Miracle-dz/UniFAR-FTUs huggingface dataset:
Migraine in students of a US medical school.

BACKGROUND AND OBJECTIVES The study's aim was to determine migraine prevalence, severity, and educational impact in medical students. METHODS A 23-item survey of all medical students of one US medical school addressed migraine frequency, triggers, pattern and severity of symptoms, medication use, impact on educational activities and career choices. RESULTS The response rate was 48% (359 students). Eighty-nine (24.8%) self-reported migraine, and all were confirmed by reported symptoms. Of these students, 54% had a physician-confirmed diagnosis. Migraine prevalence was 35% in women and 14.1% in men. About half of migraineurs reported one or more attacks monthly. Stress and sleep disturbances were the most common triggers. Pain severity was rated 7 or higher on a 10-point scale by 73%. NSAIDs were the most common medications used. Only 13% used prophylaxis. More than 80% reported reduced productivity during migraine, but 76% felt obliged to attend educational activities despite symptoms. A total of 81% of students did not endorse informing faculty about migraine status, and 95% would not disclose it in residency application. Migraine was not a factor in choice of medical specialty or specific residency program. CONCLUSIONS Migraine is common in medical students, especially women. Only half have consulted a physician for migraine, and students may be using suboptimal treatments. Although more than half experience severe symptoms that impair performance, the majority attend educational sessions during migraine attacks, and they believe this is expected. Students are unwilling to disclose migraine status to faculty or during application to residency. Migraine does not influence career choices.
Intramolecular Telomeric G-Quadruplexes Dramatically Inhibit DNA Synthesis by Replicative and Translesion Polymerases, Revealing their Potential to Lead to Genetic Change

Recent research indicates that hundreds of thousands of G-rich sequences within the human genome have the potential to form secondary structures known as G-quadruplexes. Telomeric regions, consisting of long arrays of TTAGGG/AATCCC repeats, are among the most likely areas in which these structures might form. Since G-quadruplexes assemble from certain G-rich single-stranded sequences, they might arise when duplex DNA is unwound such as during replication. Coincidentally, these bulky structures when present in the DNA template might also hinder the action of DNA polymerases. In this study, single-stranded telomeric templates with the potential to form G-quadruplexes were examined for their effects on a variety of replicative and translesion DNA polymerases from humans and lower organisms. Our results demonstrate that single-stranded templates containing four telomeric GGG runs fold into intramolecular G-quadruplex structures. These intramolecular G quadruplexes are somewhat dynamic in nature and stabilized by increasing KCl concentrations and decreasing temperatures. Furthermore, the presence of these intramolecular G-quadruplexes in the template dramatically inhibits DNA synthesis by various DNA polymerases, including the human polymerase δ employed during lagging strand replication of G-rich telomeric strands and several human translesion DNA polymerases potentially recruited to sites of replication blockage. Notably, misincorporation of nucleotides is observed when certain translesion polymerases are employed on substrates containing intramolecular G-quadruplexes, as is extension of the resulting mismatched base pairs upon dynamic unfolding of this secondary structure. These findings reveal the potential for blockage of DNA replication and genetic changes related to sequences capable of forming intramolecular G-quadruplexes.
Innovation for biorefineries – Networks, narratives, and new institutions for the transition to a bioeconomy

The transition to a bioeconomy is dependent on transformative changes to technologies, organisations, and institutions, which jointly can be described as a socio-technical change. The thesis contributes to the understanding of how the transition is shaped by expectations on and collaborations for innovation forbiorefineries, which can produce chemicals, fuels, and materials needed in a bioeconomy.The thesis poses three research questions: i) what are the systemic characteristics of innovation for biorefineries? ii) how do collaborations and networks shape innovation for biorefineries? and iii) in what ways are expectations and institutions shaping pathways of innovation for biorefineries? These questions are answered with a mixed methods approach.Reorienting the socio-technical system for production and utilisation of chemicals, fuels, and materials towards a bioeconomy requires the overcoming of significant technological and institutional barriers. Though collaborations on innovation for biorefineries are needed to combine knowledge about technologies, materials, and markets they are costly and difficult. Expectations on biorefineries in the bioeconomy are divergent and conflictual. Acknowledging and resolving these conflicts is thus key to build effective and stable partnerships, which has proven to be difficult in the biorefinery field. Further, actors meet barriers to local transformative innovation in the global institutional context in which they are embedded. The thesis shows that transition initiatives are shaped by and dependent on institutional structures on multiple scales, but that opportunities exist for actors to build new networks which can enable the transition to a bioeconomy. (Less)
Single Infrared Image Optical Noise Removal Using a Deep Convolutional Neural Network

In this paper, we propose a deep learning method for single infrared image optical noise removal. With a fully convolutional neural network, it is able to eliminate the optical noise in single infrared image. Our architecture consists of two networks: a denoising network and a conditional discriminator. The denoising network takes a noise image as input and outputs a denoising result, while the discriminator tries to make the output look more like the target. Actually, only in the testing phase, this method is feed-forward. Significant image quality in experiments is achieved compared with the existing method.
PHOTOPROTECTION of PLANTS via OPTICAL SCREENING

Plants as photoautotrophic organisms cannot exist without solar radiation energy. On the other hand, photosynthesis proceeds with an optimal rate only within a narrow irradiance range, which is often lower than fluxes of solar radiation reaching plants under natural conditions. Therefore, the light energy absorbed by the photosynthetic apparatus often could not be utilized completely in the course of photochemical reactions. The imbalance between the amount of the absorbed light energy and the plant's capacity for its utilization occurs under high fluxes of solar radiation and/or even under moderate irradiance combined with stresses of different nature, such as extreme temperatures, drought or mineral nutrition deficiencies. There are also other situations when plants are rendered sensitive to damage by excessive fluxes of solar radiation. Thus, in juvenile and senescing plants, the regulation of photosynthetic apparatus functioning is not so perfect in comparison with mature leaves, making them less efficient in the utilization of the absorbed light, and therefore, prone to photodamage by radiation fluxes that usually do not harm mature plants.
Predicting Lesion Growth and Patient Survival in Colorectal Cancer Patients using Deep Neural Networks

Being responsible for over 50,000 death per year within the U.S. alone, colorectal cancer (CRC) is the second leading cause of cancer related deaths in industry nations with increasing prevalence. Within the scope of personalized medicine, precise estimates on future progress are crucial. We thus propose a novel deep learning based system using deep convolutional sparse autoencoders for estimating future lesion growth for CRC liver lesions based on single slice CT tumor images for early therapy assessment. Furthermore, we show that our system can be used for one-year survival prediction in CRC patients. While state of the art treatment assessment (RECIST) is premised on retrospective lesion analysis, our proposed system delivers an estimate on future response, thus prospectively allowing to adapt therapy before further progress. We compare our system to single-lesion assessment through RECIST diameter and Radiomics. With our approach we archieve a φ-coefficient of 40.0 % compared to 27.3 % / 29.4 % and an AUC of . 784 vs . 744/. 737 for growth prediction, as well as a φ-coefficient of 44.9 % vs 32.1 % / 18.0 % and an AUC of . 710 vs. . 688/. 568 for survival prediction.
Super-Resolution Imaging of Mammograms Based on the Super-Resolution Convolutional Neural Network

Purpose: To apply and evaluate a super-resolution scheme based on the super-resolution convolutional neural network (SRCNN) for enhancing image resolution in digital mammograms. Materials and Methods: A total of 711 mediolateral oblique (MLO) images including breast lesions were sampled from the Curated Breast Imaging Subset of the Digital Database for Screening Mammography (CBIS-DDSM). We first trained the super-resolution convolutional neural network (SRCNN), which is a deep-learning based super-resolution method. Using this trained SRCNN, high-resolution images were reconstructed from low-resolution images. We compared the image quality of the super-resolution method and that obtained using the linear interpolation methods (nearest neighbor and bilinear interpolations). To investigate the relationship between the image quality of the SRCNN-processed images and the clinical features of the mammographic lesions, we compared the image quality yielded by implementing the SRCNN, in terms of the breast density, the Breast Imaging-Reporting and Data System (BI-RADS) assessment, and the verified pathology information. For quantitative evaluation, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) were measured to assess the image restoration quality and the perceived image quality. Results: The super-resolution image quality yielded by the SRCNN was significantly higher than that obtained using linear interpolation methods (p < 0.001). The SRCNN-processed image quality in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases were significantly higher than that in low-density breasts, low-risk BI-RADS assessment groups, and benign cases, respectively (p < 0.01). Conclusion: SRCNN can significantly outperform conventional interpolation methods for enhancing image resolution in digital mammography. SRCNN can significantly improve the image quality of magnified mammograms, especially in dense breasts, high-risk BI-RADS assessment groups, and pathology-verified malignant cases.
Relativistic allowed and forbidden transition probabilities for fluorine-like Fe XVIII

Energy levels and the corresponding transition probabilities for allowed and forbidden transitions among the levels of the ground configuration and first 23 excited configurations of fluorine-like Fe  have been calculated using the mul- ticonfigurational Dirac-Fock  code. A total of 379 lowest bound levels of Fe  is presented, and the energy levels are identified in spectroscopic notations. Transition probabilities, oscillator strengths and line strengths for electric dipole (E1), electric quadrupole (E2) and magnetic dipole (M1) transitions among these 379 levels are also presented. The calculated energy levels and transition probabilities are compared with experimental data.
Precipitation thresholds for CO2 uptake in grass and shrub plant communities on Walnut Gulch Experimental Watershed

In semiarid ecosystems, precipitation is the major driving force for carbon uptake and subsequent plant growth. The hypothesis for this study was that the timing, frequency, and precipitation amount would produce different precipitation thresholds for uptake of carbon dioxide in grass and shrub plant communities. Eight years of precipitation data were used to determine the amount needed for carbon dioxide uptake thresholds in spring and summer seasons. Bowen ratio energy balance systems were used to measure carbon dioxide and moisture fluxes. In spring at the shrub site, close to or above long‐term average spring precipitation of 59 mm was required to produce an uptake response. At the grass site a minimum of 23 mm was needed to produce an uptake response, which was much less than the long‐term precipitation average of 68 mm. At both sites, spring or multiple summer responses reduced the threshold values for a summer response. Summer threshold ranges for the shrub site were 57–94 mm with a spring response and 123–140 mm without. Grass site summer thresholds were 51–95 mm with a spring response and 80–148 mm without. Summer precipitation threshold values were higher than spring values relating to the high summer evapotranspiration demand. The influence and variability of precipitation timing and frequency on carbon dioxide uptake threshold values resulted in no definitive conclusions as to differences between the grass and shrub plant communities, except that the grass site had slightly lower thresholds. Precipitation timing and frequency influence on total carbon uptake in some situations were more important than total precipitation. The lower grass site threshold values, along with a shift in climate toward more frequent and smaller precipitation events, may give grass ecosystems a competitive growth advantage.
An assessment of motion artefacts in multi band EPI for high spatial and temporal resolution resting state FMRI

Purpose: Multiband (MB) echo planar imaging (EPI) is a recent MRI technique capable of acquiring multi-slice, whole brain FMRI data with subsecond temporal resolution. The technique employs MB pulses to simultaneously excite n non-contiguous slices which are then separated by utilizing spatial information in multi-channel receive arrays with parallel imaging reconstruction. MB-EPI thereby acquires n images in each EPI echo train as opposed to the single image acquired by conventional “single-band” EPI, and therefore offers increased temporal and/or spatial resolution. Further, by increasing the temporal degrees-of-freedom, MB-EPI increases the temporal SNR, improving multivariate analyses such as independent component analysis (ICA). However, it is reasonable to expect MB-EPI to exhibit increased motion sensitivity due to the combination of short TR (spin history) and parallel imaging. In this study, the performance of MB-EPI with different acceleration factors was compared to that of standard EPI with respect to sensitivity to subject motion.
Understanding design for dynamic and Diverse Use Situations

The design research community acknowledges that usability and user experience are largely influenced by user characteristics, goals and contexts of use. For industrially manufactured products, these use situations are often dynamic and diverse. However, little guidance can be found in literature on the incorporation of dynamic and diverse use situations (DDUS) into the design process. This paper explores this issue by means of an analysis of user centred design literature and an empirical study of design for DDUS in design practice. We retrospectively studied three projects in which a product with DDUS was designed. Based on this study, we identified different effective strategies executed by design practitioners to analyse DDUS. We also observed difficulties in designing for DDUS. These difficulties include the identification of relevant aspects within this broad spectrum of use situations and sharing knowledge of product use between team members. We conclude that there is a need for guidance in the creation of flexible frames of reference of product use that evolve with solutions in the design process, particularly for design projects that cannot rely on the reuse of such a frame of reference from similar previous projects.
On the r>h shift in Kiên Giang Khmer: supplementary materials

This paper presents an acoustic and perceptual study of the r>h shift in the variety of Khmer spoken in Giồng Riềng district, Kiên Giang province, Vietnam. In Phnom Penh Khmer, /r/ is realized as [h] in syllable onsets and onset clusters, and accompanied by lowered pitch, breathiness, and in some cases a change in the quality of the following vowel. In Kiên Giang Khmer, the r>h shift is accompanied by pitch lowering, but without changes in aspiration or vowel quality, and spectral measures did not indicate substantial differences in voice quality. Consistent with their productions, users of this dialect appear to rely solely on differences pitch to identify these lexical items. We discuss the implications of our findings for Khmer dialectology, mechanisms of sound change, and variation in the realization of rhotics more generally.
Rangeland Hydrology and Erosion Model

16 Machine learning (ML) is becoming an ever more important tool in hydrologic model- 17 ing. Many studies have shown the higher prediction accuracy of the ML models over tra- 18 ditional process-based ones. However, there is another advantage of ML which is its lower 19 computer time of execution. This is important for the applications such as hydraulic soil 20 erosion estimation over a large area and at a finer spatial scale. Using traditional mod- 21 els like Rangeland Hydrology and Erosion Model (RHEM) requires too much computa- 22 tion time and resources. In this study, we designed an Artificial Neural Network that is 23 able to recreate the RHEM outputs (runoff, soil loss, and sediment yield) with high ac- 24 curacy (Nash-Sutcliffe Efficiency ≈ 1.0) and a very low computational time (13 billion 25 times faster on average). We ran the RHEM for more than a million synthetic scenar- 26 ios and train the Emulator with them. We also, fine-tuned the trained Emulator with 27 the RHEM runs of the real-world scenarios (more than 32,000) so the Emulator remains 28 comprehensive while it works specifically accurately for the real-world cases. We
Fast Low-Rank Bayesian Matrix Completion With Hierarchical Gaussian Prior Models

The problem of low-rank matrix completion is considered in this paper. To exploit the underlying low-rank structure of the data matrix, we propose a hierarchical Gaussian prior model, where columns of the low-rank matrix are assumed to follow a Gaussian distribution with zero mean and a common precision matrix, and a Wishart distribution is specified as a hyperprior over the precision matrix. We show that such a hierarchical Gaussian prior has the potential to encourage a low-rank solution. Based on the proposed hierarchical prior model, we develop a variational Bayesian matrix completion method, which embeds the generalized approximate massage passing technique to circumvent cumbersome matrix inverse operations. Simulation results show that our proposed method demonstrates superiority over some state-of-the-art matrix completion methods.
Towards Nanowire Tandem Junction Solar Cells on Silicon

The development of photovoltaics as a serious means of producing renewable energy has accelerated greatly in the last ten years, with prices for silicon-based solar cell systems dropping dramatically in the last few years. The next great opportunity for photovoltaics following this competitiveness in prices will be to enhance the cell and panel efficiencies. It is quite generally seen that the most viable platform on which this should be realized will be as augmented silicon solar cells, in which a top cell will be combined with the silicon bottom cell in a tandem configuration, by which the efficiency can be enhanced by a factor from 20% to 50%, depending on details of the approach. In this paper, we report on the status of one such approach, namely, with a top cell comprising III–V nanowires, connected to the bottom silicon cell in a two-terminal or four-terminal configuration. Among the most important opportunities, we show that a substrate-free growth, called Aerotaxy, offers a radical reduction in the total price picture. Besides the description of the key technical approaches, we also discuss the environmental issues.
Gate driver design and continuous operation of an improved 1200V/200A FREEDM-pair half-bridge power module

FREEDM-Pair is an innovative power semiconductor switch which reduces the loss and cost through the hybrid integration of a Si IGBT and a SiC MOSFET. During the turn-off of the FREEDM-Pair, the Si IGBT is turned off first under ZVS condition and after a carefully selected delay time, the SiC MOSFET is turn-off. In this way, the turn-off loss of the Si IGBT is significantly reduced due to the ZVS turn-off condition. During the delay time, the current will be carried by the SiC MOSFET only. During the turn-on, the SiC MOSFET and the IGBT can be turned on at the same time. Due to faster turn-on speed of the MOSFET, the IGBT is also turned on under the ZVS condition. FREEDM-Pair also has better conduction characteristics compared to the Si IGBT by the combined current conduction capability of the unipolar and bipolar devices. Therefore the FREEDM-Pair provides an ideal option to realize the tradeoff between the cost and performance, and can be applied to main stream applications which currently use IGBT. Previously, the 1200V/200A FREEDM-Pair with individual gate terminals for IGBT and MOSFET have been designed and published. In this paper, a three terminal 1200V/200A FREEDM-Pair is presented and analyzed for the first time. In the three terminal FREEDM-Pair, a passive delay circuit is designed and integrated into the power module, so only one gate driver signal is needed. The designed delay circuit will generate the desired delay gate signal for the MOSFET automatically. To verify the designed gate driver function and the FREEDM-Pair performance in continuous operation, a half-bridge (HB) power module consisting of two FREEDM-Pair switches is developed. The continuous operation of the 1200V/200A FREEDM-Pair in a buck converter topology is reported for the first time.
Power System Voltage Stability Evaluation Considering Renewable Energy With Correlated Variabilities

Power system operation will encounter numerous variabilities as the proliferation of renewable energy continues. Such random events will require the evaluation of corresponding variables and the assessment of their impacts on the monitoring and the control of stochastic power systems. In this paper, a global sensitivity analysis (GSA) method is proposed to perform a priority ranking of renewable energy variabilities that will affect the voltage stability of power systems. First, a probabilistic model for the load margin calculation is presented considering the renewable power generation. Then, GSA is applied to models with correlated random input variables and the importance index is designed for evaluating the impacts of such variables. Next, the stochastic response surface method is adopted in GSA to improve the computation efficiency of the proposed evaluation method. Finally, the overall procedure is presented to identify critical variables that can affect the variability of load margins in voltage stability analyses. The proposed method is applied to power systems with a large penetration of renewable power and the influences of critical variabilities on voltage stability are analyzed. In this paper, the proposed method is tested using the IEEE 9-bus and the IEEE 118-bus systems, and its accuracy is compared with those of the local sensitivity analysis method and the commonly used GSA methods.