in the metaanalysis. Results: The effect sizes for antidepressant treatment are moderately larger than for placebo. A larger effect size was observed in studies where objective diagnostic criteria for depression were used. Conclusions: We conclude that the superior efficacy of antidepressants over placebo can be demonstrated.
Strength or Accuracy ?

Wilson's XCS is a clear departure from earlier classi er systems in terms of the way it calculates the tness of classi ers for use in the genetic algorithm. Despite the growing body of work on XCS and the advantages claimed for it there has been no detailed comparison of XCS and traditional strength-based systems. This work takes a step towards rectifying this situation by surveying a number of issues related to the change in tness. I distinguish di erent de nitions of overgenerality for strength and accuracy-based tness and analyse some implications of the use of accuracy, including an apparent advantage in addressing the explore/exploit problem. I analyse the formation of strong overgenerals, a major problem for strength-based systems, and illustrate their dependence on biased reward functions. I consider motivations for biasing reward functions in single step environments, and show that non-trivial multi step environments have biased Q-functions. I conclude that XCS's accuracy-based tness appears to have a number of signi cant advantages over traditional strength-based tness.
Remote Surveillance and Face Tracking with Mobile Phones (Smart Eyes)

Remote Surveillance and Face Tracking with Mobile Phones By: Sandro da Silva M.Sc. Thesis Department of Computer Science Faculty of Science University of the Western Cape This thesis addresses analysis, evaluation and simulation of low complexity face detection algorithms and tracking that could be used on mobile phones. Network access control using face recognition increases the user-friendliness in human-computer interaction. In order to realize a real time system implemented on handheld devices with low computing power, low complexity algorithms for face detection and face tracking are implemented. Skin color detection algorithms and face matching have low implementation complexity suitable for authentication of cellular network services. Novel approaches for reducing the complexities of these algorithms and fast implementation are introduced in this thesis. This includes a fast algorithm for face detection in video sequences, using a skin color model in the HSV (Hue-SaturationValue) color space. It is combined with a Gaussian model of the H and S statistics and adaptive thresholds. These algorithms permit segmentation and detection of multiple faces in thumbnail images. Furthermore we evaluate and compare our results with those of a method implemented in the Chromatic Color space (YCbCr). We also test our test data on face detection method using Convolutional Neural Network architecture to study the suitability of using other approaches besides skin color as the basic feature for face detection. Finally, face tracking is done in 2D color video streams using HSV as the histogram color space. The program is used to compute 3D trajectories for a remote surveillance system.
Visual Understanding via Multi-Feature Shared Learning With Global Consistency

Image/video data is usually represented with multiple visual features. Fusion of multi-source information for establishing attributes has been widely recognized. Multi- feature visual recognition has recently received much attention in multimedia applications. This paper studies visual understanding via a newly proposed l2-norm-based multi-feature shared learning framework, which can simultaneously learn a global label matrix and multiple sub-classifiers with the labeled multi-feature data. Additionally, a group graph manifold regularizer composed of the Laplacian and Hessian graph is proposed. It can better preserve the manifold structure of each feature, such that the label prediction power is much improved through semi-supervised learning with global label consistency. For convenience, we call the proposed approach global-label- consistent classifier (GLCC). The merits of the proposed method include the following: 1) the manifold structure information of each feature is exploited in learning, resulting in a more faithful classification owing to the global label consistency; 2) a group graph manifold regularizer based on the Laplacian and Hessian regularization is constructed ; and 3) an efficient alternative optimization method is introduced as a fast solver owing its speed to convex sub-problems. Experiments on several benchmark visual datasets-the 17-category Oxford Flower dataset, the challenging 101- category Caltech dataset, the YouTube and Consumer Videos dataset, and the large-scale NUS-WIDE dataset-have been used for multimedia understanding . The results demonstrate that the proposed approach compares favorably with state-of-the-art algorithms. An extensive experiment using the deep convolutional activation features also shows the effectiveness of the proposed approach. The code will be available on http://www.escience. cn/people/lei/index.html.
Unprecedented Sensitivity in a Probe for Monitoring Cathepsin B: Chemiluminescence Microscopy Cell-Imaging of a Natively Expressed Enzyme.

Until recently, chemiluminescence cell images could only be obtained using luciferase-activated probes. Moreover, chemiluminescence microscopy cell-imaging has not been demonstrated for natively expressed enzymes like cathepsin B. Herein, we describe the design, synthesis, and evaluation of the first chemiluminescence probe for the detection and imaging of cathepsin B. The probe activation mechanism relies on the release of a dioxetane intermediate, which undergoes chemiexcitation to emit green light with high efficiency under physiological conditions. Using the probe, we obtained clear images of cancerous leukemia and colon cells. This is the first demonstration of chemiluminescence cell images obtained by a probe for a natively expressed endogenous enzyme. We anticipate that the concept presented in this study will be broadly used to develop analogous probes for other important proteases relevant to biomolecular processes.
Phylogenetic conservatism of environmental niches in mammals

Phylogenetic niche conservatism is the pattern where close relatives occupy similar niches, whereas distant relatives are more dissimilar. We suggest that niche conservatism will vary across clades in relation to their characteristics. Specifically, we investigate how conservatism of environmental niches varies among mammals according to their latitude, range size, body size and specialization. We use the Brownian rate parameter, σ2, to measure the rate of evolution in key variables related to the ecological niche and define the more conserved group as the one with the slower rate of evolution. We find that tropical, small-ranged and specialized mammals have more conserved thermal niches than temperate, large-ranged or generalized mammals. Partitioning niche conservatism into its spatial and phylogenetic components, we find that spatial effects on niche variables are generally greater than phylogenetic effects. This suggests that recent evolution and dispersal have more influence on species' niches than more distant evolutionary events. These results have implications for our understanding of the role of niche conservatism in species richness patterns and for gauging the potential for species to adapt to global change.
Bond Market Clienteles, the Yield Curve, and the Optimal Maturity Structure of Government Debt

We propose a clientele-based model of the yield curve and optimal maturity structure of government debt. Clienteles are generations of agents at different life cycle stages in an overlapping-generations economy. An optimal maturity structure exists in the absence of distortionary taxes and induces efficient intergenerational risksharing. If agents are more risk-averse than log, then an increase in the long-horizon clientele raises the price and optimal supply of long-term bonds. But while a welfare-maximizing government caters to clienteles, it does not accommodate fully their demand, and limits issuance of long-term bonds to a level where these earn negative expected excess returns.
A State Synchronization Mechanism for Orchestrated Processes

Two orchestrated processes interacting with each other have to maintain their own states. Messages are used to synchronize states between orchestrated processes. Server crash and network failure may result in loss of messages and therefore result in a state change performed by only one party. Thus, the states of the parties are no longer synchronized, resulting in state inconsistencies and in worst case deadlocks. In this paper, we propose a mechanism for guaranteed state synchronization of orchestrated processes with system and network failures. Our mechanism is based on interaction patterns and process transformations. The basic idea is to redesign the original processes into their state synchronization-enabled counterparts via process transformations that can be automated. The transformation mechanism is formalized based on Colored Petri Nets. We present the formal proof of the correctness of our mechanism and give the overhead analysis to illustrate its practicability.
Serious and Violent Young Offenders’ Decisions to Recidivate: An Assessment of Five Sentencing Models

Five models of sentencing were assessed with respect to their impact on the decisions of young offenders to recidivate. The five sentencing models tested were fairness, deterrence, chronic offender lifestyle, special needs, and procedural rights. A sample of 400 incarcerated young offenders from the Vancouver, British Columbia, metropolitan area were asked questions regarding their attitudes toward these sentencing models and their intentions to recidivate after serving a period of incarceration. Principal components analyses suggested that although these models do not function independently, two composite models do shed some light on the issues that young offenders consider when contemplating their decisions and intentions to recidivate. Despite the ability of these models to predict half of the explained variance in young offenders’ decisions regarding recidivism, a majority of the sample appeared to not be affected exclusively by cost-benefit analysis, punishment, or reintegrative motivations. The authors conclude that without additional variables and even higher predictive validity, it is premature for policy makers to focus on any single model of sentencing in constructing juvenile justice laws.
Deep evolution of image representations for handwritten digit recognition

A training protocol for learning deep neural networks, called greedy layer-wise training, is applied to the evolution of a hierarchical, feed-forward Genetic Programming based system for feature construction and object recognition. Results on a popular handwritten digit recognition benchmark clearly demonstrate that two layers of feature transformations improves generalisation compared to a single layer. In addition, we show that the proposed system outperforms several standard Genetic Programming systems, which are based on hand-designed features, and use different program representations and fitness functions.