Miracle-dz/UniFAR-FTUs huggingface dataset:
Small molecule selectively suppresses MYC transcription in cancer cells

Significance Oncogene MYC is up-regulated in up to 75% of all cancers. Suppression of MYC expression or inhibition of MYC function by mutant protein has been shown efficacious in a variety of cancer models. Even though selective MYC-targeting agents have been long sought after, no drugs are available yet. Herein, we demonstrate that a pharmacological agent, stauprimide, selectively suppresses MYC transcription in a variety of cancer cells in vitro and inhibits tumor growth in xenograft mouse models. This study provides not only a proof-of-concept for pharmacologically targeting MYC transcription as an effective strategy for cancer treatment, but also evidence to support further development of stauprimide as an anticancer drug candidate. Stauprimide is a staurosporine analog that promotes embryonic stem cell (ESC) differentiation by inhibiting nuclear localization of the MYC transcription factor NME2, which in turn results in down-regulation of MYC transcription. Given the critical role the oncogene MYC plays in tumor initiation and maintenance, we explored the potential of stauprimide as an anticancer agent. Here we report that stauprimide suppresses MYC transcription in cancer cell lines derived from distinct tissues. Using renal cancer cells, we confirmed that stauprimide inhibits NME2 nuclear localization. Gene expression analysis also confirmed the selective down-regulation of MYC target genes by stauprimide. Consistent with this activity, administration of stauprimide inhibited tumor growth in rodent xenograft models. Our study provides a unique strategy for selectively targeting MYC transcription by pharmacological means as a potential treatment for MYC-dependent tumors.
Commitment and Capacity for Providing Evidence-Based Tobacco Treatment in Us Drug Treatment Facilities

Background: Although people with mental illness, including substance use disorders, consume 44% of cigarettes in the United States, few facilities provide tobacco treatment. This study assesses staff- and facility-level drivers of tobacco treatment in substance use treatment. Methods: Surveys were administered to 405 clinic directors selected from a comprehensive inventory of 3800 US outpatient facilities. The main outcome was the validated 7-item Index of Tobacco Treatment Quality. Other measures included the validated Tobacco Treatment Commitment Scale and indicators of facility resources for providing tobacco treatment. Results: Stepwise model selection was used to determine the relationship between capacity/resources and treatment quality. The final model retained 7 items and had good fit (adjusted R2 = 0.43). Four capacities significantly predicted treatment quality. Structural equation modeling (SEM) was used to test the impact of staff commitment on treatment quality; the model had good fit and the relationship was significant (comparative fit index [CFI] = 0.951, root mean square error of approximation [RMSEA] = 0.054). Adding the 7 capacity/resources maintained similar model fit (CFI = 0.922, RMSEA = 0.053). Staff commitment was slightly strengthened in this model, with a rise in parameter estimate from 0.449 to 0.560. All resource/capacity items were also significant predictors of treatment quality; the strongest was receiving training in how to provide tobacco treatment (0.360), followed by dedicated staff time (0.279) and having a policy that requires staff to offer treatment (0.272). Conclusions: Staff commitment to providing tobacco treatment was the strongest predictor of tobacco treatment quality, followed by resources for providing treatment. Interventions to change staff attitudes and improve resources for tobacco treatment have the strongest potential for improving quality of care.
Transcriptional Roadmap to Seasonal Variation in Wood Formation of Norway Spruce1

Seasonal wood transcriptomes of Norway spruce suggest mechanisms underlying specific aspects of gymnosperm wood biology, such as latewood formation, lignin biosynthesis, and tracheid longevity. Seasonal cues influence several aspects of the secondary growth of tree stems, including cambial activity, wood chemistry, and transition to latewood formation. We investigated seasonal changes in cambial activity, secondary cell wall formation, and tracheid cell death in woody tissues of Norway spruce (Picea abies) throughout one seasonal cycle. RNA sequencing was performed simultaneously in both the xylem and cambium/phloem tissues of the stem. Principal component analysis revealed gradual shifts in the transcriptomes that followed a chronological order throughout the season. A notable remodeling of the transcriptome was observed in the winter, with many genes having maximal expression during the coldest months of the year. A highly coexpressed set of monolignol biosynthesis genes showed high expression during the period of secondary cell wall formation as well as a second peak in midwinter. This midwinter peak in expression did not trigger lignin deposition, as determined by pyrolysis-gas chromatography/mass spectrometry. Coexpression consensus network analyses suggested the involvement of transcription factors belonging to the ASYMMETRIC LEAVES2/LATERAL ORGAN BOUNDARIES and MYELOBLASTOSIS-HOMEOBOX families in the seasonal control of secondary cell wall formation of tracheids. Interestingly, the lifetime of the latewood tracheids stretched beyond the winter dormancy period, correlating with a lack of cell death-related gene expression. Our transcriptomic analyses combined with phylogenetic and microscopic analyses also identified the cellulose and lignin biosynthetic genes and putative regulators for latewood formation and tracheid cell death in Norway spruce, providing a toolbox for further physiological and functional assays of these important phase transitions.
Novel functionalized nanoparticles for tumor-targeting co-delivery of doxorubicin and siRNA to enhance cancer therapy

Human homeobox protein (Nanog) is highly expressed in most cancer cells and has gradually emerged as an excellent target in cancer therapy, owing to its regulation of cancer cell proliferation, metastasis and apoptosis. In this study, we prepared tumor-targeting functionalized selenium nanoparticles (RGDfC-SeNPs) to load chemotherapeutic doxorubicin (DOX) and Nanog siRNA. Herein, RGDfC peptide was used as a tumor-targeting moiety which could specifically bind to αvβ3 integrins overexpressed on various cancer cells. The sizes of RGDfC-SeNPs@DOX nanoparticles (~12 nm) were confirmed by both dynamic light scattering and transmission electron microscopy. The chemical structure of RGDfC-SeNPs@DOX was characterized via Fourier-transform infrared spectroscopy. The RGDfC-SeNPs@DOX was compacted with siRNA (anti-Nanog) by electrostatic interaction to fabricate the RGDfC-SeNPs@DOX/siRNA complex. The RGDfC-SeNPs@DOX/siRNA complex nanoparticles could efficiently enter into HepG2 cells via clathrin-associated endocytosis, and showed high gene transfection efficiency that resulted in enhanced gene silencing. The in vivo biodistribution experiment indicated that RGDfC-SeNPs@DOX/siRNA nanoparticles were capable of specifically accumulating in the tumor site. Furthermore, treatment with RGDfC-SeNPs@DOX/siRNA resulted in a more significant anticancer activity than the free DOX, RGDfC-SeNPs@DOX or RGDfC-SeNPs/siRNA in vitro and in vivo. In summary, this study shows a novel type of DOX and siRNA co-delivery system, thereby providing an alternative route for cancer treatment.
Development of a Specification for Bridge Seismic Retrofit with Carbon Fiber Reinforced Polymer Composites

The U.S. Interstate 80 bridge over State Street in Salt Lake City is very near the Wasatch fault, which is active and capable of producing large earthquakes. The bridge was designed and built in 1965 according to the 1961 American Association of State Highway Officials specifications, which did not consider earthquake-induced forces or displacements. The bridge consists of reinforced concrete bents supporting steel plate welded girders. The bents are supported on cast-in-place concrete piles and pile caps. A seismic retrofit design was developed using carbon fiber reinforced polymer ~CFRP! composites, which was implemented in the summer of 2000 and the summer of 2001, to improve the displacement ductility of the bridge. The seismic retrofit included column jacketing, as well as wrapping of the bent cap and bent cap-column joints for confinement, flexural, and shear strength increase. This paper describes the specifications developed for the CFRP composite column jackets and composite bent wrap. The specifications included provisions for materials, constructed thickness based on strength capacity, and an environmental durability reduction factor. Surface preparation, finish coat requirements, quality assurance provisions, which included sampling and testing, and constructability issues regarding the application of fiber composite materials in the retrofit of concrete bridges are also described.
Mining Logs to Model the Use of a System

Background. Process mining is a technique to build process models from "execution logs" (i.e., events triggered by the execution of a process). State-of-the-art tools can provide process managers with different graphical representations of such models. Managers use these models to compare them with an ideal process model or to support process improvement. They typically select the representation based on their experience and knowledge of the system. Aim. This work studies how to automatically build process models representing the actual intents (or uses) of users while interacting with a software system. Such intents are expressed as a set of actions performed by a user to a system to achieve specific use goals. Method. This work applies the theory of Hidden Markov Models to mine use logs and automatically model the use of a system. Results. Unlike the models generated with process mining tools, the Hidden Markov Models automatically generated in this study provide the intents of a user and can be used to recommend managers with a faithful representation of the use of their systems. Conclusions. The automatic generation of the Hidden Markov Models can achieve a good level of accuracy in representing the actual user's intents provided the log dataset is carefully chosen. In our study, the information contained in one-month set of logs helped automatically build Hidden Markov Models with superior accuracy and similar expressiveness of the models built together with the company's stakeholder.
STNet: Selective Tuning of Convolutional Networks for Object Localization

Visual attention modeling has recently gained momentum in developing visual hierarchies provided by Convolutional Neural Networks. Despite recent successes of feed-forward processing on the Abstraction of concepts form raw images, the inherent nature of feedback processing has remained computationally controversial. Inspired by the computational models of covert visual attention, we propose the Selective Tuning of Convolutional Networks (STNet). It is composed of both streams of Bottom-Up and Top-Down information processing to selectively tune the visual representation of convolutional networks. We experimentally evaluate the performance of STNet for the weakly-supervised localization task on the ImageNet benchmark dataset. We demonstrate that STNet not only successfully surpasses the state-of-the-art results but also generates attention-driven class hypothesis maps.
Rapid gate sizing with fewer iterations of Lagrangian Relaxation

Existing Lagrangian Relaxation (LR) based gate sizers take many iterations to converge to a competitive solution. In this paper, we propose a novel LR based gate sizer which dramatically reduces the number of iterations while achieving a similar reduction in leakage power and meeting the timing constraints. The decrease in the iteration count is enabled by an elegant Lagrange multiplier update strategy for rapid coarse-grained optimization as well as finer-grained timing and power recovery techniques, which allow the coarse-grained optimization to terminate early without compromising the solution quality. Since LR iterations dominate the total runtime, our gate sizer achieves an average speedup of 2.5x in runtime and saves 1% more power compared to the previous fastest work.
Cleavage of Rubber by the Latex Clearing Protein (Lcp) of Streptomyces sp. Strain K30: Molecular Insights

ABSTRACT Gram-positive rubber degraders such as Streptomyces sp. strain K30 cleave rubber [poly(cis-1,4-isoprene)] to low-molecular-mass oligoisoprenoid products with terminal keto and aldehyde groups by the secretion of a latex clearing protein (Lcp) designated rubber oxygenase. LcpK30 is a heme b cytochrome and has a domain of unknown function (DUF2236) that is characteristic of orthologous Lcps. Proteins with a DUF2236 domain are characterized by three highly conserved residues (R164, T168, and H198 in LcpK30). Exchange of R164 or T168 by alanine and characterization of the purified LcpK30 muteins revealed that both were stable and contained a heme group (red color) but were inactive. This finding identifies both residues as key residues for the cleavage reaction. The purified H198A mutein was also inactive and stable but was colorless due to the absence of heme. We constructed and characterized alanine muteins of four additional histidine residues moderately conserved in 495 LcpK30 homologous sequences (H203A, H232A, H259A, H266A). All muteins revealed wild-type properties, excluding any importance for activity and/or heme coordination. Since LcpK30 has only eight histidines and the three remaining residues (H103, H184, and H296) were not conserved (<11%), H198 presumably is the only essential histidine, indicating its putative function as a heme ligand. The second axial position of the heme is likely occupied by a not yet identified molecule. Mutational analysis of three strictly conserved arginine residues (R195, R202, R328) showed that R195A and R202A muteins were colorless and instable, suggesting that these residues are important for the protein stability. IMPORTANCE Large amounts of rubber waste materials have been permanently released into the environment for more than a century, yet accumulation of rubber particles released, e.g., by abrasion of tires along highways has not been observed. This is indicative of the ubiquitous presence and activity of rubber-degrading microorganisms. Despite increasing research activities on rubber biodegradation during the last 2 decades, the knowledge of the enzymatic cleavage mechanism of rubber by latex clearing protein (Lcp) still is limited. In particular, the catalytic cleavage mechanism and the amino acids of Lcp proteins (Lcps) that are involved have not yet been identified for any Lcp. In this study, we investigated the importance of 10 amino acid residues of Lcp from Streptomyces sp. K30 (LcpK30) by mutagenesis, mutein purification, and biochemical characterization. We identified several essential residues, one of which most likely represents an axial heme ligand in Lcp of Streptomyces sp. K30.
ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes

Exploiting synthetic data to learn deep models has attracted increasing attention in recent years. However, the intrinsic domain difference between synthetic and real images usually causes a significant performance drop when applying the learned model to real world scenarios. This is mainly due to two reasons: 1) the model overfits to synthetic images, making the convolutional filters incompetent to extract informative representation for real images; 2) there is a distribution difference between synthetic and real data, which is also known as the domain adaptation problem. To this end, we propose a new reality oriented adaptation approach for urban scene semantic segmentation by learning from synthetic data. First, we propose a target guided distillation approach to learn the real image style, which is achieved by training the segmentation model to imitate a pretrained real style model using real images. Second, we further take advantage of the intrinsic spatial structure presented in urban scene images, and propose a spatial-aware adaptation scheme to effectively align the distribution of two domains. These two modules can be readily integrated with existing state-of-the-art semantic segmentation networks to improve their generalizability when adapting from synthetic to real urban scenes. We evaluate the proposed method on Cityscapes dataset by adapting from GTAV and SYNTHIA datasets, where the results demonstrate the effectiveness of our method.
Transcriptional landscape of the human cell cycle

Significance Our study provided a comprehensive view of the transcriptional landscape across the cell cycle. We revealed lag between transcription and steady-state RNA expression at the cell-cycle level and characterized a large amount of active transcription during early mitosis. In addition, our analysis identified thousands of enhancer RNAs and related transcription factors that are highly correlated with cell-cycle–regulated transcription but not with steady-state expression, thus highlighting the importance of transcriptional and epigenetic dynamics during cell-cycle progression. Steady-state gene expression across the cell cycle has been studied extensively. However, transcriptional gene regulation and the dynamics of histone modification at different cell-cycle stages are largely unknown. By applying a combination of global nuclear run-on sequencing (GRO-seq), RNA sequencing (RNA-seq), and histone-modification Chip sequencing (ChIP-seq), we depicted a comprehensive transcriptional landscape at the G0/G1, G1/S, and M phases of breast cancer MCF-7 cells. Importantly, GRO-seq and RNA-seq analysis identified different cell-cycle–regulated genes, suggesting a lag between transcription and steady-state expression during the cell cycle. Interestingly, we identified genes actively transcribed at early M phase that are longer in length and have low expression and are accompanied by a global increase in active histone 3 lysine 4 methylation (H3K4me2) and histone 3 lysine 27 acetylation (H3K27ac) modifications. In addition, we identified 2,440 cell-cycle–regulated enhancer RNAs (eRNAs) that are strongly associated with differential active transcription but not with stable expression levels across the cell cycle. Motif analysis of dynamic eRNAs predicted Kruppel-like factor 4 (KLF4) as a key regulator of G1/S transition, and this identification was validated experimentally. Taken together, our combined analysis characterized the transcriptional and histone-modification profile of the human cell cycle and identified dynamic transcriptional signatures across the cell cycle.
The role of thoracic surgery in the management of mesothelioma: an expert opinion on the limited evidence

ABSTRACT Surgery has a key role at different points in the management of Malignant Pleural Mesothelioma. Diagnosis with video assisted thoracoscopy offers excellent sensitivity and specificity and a direct view of the pleural cavity to verify the extent of the tumor. Nodal involvement can be assessed by mediastinoscopy and either talc pleurodesis or partial pleurectomy can be used for symptom control in advanced stage disease. Extra Pleural Pneumonectomy (EPP) and Extended Pleurectomy Decortication (EPD) are used to prolong survival although the benefit of radical surgery has not has been fully clarified; EPP failed to show its benefit in the MARS trial and EPD is currently under investigation in the MARS2 trial. More randomized prospective trial data are needed to fully understand the role of radical surgery in the treatment of pleural mesothelioma.
Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer

The state-of-charge (SOC) observer with online model adaption generally has high accuracy and robustness. However, the unexpected sensing of noises is shown to cause the biased identification of model parameters. To address this problem, a novel technique which integrates a recursive total least squares (RTLS) with an SOC observer is proposed to enhance the online model identification and SOC estimate. An efficient method is exploited to solve the Rayleigh quotient minimization which lays the basis of the RTLS. The number of multiplies, divides, and square roots is elaborated to show the low computational complexity of the developed RTLS. Simulation and experimental results show that the proposed RTLS-based observer attenuates the model identification bias caused by noise corruption effectively, and, thereby, provides a more reliable estimation of SOC. The proposed method is further compared with several available methods to highlight its superiority in terms of accuracy and the robustness to noise corruption.
Quantifying the properties of a dosimeter responsive toblue light hazard effective wavelengths

The research reported in this dissertation characterizes the properties of a proposed blue light dosimeter suitable for measuring the ocular blue light hazard. Blue light of high intensity with shorter wavelengths (400-500 nm) has been shown to produce an adverse effect on the healthy human eye, and the hypothesis proposed is that a dosimeter based on polysulphone and phenothiazine has the properties to act as a dosimeter to measure exposure to the blue light hazard. Exposure to the shorter wavelengths of blue light is measured by weighting the spectral irradiance of a light source against the blue light hazard action spectrum, and the dosimeter employed in this study uses a long pass filter to remove ultraviolet wavelengths shorter than 380 nm. An examination of the polysulphone and phenothiazine dosimeters was completed for the change in absorbance, dose response, the dark reaction, repeatability of measurement, the influence of the angle of the receiving plane, and stability against changes in irradiation and temperature. The results show that a change in the dosimeter optical transmission occurs as a result of exposure to blue light, with maximum dosimeter response after exposure to wavelengths around 420 nm. The polysulphone and phenothiazine dosimeters were exposed to four light sources and dose response calibration curves established that relate blue light exposure to the change in absorbance. The effect of the angle of the receiving plane was found to be approximated by a cosine function for angles up to 70o from the normal, with the difference between measured and theoretical values in this angle range being less than about 0.2. Dosimeter response to repeated solar radiation exposure was found to be reproducible with a standard error in the A of 0.005. The dark reaction of the dosimeter in terms of the average change in absorbance in darkness was also found to be small, with a change of 0.056 at zero hours being 0.058 after 24 hours and 0.067 after one week. Dosimeter rate variations with irradiance variations between 10 W/m² and 15 W/m² were found to be only ± 10 %, and thus within the measurement error of the badges. Dosimeter responses were also found to be stable for temperatures ranging from 23 oC to 37 oC. Taken together, all these results lead to the conclusion that polysulphone and phenothiazine dosimeters have characteristics that enable their widespread use for rapid, convenient and low-cost assessment of the blue light hazard.