gebhart/patrepeval huggingface dataset:
72399 Epigenetic Modification of Macrophages Contribute to Protective Memory in Against Staphylococcus aureus (2021)

ABSTRACT IMPACT: This work may provide new targets for vaccine and immunotherapeutic development against MRSA infections. OBJECTIVES/GOALS: Staphylococcus aureus is the leading cause of skin and skin structure infection (SSSI), a primary portal of entry for invasive infection. Patients with SA SSSI have a high 1-year recurrence. We have shown innate memory protects mice against SA SSSI. The goal of this project is to determine epigenetic mechanisms of protective memory against SA SSSI. METHODS/STUDY POPULATION: We have shown macrophages (Mf) afford protective memory against recurrent SA SSSI in mice. Priming by prior infection reduced skin lesion size and MRSA burden, which correlated with increased Mf in abscesses and lymph nodes. Priming potentiated the opsonophagocytic killing of SA by bone-marrow derived Mf (BMDM) in vitro, and their adoptive transfer into naive skin afforded protective efficacy in vivo. Here, we investigated epigenetic mechanisms of anti-SA efficacy in BMDMs. BMDM from naive (uninfected) or primed (SA SSSI) wild-type C57Bl/6 mice were cultured ex vivo. DNA from BMDM groups were isolated and analyzed for methylation changes using reduced representation bisulfite sequencing (RRBS). Pathway analyses of methylation changes were determined with Panther. RESULTS/ANTICIPATED RESULTS: Present findings indicate the protective memory afforded by BMDM was mediated by epigenetic modifications of the DNA. Using RRBS, we profiled differentially methylated regions (DMR) in DNA from naive vs. primed BMDM. Primed BMDM exhibited significantly different DMRs as compared to naive BMDM. Proximity to known genes were mapped using GREAT. Pathway analyses revealed DMRs predominant in genes integral to immune modulation, such as integrin signaling, cytokine/chemokine networks, and growth regulation. For example, SA-primed BMDM were hypermethylated proximate to GIMAP8 versus naive BMDM, suggesting repression of this protein. Gimap family ligands are small GTPase immune-associated proteins expressed in immune cells known to regulate macrophage lysosomal fusion during parasite infection. DISCUSSION/SIGNIFICANCE OF FINDINGS: These findings reveal epigenetic mechanisms of macrophage innate memory against recurrent MRSA infection. Functional testing of these genes in response to SA infection is needed to confirm their protective role. These insights may provide new targets for vaccine and immunotherapeutic development against MRSA.
Toward Foundational Deep Learning Models for Medical Imaging in the New Era of Transformer Networks (2022)

Deep learning models are currently the cornerstone of artificial intelligence in medical imaging. While progress is still being made, the generic technological core of convolutional neural networks (CNNs) has had only modest innovations over the last several years, if at all. There is thus a need for improvement. More recently, transformer networks have emerged that replace convolutions with a complex attention mechanism, and they have already matched or exceeded the performance of CNNs in many tasks. Transformers need very large amounts of training data, even more than CNNs, but obtaining well-curated labeled data is expensive and difficult. A possible solution to this issue would be transfer learning with pretraining on a self-supervised task using very large amounts of unlabeled medical data. This pretrained network could then be fine-tuned on specific medical imaging tasks with relatively modest data requirements. The authors believe that the availability of a large-scale, three-dimension-capable, and extensively pretrained transformer model would be highly beneficial to the medical imaging and research community. In this article, authors discuss the challenges and obstacles of training a very large medical imaging transformer, including data needs, biases, training tasks, network architecture, privacy concerns, and computational requirements. The obstacles are substantial but not insurmountable for resourceful collaborative teams that may include academia and information technology industry partners. © RSNA, 2022 <b>Keywords:</b> Computer-aided Diagnosis (CAD), Informatics, Transfer Learning, Convolutional Neural Network (CNN).
Prognostic Serum Cytokines in Classical Hodgkin Lymphoma—Letter (2014)

We read with interest the study by Marri and colleagues ([1][1]) suggesting that in patients with classical Hodgkin lymphoma (cHL), the pretreatment cytokine profile, particularly serum levels of IL6 and IL2R, may be used to identify those at high risk for early-disease relapse. The authors further
Genetic Stability of Parainfluenza Virus 5-Vectored Human Respiratory Syncytial Virus Vaccine Candidates after <i>In Vitro</i> and <i>In Vivo</i> Passage (2017)

Human respiratory syncytial virus (RSV) is the leading etiologic agent of lower respiratory tract infections in children, but no licensed vaccine exists. Previously, we developed two parainfluenza virus 5 (PIV5)-based RSV vaccine candidates that protect mice against RSV challenge. PIV5 was engineered to express either the RSV fusion protein (F) or the RSV major attachment glycoprotein (G) between the hemagglutinin-neuraminidase (HN) and RNA-dependent RNA polymerase (L) genes of the PIV5 genome [PIV5-RSV-F (HN-L) and PIV5-RSV-G (HN-L), respectively]. To investigate the stability of the vaccine candidates <i>in vitro</i>, they were passaged in Vero cells at high and low multiplicities of infection (MOIs) for 11 generations and the genome sequences, growth kinetics, and protein expression of the resulting viruses were compared with those of the parent viruses. Sporadic mutations were detected in the consensus sequences of the viruses after high-MOI passages, and mutation rates increased under low-MOI-passage conditions. None of the mutations abolished antigen expression. Increased numbers of mutations correlated with increased growth rates <i>in vitro</i>, indicating that the viruses evolved through the course of serial passages. We also examined the <i>in vivo</i> stability of the vaccine candidates after a single passage in African green monkeys. No mutations were detected in the consensus sequences of viruses collected from the bronchoalveolar lavage (BAL) fluid of the animals. <i>In vivo</i>, mutations in RSV G and PIV5 L were found in individual isolates of PIV5-RSV-G (HN-L), but plaque isolates of PIV5-RSV-F (HN-L) had no mutations. To improve upon the PIV5-RSV-F (HN-L) candidate, additional vaccine candidates were generated in which the gene for RSV F was inserted into earlier positions in the PIV5 genome. These insertions did not negatively impact the sequence stability of the vaccine candidates. The results suggest that the RSV F and G gene insertions are stable in the PIV5 genome. However, the function of the foreign gene insertion may need to be considered when designing PIV5-based vaccines.<b>IMPORTANCE</b> The genetic stability of live viral vaccines is important for safety and efficacy. PIV5 is a promising live viral vector and has been used to develop vaccines. In this work, we examined the genetic stability of a PIV5-based RSV vaccine <i>in vitro</i> and <i>in vivo</i> We found that insertions of foreign genes, such as the RSV F and G genes, were stably maintained in the PIV5 genome and there was no mutation that abolished the expression of RSV F or G. Interestingly, the function of the inserted gene may have an impact on PIV5 genome stability.
Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation (2021)

In autonomous driving, learning a segmentation model that can adapt to various environmental conditions is crucial. In particular, copying with severe illumination changes is an impelling need, as models trained on daylight data will perform poorly at nighttime. In this paper, we study the problem of Domain Adaptive Nighttime Semantic Segmentation (DANSS), which aims to learn a discriminative nighttime model with a labeled daytime dataset and an unlabeled dataset, including coarsely aligned day-night image pairs. To this end, we propose a novel Bidirectional Mixing (Bi-Mix) framework for DANSS, which can contribute to both image translation and segmentation adaptation processes. Specifically, in the image translation stage, Bi-Mix leverages the knowledge of day-night image pairs to improve the quality of nighttime image relighting. On the other hand, in the segmentation adaptation stage, Bi-Mix effectively bridges the distribution gap between day and night domains for adapting the model to the night domain. In both processes, Bi-Mix simply operates by mixing two samples without extra hyper-parameters, thus it is easy to implement. Extensive experiments on Dark Zurich and Nighttime Driving datasets demonstrate the advantage of the proposed Bi-Mix and show that our approach obtains state-of-the-art performance in DANSS. Our code is available at https://github.com/ygjwd12345/BiMix.
Table S6 from Chemogenomic Landscape of &lt;i&gt;RUNX1&lt;/i&gt;-mutated AML Reveals Importance of &lt;i&gt;RUNX1&lt;/i&gt; Allele Dosage in Genetics and Glucocorticoid Sensitivity (2023)

&lt;p&gt;Table S6&lt;/p&gt;
<i>mda-</i> 7/IL-24 Expression Inhibits Breast Cancer through Upregulation of Growth Arrest-Specific Gene 3 ( <i>gas3</i> ) and Disruption of β1 Integrin Function (2013)

Melanoma differentiation-associated gene (MDA)-7)/interleukin (IL)-24, a member of the IL-10 family of cytokines, inhibits growth of various human cancer cells, yet the underlying mechanism is largely unknown. Here, we report that mda-7/IL-24 efficiently suppresses the development of rat mammary tumors in vivo. Microarray analysis for genes differentially expressed in rat mammary tumor cells overexpressing MDA-7/IL-24 compared with those that do not express this cytokine identified growth arrest-specific gene-3 (gas3) as a target for mda-7/IL-24. Upregulation of gas3 by mda-7/IL-24 was STAT3 dependent. Induction of gas3 inhibited attachment and proliferation of tumor cells in vitro and in vivo by inhibiting the interaction of β1 integrin with fibronectin. A mutated GAS3, which is unable to bind β1 integrin, was also unable to inhibit fibronectin-mediated attachment and cell growth both in adherent and suspension cultures, suggesting that GAS3 exerts its effects through interaction with and regulation of β1 integrin. Thus, mda-7/IL-24 inhibits breast cancer growth, at least in part, through upregulation of GAS3 and disruption of β1 integrin function. Importantly, the expression of the mda-7/IL-24 receptor, IL-20R1, is highly correlated with GAS3 expression in human breast cancer (P = 1.02 × 10(-9)), and the incidence of metastases is significantly reduced in patients with HER2(+) breast cancer expressing high-levels of IL-20R1. Together, our results identify a novel MDA-7/IL-24-GAS3-β1integrin-fibronectin signaling pathway that suppresses breast cancer growth and can be targeted for therapy.
Supplementary Data-Data from Amiloride, An Old Diuretic Drug, Is a Potential Therapeutic Agent for Multiple Myeloma (2023)

&lt;p&gt;Supplementary methods, tables and references.&lt;/p&gt;
Daylight Sensing LED Lighting System (2016)

Adaptation of artificial light based on the available amount of daylight is known to be effective for energy savings. To achieve such daylight control, the state-of-the-art lighting systems use external photodetectors. The photodetector measures the combined contribution of artificial light and daylight, and closed-loop control schemes are used to determine the dimming levels of luminaires to produce the right amount of artificial light. In this paper, we propose LED luminaires that can perform the dual function of illumination and daylight sensing, obviating the need of additional photodetectors. We present a daylight sensing luminaire prototype and consider two driving protocols for sensing and illumination. An open-loop control scheme is then considered for daylight control. The proposed system is shown to be more robust to reflectance changes in comparison with a photodetector-based closed-loop lighting control system.
PATH-05. IMPLEMENTATION OF A TARGETED NEXT-GENERATION SEQUENCING PANEL FOR THE DIAGNOSIS AND PRECISION MEDICINE TREATMENT OF ADULT PATIENTS WITH WHO GRADE IV DIFFUSE GLIOMAS (2018)

Analysis of tumors via next-generation sequencing is now routinely used in clinical practice. The UCSF500 NGS panel became available starting in June 2015. In Dec 2017, a glioblastoma precision medicine initiative started at our institution to sequence all newly-diagnosed WHO grade IV diffuse gliomas. We review our experience over a 3-year period. The UCSF500 Cancer Panel assesses approximately 500 cancer-associated genes for mutations, copy number alterations, and structural rearrangements, including fusions. The test can be run on tumor DNA alone or compared with normal DNA, allowing for discrimination of germline variants. Sequencing results are analyzed by a neuropathologist with genomics expertise (D.A.S.). Results from the 165 adult WHO grade IV diffuse glioma cases sequenced to date were analyzed, including 136 glioblastomas, IDH-wildtype; 19 glioblastomas, IDH-mutant; and 10 diffuse midline gliomas, H3 K27M-mutant. Among the 136 IDH-wildtype glioblastomas, the most common alterations were in TERT, EGFR, CDKN2A, PTEN, NF1, TP53, PIK3R1, PDGFRA, CDK4, MDM2, LZTR1, and STAG2. Among the 19 IDH-mutant glioblastomas, the most common additional alterations were in TP53, ATRX, CDKN2A, and PDGFRA. Paired germline sequencing was performed on 71 patients, ten of which were found to harbor a germline mutation associated with increased cancer risk, including the CHEK2, MSH2, and NF1 genes. Somatic hypermutation was present in nine cases, four at initial resection and five at recurrence with a temozolomide-associated mutational signature. Among the four treatment-naïve glioblastomas with hypermutation, two were Lynch syndrome-associated in patients with damaging germline mutations in MSH2, and two were sporadic tumors that harbored somatic mutations in mismatch repair genes. CONCLUSIONS: Genomic profiling in adult glioblastoma patients results in identification of potentially actionable genetic alterations and also previously unknown germline mutations associated with increased cancer risk. A subset of glioblastomas (approximately 5%) harbor somatic hypermutation, indicating potential utility of immune checkpoint inhibition.
Optimal Design of Energy-Efficient Multi-User MIMO Systems: Is Massive\n MIMO the Answer? (2014)

Assume that a multi-user multiple-input multiple-output (MIMO) system is\ndesigned from scratch to uniformly cover a given area with maximal energy\nefficiency (EE). What are the optimal number of antennas, active users, and\ntransmit power? The aim of this paper is to answer this fundamental question.\nWe consider jointly the uplink and downlink with different processing schemes\nat the base station and propose a new realistic power consumption model that\nreveals how the above parameters affect the EE. Closed-form expressions for the\nEE-optimal value of each parameter, when the other two are fixed, are provided\nfor zero-forcing (ZF) processing in single-cell scenarios. These expressions\nprove how the parameters interact. For example, in sharp contrast to common\nbelief, the transmit power is found to increase (not to decrease) with the\nnumber of antennas. This implies that energy-efficient systems can operate in\nhigh signal-to-noise ratio regimes in which interference-suppressing signal\nprocessing is mandatory. Numerical and analytical results show that the maximal\nEE is achieved by a massive MIMO setup wherein hundreds of antennas are\ndeployed to serve a relatively large number of users using ZF processing. The\nnumerical results show the same behavior under imperfect channel state\ninformation and in symmetric multi-cell scenarios.\n
Optimal design of sensor micro-structures (2002)

In this paper, the problem of designing the shape of a resonant device for minimum mass under multiple frequency and manufacturing constraints, is considered. An optimality criterion method has been used in optimization. The developed program is designed to interface and exploit the results of a commercial finite element code, ANSYS.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>
ELECTRICALLY CONDUCTIVE ADHESIVES MODIFIED USING IONS AND NANOPARTICLES (2011)

Ordinary electrically conductive adhesives (ECAs) cannot cope with solders due to their worse electrical and also mechanical properties. The general goal of this work is to improve electrical conductivity which is nowadays up to two orders worse when compared to lead or lead free solders. This work summarizes the results of experiments, where two basic adhesives AX20 and AX70MN are either additionally filled with silver nanoparticles, carbon nanotubes or AgNO3, or modified using nitric acid (HNO3), chloride acid (HCl), potassium cyanide (KCN) or treated with elevated temperature (200 °C) for the duration of 20 minutes (annealed).
Reduced basis method for source mask optimization (2010)

Image modeling and simulation are critical to extending the limits of leading edge lithography technologies used for IC making. Simultaneous source mask optimization (SMO) has become an important objective in the field of computational lithography. SMO is considered essential to extending immersion lithography beyond the 45nm node. However, SMO is computationally extremely challenging and time-consuming. The key challenges are due to run time vs. accuracy tradeoffs of the imaging models used for the computational lithography. We present a new technique to be incorporated in the SMO flow. This new approach is based on the reduced basis method (RBM) applied to the simulation of light transmission through the lithography masks. It provides a rigorous approximation to the exact lithographical problem, based on fully vectorial Maxwell's equations. Using the reduced basis method, the optimization process is divided into an offline and an online steps. In the offline step, a RBM model with variable geometrical parameters is built self-adaptively and using a Finite Element (FEM) based solver. In the online step, the RBM model can be solved very fast for arbitrary illumination and geometrical parameters, such as dimensions of OPC features, line widths, etc. This approach dramatically reduces computational costs of the optimization procedure while providing accuracy superior to the approaches involving simplified mask models. RBM furthermore provides rigorous error estimators, which assure the quality and reliability of the reduced basis solutions. We apply the reduced basis method to a 3D SMO example. We quantify performance, computational costs and accuracy of our method.
Single-Electron Currents in Designer Single-Cluster Devices (2020)

Atomically precise clusters can be used to create single-electron devices wherein a single redox-active cluster is connected to two macroscopic electrodes via anchoring ligands. Unlike single-electron devices comprising nanocrystals, these cluster-based devices can be fabricated with atomic precision. This affords an unprecedented level of control over the device properties. Herein, we design a series of cobalt chalcogenide clusters with varying ligand geometries and core nuclearities to control their current-voltage (I-V) characteristics in a scanning tunneling microscope-based break junction (STM-BJ) device. First, the device geometry is modified by precisely positioning junction-anchoring ligands on the surface of the cluster. We show that the I-V characteristics are independent of ligand placement, confirming a sequential, single-electron tunneling mechanism. Next, we chemically fuse two clusters to realize a larger cluster dimer that behaves as a single electronic unit, possessing a smaller reorganization energy and more accessible redox states than the monomeric analogues. As a result, dimer-based devices exhibit significantly higher currents and can even be pushed to current saturation at high bias. Owing to these controllable properties, single-cluster junctions serve as an excellent platform for exploring incoherent charge transport processes at the nanoscale. With this understanding, as well as properties such as nonlinear I-V characteristics and rectification, these molecular clusters may function as conductive inorganic nodes in new devices and materials.
Measurements of Secondary Losses in a High-Lift Front-Loaded Turbine Cascade With the Implementation of Non-Axisymmetric Endwall Contouring (2009)

This paper is the second in a series from the same authors studying the mitigation of endwall losses using the low-speed linear cascade test facility at Carleton University. The previous paper documented the baseline test case for the study. The current work investigates the secondary flow in a cascade of more highly-loaded low-pressure turbine airfoils with and without the implementation of endwall profiling. This study is novel in two regards. First, the contouring is applied to low-pressure turbine airfoils, whereas studies conducted by other researchers have focused their endwall profiling efforts on the high-pressure turbine. Second, while previous researchers have optimized contouring designs for a given airfoil, the current work demonstrates the potential to open the design space by employing high-lift airfoils in conjunction with endwall contouring. Seven-hole pneumatic probe measurements taken within the blade passage and downstream of the trailing edge track the progression of the secondary flow and losses generated. The contouring divides the vorticity associated with the passage vortex into two weaker vortices, and reduces the secondary kinetic energy. Overall the secondary losses are reduced and the loss reduction is discussed with regards to changes in the flow physics. A detailed breakdown of the mixing losses further demonstrates the benefits of endwall contouring.
Deep learning based multiple sclerosis lesion detection utilizing synthetic data generation and soft attention mechanism (2022)

In this work we focus on identifying healthy brain slices vs brain slices with Multiple sclerosis (MS) lesions. MS is an autoimmune, demyelinating disease characterized by inflammatory lesions in the central nervous system. MRI is commonly used for diagnosis of MS, and enables accurate detection and classification of lesions for early diagnosis and treatment. Visual attention mechanisms may be beneficial for the detection of MS brain lesions, as they tend to be small. The attention mechanism prevents overfitting of the background when the amount of data is limited. In addition, enough data is necessary for training a successful machine learning algorithms for medical image analysis. Data with insufficient variability leads to poor classification performance. This is problematic in medical imaging where abnormal findings are uncommon and data labeling requires expensive expert’s time. In this work, we suggest a new network architecture, based on Y-net and EfficientNet models, with attention layers to improve the network performance and reduce overfitting. Furthermore, the attention layers allow extraction of lesion locations. In addition, we show an innovative regularization scheme on the attention weight mask to make it focus on the lesions while letting it search in different areas. Finally, we explore an option to add synthetic lesions in the training process. Based on recent work, we generate artificial lesions in healthy brain MRI scans to augment our training data. Our system achieves 91% accuracy in identifying cases that contain lesions (vs. healthy cases) with more than 13% improvement over an equivalent system without the attention and the data added.
Supplementary Data from Results and Molecular Correlates from a Pilot Study of Neoadjuvant Induction FOLFIRINOX Followed by Chemoradiation and Surgery for Gastroesophageal Adenocarcinomas (2023)

&lt;p&gt;Supplemental Appendix 5. Acute toxicity related to chemotherapy and chemoradiation.&lt;/p&gt;