gebhart/patrepeval huggingface dataset:
The Unique Characteristics of “Upper” Lumbar Disc Herniations (2004)

Herniated discs at the L1-L2 or L2-L3 level are different entities from those at lower levels of the lumbar spine. The surgical outcome in terms of postoperative back and radicular pain is worse for herniated discs at L1-L2 and L2-L3 compared with those treated at L3-L4. Our patients with L1-L2 or L2-L3 surgically treated herniated discs were more likely to have had previous lumbar surgery and required a fusion more often than their counterparts with L3-L4 herniated discs.
D2-Net: A Trainable CNN for Joint Description and Detection of Local Features (2019)

In this work we address the problem of finding reliable pixel-level correspondences under difficult imaging conditions. We propose an approach where a single convolutional neural network plays a dual role: It is simultaneously a dense feature descriptor and a feature detector. By postponing the detection to a later stage, the obtained keypoints are more stable than their traditional counterparts based on early detection of low-level structures. We show that this model can be trained using pixel correspondences extracted from readily available large-scale SfM reconstructions, without any further annotations. The proposed method obtains state-of-the-art performance on both the difficult Aachen Day-Night localization dataset and the InLoc indoor localization benchmark, as well as competitive performance on other benchmarks for image matching and 3D reconstruction.
Network Reconstruction (2015)

Cellular functions rely on the interactions of their chemical constituents. Various high-throughput experimental methods now allow us to determine the chemical composition of cells on a genome-scale. These methods include whole-genome sequencing and annotation (genomics), the measurement of the messenger RNA molecules that are synthesized under a given condition (transcriptomics), the protein abundance, interactions, and functional states (proteomics),measurements of the presence and concentration of metabolites (metabolomics), and metabolic fluxes (fluxomics). In addition, methods now exist to determine the binding sites of proteins on the DNA (location analysis), to determine transcription start sites (TSSs), transcription breakage points, and the location of ribosomes on the transcripts. Furthermore, the physical location of protein products and segments of the DNA can be determined using various fluorescent reporting molecules. All these omic data types along with bibliomic data (primary literature information) enable the reconstruction of the biochemical reaction networks that operate in cells. Part I will discuss the process of network reconstruction, that effectively amounts to a 2D annotation of genomes.
InP/InGaAs Photodetector on SOI Photonic Circuitry (2010)

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> We present an InP-based membrane p-i-n photodetector on a silicon-on-insulator sample containing a Si-wiring photonic circuit that is suitable for use in optical interconnections on Si integrated circuits (ICs). The detector mesa footprint is 50 <formula formulatype="inline"><tex Notation="TeX">$\mu\hbox{m}^{2}$</tex></formula>, which is the smallest reported to date for this kind of device, and the junction capacitance is below 10 fF, which allows for high integration density and low dynamic power consumption. The measured detector responsivity and 3-dB bandwidth are 0.45 A/W and 33 GHz, respectively. The device fabrication is compatible with wafer-scale processing steps, guaranteeing compatibility toward future-generation electronic IC processing. </para>
Mechanistic study of the superior anti-cancer properties of a first-in-class small molecule targeting PCNA. (2019)

e14636 Background: Proliferating cell nuclear antigen (PCNA), through its interaction with various proteins involved in DNA synthesis, cell cycle regulation, and DNA repair, plays a central role in maintaining genome stability. We previously reported a novel cancer associated PCNA isoform (dubbed caPCNA), which was predominantly expressed in a broad range of cancer cells and tumor tissues, but not significantly in non-malignant cells. We found that the caPCNA-specific antigenic site lies between L126 and Y133, a region within the interdomain connector loop of PCNA that is known to be a major binding site for many of PCNA’s interacting proteins. A cell permeable peptide harboring the L126-Y133 sequence inhibited PCNA function in cancer cells and selectively kills cancer cells and xenograft tumors. Methods: Based on these observations, we sought small molecules targeting this peptide region of PCNA as potential broad-spectrum anti-cancer agents. Results: Our effort led to a drug candidate, AOH1996, which selectively kills a broad range of cancer cells at high nanomolar concentrations, but is not associated with significant toxicity to non-malignant cells. It also works synergistically with DNA damaging chemotherapeutic drugs, such as cisplatin and irinotecan, to selectively kill cancer cells. This compound is orally available to animals and suppresses tumor growth in a dosage form compatible to clinical applications. Importantly, it doesn’t cause significant toxicity at 2.5 times its effective dose. Mechanistically, AOH1996 competes with T3, a known PCNA ligand, for binding to PCNA. However, the mechanism by which AOH1996 exerts its effect on cancer cells may not be identical to what have been reported for the T3 analogs. In particular, we found that AOH1996 interferes with the association of PCNA and MCM7 to euchromatin, leading to DNA replication stress, blockade of homologous recombination-mediated DNA repair, and induction of apoptosis in cancer cells. Conclusions: These findings demonstrated the potential of this compound as a novel therapeutic agent warranting clinical investigation for cancer treatment. We have started planning a phase 1 clinical study for this compound.
A MEMS slip sensor: Estimations of triaxial force and coefficient of static friction for prediction of a slip (2017)

We report on a MEMS slip sensor that estimates triaxial force as well as coefficient of static friction for prediction of a slip. The sensor was composed of the outer and inner elastomers separated by a hard substrate, which enable to measure triaxial force independently of coefficient of static friction. We fabricated a prototype of the sensor and evaluated it by pressing and sliding it on various conditions of coefficient of static friction.
Crash Characteristics of Partially Quenched Curved Products by Three-Dimensional Hot Bending and Direct Quench (2020)

Recently, improvement of hybrid and electric vehicle technologies, equipped with batteries, continues to solve energy and environmental problems. Lighter weight and crash safety are required in these vehicles body. In order to meet these requirements, three-dimensional hot bending and direct quench (3DQ) technology, which enables to form hollow tubular automotive parts with a tensile strength of 1470 MPa or over, has been developed. In addition, this technology enables to produce partially quenched automotive parts. In this study, the crash characteristics of 3DQ partially quenched products were investigated as the fundamental research of the design for improving the energy absorption. Main results are as follows: (1) for partially quenched straight products in axial crash test, buckling that occurs at nonquenched portion can be controlled; (2) for the nonquenched conventional and overall-quenched curved products, buckling occurs at the bent portion at the initial stage in axial crash tests, and its energy absorption is low; (3) by optimizing partially quench conditions, buckling occurrence can be controlled; and (4) In this study, the largest energy absorption was obtained from the partially quenched curved product, which was 84.6% larger than the energy absorption of the conventional nonquenched bent product in crash test.
Formation of hybrid polaritons in an organic-inorganic microcavity at room temperature (2013)

We demonstrate hybridization of organic and inorganic excitons via strong coupling to a common microcavity mode. The system consists of 3,4,7,8-napthalenetetracarboxylic dianhydride (NTCDA) and ZnO nanocrystals embedded in a dielectric microcavity held at room temperature.
A low complexity stopping criterion for reducing power consumption in turbo decoders (2011)

Turbo codes are proposed in most of the advanced digital communication standards, such as 3GPP-LTE. However, due to its computational complexity, the turbo decoder is one of the most power hungry blocks in digital baseband. To alleviate this issue, one way is to avoid surplus computing phases thanks to the early termination of the iterative decoding process. The use of stopping criteria is one of the most common algorithm level power reduction methods in literature. These methods always come with some hardware overhead. In this paper, a new trellis based stopping criterion is proposed. The novelty of this approach is the lower hardware overhead thanks to the use of trellis states as key parameter to stop the iterative process. Results are showing the importance of this added hardware in terms of method efficiency. Compared to state-of-the-art Log Likelihood Ratio (LLR) based techniques, proposed Low Complexity Trellis Based (LCTB) is demonstrating 23% less power consumption on average, for comparable performance level in terms of Bit Error Rate (BER) and Frame Error Rate (FER).
Fuzzy simplification of non-numeric expressions containing some intervals and/or floating point numbers (2014)

This article describes a Mathematica package that improves simplification of general non-numeric expressions containing any mixture of Gaussian rational numbers, symbolic constants, machine and arbitrary-precision floating-point numbers, together with intervals having any mixture of such endpoints. Such generalized numbers are not automatically all converted to floats or to intervals. Expressions can be multivariate and non-polynomial. Techniques include:
Intercalation of Capsaicin into Layered Double Hydroxide and Their Bio-repellent Properties (2014)

Capsaicin was successfully intercalated into layered double hydroxide (LDH) by coprecipitation and ion-exchange method. The intercalation of capsaicin was confirmed by XRD, FT-IR, and elemental analysis. It was confirmed by XRD that the basal spacing of the host LDH increased from 0.88 to 1.50 nm after the intercalation of capsaicin into the LDH. Capsaicin-intercalated LDH (LDH-Cap) powder was readily dispersed in polymer materials such as silicone rubber, acrylic lacquer, epoxy lacquer, and polystyrene to produce composites filled with LDH-Cap. It was found that the amount of released capsaicin from the composite materials was controlled by intercalation into LDH. The composites possess bio-repellent properties against acorn barnacle and protective ability against rat gnawing damage.
Principle of Self-Complementarity and Application to Broadband Antennas (2017)

Theory of self-complementarity which was derived through an investigation of the input impedance of slot antennas with arbitrary shape is described. Examples of self-complementary antennas, modified self-complementary antennas, and their application are presented. It is pointed out that the self-complementarity is very important to obtain the wide frequency band.
Early warning signals regarding environmental suitability in the Drosophila antenna (2017)

Highlights A novel geotaxis assay showed high intensity odorant exposures are harmful to flies Repulsion at high odor intensities can be a protective mechanism Olfactory receptor neuron (ORN) excitability abruptly changes with odor intensity A linear combination of ORN activities can robustly predict intensity-dependent behavioral repulsion Summary The olfactory system is uniquely positioned to warn an organism of environmental threats. Whether and how it encodes such information is not understood. Here, we examined this issue in the fruit fly Drosophila melanogaster. We found that intensity-dependent repulsion to chemicals safeguarded flies from harmful, high-intensity vapor exposures. To understand how sensory input changed as the odor valence switched from innocuous to threatening, we recorded from olfactory receptor neurons (ORNs) in the fly antenna. Primarily, we observed two response non-linearities: recruitment of non-active ORNs at higher intensities, and abrupt transitions in neural excitability from regular spiking to high-firing oscillatory regime. Although non-linearities observed in any single ORN was not a good indicator, a simple linear combination of firing events from multiple neurons provided robust recognition of threating/repulsive olfactory stimuli. In sum, our results reveal how information necessary to avoid environmental threats may also be encoded in the insect antenna.
Effects of Exercise on EEG Activity and Standard Tools Used to Assess Concussion (2019)

A variety of cognitive assessment tools are used to determine the functional status of the brain before and after injury in athletes. Questionnaires, neuropsychological tests, and electroencephalographic (EEG) measures have been recently used to directly assess brain function on and near the playing field. However, exercise can affect cognitive performance and EEG measures of cortical activity. To date, little empirical evidence exists on the effects of acute exercise on these measures of neurological function. We therefore quantified athlete performance on a standardized battery of concussion assessment tools and EEG measurements immediately before and after acute exercise to simulate conditions of athletic competition. Heart rate and arterial oxygen levels were collected before and after the exercise challenge consisting of a 1-mile run. Together these data, from a gender-balanced cohort of collegiate athletes, demonstrated that moderate to hard levels of acute exercise improved performance on the King-Devick test (K-D test) and Standardized Assessment of Concussion (SAC) component of the Sport Concussion Assessment Tool (SCAT3). Gender played an important role in these effects, and performance was most affected by exercise in female athletes. EEG activity in the theta band (4-8 Hz) was decreased during periods of quiet resting with eyes open or eyes closed. Additionally, exercise produced a slowing of the EEG during the K-D test and a shift to higher frequencies during the balance assessment of the SCAT3. Together, these data indicate that exercise alone can influence outcome measures of cognitive assessment tools used to assess brain function in athletes. Finally, care must be taken to acquire postinjury measurements during a comparable physiologic state to that in which baseline assessment data were measured, and further research is needed into the factors influencing outcome measures of these tests.
Vaccination induced changes in pro-inflammatory cytokine levels as an early putative biomarker for cognitive improvement in a transgenic mouse model for Alzheimer disease (2014)

Several pieces of experimental evidence suggest that administration of anti-β amyloid (Aβ) vaccines, passive anti-Aβ antibodies or anti-inflammatory drugs can reduce Aβ deposition as well as associated cognitive/behavioral deficits in an Alzheimer disease (AD) transgenic (Tg) mouse model and, as such, may have some efficacy in human AD patients as well. In the investigation reported here an Aβ 1-42 peptide vaccine was administered to 16-month old APP+PS1 transgenic (Tg) mice in which Aβ deposition, cognitive memory deficits as well as levels of several pro-inflammatory cytokines were measured in response to the vaccination regimen. After vaccination, the anti-Aβ 1-42 antibody-producing mice demonstrated a significant reduction in the sera levels of 4 pro-inflammatory cytokines (TNF-α, IL-6, IL-1 α, and IL-12). Importantly, reductions in the cytokine levels of TNF-α and IL-6 were correlated with cognitive/behavioral improvement in the Tg mice. However, no differences in cerebral Aβ deposition in these mice were noted among the different control and experimental groups, i.e., Aβ 1-42 peptide vaccinated, control peptide vaccinated, or non-vaccinated mice. However, decreased levels of pro-inflammatory cytokines as well as improved cognitive performance were noted in mice vaccinated with the control peptide as well as those immunized with the Aβ 1-42 peptide. These findings suggest that reduction in pro-inflammatory cytokine levels in these mice may be utilized as an early biomarker for vaccination/treatment induced amelioration of cognitive deficits and are independent of Aβ deposition and, interestingly, antigen specific Aβ 1-42 vaccination. Since cytokine changes are typically related to T cell activation, the results imply that T cell regulation may have an important role in vaccination or other immunotherapeutic strategies in an AD mouse model and potentially in AD patients. Overall, these cytokine changes may serve as a predictive marker for AD development and progression as well as having potential therapeutic implications.
Importance of Recalibrating Models for Type 2 Diabetes Onset Prediction: Application of the Diabetes Population Risk Tool on the Health and Retirement Study (2018)

A timely prediction of type 2 diabetes (T2D) onset is important for early intervention to prevent, or at least postpone, its incidence. Several models to predict T2D onset according to individual risk factors were proposed. However, their practical applicability is limited by the fact that they often perform suboptimally when applied to a different population. A solution to overcome this limitation is model recalibration, which consists in updating the model parameters. The aim of this work is to demonstrate the benefits of T2D predictive model recalibration. For the purpose, we considered as case study the Diabetes Population Risk Tool (DPoRT), originally tuned for the Canadian population, and we applied it to data collected in older Americans in the Health and Retirement Study (HRS). A subset of 30,274 subjects was extracted from HRS and divided into a training (N=24,219) and a test set (N=6,055) stratifying for sex and diabetes incidence. The DPoRT was recalibrated by re-estimating all model coefficients on the training set, and then assessed on the test set by comparing the performance of recalibrated vs original model. Model discriminatory ability and calibration were assessed by the concordance index (C-index) and the expected to observed event probability ratio (E/O), respectively. Results show that the recalibrated DPoRT presents similar discriminatory ability to the original model, with C-index equal to 0.68 vs. 0.67 in men, 0.73 vs. 0.73 in women, and better calibration than the original model, with E/O ratio equal to 0.75 vs. 4.57 in men, 0.81 vs. 2.53 in women. Results confirm that recalibration is a key step to be performed before the application of predictive models to different populations in order to guarantee an accurate prediction of diabetes incidence.
Assessing the Capability of Distance Measuring Equipment (DME) to Support Future Air Traffic Capacity (2012)

Alternative position, navigation, and timing (APNT) systems are needed by aviation to maintain operations for prolonged periods during an absence of Global Positioning System (GPS) service. This is especially important in the future as the Federal Aviation Administration (FAA) Next Generation Air Transportation System (NextGen) utilizes GPS to efficiently handle the anticipated twofold increase in air traffic over the next 15 years. Thus, an APNT system must be able to support the full capacity of future airspace. One candidate APNT system is distance measuring equipment (DME). Since DME has capacity limits, it is important to understand these limits and how they may be increased. This paper models DME capacity and uses the model to examine potential means of improving capacity. Results indicate that increasing DME station reply rate reduces availability for avionics at the current minimum operational performance standards (MOPS). However, with reasonable MOPS changes, DME could support anticipated air traffic levels. Copyright © 2012 Institute of Navigation.
Drum Sound Detection in Polyphonic Music with Hidden Markov Models (2009)

This paper proposes a method for transcribing drums from polyphonic music using a network of connected hidden Markov models (HMMs). The task is to detect the temporal locations of unpitched percussive sounds (such as bass drum or hi-hat) and recognise the instruments played. Contrary to many earlier methods, a separate sound event segmentation is not done, but connected HMMs are used to perform the segmentation and recognition jointly. Two ways of using HMMs are studied: modelling combinations of the target drums and a detector-like modelling of each target drum. Acoustic feature parametrisation is done with mel-frequency cepstral coefficients and their first-order temporal derivatives. The effect of lowering the feature dimensionality with principal component analysis and linear discriminant analysis is evaluated. Unsupervised acoustic model parameter adaptation with maximum likelihood linear regression is evaluated for compensating the differences between the training and target signals. The performance of the proposed method is evaluated on a publicly available data set containing signals with and without accompaniment, and compared with two reference methods. The results suggest that the transcription is possible using connected HMMs, and that using detector-like models for each target drum provides a better performance than modelling drum combinations.
Segmentation of complex 3-D medical objects, a challenge and a requirement for computer assisted surgery planning and performing (1994)

No abstract
User-Based Solutions for Android Malware Detection (2018)

The existence of Android malware is increasing day by day. These software are able to access the information of users in the background without being aware of the users on smart devices, send SMS to the user name and violate the privacy of users. There are different methods for detecting malware such as static, dynamic and hybrid. However, each method has its own specific shortcomings. In this paper, it is aimed to create a detection rule with dynamic methods. In this way, both the user and the user will be informed about the instant attacks. For this purpose, firstly, statistics about the usage of operating system methods in a device are examined and average value ranges were determined. Then, this rule is updated with the usage frequency of the users and a specific statistical rule is obtained for the user. The abnormally defined unregistered operating system movements on the mobile device are detected and the user is informed instantly.