gebhart/patrepeval huggingface dataset:
Shell morphology and Raman spectra of epitaxial Ge−SixGe1−x and Si−SixGe1−x core-shell nanowires (2017)

We investigate the shell morphology and Raman spectra of epitaxial Ge−SixGe1−x and Si−SixGe1−x core−shell nanowire heterostructures grown using a combination of a vapor−liquid−solid (VLS) growth mechanism for the core, followed by in-situ epitaxial shell growth using ultra-high vacuum chemical vapor deposition. Cross-sectional transmission electron microscopy reveals that the VLS growth yields cylindrical Ge, and Si nanowire cores grown along the ⟨111⟩, and ⟨110⟩ or ⟨112⟩ directions, respectively. A hexagonal cross-sectional morphology is observed for Ge-SixGe1-x core-shell nanowires terminated by six {112} facets. Two distinct morphologies are observed for Si-SixGe1-x core-shell nanowires that are either terminated by four {111} and two {100} planes associated with the ⟨110⟩ growth direction or four {113} and two {111} planes associated with the ⟨112⟩ growth direction. We show that the Raman spectra of Si- SixGe1-x are correlated with the shell morphology thanks to epitaxial growth-induced strain, with the core Si-Si mode showing a larger red shift in ⟨112⟩ core-shell nanowires compared to their ⟨110⟩ counterparts. We compare the Si-Si Raman mode value with calculations based on a continuum elasticity model coupled with the lattice dynamic theory.
A SiC Trench MOSFET concept offering improved channel mobility and high reliability (2017)

This work discusses the challenges in the design of a SiC Power MOSFET compared to their silicon-based relatives and describes a novel SiC Trench MOSFET concept. The most prominent difficulties being identified are related to the properties of the channel and the gate dielectric as well as their interface. Different approaches to realize a SiC MOSFET are briefly discussed and the CoolSiC™ MOSFET concept is introduced which balances low conduction losses with an IGBT-like reliability. Long term gate oxide tests reveal that the extrinsic failure rate can be confidently predicted to be less than 1 FIT per die in 20 years under specified use conditions for industrial applications.
Home Blood‐Glucose Monitoring A New Approach to the Management of Diabetes Mellitus (1980)

The results in 116 diabetics who were performing long-term home blood-glucose monitoring (HBGM) are presented. By means of Dextrostix and portable glucose meters at home and at work, patients recorded blood glucose (BG) profiles during original treatment and subsequent adjustment, and were followed up for four to 18 months. Initially, marked fluctuations in BG levels occurred, with 67% of patients demonstrating hypoglycaemia (clinically silent in 53%) followed by rebound hyperglycaemia (the Somogyi effect). Adjustment of the insulin regimen resulted in reduction in standard deviation of BG concentration from a mean of 4.4 mmol/L to 2.7 mmol/L (P<0.001). thus avoiding both hypoglycaemia and extreme hyperglycaemia. Daily insulin dose decreased from a mean of 44 ($pM21 SD) units to 30 ($pM12 SD) units per day (P<0.001), and the insulin regimens used are described. The results of urine tests correlated poorly with those of HBGM in 67% of patients. Patient acceptance of HBGM was excellent, and it has permanently replaced urine testing in 96% of the subjects. Management based on HBGM resulted in improved diabetic control and lifestyle, and reduced the frequency of hospital admissions. The need for adequate education in technique and supervision is stressed. HBGM is superior to, and more acceptable than, urine testing as a method of monitoring diabetic control.
What Can a JM<sup>3</sup>er Do for the Green Earth? (2008)

The <i>Journal of Micro/Nanopatterning, Materials, and Metrology</i> (JM3) publishes peer-reviewed papers on the core enabling technologies that address the patterning needs of the electronics industry.
β1-Adrenoceptor blockade mitigates excessive norepinephrine release into cardiac interstitium in mitral regurgitation in dog (2006)

Mitral regurgitation (MR) is associated with increased neuronal release of norepinephrine (NE) and epinephrine (EP) into myocardial interstitial fluid (ISF) that may be necessary in sustaining left ventricular (LV) function via activation of cardiomyocyte beta-adrenergic receptors (ARs). However, activation of neuronal beta-ARs on cardiac neurons may lead to further catecholamine release, with an attendant risk of functional deterioration. We hypothesize that a beneficial effect of beta-AR blockade may therefore mitigate excessive catecholamine release from cardiac adrenergic neurons in dogs with MR. We measured the effects of chronic beta-receptor blockade (beta-RB) on ISF NE and EP release using in vivo microdialysis in open-chest anesthetized dogs after 4 wk of MR with or without extended release of metoprolol succinate (100 mg/day) as well as in control dogs. Fractional shortening increased by 30% in both MR and MR + beta-RB dogs after 4 wk of MR. In MR + beta-RB dogs, stellate-stimulated heart rate change was attenuated compared with control and MR dogs, whereas peak change of LV pressure over time (+dP/dt) increased equally in all groups. Stellate-stimulated ISF NE increased fivefold over baseline in MR versus twofold in control dogs (< 0.05), but the NE release was significantly attenuated in MR + beta-RB dogs. In contrast, stellate-stimulated increases in ISF EP did not differ in control, MR, and MR + beta-RB dogs. This study demonstrates that beta-RB attenuates ISF NE release from cardiac neurons and that the LV functional response to MR is not dependent on an excess increase in ISF NE. Thus beta1-RB may exert a beneficial effect by attenuating untoward effects of excessive sympathetic efferent neural NE release while sustaining early LV functional adaptation to MR.
Recycle continuous‐flow electrophoresis: Zero‐diffusion theory (1988)

Abstract Recycle continuous‐flow electrophoresis (RCFE) is a modification of Hannig's thin‐film CFE device in which eluant is continuously recycled to the chamber inlet. Shifting the recycled eluent laterally a specific distance prior to reinjection enables the RCFE to separate any two solutes to arbitrary purity. This paper studies solute transport in an idealized RCFE in the absence of diffusion. The model is solved analytically by means of a two‐sided Laplace transform. Three distinct types of solute distribution are found, and guidelines for construction and operation of an RCFE are derived.
Treatment of a benign colorectal anastomotic stricture with a biodegradable stent (2011)

It is routine practice to use metal stents in the treatment of malign obstructions in the lower gastrointestinal tract [1]. However, metal stents are not suitable for benign conditions. Recently, treatment with biodegradable stents has been implemented for non-malignant diseases in the upper gastrointestinal tract [2] [3], but there is no case in the literature reporting on the use of biodegradable stents for benign conditions in the lower gastrointestinal tract. Here we describe a case with a benign stricture in a colorectal anastomosis, treated with a biodegradable stent.
1H NMR-Based Metabonomics for the Classification of Greek Wines According to Variety, Region, and Vintage. Comparison with HPLC Data (2009)

A sensitive and simple method was developed for the classification of wines according to variety, geographical origin, and vintage using NMR-based metabonomics. Polyphenol-rich extracts were prepared from 67 varietal wines from the principal wine-producing regions of Greece, using adsorption resin XAD-4. 1D (1)H NMR spectra obtained from the corresponding extracts were segmented, integrated, and normalized, and the data were subjected to principal component analysis. The chemometric classification of wines according to their phenolic profile allows discrimination between wines from different wineries of the same wine-producing zone and between different vintages for wines of the same variety.
Harvesting raindrop energy: experimental study (2008)

At present, the energy autonomy of a microsystem is limited by the lifespan of the battery. Hence the development of the energy harvesting concept, whereby the energy needed to power the sensor is taken from the operating environment. However, there is no single solution suitable for all types of environment. In this paper, we look at a still unexploited source of energy: rain. Our system scavenges the vibration energy from a piezoelectric flexible structure impacted by a water drop. We present an experimental device that validates the aforementioned theoretical results.
Getting oriented with antibodies (2017)

Neisseria meningitidis is a Gram-negative bacterium capable of causing deadly invasive disease. Two recently developed vaccines against N. meningitidis serogroup B include recombinant factor H binding protein (fHbp), a surface protein that meningococci use to evade the host immune system. Many anti-fHbp monoclonal antibodies (mAbs) produced against fHbp fail to trigger complement-mediated bacteriolysis when used alone in vitro, but are highly synergistic and bactericidal when used in combination. This opened the door to defining the structural basis by which mAbs activate complement synergistically when binding to different epitopes on the same antigen, a story that is told by Malito et al. in a recent issue of the Biochemical Journal. Using two separate crystal structures of fHbp bound to Fabs from synergistic mAbs, they were able to model the structure of both full length antibodies bound simultaneously to fHbp. This revealed that the bound antibodies orient their Fc domains 115-130 Å apart, a distance that is compatible with multivalent C1q binding. The need for a precise orientation of Fc domains in order to efficiently activate effector functions is an emerging theme across multiple fields, and its implications could have broad impacts on vaccinology and immunotherapy.
Nanostructured Organic–Inorganic Composite Materials by Twin Polymerization of Hybrid Monomers (2009)

Abstract Forming two structurally different but associated polymer structures in a single step is a possible route for the production of nanostructured materials. By means of twin polymerization of specially constructed monomers consisting of two different covalently bonded building blocks (hybrid monomers), this route is realized. What is important is that two different macromolecular structures are formed from one monomer in a single process. The two polymers formed can be linear, branched, or cross‐linked structures. The molecular composition of the hybrid monomer defines the degree of cross‐linking of the corresponding macromolecular structures that is theoretically possible.
Post Selection Inference with Incomplete Maximum Mean Discrepancy Estimator (2018)

Measuring divergence between two distributions is essential in machine learning and statistics and has various applications including binary classification, change point detection, and two-sample test. Furthermore, in the era of big data, designing divergence measure that is interpretable and can handle high-dimensional and complex data becomes extremely important. In the paper, we propose a post selection inference (PSI) framework for divergence measure, which can select a set of statistically significant features that discriminate two distributions. Specifically, we employ an additive variant of maximum mean discrepancy (MMD) for features and introduce a general hypothesis test for PSI. A novel MMD estimator using the incomplete U-statistics, which has an asymptotically Normal distribution (under mild assumptions) and gives high detection power in PSI, is also proposed and analyzed theoretically. Through synthetic and real-world feature selection experiments, we show that the proposed framework can successfully detect statistically significant features. Last, we propose a sample selection framework for analyzing different members in the Generative Adversarial Networks (GANs) family.
Data from Efficacy of a Cancer Vaccine against <i>ALK</i>-Rearranged Lung Tumors (2023)

&lt;div&gt;Abstract&lt;p&gt;Non–small cell lung cancer (NSCLC) harboring chromosomal rearrangements of the anaplastic lymphoma kinase (&lt;i&gt;ALK&lt;/i&gt;) gene is treated with ALK tyrosine kinase inhibitors (TKI), but the treatment is successful for only a limited amount of time; most patients experience a relapse due to the development of drug resistance. Here, we show that a vaccine against ALK induced a strong and specific immune response that both prophylactically and therapeutically impaired the growth of ALK-positive lung tumors in mouse models. The ALK vaccine was efficacious also in combination with ALK TKI treatment and significantly delayed tumor relapses after TKI suspension. We found that lung tumors containing &lt;i&gt;ALK&lt;/i&gt; rearrangements induced an immunosuppressive microenvironment, regulating the expression of PD-L1 on the surface of lung tumor cells. High PD-L1 expression reduced ALK vaccine efficacy, which could be restored by administration of anti–PD-1 immunotherapy. Thus, combinations of ALK vaccine with TKIs and immune checkpoint blockade therapies might represent a powerful strategy for the treatment of ALK-driven NSCLC. &lt;i&gt;Cancer Immunol Res; 3(12); 1333–43. ©2015 AACR&lt;/i&gt;.&lt;/p&gt;&lt;/div&gt;
Effect of spatial smoothing on the performance of MUSIC and the minimum-norm method (1990)

The effect of using a spatially smoothed forward-backward covariance matrix on the performance of MUSIC and the minimum-norm method for estimating the direction of arrival of plane waves in white noise using a uniformly spaced linear array (ULA) is analysed. In particular, asymptotic results for the mean squared error in the estimates of the signal zeros and the direction of arrival are derived. It is shown that in general for subspace methods a forward-backward smoothing approach is preferable to a forward smoothing approach. An important outcome of this analysis is that for MUSIC, the error in the signal zeros is shown to exhibit a different trend compared to the error in the DOA estimates and this leads to difficulty in interpreting the spatial spectrum. For instance, when smoothing is used, the peaks in the spatial spectrum become sharper, giving the impression of higher resolution, whereas in reality the estimates of the DOA may in fact have deteriorated compared with the ones obtained using minimal or no smoothing. With regard to the minimum-norm method, the errors in the signal zeros exhibit the same trend as the DOA estimates so that no such problem is created. As to the relative comparison of the methods, it is shown that proper spatial smoothing enables the performance of the minimum-norm method to be made comparable to MUSIC.
Microtensile Bond Strength and Interfacial Characterization of 11 Contemporary Adhesives Bonded to Bur-cut Dentin (2010)

When bonded to dentin, the adhesives with simplified application procedures (in particular, one-step self-etch adhesives) still underperform as compared to conventional three-step adhesives. "Mild" two-step self-etch adhesives that provide additional chemical bonding appear to most optimally combine bonding effectiveness with a simplified application protocol.
Phototriggered Guest Release from a Nonporous Organic Crystal: Remarkable Single-Crystal-to-Single-Crystal Transformation of a Binary Cocrystal Solvate to a Ternary Cocrystal (2020)

The development of organic solids for applications in materials science requires a fundamental understanding of how close packing of molecules can affect structure and function. We report here nonporous organic crystals that release entrapped guest molecules upon application of UV light. We show components of binary cocrystal solvates to undergo an intermolecular photoreaction to generate ternary cocrystals that results in release of entrapped solvent molecules. The phototriggered guest release occurs in a single-crystal-to-single-crystal transformation that is in the absence pores and channels in the solid. The cocrystals are composed of a tetratopic hydrogen-bond-acceptor molecule synthesized in the solid state. The UV-light results in [2 + 2] photodimerization of an isocoumarin to generate a ternary cocrystal with cyclobutane molecules that support guest release.
Segmental discriminative analysis for american sign language recognition and verification (2010)

This dissertation presents segmental discriminative analysis techniques for American Sign Language (ASL) recognition and verification. ASL recognition is a sequence classification problem. One of the most successful techniques for recognizing ASL is the hidden Markov model (HMM) and its variants. This dissertation addresses two problems in sign recognition by HMMs. The first is discriminative feature selection for temporally-correlated data. Temporal correlation in sequences often causes difficulties in feature selection. To mitigate this problem, this dissertation proposes segmentally-boosted HMMs (SBHMMs), which construct the state-optimized features in a segmental and discriminative manner. The second problem is the decomposition of ASL signs for efficient and accurate recognition. For this problem, this dissertation proposes discriminative state-space clustering (DISC), a data-driven method of automatically extracting sub-sign units by state-tying from the results of feature selection. DISC and SBHMMs can jointly search for discriminative feature sets and representation units of ASL recognition. 
ASL verification, which determines whether an input signing sequence matches a predefined phrase, shares similarities with ASL recognition, but it has more prior knowledge and a higher expectation of accuracy. Therefore, ASL verification requires additional discriminative analysis not only in utilizing prior knowledge but also in actively selecting a set of phrases that have a high expectation of verification accuracy in the service of improving the experience of users. This dissertation describes ASL verification using CopyCat, an ASL game that helps deaf children acquire language abilities at an early age. It then presents the probe technique which automatically searches for an optimal threshold for verification using prior knowledge and BIG, a bi-gram error-ranking predictor which efficiently selects/creates phrases that, based on the previous performance of existing verification systems, should have high verification accuracy. 
This work demonstrates the utility of the described technologies in a series of experiments. SBHMMs are validated in ASL phrase recognition as well as various other applications such as lip reading and speech recognition. DISC-SBHMMs consistently produce fewer errors than traditional HMMs and SBHMMs in recognizing ASL phrases using an instrumented glove. Probe achieves verification efficacy comparable to the optimum obtained from manually exhaustive search. Finally, when verifying phrases in CopyCat, BIG predicts which CopyCat phrases, even unseen in training, will have the best verification accuracy with results comparable to much more computationally intensive methods.
Fifteen-year survival and recurrence rates after radiotherapy for localized prostate cancer. (1997)

The cancer-specific mortality rate for patients with stage A2 and B tumors and negative nodes compares favorably with other series of patients treated with radiation therapy and > or = 15 years' follow-up evaluation. While local progression rates are high and associated with a substantial risk of prostate cancer death, many patients live with the disease and ultimately die of causes other than prostate cancer.
Monolithic columns with a gradient of functionalities prepared via photoinitiated grafting for separations using capillary electrochromatography (2004)

Stationary phases for capillary electrochromatography with a longitudinal gradient of functionalities have been prepared via photoinitiated grafting of polymer chains onto the pore surface of a porous polymer monolith. In order to achieve the desired retention and electroosmotic flow, the hydrophobic poly(butyl methacrylate-co-ethylene dimethacrylate) monolith with optimized porous properties was grafted with a layer of ionizable polymer, poly(2-acrylamido-2-methyl-1-propanesulfonic acid). A moving shutter and a neutral density filter were used to control the dose of UV light received at different locations along the monolith in order to create the longitudinal gradient of functionalities. Formation of the desired gradients was confirmed using electron probe microanalysis of different locations along the column. The preparation technique significantly affects performance in the CEC mode as demonstrated on the separations of a model mixture using columns both with homogeneous distribution of grafts and with a gradient of functionality. Columns grafted with the gradient of functionalities were found superior to those functionalized uniformly. A comparison of the performance of the gradient column with another containing evenly distributed functionalities showed the performance benefits of the "gradient" column.
When 5G Meets Deep Learning: A Systematic Review (2020)

This last decade, the amount of data exchanged in the Internet increased by over a staggering factor of 100, and is expected to exceed well over the 500 exabytes by 2020. This phenomenon is mainly due to the evolution of high speed broadband Internet and, more specifically, the popularization and wide spread use of smartphones and associated accessible data plans. Although 4G with its long-term evolution (LTE) technology is seen as a mature technology, there is continual improvement to its radio technology and architecture such as in the scope of the LTE Advanced standard, a major enhancement of LTE. But for the long run, the next generation of telecommunication (5G) is considered and is gaining considerable momentum from both industry and researchers. In addition, with the deployment of the Internet of Things (IoT) applications, smart cities, vehicular networks, e-health systems, and Industry 4.0, a new plethora of 5G services has emerged with very diverging and technologically challenging design requirements. These include: high mobile data volume per area, high number of devices connected per area, high data rates, longer battery life for low-power devices, and reduced end-to-end latency. Several technologies are being developed to meet these new requirements. Among these we list ultra-densification, millimeter Wave usage, antennas with massive multiple-input multiple-output (MIMO), antenna beamforming to increase spacial diversity, edge/fog computing, among others. Each of these technologies brings its own design issues and challenges. For instance, ultra-densification and MIMO will increase the complexity to estimate channel condition and traditional channel state information (CSI) estimation techniques are no longer suitable due to the complexity of the new scenarios. As a result, new approaches to evaluate network condition such as by continuously collecting and monitoring key performance indicators become necessary. Timely decisions are needed to ensure the correct operation of such network. In this context, deep learning (DL) models could be seen as one of the main tools that can be used to process monitoring data and automate decisions. As these models are able to extract relevant features from raw data (images, texts, and other types of unstructured data), the integration between 5G and DL looks promising and one that requires exploring. As main contributions, this paper presents a systematic review about how DL is being applied to solve some 5G issues. We examine data from the last decade and the works that addressed diverse 5G problems, such as physical medium state estimation, network traffic prediction, user device location prediction, self network management, among others. We also discuss the main research challenges when using DL models in 5G scenarios and identify several issues that deserve further consideration.