Miracle-dz/UniFAR-FTUs huggingface dataset:
"FINAL ACCEPTED VERSION" Serial Analysis of Gene Expression (SAGE) During Elongation of the Peri- Implantation Porcine Trophectoderm (Conceptus) Running Head: SAGE in Elongating Porcine Conceptus

Conceptus loss during the pre-implantation and early post-implantation period hinders the efficiency of swine reproduction. Significant conceptus loss occurs during trophectoderm elongation between gestational day (D) 11 and D12. Elongation of the porcine conceptus is a key stage of development during which maternal recognition of pregnancy, initial placental development, and preparation for implantation occurs. The objective of this study was to establish comparative transcriptome profiles of D11 ovoid and D12 filamentous conceptuses and thereby identify temporally regulated genes essential for developmental progression during conceptus elongation. Serial analysis of gene expression (SAGE) libraries were constructed from in vivo derived ovoid and filamentous swine conceptuses to yield a total of 42,389 tags (ovoid) and 42,391 tags (filamentous) representing 14,464 and 13,098 putative unique transcripts, respectively. Statistical analysis of tag frequencies revealed the differential expression of 431 tags between libraries (p<0.05). Nucleotide sequence alignment searches on public databases provided SAGE tag annotation and gene ontology assignments. Comparisons between the SAGE profiles of ovoid and filamentous conceptuses revealed increased expression of key genes in the steroidogenesis (cytochrome p450 scc (CYP11A1), aromatase (CYP19A), and steroidogenic acute regulatory protein (STAR)) and oxidative stress response pathways (microsomal glutathione S-transferase 1 (MGST1) and copper-zinc SOD (SOD1)). Differential expression of these genes in the steroidogenic and oxidative stress response pathways was confirmed by real time PCR. These results validate the utility of SAGE in the pig and establish an initial model linking gene expression profiles at the pathway level with phenotypic progression from ovoid to filamentous stages of conceptus development.
Industrialized Application of Rheo-HPDC Process for the Production of Large Thin-Walled Aluminum Alloy Parts

A simplified and efficient process, namely air-cooled stirring rod (ACSR), was proposed to prepare semisolid slurry of aluminum alloys. An advanced integrated rheological high pressure die-casting (Rheo-HPDC) technology was established by combining the ACSR equipment with HPDC machines to produce high quality aluminum alloy products. Microstructures, surface qualities, mechanical properties, corrosion resistances and thermal conductivities of the Al-8Si alloy parts prepared by Rheo-HPDC were investigated and compared to those prepared by traditional HPDC. The results indicated that the Rheo-HPDC process can prepare aluminum alloy parts in which the primary particles are fine and spherical, and there is few shrinkage porosity. Multifarious high quality large thin-walled aluminum alloy parts, such as filter shells, cooling shells, antenna crates and mounting brackets for communication, were produced by the process. Rheo-HPDC alloys showed improved surface quality to those formed by traditional HPDC, and the surface roughness is small and avoid employing CNC to surface finish. Also, compared with HPDC alloys, the alloys prepared by Rheo-HPDC have an increased mechanical properties and thermal conductivity due to high density and refined microstructure. Furthermore, Rheo-HPDC aluminum alloys indicated a remarkable improvement in corrosion resistance as shown by the results of electrochemical and weight loss experiments.
Financial Bilateral Contract Negotiation in Wholesale Electricity Markets Using Nash Bargaining Theory

Bilateral contracts are important risk-hedging instruments constituting a major component in the portfolios held by many electric power market participants. However, bilateral contract negotiation is a complicated process as it involves risk management, strategic bargaining, and multi-market participation. This study analyzes a financial bilateral contract negotiation process between a generation company and a load-serving entity in a wholesale electric power market with congestion managed by locational marginal pricing. Nash bargaining theory is used to model a Pareto-efficient settlement point. The model predicts negotiation outcomes under various conditions and identifies circumstances in which the two parties might fail to reach an agreement. Both analysis and simulation are used to gain insight regarding how these negotiation outcomes systematically vary in response to changes in the participants' risk preferences and price biases.
On dynamic diode characteristics and EMI performance of an inverter with Undeland snubber

In this paper, two different power diodes were modeled in SPICE and implemented in a 100kW inverter. The reverse and forward recovery characteristics of the diodes were simulated and compared with hardware measurement to verify the validity of the SPICE diode models. The diodes showed different dynamic and static performance and simulations were performed to investigate which parameters that influence the losses and EMI-performance most. The simulations showed that the conducted emissions were reduced with approximately 3dB by selecting a diode with softer dynamic characteristics on expense of increased losses. Furthermore, an Undeland snubber was implemented which resulted in a reduction of the conducted emissions, recovery of the snubber energy and reduction of the losses in the switching elements. However, the forward and reverse recovery characteristics of the snubber diodes must be considered when designing the circuit since they also have an impact on the conducted emissions and the losses in the circuit.
Target-Specific Support Vector Machine Scoring in Structure-Based Virtual Screening: Computational Validation, In Vitro Testing in Kinases, and Effects on Lung Cancer Cell Proliferation

We assess the performance of our previously reported structure-based support vector machine target-specific scoring function across 41 targets, 40 among them from the Directory of Useful Decoys (DUD). The area under the curve of receiver operating characteristic plots (ROC-AUC) revealed that scoring with SVM-SP resulted in consistently better enrichment over all target families, outperforming Glide and other scoring functions, most notably among kinases. In addition, SVM-SP performance showed little variation among protein classes, exhibited excellent performance in a test case using a homology model, and in some cases showed high enrichment even with few structures used to train a model. We put SVM-SP to the test by virtual screening 1125 compounds against two kinases, EGFR and CaMKII. Among the top 25 EGFR compounds, three compounds (1-3) inhibited kinase activity in vitro with IC₅₀ of 58, 2, and 10 μM. In cell cultures, compounds 1-3 inhibited nonsmall cell lung carcinoma (H1299) cancer cell proliferation with similar IC₅₀ values for compound 3. For CaMKII, one compound inhibited kinase activity in a dose-dependent manner among 20 tested with an IC₅₀ of 48 μM. These results are encouraging given that our in-house library consists of compounds that emerged from virtual screening of other targets with pockets that are different from typical ATP binding sites found in kinases. In light of the importance of kinases in chemical biology, these findings could have implications in future efforts to identify chemical probes of kinases within the human kinome.
Sparse Uncorrelated Linear Discriminant Analysis for Undersampled Problems

Linear discriminant analysis (LDA) as a well-known supervised dimensionality reduction method has been widely applied in many fields. However, the lack of sparsity in the LDA solution makes interpretation of the results challenging. In this paper, we propose a new model for sparse uncorrelated LDA (ULDA). Our model is based on the characterization of all solutions of the generalized ULDA. We incorporate sparsity into the ULDA transformation by seeking the solution with minimum ℓ1-norm from all minimum dimension solutions of the generalized ULDA. The problem is then formulated as an ℓ1-minimization problem with orthogonality constraint. To solve this problem, we devise two algorithms: 1) by applying the linearized alternating direction method of multipliers and 2) by applying the accelerated linearized Bregman method. Simulation studies and high-dimensional real data examples demonstrate that our algorithms not only compute extremely sparse solutions but also perform well in classification.
Common Sense Reasoning for Detection, Prevention, and Mitigation of Cyberbullying

Cyberbullying (harassment on social networks) is widely recognized as a serious social problem, especially for adolescents. It is as much a threat to the viability of online social networks for youth today as spam once was to email in the early days of the Internet. Current work to tackle this problem has involved social and psychological studies on its prevalence as well as its negative effects on adolescents. While true solutions rest on teaching youth to have healthy personal relationships, few have considered innovative design of social network software as a tool for mitigating this problem. Mitigating cyberbullying involves two key components: robust techniques for effective detection and reflective user interfaces that encourage users to reflect upon their behavior and their choices. Spam filters have been successful by applying statistical approaches like Bayesian networks and hidden Markov models. They can, like Google’s GMail, aggregate human spam judgments because spam is sent nearly identically to many people. Bullying is more personalized, varied, and contextual. In this work, we present an approach for bullying detection based on state-of-the-art natural language processing and a common sense knowledge base, which permits recognition over a broad spectrum of topics in everyday life. We analyze a more narrow range of particular subject matter associated with bullying (e.g. appearance, intelligence, racial and ethnic slurs, social acceptance, and rejection), and construct BullySpace, a common sense knowledge base that encodes particular knowledge about bullying situations. We then perform joint reasoning with common sense knowledge about a wide range of everyday life topics. We analyze messages using our novel AnalogySpace common sense reasoning technique. We also take into account social network analysis and other factors. We evaluate the model on real-world instances that have been reported by users on Formspring, a social networking website that is popular with teenagers. On the intervention side, we explore a set of reflective user-interaction paradigms with the goal of promoting empathy among social network participants. We propose an “air traffic control”-like dashboard, which alerts moderators to large-scale outbreaks that appear to be escalating or spreading and helps them prioritize the current deluge of user complaints. For potential victims, we provide educational material that informs them about how to cope with the situation, and connects them with emotional support from others. A user evaluation shows that in-context, targeted, and dynamic help during cyberbullying situations fosters end-user reflection that promotes better coping strategies.
The Bipolar Kinesin, KLP61F, Cross-links Microtubules within Interpolar Microtubule Bundles of Drosophila Embryonic Mitotic Spindles

Previous genetic and biochemical studies have led to the hypothesis that the essential mitotic bipolar kinesin, KLP61F, cross-links and slides microtubules (MTs) during spindle assembly and function. Here, we have tested this hypothesis by immunofluorescence and immunoelectron microscopy (immunoEM). We show that Drosophila embryonic spindles at metaphase and anaphase contain abundant bundles of MTs running between the spindle poles. These interpolar MT bundles are parallel near the poles and antiparallel in the midzone. We have observed that KLP61F motors, phosphorylated at a cdk1/cyclin B consensus domain within the BimC box (BCB), localize along the length of these interpolar MT bundles, being concentrated in the midzone region. Nonphosphorylated KLP61F motors, in contrast, are excluded from the spindle and display a cytoplasmic localization. Immunoelectron microscopy further suggested that phospho-KLP61F motors form cross-links between MTs within interpolar MT bundles. These bipolar KLP61F MT-MT cross-links should be capable of organizing parallel MTs into bundles within half spindles and sliding antiparallel MTs apart in the spindle midzone. Thus we propose that bipolar kinesin motors and MTs interact by a “sliding filament mechanism” during the formation and function of the mitotic spindle.
Diversity-multiplexing tradeoff optimal codes for OFDM-based asynchronous cooperative networks

An asynchronous cooperative network where different time delays among nodes exist is considered in this paper. With OFDM modulation, it is first shown that the diversity-multiplexing tradeoff (DMT) achieved by the non-orthogonal selection decode-and-forward (NSDF) protocol for this network is the same as that for the synchronous one. In sharp contrast to the currently available complicated approximately universal "matrix" codes, a systematic construction of an extremely simple "vector" code is proposed. Given the transmitted codeword vector, the same codeword will be sent by all nodes during transmission, hence it will greatly reduce the complexities of relay deployment and decoding. Furthermore, it is proved that the proposed scheme achieves the optimal DMT of the NSDF protocol for this asynchronous cooperative network.
Volumetric bioimaging based on light field microscopy with temporal focusing illumination

Light field technique at a single shot can get the whole volume image of observed sample. Therefore, the original frame rate of the optical system can be taken as the volumetric image rate. For dynamically imaging whole micron-scale biosample, a light field microscope with temporal focusing illumination has been developed. In the light field microscope, the f-number of the microlens array (MLA) is adopted to match that of the objective; hence, the subimages via adjacent lenslets do not overlay each other. A three-dimensional (3D) deconvolution algorithm is utilized to deblur the out-of-focusing part. Conventional light field microscopy (LFM) illuminates whole volume sample even noninteresting parts; nevertheless, whole volume excitation causes even more damage on bio-sample and also increase the background noise from the out of range. Therefore, temporal focusing is integrated into the light field microscope for selecting the illumination volume. Herein, a slit on the back focal plane of the objective is utilized to control the axial excitation confinement for selecting the illumination volume. As a result, the developed light field microscope with the temporal focusing multiphoton illumination (TFMPI) can reconstruct 3D images within the selected volume, and the lateral resolution approaches to the theoretical value. Furthermore, the 3D Brownian motion of two-micron fluorescent beads is observed as the criterion of dynamic sample. With superior signal-to-noise ratio and less damage to tissue, the microscope is potential to provide volumetric imaging for vivo sample.
Density ridge manifold traversal

The density ridge framework for estimating principal curves and surfaces has in a number of recent works been shown to capture manifold structure in data in an intuitive and effective manner. However, to date there exists no efficient way to traverse these manifolds as defined by density ridges. This is unfortunate, as manifold traversal is an important problem for example for shape estimation in medical imaging, or in general for being able to characterize and understand state transitions or local variability over the data manifold. In this paper, we remedy this situation by introducing a novel manifold traversal algorithm based on geodesics within the density ridge approach. The traversal is executed in a subspace capturing the intrinsic dimensionality of the data using dimensionality reduction techniques such as principal component analysis or kernel entropy component analysis. A mapping back to the ambient space is obtained by training a neural network. We compare against maximum mean discrepancy traversal, a recent approach, and obtain promising results.
Photopolymerization in 3D Printing

The field of 3D printing is continuing its rapid development in both academic and industrial research environments. The development of 3D printing technologies has opened new implementations in rapid prototyping, tooling, dentistry, microfluidics, biomedical devices, tissue engineering, drug delivery, etc. Among different 3D printing techniques, photopolymerization-based process (such as stereolithography and digital light processing) offers flexibility over the final properties of the 3D printed materials (such as optical, chemical, and mechanical properties) using versatile polymer chemistry. The strategy behind the 3D photopolymerization is based on using monomers/oligomers in liquid state (in the presence of photoinitiators) that can be photopolymerized (via radical or cationic mechanism) upon exposure to light source of different wavelengths (depending on the photoinitiator system). An overview of recent evolutions in the field of photopolymerization-based 3D printing and highlights of novel 3D print.
Probabilistic Incremental Program Evolution

Probabilistic incremental program evolution (PIPE) is a novel technique for automatic program synthesis. We combine probability vector coding of program instructions, population-based incremental learning, and tree-coded programs like those used in some variants of genetic programming (GP). PIPE iteratively generates successive populations of functional programs according to an adaptive probability distribution over all possible programs. Each iteration, it uses the best program to refine the distribution. Thus, it stochastically generates better and better programs. Since distribution refinements depend only on the best program of the current population, PIPE can evaluate program populations efficiently when the goal is to discover a program with minimal runtime. We compare PIPE to GP on a function regression problem and the 6-bit parity problem. We also use PIPE to solve tasks in partially observable mazes, where the best programs have minimal runtime.
Hardware architecture of an Internet Protocol Version 6 processor

This paper presents the architecture of an Internet Protocol Version 6 processor for FPGA-based data processing and acquisition applications. Design choices and limitations are discussed together with a thorough simulation and verification methodology. The processor is demonstrated to frame and parse UDP over IPv6 traffic at 1Gb/s line-speed on a Virtex 5 FPGA, outperforming a reference soft-processor solution for UDP over IPv4. RTL-simulations show that 10Gb/s operation is attainable with the same architecture.
Kullback-Leibler Divergence and moment matching for hyperspherical probability distributions

When approximating one probability density with another density, it is desirable to minimize the information loss of the approximation as quantified by, e.g., the Kullback-Leibler divergence (KLD). It has been known for some time that in the case of the Gaussian distribution, matching the first two moments of the original density yields the optimal approximation in terms of minimizing the KLD. In this paper, we will show that a similar property can be proven for certain hyperspherical probability distributions, namely the von Mises-Fisher and the Watson distribution. This result has profound implications for moment-based filtering on the unit hypersphere as it shows that moment-based approaches are optimal in the information-theoretic sense.
Impaired defenses of neonatal mouse alveolar macrophage with cftr deletion are modulated by glutathione and TGFβ1

Our understanding of the intrinsic effects of cystic fibrosis (CF) transmembrane conductance regulator (cftr) deletion on resident neonatal alveolar macrophage (AM) remains limited. We previously demonstrated that diminished glutathione (GSH) or excessive AM transforming growth factor beta one (TGFβ1) contributes to AM dysfunction in a variety of disease states. In this study, using a gut‐corrected cftr neonatal knockout (KO) mouse model and a siRNA‐manipulated macrophage‐like cell line (THP‐1 cell), we hypothesized (1) that cftr mutation alone increases neonatal AM oxidant stress and cellular TGFβ1 signaling via altered GSH, thereby impairing cellular function, and (2) that exogenous GSH attenuates AM alterations and dysfunction in the KO AM. In neonatal KO mice, the baseline bronchoalveolar lavage fluid demonstrated a near doubling in mixed disulfides (P ≤ 0.05) and oxidized GSSG (P ≤ 0.05) without concurrent inflammation compared to WT littermates. KO AM demonstrated diminished AM thiols (P ≤ 0.05), increased AM mitochondrial ROS (P ≤ 0.05), increased AM TGFβ1 (P ≤ 0.05) with increased TGFβ1 signaling (P ≤ 0.05), and impaired phagocytosis (P ≤ 0.05). KO AM mitochondrial ROS was modulated by exogenous GSH (P ≤ 0.05). Conversely, TGFβ1 was reduced (P ≤ 0.05) and impaired phagocytosis was rescued (P ≤ 0.05) by exogenous GSH in the KO AM. These results suggest that an altered neonatal AM phenotype may contribute to the initiation of lung inflammation/infection in the CF lung. Modulation of the AM in the neonatal CF lung may potentially alter progression of disease.
DEVELOPMENT OF RITONAVIR LOADED NANOPARTICLES: IN VITRO AND IN VIVO CHARACTERIZATION

Objective: The purpose of the present investigation was to develop a nanosuspension to improve dissolution rate and oral bioavailability of ritonavir. Methods: Extended-release ritonavir loaded nanoparticles were prepared using the polymeric system by nanoprecipitation technique. Further, the effect of Eudragit RL100 (polymeric matrix) and polyvinyl alcohol (surfactant) was investigated on particle size and distribution, drug content, entrapment efficiency, and in vitro drug release from nanosuspension where a strong influence of polymeric contents was observed. Drug-excipient compatibility and amorphous nature of drug in prepared nanoparticles were confirmed by Fourier transform infrared spectroscopy, differential scanning calorimetry, and powder X-ray diffraction studies, respectively. Results: Hydrophobic portions of Eudragit RL100 could result in enhanced encapsulation efficiency. However, increase in polymer and surfactant contents lead to enlarged particle size proportionately as confirmed by transmission electron microscopy. Nanosuspension showed a significant rise in dissolution rate with complete in vitro drug release as well as higher bioavailability in rats compared to the pure drug. Conclusion: The nanoprecipitation technique used in present research could be further explored for the development of different antiretroviral drug carrier therapeutics.
Challenges in clinical prostate cancer: role of imaging.

OBJECTIVE This article reviews a recent 2-day workshop on prostate cancer and imaging technology that was conducted by the Cancer Imaging Program of the National Cancer Institute. The workshop dealt with research trends and avenues for improving imaging and applications across the clinical spectrum of the disease. CONCLUSION After a summary of prostate cancer incidence and mortality, four main clinical challenges in prostate cancer treatment and management-diagnostic accuracy; risk stratification, initial staging, active surveillance, and focal therapy; prostate-specific antigen relapse after radiation therapy or radical prostatectomy; and assessing response to therapy in advanced disease-were discussed by the 55-member panel. The overarching issue in prostate cancer is distinguishing lethal from nonlethal disease. New technologies and fresh uses for established procedures make imaging effective in both assessing and treating prostate cancer.