Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving In Vitro Generated Cartilage-Carrier-Constructs by Optimizing Growth Factor Combination

The presented study is focused on the generation of osteochondral implants for cartilage repair, which consist of bone substitutes covered with in vitro engineered cartilage. Re-differentiation of expanded porcine cells was performed in alginate gel followed by cartilage formation in high-density cell cultures. In this work, different combinations of growth factors for the stimulation of re-differentiation and cartilage formation have been tested to improve the quality of osteochondral implants. It has been demonstrated that supplementation of the medium with growth factors has significant effects on the properties of the matrix. The addition of the growth factors IGF-I (100 ng/mL) and TGF-β1 (10 ng/mL) during the alginate culture and the absence of any growth factors during the high-density cell culture led to significantly higher GAG to DNA ratios and Young’s Moduli of the constructs compared to other combinations. The histological sections showed homogenous tissue and intensive staining for collagen type II.
A OMA DM based Software Defined Radio Proof-of-Concept Demonstration Platform

This paper presents our work on a demonstration platform for a software defined radio proof-of-concept, and how OMA Device Management protocol and Functional Description Language can be used to support RAT reconfiguration on SDR terminals. Open Mobile Alliance is an open standards body for the mobile phone industry, and OMA DM protocol is designed to manage small mobile devices such as mobile phones, PDAs and laptop computers. The Functional Description Language is based on XML and is used to describe functional configurations for reconfigurable equipment. The description file is interpreted by the Configuration Control Module using Signal Processing Modules to create a binary configuration file for the target platform. An SDR platform integrating these ideas is introduced. The adaptive execution environment concept is evaluated on a heterogeneous multi-processor hardware test platform known as the Real-Time Research Platform.
Spinal cord stimulation for neuropathic pain: current perspectives

Neuropathic pain constitutes a significant portion of chronic pain. Patients with neuropathic pain are usually more heavily burdened than patients with nociceptive pain. They suffer more often from insomnia, anxiety, and depression. Moreover, analgesic medication often has an insufficient effect on neuropathic pain. Spinal cord stimulation constitutes a therapy alternative that, to date, remains underused. In the last 10 to 15 years, it has undergone constant technical advancement. This review gives an overview of the present practice of spinal cord stimulation for chronic neuropathic pain and current developments such as high-frequency stimulation and peripheral nerve field stimulation.
Electroantennogram response of the parasitoid, Microplitis croceipes to host-related odors: The discrepancy between relative abundance and level of antennal responses to volatile compound.

Herbivores emit volatile organic compounds (VOCs) after feeding on plants. Parasitoids exploit these VOCs as odor cues to locate their hosts. In nature, host-related odors are emitted as blends of various compounds occurring in different proportions, and minor blend components can sometimes have profound effects on parasitoid responses. In a previous related study, we identified and quantified VOCs emitted by cotton plant-fed Heliothis virescens (Lepidoptera: Noctuidae) larvae, an herbivore host of the parasitoid Microplitis croceipes (Hymenoptera: Braconidae). In the present study, the olfactory response of female M. croceipes to synthetic versions of 15 previously identified compounds was tested in electroantennogram (EAG) bioassays. Using M. croceipes as a model species, we further asked the question: does the relative abundance of a volatile compound match the level of antennal response in parasitoids? Female M. croceipes showed varying EAG responses to test compounds, indicating different levels of bioactivity in the insect antenna. Eight compounds, including decanal, 1-octen-3-ol, 3-octanone, 2-ethylhexanol, tridecane, tetradecane, α-farnesene and bisabolene, elicited EAG responses above or equal to the 50 th percentile rank of all responses. Interestingly, decanal, which represented only 1% of the total amount of odors emitted by cotton-fed hosts, elicited the highest (0.82 mV) EAG response in parasitoids. On the other hand, ( E)-β-caryophyllene, the most abundant (29%) blend component, elicited a relatively low (0.17 mV) EAG response. The results suggest that EAG response to host-related volatiles in parasitoids is probably more influenced by the ecological relevance or functional role of the compound in the blend, rather than its relative abundance.
Resource Allocation in Successive Relaying for Half-Duplex Relay-Based OFDMA Systems

In this paper, we consider a four-node relay-based OFDMA system. The expression of the upper bound for the achievable rate in Successive Relaying (SUR) protocol is derived by using the cut-set theorem for half-duplex systems. Based on this expression, the near-optimal solution of the achievable rate is obtained in the joint power and subcarrier allocation constraint, according to the dual problem decomposition approach and the subgradient method. Moreover, we make a comparison on the achievable rate between SUR and Simultaneous Relaying (SIR) in the pathloss model accepted by the 3rd Generation Partnership Project (3GPP) Long Term Evolution (LTE) Advanced system. The simulation results demonstrate the enhancement of the achievable rate in SUR protocol is expanded in high signal-to-noise ratio (SNR) compared with the achievable rate in SIR protocol under both the symmetric and asymmetric cases. And for SUR protocol , the achievable rate in symmetric case performs better in high SNR.
GINI: From ISH Images to Gene Interaction Networks

Accurate inference of molecular and functional interactions among genes, especially in multicellular organisms such as Drosophila, often requires statistical analysis of correlations not only between the magnitudes of gene expressions, but also between their temporal-spatial patterns. The ISH (in-situ-hybridization)-based gene expression micro-imaging technology offers an effective approach to perform large-scale spatial-temporal profiling of whole-body mRNA abundance. However, analytical tools for discovering gene interactions from such data remain an open challenge due to various reasons, including difficulties in extracting canonical representations of gene activities from images, and in inference of statistically meaningful networks from such representations. In this paper, we present GINI, a machine learning system for inferring gene interaction networks from Drosophila embryonic ISH images. GINI builds on a computer-vision-inspired vector-space representation of the spatial pattern of gene expression in ISH images, enabled by our recently developed system; and a new multi-instance-kernel algorithm that learns a sparse Markov network model, in which, every gene (i.e., node) in the network is represented by a vector-valued spatial pattern rather than a scalar-valued gene intensity as in conventional approaches such as a Gaussian graphical model. By capturing the notion of spatial similarity of gene expression, and at the same time properly taking into account the presence of multiple images per gene via multi-instance kernels, GINI is well-positioned to infer statistically sound, and biologically meaningful gene interaction networks from image data. Using both synthetic data and a small manually curated data set, we demonstrate the effectiveness of our approach in network building. Furthermore, we report results on a large publicly available collection of Drosophila embryonic ISH images from the Berkeley Drosophila Genome Project, where GINI makes novel and interesting predictions of gene interactions. Software for GINI is available at http://sailing.cs. cmu.edu/Drosophila_ISH_images/
Targeted delivery of antigen to hamster nasal lymphoid tissue with M-cell-directed lectins

The nasal cavity of a rodent is lined by an epithelium organized into distinct regional domains responsible for specific physiological functions. Aggregates of nasal lymphoid tissue (NALT) located at the base of the nasal cavity are believed to be sites of induction of mucosal immune responses to airborne antigens. The epithelium overlying NALT contains M cells which are specialized for the transcytosis of immunogens, as demonstrated in other mucosal tissues. We hypothesized that NALT M cells are characterized by distinct glycoconjugate receptors which influence antigen uptake and immune responses to transcytosed antigens. To identify glycoconjugates that may distinguish NALT M cells from other cells of the respiratory epithelium (RE), we performed lectin histochemistry on sections of the hamster nasal cavity with a panel of lectins. Many classes of glycoconjugates were found on epithelial cells in this region. While most lectins bound to sites on both the RE and M cells, probes capable of recognizing alpha-linked galactose were found to label the follicle-associated epithelium (FAE) almost exclusively. By morphological criteria, the FAE contains >90% M cells. To determine if apical glycoconjugates on M cells were accessible from the nasal cavity, an M-cell-selective lectin and a control lectin in parallel were administered intranasally to hamsters. The M-cell-selective lectin was found to specifically target the FAE, while the control lectin did not. Lectin bound to M cells in vivo was efficiently endocytosed, consistent with the role of M cells in antigen transport. Intranasal immunization with lectin-test antigen conjugates without adjuvant stimulated induction of specific serum immunoglobulin G, whereas antigen alone or admixed with lectin did not. The selective recognition of NALT M cells by a lectin in vivo provides a model for microbial adhesin-host cell receptor interactions on M cells and the targeted delivery of immunogens to NALT following intranasal administration.
Boron and phosphorous implantation into (100) germanium : modeling and investigation of dopant annealing behavior

BORON AND PHOSPHORUS IMPLANTATION INTO (100) GERMANIUM: MODELING AND INVESTIGATION OF DOPANT ANNEALING BEHAVIOR by Yong Seok Suh Germanium is increasingly being considered at this time for future silicon compatible optoelectronic and complementary metal oxide semiconductor (CMOS) device application. Germanium implantation will be a critical process for future device fabrication. However, critical properties like Pearson parameters and dopant activation temperatures are not well established. In this study, boron and phosphorus were implanted into (100) germanium with energies ranging from 20 to 320 keV and doses of 5x10 13 to 5x 1016 cm-2. The behavior of the boron and phosphorus before and after annealing for 3 hours at 400, 600 or 800°C in ultra high purity nitrogen were characterized using secondary ion mass spectrometry (SIMS), spreading resistance profiling (SRP) measurements, Hall Effect measurement, X-ray diffraction (XRD) measurement, and Rutherford backscattering spectrometry (RBS). A predictive model for the implanted dopant distribution's dependence on energy was developed using the experimentally determined implant moments combined with Pearson distributions and the post-annealing electrical, structural and diffusion behavior was characterized. Results from numeric simulation and analytic calculations using Lindard-Scharff-Schiott (LSS) theory are presented to offer insight into the physics of the pre-annealed implanted dopant distributions. BORON AND PHOSPHORUS IMPLANTATION INTO (100) GERMANIUM: MODELING AND INVESTIGATION OF DOPANT ANNEALING BEHAVIOR
Biomimetic Adaptations of GA and SA for the Robust MO Optimization of an Industrial Nylon-6 Reactor

A few jumping gene adaptations have been developed for genetic algorithm and simulated annealing. These are inspired by biology and speed up the convergence. These are used to optimize, multiobjectively, an industrial nylon-6 reactor. Nylon-6 is an important commodity plastic, used for fibers, automobile parts, support for electronics materials, gears for toys, etc. The reaction time (increases the profit) and the final concentration of the undesirable cyclic dimer (improves product quality) are minimized. Robust Pareto solutions are obtained in which a ± 1% variation of the decision variables does not lead to significant deviations in the objective functions.
Inertial Structure From Motion with Autocalibration

We present a technique to fuse inertial and visual information for real-time navigation applications. The combined model exhibits bounded bias, autocalibrates the camera-to-IMU transformation (eliminating the need for precise measurement or construction), and updates an estimate of the gravity vector. We analyze of the observability of the combined system, and discuss the implementation of a filter to estimate ego-motion from inertial and vision measurements as part of an integrated sensor system.
Novel optical fingerprint sensor utilizing optical characteristics of skin tissue under fingerprints

We have developed an optical fingerprint sensor for personal identification. Conventional sensors detect contact between the convex parts of fingerprints and the input surface of the sensor, however, we have devised a novel sensor that utilizes the optical characteristics of the skin tissue under fingerprints. We obtained tomographic images from under fingerprints by optical coherence tomography (OCT), and discovered that the reflected and scattered light from the skin tissue under the concave parts of fingerprints was lower than the convex parts. In other words, the concave parts had a higher light transmittance than the convex parts. Moreover, even when there were wrinkles in a fingerprint, the same optical characteristics were present. Based on this, we made an experimental sensor that detected fingerprint patterns using light transmittance dispersion in the skin tissue. This sensor consists of light emitting diodes (LED) that irradiate red light from the side of a fingernail and an image formation system that forms an image onto an imaging device, by using the light that penetrated the finger. Using this sensor, we obtained fingerprint pattern images in which the concave parts were brighter than the convex parts. These results showed good agreement with the transmittance dispersion described above. Consequently, it has been demonstrated that a fingerprint sensor utilizing the optical can efficiently increase the recognition of fingerprint patterns of wrinkled or wet fingers, which conventional sensors have difficulty recognizing.
Temperature Field Reconstruction for the Application of Wireless Implantable Temperature Sensors in Cryosurgery

This study is a part of an ongoing effort to develop means to improve monitoring and control of cryosurgery—the destruction of undesired biological tissues by freezing. Cryosurgery is being developed as a minimally invasive procedure since the mid 1980’s, following technological developments in medical imaging. Prostate cryosurgery was the first to become a routine minimally invasive cryosurgical procedure [1]. Copyright © 2011 by ASME
Functional Interactions between Stress and the Endocannabinoid System: From Synaptic Signaling to Behavioral Output

Endocannabinoid signaling is distributed throughout the brain, regulating synaptic release of both excitatory and inhibitory neurotransmitters. The presence of endocannabinoid signaling within stress-sensitive nuclei of the hypothalamus, as well as upstream limbic structures such as the amygdala, suggests it may play an important role in regulating the neuroendocrine and behavioral effects of stress. The evidence reviewed here demonstrates that endocannabinoid signaling is involved in both activating and terminating the hypothalamic-pituitary-adrenal axis response to both acute and repeated stress. In addition to neuroendocrine function, however, endocannabinoid signaling is also recruited by stress and glucocorticoid hormones to modulate cognitive and emotional processes such as memory consolidation and extinction. Collectively, these data demonstrate the importance of endocannabinoid signaling at multiple levels as both a regulator and an effector of the stress response.
Adapter les zones côtières aux changements climatiques: de la consultation à l'action

ABSTRACT The population of the maritime region of Quebec is vulnerable to coastal erosion that will be affected by climate change (CC). Sea level rise, extreme events such as storms and intense rainfall, ice climate, thaw and frost cycles can increase erosion and affect coastal infrastructures. In 2005, a three-year research project was initiated to facilitate the adaptation process of coastal communities. The project team addressed the issue though a multidisciplinary approach and a participative method that promotes transfer of scientific information and research of consensus. This participative process was designed to facilitate the transfer of socio economic, environmental, technical and scientific information to the policy makers. Project benefits include the following: inserting science of climate and coastal dynamics in the decision process at regional and national levels, improving communications between stake holders and accelerating the implementation of adaptation solutions.
p53-R273H gains new function in induction of drug resistance through down-regulation of procaspase-3

Development of drug resistance is one of the major obstacles in cancer chemotherapy. The molecular mechanism leading to drug resistance is still not fully understood. A10A cells, a doxorubicin-resistant subline of human squamous cell carcinoma A431 cells, showed cross-resistance to methotrexate and also resistance to the drug-induced apoptosis. The cells also showed overexpression of a mutated form of p53, p53-R273H (Arg to His at codon 273), and down-regulation of procaspase-3. Knockdown of p53-R273H by p53 small interfering RNA in A431 cells increased procaspase-3 level and sensitized the cells to drug-induced apoptosis. On the other hand, transfection of p53-R273H into p53 null human osteosarcoma Saos-2 cells down-regulated procaspase-3 level and induced resistance to the drug toxicity and drug-induced apoptosis. The results support the idea that p53-R273H may gain new functions in induction of drug resistance and impairment in drug-induced apoptosis through down-regulation of procaspase-3 level. The study sheds new light on the understanding of the gain of function and drug resistance mechanisms associated with mutant p53. [Mol Cancer Ther 2007;6(3):1054–8]
Photon Consumption in Minihalos during Cosmological Reionization

At the earliest epochs of structure formation in cold dark matter (CDM) cosmologies, the smallest nonlinear objects are the numerous small halos that condense with virial temperatures below ~104 K. Such "minihalos" are not yet resolved in large-scale three-dimensional cosmological simulations. Here we employ a semianalytic method, combined with three-dimensional simulations of individual minihalos, to examine their importance during cosmological reionization. We show that, depending on when reionization takes place, they potentially play an important role as sinks of ionizing radiation. If reionization occurs at sufficiently high redshifts (zr ≳ 20), the intergalactic medium is heated to ~104 K and most minihalos never form. On the other hand, if zr ≲ 20, a significant fraction (≳10%) of all baryons have already collapsed into minihalos, and are subsequently removed from the halos by photoevaporation as the ionizing background flux builds up. We show that this process can require a significant budget of ionizing photons, exceeding the production by straightforward extrapolations back in time of known quasar and galaxy populations by a factor of up to ~10 and ~3, respectively.
Beyond Bag of Features : Adaptive Hilbert Scan Based Tree for Image Retrieval

AbstractOne fundamental problem in large scale image retrieval with the bag-of-features is its lack of spatial information, which affects accuracy of image retrieval. Depending on distribution of local features in an image, we propose a novel adaptive Hilbert-scan strategy which computes weight of each path at increasingly fine resolutions. Owing to merits of this strategy, spatial information of object will be preserved more precisely at Hilbert order. Extensive experiments on Caltech-256 show that our method obtains higher accuracy.
Designing Statistical Estimators That Balance Sample Size, Risk, and Computational Cost

This paper proposes a tradeoff between computational time, sample complexity, and statistical accuracy that applies to statistical estimators based on convex optimization. When we have a large amount of data, we can exploit excess samples to decrease statistical risk, to decrease computational cost, or to trade off between the two. We propose to achieve this tradeoff by varying the amount of smoothing applied to the optimization problem. This work uses regularized linear regression as a case study to argue for the existence of this tradeoff both theoretically and experimentally. We also apply our method to describe a tradeoff in an image interpolation problem.
Alignment of bilingual named entities in parallel corpora using statistical models and multiple knowledge sources

Named entity (NE) extraction is one of the fundamental tasks in natural language processing (NLP). Although many studies have focused on identifying NEs within monolingual documents, aligning NEs in bilingual documents has not been investigated extensively due to the complexity of the task. In this article we introduce a new approach to aligning bilingual NEs in parallel corpora by incorporating statistical models with multiple knowledge sources. In our approach, we model the process of translating an English NE phrase into a Chinese equivalent using lexical translation/transliteration probabilities for word translation and alignment probabilities for word reordering. The method involves automatically learning phrase alignment and acquiring word translations from a bilingual phrase dictionary and parallel corpora, and automatically discovering transliteration transformations from a training set of name-transliteration pairs. The method also involves language-specific knowledge functions, including handling abbreviations, recognizing Chinese personal names, and expanding acronyms. At runtime, the proposed models are applied to each source NE in a pair of bilingual sentences to generate and evaluate the target NE candidates; the source and target NEs are then aligned based on the computed probabilities. Experimental results demonstrate that the proposed approach, which integrates statistical models with extra knowledge sources, is highly feasible and offers significant improvement in performance compared to our previous work, as well as the traditional approach of IBM Model 4.