Miracle-dz/UniFAR-FTUs huggingface dataset:
Autologous Fibrin Glue as an Encapsulating Scaffold for Delivery of Retinal Progenitor Cells

The retina is a highly sophisticated piece of the neural machinery that begins the translation of incoming light signals into meaningful visual information. Several degenerative diseases of the retina are characterized by photoreceptor loss and eventually lead to irreversible blindness. Regenerative medicine, using tissue engineering-based constructs to deliver progenitor cells or photoreceptors along with supporting carrier matrix is a promising approach for restoration of structure and function. Fresh fibrin glue (FG) produced by the CryoSeal®FS system in combination with mouse retinal progenitor cells (RPCs) were evaluated in this study. In vitro expanded RPCs isolated from postnatal mouse retina were encapsulated into FG and cultured in the presence of the protease inhibitor, tranexamic acid. Encapsulation of RPCs into FG did not show adverse effects on cell proliferation or cell survival. RPCs exhibited fibroblast-like morphology concomitantly with attachment to the encapsulating FG surface. They expressed α7 and β3 integrin subunits that could mediate attachment to fibrin matrix via an RGD-independent mechanism. The three-dimensional environment and the attachment surface provided by FG was associated with a rapid down-regulation of the progenitor marker SOX2 and enhanced the expression of the differentiation markers cone-rod homeobox and recoverin. However, the in vitro culture conditions did not promote full differentiation into mature photoreceptors. Nevertheless, we have shown that autologous fibrin, when fabricated into a scaffold for RPCs for delivery to the retina, provides the cells with external cues that could potentially improve the differentiation events. Hence, transient encapsulation of RPCs into FG could be a valid and potential treatment strategy to promote retinal regeneration following degenerative diseases. However, further optimization is necessary to maximize the outcomes in terms of mature photoreceptors.
A Hybrid Method for Obtaining the Distribution of Report Latency in Wireless Sensor Networks

Efficient event reporting is of vital importance in wireless sensor networks (WSNs). This is especially true in critical-time applications, where ensuring an adequate report latency allows a proper reaction from the network to the occurring phenomena. Despite a QoS analysis based on mean report latency is clearly insufficient for critical-time applications, it is oftentimes presented as the main performance indicator. For instance, high percentiles or the whole distribution of the report latency are much better suited. Furthermore, several applications, such as target positioning, require the transmission of a certain number of event packets to accurately characterize occurring events. Building on this, we present a hybrid method for obtaining the report latency distribution of random access (RA) WSN protocols. Here, the distribution of detecting nodes is obtained by simulation and then we perform the mathematical analysis of QoS parameters. Since the proposed method comprises a general structure, it can be easily adapted for analyzing the QoS of a wide range of RA WSN protocols. We use this method for evaluating the report latency, along with the energy consumption of RA protocols in applications that require the transmission of, at least, k event messages. Results show that the combination of higher detection ranges with low congested wireless environments leads to a clear reduction in report latency and event overlooking probability. This leads to an overall performance increase.
Rv2131c from Mycobacterium tuberculosis Is a CysQ 3′-Phosphoadenosine-5′-phosphatase

Mycobacterium tuberculosis (Mtb) produces a number of sulfur-containing metabolites that contribute to its pathogenesis and ability to survive in the host. These metabolites are products of the sulfate assimilation pathway. CysQ, a 3′-phosphoadenosine-5′-phosphatase, is considered an important regulator of this pathway in plants, yeast, and other bacteria. By controlling the pools of 3′-phosphoadenosine 5′-phosphate (PAP) and 3′-phosphoadenosine 5′-phosphosulfate (PAPS), CysQ has the potential to modulate flux in the biosynthesis of essential sulfur-containing metabolites. Bioinformatic analysis of the Mtb genome suggests the presence of a CysQ homologue encoded by the gene Rv2131c. However, a recent biochemical study assigned the protein’s function as a class IV fructose-1,6-bisphosphatase. In the present study, we expressed Rv2131c heterologously and found that the protein dephosphorylates PAP in a magnesium-dependent manner, with optimal activity observed between pH 8.5 and pH 9.5 using 0.5 mM MgCl2. A sensitive electrospray ionization mass spectrometry-based assay was used to extract the kinetic parameters for PAP, revealing a Km (8.1 ± 3.1 μM) and kcat (5.4 ± 1.1 s−1) comparable to those reported for other CysQ enzymes. The second-order rate constant for PAP was determined to be over 3 orders of magnitude greater than those determined for myo-inositol 1-phosphate (IMP) and fructose 1,6-bisphosphate (FBP), previously considered to be the primary substrates of this enzyme. Moreover, the ability of the Rv2131c-encoded enzyme to dephosphorylate PAP and PAPS in vivo was confirmed by functional complementation of an Escherichia coli ΔcysQ mutant. Taken together, these studies indicate that Rv2131c encodes a CysQ enzyme that may play a role in mycobacterial sulfur metabolism.
Multiple Feature-Based Classifier and Its Application to Image Classification

A new image classification method with multiple feature-based classifier (MFC) is proposed in this paper. MFC does not use the entire feature vectors extracted from the original data in a concatenated form to classify each datum, but rather uses groups of features related to each feature vector separately. In the training stage, a confusion table calculated from each local classifier that uses a specific feature vector group is drawn throughout the accuracy of each local classifier and then, in the testing stage, the final classification result is obtained by applying weights corresponding to the confidence level of each local classifier. The proposed MFC algorithm is applied to the problem of image classification on a set of image data. The results demonstrate that the proposed MFC scheme can optimally enhance the classification accuracy of individual classifiers that use specific feature vector group.
A pose adaptive eye detection method using 3D face information

This paper proposes an eye detection algorithm robust to head pose variation. First, face region is detected by Viola-Jones algorithm. Second, in order to detect eye robust to pose variation, a window template is generated from the 3D mean face model. Finally, eye is detected and localized by using this window template. In the experiments, the proposed method is tested on CAS-PEAL database.
A filtering method of gyroscope random drift for Miniature Unmanned Helicopter

Low-cost MEMS gyroscopes used in the Miniature Unmanned Helicopter (MUH) have great random drift. In order to improve the performance of MEMS gyroscopes, a one-order AR model was established for the random drift. Then Sage-Husa adaptive Kalman filter was applied to process the random drift signal. Experiments were carried out to verify the validity of the method. Tests results demonstrate that the method based on AR model and Sage-Husa adaptive Kalman filter is convenient and effective and it significantly reduces random drift. Compared to conventional Kalman filter, Sage-Husa adaptive Kalman filter can estimate statistic characteristics of system noise and measurement noise and modify filter parameters on-time. It improves stability and adaptability and thus can give a more accurate filtering result.
Repurposing an Erstwhile Cancer Drug: A Quantitative and Therapeutic Evaluation of Alternative Nanosystems for the Delivery of Colchicine to Solid Tumors

Background: Antimitotic drugs represent some of the most popular vascular disrupting agents used today in the fight against cancer. Colchicine is the first known alkaloid. The drug binds to tubulin protein and depolymerizes microtubules. Despite the impressive therapeutic activity as an antimitotic agent, colchicine is fatally toxic when administered to cancer patients intravenously due to its low therapeutic index. This study supports the early development of a relatively safe and target-specific nanoparticle for the IV administration of colchicine to lung tumors. Methods: For in vitro studies, lung cancer (LLC, Chago-k-1 and MCA-205), and endothelial cell lines (MS1- VEGF, HMEC-1) were employed. The qualitative and quantitative analyses of the cytoskeleton and nuclear areas were performed using FITC-labeled β-tubulin antibody; the mean area of cytoskeleton and nucleus per cell was determined by fluorescence microscopy and BIOQUANT. In vivo, the biodistribution and therapeutic efficacy studies of the pegylated cationic liposomes (PCLs) were performed in C57BL/6 mice bearing pseudo-orthotopic lung tumors. The biodistribution of colchicine and PCL-colchicine was determined by dual labeling. 111In labeled PCLs and 3H-Colchicine were employed to simultaneously track the vehicle and drug, respectively. The therapeutic efficacy was determined by monitoring animal survival. Results: The disruption of microtubules was most evident when colchicine was loaded in DOTAP-PCLs. We report a ~2 fold (p=0.0214, 95% CI is 2.923 to 13.247) increase in the accumulation of PCL-colchicine in tumor-bearing lung compared to the normal lung, resulting in significantly extended survival times in PCL treated group (p=0.0052, Log Rank (Mantel-Cox test)). Conclusions: The PCL-based colchicine-loaded nanosystem can renew the clinical potential of abandoned antimitotic agents such as colchicine. The PCL platform can enable the accumulation of clinically relevant doses of colchicine in solid tumors. This will ensure antimitotic activity while decreasing the uptake of the drug in healthy tissues.
Robust Eye Localization on Multi-View Face in Complex Background Based on SVM Algorithm

Focused on multi-view eye localization in complex background, a new detection method based improved SVM is proposed. First, the face is located by AdaBoost detector and the eye searching range on the face is determined. Then, the crossing detection method, which uses the feature of eye and brow integrated as a whole, and the improved SVM detectors trained by large scale multi-view eye examples are adopted to find the candidate eye regions. Based on the fact that the window region with higher weight in SVM classifier is relatively closer to the eye, and the same eye tends to be repeatedly detected by near windows, the candidate eye regions are filtered to refine the eye location on the multi-view face. Experiments show that the method has very good accuracy and robustness to the eye localization with various face post and expression in the complex background.
What Predicts Media Coverage of Health Science Articles?

An important aspect of health science is communicating research findings to the public. The media is a critical instrument in disseminating research. Yet the process by which a scientific article becomes “newsworthy” is not well understood. In this study, we use large-scale text analysis to characterize the content features of articles that are predictive of newsworthiness. We experiment with two novel corpora: (i) 28,910 articles from a di- verse range of biomedical and health journals, of which 1,343 were covered by the news agency Reuters, and (ii) 10,760 articles from the JAMA journals, of which 846 were given press releases by the journal editors. We show that media coverage can be predicted reasonably well: logistic regression achieves mean AUCs of 0.783 and 0.882 on the Reuters and JAMA datasets, respec- tively. We present and discuss interesting findings con- cerning the most predictive content features.
Efficient Pairwise Learning Using Kernel Ridge Regression: an Exact Two-Step Method

Pairwise learning or dyadic prediction concerns the prediction of properties for pairs of objects. It can be seen as an umbrella covering various machine learning problems such as matrix completion, collaborative filtering, multi-task learning, transfer learning, network prediction and zero-shot learning. In this work we analyze kernel-based methods for pairwise learning, with a particular focus on a recently-suggested two-step method. We show that this method offers an appealing alternative for commonly-applied Kronecker-based methods that model dyads by means of pairwise feature representations and pairwise kernels. In a series of theoretical results, we establish correspondences between the two types of methods in terms of linear algebra and spectral filtering, and we analyze their statistical consistency. In addition, the two-step method allows us to establish novel algorithmic shortcuts for efficient training and validation on very large datasets. Putting those properties together, we believe that this simple, yet powerful method can become a standard tool for many problems. Extensive experimental results for a range of practical settings are reported.
Compositional polarity classification approach for product reviews

In this paper, we examine the effectiveness of compositional polarity classification technique which uses semi-supervised classifier with the help of a domain-independent unsupervised classifier for sentiment classification problem. For compositional polarity classifiers, we create a pseudo-labeled training set by using an unsupervised classifier that relies on a lexical resource and train a base SVM classifier over the training set, and then investigate four semi-supervised learning methods (self-training, Transductive SVM, spectral graph transduction and semi-supervised learning based on a Deterministic Annealing approach) on four Chinese datasets which span two different domains: digital products and hotel. Through comparative experiments, we conclude that compositional classification technique is effective and helpful to improve the accuracy of sentiment classification without using labeled data.
Extended touch mobile user interfaces through sensor fusion

This article explores an efficient sensor fusion algorithm for detecting and classifying user taps on any neighboring surface even in the presence of various background acoustics. The fusion algorithm employs a tier classifier combining microphone and accelerometer detection of user taps on iOS platform resulting into 100%success rate for the datasets studied in this paper. Fusion of these two sensors eliminates the need for any added filtering, knowledge of precise sensor positioning or use of any specialized piezoelectric sensors, as has been done in past research, as well as gives a robust classification with high success-rate even as the signal to noise ratio significantly degrades.
Efficacy of mefloquine intermittent preventive treatment in pregnancy against Schistosoma haematobium infection in Gabon: a nested randomized controlled assessor-blinded clinical trial.

BACKGROUND Urogenital schistosomiasis is a major public health problem in sub-Saharan Africa, and routine programs for screening and treatment of pregnant women are not established. Mefloquine-currently evaluated as a potential alternative to sulfadoxine-pyrimethamine as intermittent preventive treatment against malaria in pregnancy (IPTp)-is known to exhibit activity against Schistosoma haematobium. In this study we evaluated the efficacy of mefloquine IPTp against S. haematobium infection in pregnant women. METHODS Pregnant women with S. haematobium infection presenting at 2 antenatal health care centers in rural Gabon were invited to participate in this nested randomized controlled, assessor-blinded clinical trial comparing sulfadoxine-pyrimethamine with mefloquine IPTp. Study drugs were administered twice during pregnancy with a 1- month interval after completion of the first trimester. RESULTS Sixty-five pregnant women were included in this study. Schistosoma haematobium egg excretion rates showed a median reduction of 98% (interquartile range [IQR], 70%-100%) in the mefloquine group compared to an increase of 20% (IQR, -186% to 75%) in the comparator group. More than 80% of patients showed at least 50% reduction of egg excretion and overall cure rate was 47% (IQR, 36%-70%) 6 weeks after the second administration of mefloquine IPTp. CONCLUSION When used as IPTp for the prevention of malaria, mefloquine shows promising activity against concomitant S. haematobium infection leading to an important reduction of egg excretion in pregnant women. Provided that further studies confirm these findings, the use of mefloquine may transform future IPTp programs into a 2-pronged intervention addressing 2 of the most virulent parasitic infections in pregnant women in sub-Saharan Africa. CLINICAL TRIALS REGISTRATION NCT01132248; ATMR2010020001429343.
A VARIABLE PRF IMAGING METHOD FOR HIGH SQUINT DIVING SAR

The high squint diving SAR is widely used to provide the information in advance. Large squint angle deduces the deeper coupling of range and azimuth of SAR echoes which makes SAR imaging more di-cult. Especially, the large range migration of the deep couple heavily burdens the imaging processing time and storage units. The diving motion of platform worsens the situation. This paper proposes the varied azimuth sample frequency (Pulse Repeat Frequency, PRF) to implement the high squint diving SAR imaging. Based on the signal model of the diving squint SAR, it is analyzed that the range walk is the prominent component of range migration in the high squint SAR. The varied PRF imaging method dramatically decreases the range walk of echoes by shifting the beginning position of transmitted pulses and received echoes and the shift is implemented by the PRF variation. Then the range migration is decreased and the couple of range and azimuth of SAR echoes is reduced. The PRF variation law is deduced and the applicable condition of varied PRF is presented. The simulation results show that the variable PRF method is e-cient to reduce range walk of echoes. Comparison to the traditional constant PRF, the novel variable PRF method for high squint needs less storage and less time expense, which is helpful to real time SAR imaging. The non-uniform FFT can be used for the azimuth compressing of the variable PRF SAR. It will simplify the implementation of the variable PRF SAR imaging.
A double optimal descent algorithm for iteratively solving ill-posed linear inverse problems

In the iterative solution of an ill-posed linear system , how to select a fast and easily established descent direction to reduce the residual is an important issue. A mathematical procedure to find a double optimal descent direction in , without inverting , is developed in an -dimensional Krylov subspace. The novelty is that we expand in an affine Krylov subspace with undetermined coefficients, and then two optimization techniques are used to determine these coefficients in closed form, which can greatly accelerate the convergence speed in solving the ill-posed linear problems. The double optimal descent algorithm is proven to be absolutely convergent very fast, accurate and robust against noise, which is confirmed by numerical tests of several linear inverse problems, including the heat source identification problem, the backward heat conduction problem, the inverse Cauchy problem and the external force identification problem.
New species of Choleoeimeria (Apicomplexa: Eimeriidae) from the veiled chameleon, Chamaeleo calyptratus (Sauria: Chamaeleonidae), with taxonomic revision of eimerian coccidia from chameleons.

Coprological examination of 71 samples from a breeding colony of veiled chameleons, Chamaeleo calyptratus Duméril et Duméril, 1851, revealed a presence of two species of coccidia. In 100% of the samples examined, oocysts of Isospora jaracimrmani Modrý et Koudela, 1995 were detected. A new coccidian species, Choleoeimeria hirbayah sp. n. , was discovered in 32.4% of samples from the colony. Its oocysts are tetrasporocystic, cylindrical, 28.3 (25-30) x 14.8 (13.5-17.5) microm, with smooth, bilayered, -1 microm thick wall. Sporocysts are dizoic, ovoidal to ellipsoidal, 10.1 (9-11) x 6.9 (6-7.5) microm, sporocyst wall is composed of two plates joined by a meridional suture. Endogenous development is confined to the epithelium of the gall bladder, with infected cells being typically displaced from the epithelium layer towards lumen. A taxonomic revision of tetrasporocystic coccidia in the Chamaeleonidae is provided.
‘ Is the Taylor Rule the same as the Friedman Rule ?

Our objective in this paper has been to provide more theoretically coherent microfoundations for monetary policy rules in response to Lucas’s (1976) critique of econometric policy evaluation and, more importantly, to show that the Taylor rule can be derived via Friedman’s k% money supply rule. A key di¤erence with respect to the traditional IS-LM framework, is that, the aggregate decision rules evolve explicitly from optimisation by households and . rms. We conduct counterfactual historical analysis to compare and contrast Friedman’s rule alongside Taylor’s (1993) rule. JEL classi.cations : D5, D58, E0, E5
A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems

There is a growing interest in intelligent assistants for a variety of applications from organizing tasks for knowledge workers to helping people with dementia. In our earlier work, we presented a decision-theoretic framework that captures the general notion of assistance. The objective was to observe a goal-directed agent and to select assistive actions in order to minimize the overall cost. We employed the use of POMDPs to model the assistant whose hidden state was the goal of the agent. In this work, we evaluate our model of assistance on a real world domain and establish that our model was very effective in reducing the efforts of the user. We compared the results of our model against a cost-sensitive supervised learning algorithm. We also describe our current work on extending the model to include relational hierarchies. We then analyze some problems in our model and suggest possible extensions to handle them.
Data Driven Color Mapping

In this paper we present a simple, yet effective method to map data set values of different distributions to a color map in order to reveal interesting structures. We make use of an ordering and a simple projection technique to transform the data set before color mapping. Our transformation yields convincing results for various distributions. It also removes the burden from the user to test several mappings beforehand. A simple angular interpolation technique allows to project the data values of the visualization as desired, interactively.
Sensor Selection Using Information Complexity for Multi-sensor Mobile Robot Localization

Our sensor selection algorithm targets the problem of global self-localization of multi-sensor mobile robots. The algorithm builds on the probabilistic reasoning using Bayes filters to estimate sensor measurement uncertainty and sensor validity in robot localization. For quantifying measurement uncertainty we score the Bayesian belief probability density using a model selection criterion, and for sensor validity, we evaluate belief on pose estimates from different sensors as a multi-sample clustering problem. The minimization of the combined uncertainty (measurement uncertainly score + sensor validity score) allows us to intelligently choose a subset of sensors that contribute to accurate localization of the mobile robot. We demonstrate the capability of our sensor selection algorithm in automatically switching pose recovery methods and ignoring non-functional sensors for localization on real-world mobile platforms equipped with laser scanners, vision cameras, and other hardware instrumentation for pose estimation.