Miracle-dz/UniFAR-FTUs huggingface dataset:
Loss of HIF-1β in macrophages attenuates AhR/ARNT-mediated tumorigenesis in a PAH-driven tumor model

Activation of hypoxia-inducible factor (HIF) and macrophage infiltration of solid tumors independently promote tumor progression. As little is known how myeloid HIF affects tumor development, we injected the polycyclic aromatic hydrocarbon (PAH) and procarcinogen 3-methylcholanthrene (MCA; 100 μg/100 μl) subcutaneously into myeloid-specific Hif-1α and Hif-2α knockout mice (C57BL/6J) to induce fibrosarcomas (n = 16). Deletion of Hif-1α but not Hif-2α in macrophages diminished tumor outgrowth in the MCA-model. While analysis of the tumor initiation phase showed comparable inflammation after MCA-injection, metabolism of MCA was impaired in the absence of Hif-1α. An ex vivo macrophage/fibroblast coculture recapitulated reduced DNA damage after MCA-stimulation in fibroblasts of cocultures with Hif-1αLysM−/− macrophages compared to wild type macrophages. A loss of myeloid Hif-1α decreased RNA levels of arylhydrocarbon receptor (AhR)/arylhydrocarbon receptor nuclear translocator (ARNT) targets such as Cyp1a1 because of reduced Arnt but unchanged Ahr expression. Cocultures using Hif-1αLysM−/− macrophages stimulated with the carcinogen 7,12-dimethylbenz[a]anthracene (DMBA; 2 μg/ml) also attenuated a DNA damage response in fibroblasts, while the DNA damage-inducing metabolite DMBA-trans-3,4-dihydrodiol remained effective in the absence of Hif-1α. In chemical-induced carcinogenesis, HIF-1α in macrophages maintains ARNT expression to facilitate PAH-biotransformation. This implies a metabolic activation of PAHs in stromal cells, i.e. myeloid-derived cells, to be crucial for tumor initiation.
RF/Microwave Power Amplifiers: The Development Route and State-of-the-Art

Recent decades have seen significant progresses in the field of power amplifiers (PAs). In order to present the evolution of the field, this paper shows the development trends of the PA research based on the statistical analysis from 1970 to 2017. The brief history of radio frequency (RF)/microwave PAs and a comprehensive review on the evolution of RF/microwave PAs are presented. The state-of-the-art PAs thus far based on the statistical analysis in terms of the figure of merit (FoM) of RF/microwave PAs have been discussed in this paper. A quantitative relationship of trade-off of PA performance metrics is proposed, and the potential room for improvement of the performance of the PAs is also revealed.
Randomized trial on routine vs. provisional T-stenting in the treatment of de novo coronary bifurcation lesions

Aims We investigated whether routine T-stenting reduces restenosis of the side branch as compared with provisional T-stenting in patients with de novo coronary bifurcation lesions. Methods and results Our randomized study assigned 101 patients with a coronary bifurcation lesion to routine T-stenting with sirolimus-eluting stents (SES) in both branches and 101 patients to provisional T-stenting with SES placement in the main branch followed by kissing-balloon angioplasty and provisional SES placement in the side branch only for inadequate results. Primary endpoint was per cent diameter stenosis of the side branch at 9 month angiographic follow-up. Angiographic follow-up in 192 (95%) patients revealed a per cent stenosis of the side branch of 23.0 ± 20.2% after provisional T-stenting (19% with side-branch stent) and of 27.7 ± 24.8% (P = 0.15) after routine T-stenting (98.2% with side-branch stent). The corresponding binary restenosis rates were 9.4 and 12.5% (P = 0.32), prompting re-intervention in 5.0 and 7.9% (P = 0.39), respectively. In the main branch, binary restenosis rates were 7.3% after provisional and 3.1% after routine T-stenting (P = 0.17). The overall 1 year incidence of target lesion re-intervention was 10.9% after provisional and 8.9% after routine T-stenting (P = 0.64). Conclusions Routine T-stenting with SES did not improve the angiographic outcome of percutaneous coronary intervention of coronary bifurcation lesions as compared with stenting of the main branch followed by kissing-balloon angioplasty and provisional side-branch stenting.
Relay-Selection Improves the Security-Reliability Trade-Off in Cognitive Radio Systems

We consider a cognitive radio (CR) network consisting of a secondary transmitter (ST), a secondary destination (SD) and multiple secondary relays (SRs) in the presence of an eavesdropper, where the ST transmits to the SD with the assistance of SRs, while the eavesdropper attempts to intercept the secondary transmission. We rely on careful relay selection for protecting the ST-SD transmission against the eavesdropper with the aid of both single-relay and multi-relay selection. To be specific, only the “best” SR is chosen in the single-relay selection for assisting the secondary transmission, whereas the multi-relay selection invokes multiple SRs for simultaneously forwarding the ST's transmission to the SD. We analyze both the intercept probability and outage probability of the proposed single-relay and multi-relay selection schemes for the secondary transmission relying on realistic spectrum sensing. We also evaluate the performance of classic direct transmission and artificial noise based methods for the purpose of comparison with the proposed relay selection schemes. It is shown that as the intercept probability requirement is relaxed, the outage performance of the direct transmission, the artificial noise based and the relay selection schemes improves, and vice versa. This implies a trade-off between the security and reliability of the secondary transmission in the presence of eavesdropping attacks, which is referred to as the security-reliability trade-off (SRT). Furthermore, we demonstrate that the SRTs of the single-relay and multi-relay selection schemes are generally better than that of classic direct transmission, explicitly demonstrating the advantage of the proposed relay selection in terms of protecting the secondary transmissions against eavesdropping attacks. Moreover, as the number of SRs increases, the SRTs of the proposed single-relay and multi-relay selection approaches significantly improve. Finally, our numerical results show that as expected, the multi-relay selection scheme achieves a better SRT performance than the single-relay selection.
UNCOVERING FUNDAMENTAL MECHANISMS OF ACTOMYOSIN CONTRACTILITY USING ANALYTICAL THEORY AND COMPUTER SIMULATION

Title of dissertation: UNCOVERING FUNDAMENTAL MECHANISMS OF ACTOMYOSIN CONTRACTILITY USING ANALYTICAL THEORY AND COMPUTER SIMULATION James Komianos Doctor of Philosophy, 2018 Dissertation directed by: Professor Garegin Papoian Department of Chemistry and Biochemistry Actomyosin contractility is a ubiquitous force-generating function of almost all eukaryotic organisms. While more understanding of its dynamic non-equilibrium behavior has been uncovered in recent years, little is known regarding its self-emergent structures and phase transitions that are observed in vivo. With this in mind, this thesis aims to develop a state-of-the-art computational model for the simulation of actomyosin assemblies, containing detailed cytosolic reaction-diffusion processes such as actin filament treadmilling, cross-linker (un)binding, and molecular motor walking. This is explicitly coupled with novel mechanical potentials for semi-flexible actin filaments. Then, using this simulation framework combined with other analytical approaches, we propose a novel mechanism of contractility in a fundamental actomyosin structural element, derived from a thermodynamic free energy gradient favoring overlapped actin filament states when passive cross-linkers are present. With this spontaneous cross-linking, transient motors such as non-muscle myosin II can generate robust network contractility in a collective myosin II-cross-linker ratcheting mechanism. Finally, we map the phases of contractile behavior of disordered actomyosin using this theory, showing explicitly the cross-linking, motor and boundary conditions required for geometric collapse or tension generation in a network comprised of those elements. In this theory, we move away from the sarcomeric contractility mechanism typically reconciled in disordered non-muscle structures. It is our hope that this study adds theoretical knowledge as well as computational tools to study the diverse contractile assemblies found in non-muscle actomyosin networks. UNCOVERING FUNDAMENTAL MECHANISMS OF ACTOMYOSIN CONTRACTILITY USING ANALYTICAL THEORY AND COMPUTER SIMULATION
Regulation of the MAFF Transcription Factor by Proinflammatory Cytokines in Myometrial Cells1

Abstract The MAF (proto-)oncogene family of basic-leucine zipper transcription factors plays crucial roles in the control of mammalian gene expression and development. Here we analyzed the regulation of the human MAFF gene, coding for a small MAF transcription factor, in uterine smooth muscle cells. We found that MAFF transcript levels are induced by proinflammatory cytokines in PHM1–31 myometrial cells. We observed an important induction by interleukin 1 beta (IL1B) and a weaker upregulation by tumor necrosis factor (TNF), whereas interleukin 6 (IL6) treatment had no effect. Time course experiments revealed a rapid induction of MAFF transcripts within 30 min following IL1B treatment. The presence of actinomycin D inhibited the upregulation, suggesting that regulation of MAFF mRNA levels occurs at the transcriptional level. We generated a MAFF-specific antiserum and determined that MAFF protein was also induced by TNF and IL1B in PHM1–31 cells. In contrast, it was particularly interesting that the transcript and protein levels of the highly homologous MAFG and MAFK genes are not modulated by these cytokines. Our results suggest a possible specific role for MAFF in proinflammatory cytokine-mediated control of myometrial gene expression and provide the first link between a small MAF transcription factor and the inflammatory response.
Domain Adaptation for Semantic Segmentation of Urban Scenes

During the last half decade, convolutional neural networks (CNNs) have triumphed over semantic segmentation, which is a core task of various emerging industrial applications such as autonomous driving and medical imaging. However, to train CNNs requires a huge amount of data, which is difficult to collect and laborious to annotate. Recent advances in computer graphics make it possible to train CNN models on photo-realistic synthetic data with computer-generated annotations. Despite this, the domain mismatch between the real images and the synthetic data significantly decreases the models’ performance. Hence we propose a curriculum-style learning approach to minimize the domain gap in semantic segmentation. The curriculum domain adaptation solves easy tasks first in order to infer some necessary properties about the target domain; in particular, the first task is to learn global label distributions over images and local distributions over landmark superpixels. These are easy to estimate because images of urban traffic scenes have strong idiosyncrasies (e.g., the size and spatial relations of buildings, streets, cars, etc.). We then train the segmentation network in such a way that the network predictions in the target domain follow those inferred properties. In experiments, our method significantly outperforms the baselines as well as the only known existing approach to the same problem.
Sudden Infant Death Syndrome and Left Ventricular Hypertrabeculation-Hidden Arrhythmogenic Entity?

Left ventricular noncompaction/hypertrabeculation is a condition which is characterized by a highly trabeculated, “spongy” myocardium. It can present at any age with heart failure, arrhythmia and/or thromboembolic events. A wide variety of mutations have been found to be a cause of hypertrabeculation and it is possible that there is a continuum of hypertrophic cardiomyopathy, dilated cardiomyopathy and hypertrabeculation/noncompaction. We present a case of left ventricular hypertrabeculation which presented as sudden infant death syndrome and we propose that this entity may be a hidden cause of arrhythmic death in some infants presenting as sudden infant death syndrome.
Asian Spine Journal Asian Spine Journal Radiological Outcome of Short Segment Posterior Instrumentation and Fusion for Thoracolumbar Burst Fractures

which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Study Design: Retrospective study. Purpose: To evaluate the radiological outcome of the surgical treatment of thoracolumbar burst fractures by using short segment posterior instrumentation (SSPI) and fusion. Overview of Literature: The optimal surgical treatment of thoracolumbar burst fractures remains a matter of debate. SSPI is one of a number of possible choices, yet some studies have revealed high rates of poor radiological outcome for this SSPI. Methods: Patients treated using the short segment instrumentation and fusion technique at the Spinal Injuries Center (Iizuka, Fuku­ oka, Japan) from January 1, 2006 to July 31, 2012 were selected for this study. Radiographic parameters such as local sagittal angle, regional sagittal angle, disc angle, anterior or posterior height of the vertebral body at admission, postoperation and final observation were collected for radiological outcome evaluation. Results: There were 31 patients who met the inclusion criteria with a mean follow­up duration of 22.7 months (range, 12–48 months). The mean age of this group was 47.9 years (range, 15–77 years). The mean local sagittal angles at the time of admission, post­operation and final observation were 13.1°, 7.8° and 14.8°, respectively. There were 71% good cases and 29% poor cases based on our criteria for the radiological outcome evaluation. The correction loss has a strong correlation with the load sharing classification score (Spearman rho=0.64, p<0.001). Conclusions: The loss of kyphotic correction following the surgical treatment of thoracolumbar burst fracture by short segment in­ strumentation is common and has a close correlation with the degree of comminution of the vertebral body. Patients with high load sharing scores are more susceptible to correction loss and postoperative kyphotic deformity than those with low scores.
Low infectious complications in segmental living related small bowel transplantation in adults

Clinical small bowel transplantation (SBTx) has been associated with a high rate of infectious complications. Laparotomy, preservation injury, abnormal motility, lymphatic disruption, aberrant systemic venous drainage, rejection and antibiotic therapy could all be implicated in the etiology of these complications. In addition to the underlying disease, total parental nutrition could determine infections and liver impairment. Recently, standardized techniques for segmental living related SBTx (LR‐SBTx) have been developed. This technique allows reduction of some of these factors, thus resulting in a reduced incidence of infections. We report the infectious complications observed in 3 patients with short bowel syndrome treated with LR‐SBTx at our institution. A segment of 180–200 cm of ileum was transplanted with a neglectably short cold ischemia time (CIT). The donor bowel was decontaminated. Oral tacrolimus, prednisone and IV induction with ATG were used for immunosuppression. Blood, stool, urine, sputum and peritoneal fluids were collected and cultured as a routine surveillance. All recipients are alive with a current follow‐up time up to 21 months. No bacterial infections were observed during the post‐transplant period. One patient developed cytomegalovirus (CMV) enteritis 4 months after LR‐SBTx and was treated successfully with IV ganciclovir. In our limited experience, LR‐SBTx is associated with a low infection rate. This could be due to optimal graft decontamination, short CIT and to the reduced incidence of rejection and lower immunosuppression used in this immunologically well‐matched combination.
Feature extraction and dynamic identification of driving intention adapting to multi-mode emotions

Accurate identification of driving intention and reasonable control of driver’s behavior is seen as an important mean to reduce man-made traffic accidents for the intelligent vehicle. However, the intention identification processes associated with driving emotion-related impact have received very little attention. With the aim of uncovering the emotional impact on driving intention identification, the car-following condition was taken as an example, and multi-source and dynamic data of human–vehicle–environment under different driving emotional states were obtained through the experiments of emotions induction, actual driving, and virtual driving in this study. The feature extraction and dynamic identification models based on rough set theory and back-propagation artificial neural network were built to recognize driving intentions. The results showed that there were some differences in driving intention identification under different emotional modes. The differences were mainly manifested in the complexity and accuracy of intention feature vectors. The rationality and validity of the intentions feature extraction and identification models were verified by the actual driving experiments, virtual driving experiments, and interactive simulation experiments. The research results can provide theoretic basis for the emotional intelligence research of advanced vehicle, driving assist systems, and unmanned vehicles.
Global Illumination Test Scenes

The global illumination community has discussed having a database of scenes that could be used to compare and validate different global illumination algorithms. We present a set of test scenes for global illumination algorithms and propose that they be the beginning of such a database. These scenes are designed to be as simple as possible and still test a particular aspect of energy transport. The scenes are available on a web site in a variety of formats, along with images and pixel radiance data. We feel that the simplicity and availability of the models will make it easier for the community to use them, and that the field will benefit from a standard set of scenes. Additionally, we discuss classes of models with analytic solutions.
Panic Search

The ability to rapidly detect and respond to potentially threatening stimuli has survival value that has led researchers to posit that a threat detection system should have evolved within human cognitive architecture. LeDoux and his colleagues (Armony, Servan-Schreiber, Cohen, & LeDoux, 1997; LeDoux, 2000; Phelps & LeDoux, 2005) have proposed an early-warning system that is mediated by the amygdala. Under this view, the amygdala's rapid, preattentive evaluation of the emotional relevance of a threatening stimulus produces a rapid shift of attention toward the threat. Behavioral studies demonstrating efficient visual search for threatening targets (Eastwood, Smilek, & Merikle, 2001; Ohman, Flykt, & Esteves, 2001) and the capture of attention by threatening stimuli (Koster, Crombez, Van Damme, Verschuere, & De Houwer, 2004; Smith, Most, Newsome, & Zald, 2006) support the claim that attention rapidly shifts toward threatening stimuli. Importantly, the effect of this mechanism is selective: It increases processing for only the threat-relevant object. Ideally, the detection of threat would also produce a more generalized increase in processing efficiency, thereby allowing one to rapidly identify objects that would be beneficial to fleeing or fighting the threat, objects that themselves may not be threatening. Very little research has investigated how threatening stimuli influence the processing of non-threat-relevant stimuli. Recently, Phelps, Ling, and Carrasco (2006) ran such a study and found that contrast sensitivity increased when a fearful face preceded a contrast-discrimination task. They concluded that "people actually see better in the presence of emotional stimuli" (p. 298). How increased visual processing of contrast would be advantageous in an emotional situation was not directly addressed by their study. Here we posit that increased low-level visual
AFFORDING PRESCRIPTION DRUGS: NOT JUST A PROBLEM FOR THE ELDERLY

Policymakers have devoted much attention recently to expanding outpatient prescription drug coverage for elderly persons. New findings from the 2000-01 Community Tracking Study household survey show that many nonelderly adults also have problems affording prescription drugs. The problem is particularly serious among persons who are uninsured or enrolled in Medicaid, of whom about one out of four in each group reported that they couldn’t afford a prescription medication. The high rate of access problems among Medicaid enrollees is particularly significant given that all state Medicaid programs provide coverage for prescription drugs. State efforts to control Medicaid prescription drug costs are also contributing to access problems among beneficiaries. Introduction Policymakers are focusing on ways to extend coverage for prescription medications to millions of elderly Medicare beneficiaries who currently aren’t covered through the Medicare program and have no other source of coverage. However, it is often overlooked that many nonelderly adults also have problems affording prescription medications. While most nonelderly adults have prescription drug coverage through employer-sponsored health insurance or the Medicaid program, over 26 million lack health insurance coverage for any kind of medical care. This is more than twice the number of elderly Medicare beneficiaries who don’t have prescription drug coverage.
A Novel Singing Voice Separation Method Based on Sparse Non-Negative Matrix Factorization and Low-Rank Modeling

: A new single channel singing voice separation algorithm is presented in this paper. This field of signal processing provides important capability in various areas dealing with singer identification, voice recognition, data retrieval. This separation procedure is done using a decomposition model based on the spectrogram of singing voice signals. The novelty of the proposed separation algorithm is related to different issues listed in the following: 1) The decomposition scheme employs the vocal and music models learned using sparse non-negative matrix factorization algorithm. The vocal signal and music accompaniment can be considered as sparse and low-rank components of a singing voice segment, respectively. 2) An alternating factorization algorithm is used to decompose input data based on the modeled structures of the vocal and musical components. 3) A voice activity detection algorithm is introduced based on the energy of coding coefficients matrix in the training step to learn the basis vectors that are related to instrumental parts. 4) In the separation phase, these non-vocal atoms are updated to the new test conditions using the domain transfer approach to result in a proper separation procedure with low reconstruction error. The performance evaluation of the proposed algorithm is done using different measures and leads to significantly better results in comparison with the earlier methods in this context and the traditional procedures. The average improvement values of the proposed separation algorithm for PESQ, fwSegSNR, SDI, and GNSDR measures in comparison with previous separation methods in two defined test scenario and three mentioned SMR levels are 0.53, 0.84, 0.39, and 2.19, respectively.