Miracle-dz/UniFAR-FTUs huggingface dataset:
PREVALENCE OF LONG-TERM OPIOID THERAPY IN A CHRONIC NON-CANCER PAIN POPULATION ATTENDING A UNIVERSITY-BASED TERTIARY PAIN CLINIC IN SWEDEN: A CROSS-SECTIONAL STUDY

Background Opioid therapy is a common treatment for chronic pain, despite accumulating evidence regarding harm and a lack of data to support the efficacy of long-term treatment. The prevalence of opioid therapy in Swedish patients with chronic non-cancer pain is unknown. The aim of this study was to assess a short-term period prevalence of prescribed opioid-use and long-term opioid therapy in a population with complex chronic non-cancer pain. Methods The study population comprised 1,613 patients with chronic non-cancer pain referred to a university-based tertiary pain clinic in Sweden during 2015–17. Data from a 360-day period prior to consultation were extracted from the Swedish Quality Registry for Pain Rehabilitation (SQRP) and Swedish Prescribed Drug Register (SPDR). Milligram morphine equivalents per day (MME/day) for dispensed opioids were analysed for a 90-day period preceding consultation, and long-term opioid therapy was determined for the entire 360-day period. Results The 90-day prevalence was 38% (95% confidence interval (95% CI) 36.0–40.8%) and 360-day prevalence was 22.3% (n=360, 95% CI 20.4–24.4%). Conclusion Prescribing rates of opioids in a Swedish population with complex non-cancer chronic pain were high; 2 in 5 patients were dispensed an opioid within a 90-day period prior to consultation. LAY ABSTRACT Opioid therapy is a common treatment for chronic pain, despite accumulating evidence regarding harm and a lack of data to support the efficacy of long-term treatment. The use of opioid treatments in Swedish patients with chronic non-cancer pain is unknown. Hence, the current study aimed to assess the frequency of opioid use in a population with complex chronic non-cancer pain. The study population comprised 1,613 patients referred to a Swedish specialized pain rehabilitation unit during 2015–17. Data for 1 year prior to assessment were extracted from the Swedish Quality Registry for Pain Rehabilitation and Swedish Prescribed Drug Register. Milligram morphine equivalents per day for dispensed opioids were analysed for short-term prevalence and long-term opioid therapy. In conclusion, the prescribing rates of opioids in a Swedish population with complex non-cancer chronic pain were high; 2 in 5 patients were dispensed an opioid within a 90-day period prior to consultation.
Systemic inflammation and metabolic disturbances underlie inpatient mortality among ill children with severe malnutrition

Children admitted to hospital with an acute illness and concurrent severe malnutrition [complicated severe malnutrition (CSM)] have a high risk of dying. The biological processes underlying their mortality are poorly understood. In this case-control study nested within a multicenter randomized controlled trial among children with CSM in Kenya and Malawi, we found that blood metabolomic and proteomic profiles robustly differentiated children who died (n = 92) from those who survived (n = 92). Fatalities were characterized by increased energetic substrates (tricarboxylic acid cycle metabolites), microbial metabolites (e.g., propionate and isobutyrate), acute phase proteins (e.g., calprotectin and C-reactive protein), and inflammatory markers (e.g., interleukin-8 and tumor necrosis factor–α). These perturbations indicated disruptions in mitochondria-related bioenergetic pathways and sepsis-like responses. This study identified specific biomolecular disturbances associated with CSM mortality, revealing that systemic inflammation and bioenergetic deficits are targetable pathophysiological processes for improving survival of this vulnerable population.
Evaluation of microwave heating on the mechanical properties, β-glucan, and fiber content of barley kernels.

BACKGROUND Microwave heating may affect some non-starch polysaccharides of cereal kernels. This microwave effect can be positive for functional properties and the final product. Therefore, the purpose of this work was to explore the effects of microwave heating on mechanical properties, malt extract yield, wort viscosity, β-glucan of wort, and soluble, insoluble, and total dietary fiber in malting and feed barley. METHODS The barley kernels were microwave heated for 4 and 8 s and compared with a control (0 s) with no microwave irradiation treatment. The mechanical properties were measured by compressive loadings; an Environmental Scanning Electron Microscopy was used for kernel layers. β-glucan content in the barley kernel and wort was measured with a Mixed Linkage beta-glucan (K-BGLU Megazyme International; Wicklow, Ireland). Insoluble, soluble, and total dietary fiber was determined using 32-07 of AACC method. RESULTS The thickness of barley kernel bran layers was related to the mechanical properties. The modulus of elasticity decreased after 4 s of heating but increased after 8 s. Irradiation affected non-starch polysaccharides, such as β-glucan and fiber. β-glucan decreased after 4 s as did wort viscosity. The insoluble and total dietary fiber followed the same trend as β-glucan, but the soluble fiber content increased with prolonged microwave heating. CONCLUSIONS A few seconds of microwave heating is enough to increase barley’s value in the brewing industry and improve health benefits due to minor changes in the biochemical grain components.
Reliability of Measuring Leg Segments and Joint Angles Using Smartphones during Aquatic Exercise

Objectives Aquatic therapy is a significant intervention method for both patients and healthy individuals. However, in clinical practice, quantitative measurements are rarely applied in aquatic therapy due to the disadvantages of submerging expensive instruments in water. In this study, we used readily available smartphones and armbands to measure leg segments and joint angles during aquatic gait and evaluated the reliability of these measurements. Methods Waterproof smartphones were strapped to the trunk, thighs, and shanks of 19 healthy young adults using armbands. The angles of the trunk, thigh, and shank segments were measured during aquatic gait. The measurements were repeated 1 day later. The data were analyzed to obtain the angles of the hip and knee joints. Results Measurement repeatability, calculated using the intraclass correlation coefficient (ICC), was the highest for the shank segment range of motion (ROM) (first 46.79° ± 5.50°, second 50.12° ± 9.98°, ICC = 0.78). There was high agreement in trunk segment ROM (first 6.36° ± 1.42°, second 4.29° ± 1.83°, ICC = 0.73), thigh segment ROM (first 33.49° ± 5.18°, second 37.31° ± 8.70°, ICC = 0.62), and knee joint ROM (first 52.43° ± 11.26°, second 62.19° ± 16.65°, ICC = 0.68) and fair agreement in hip joint ROM (first 34.60°±4.71°, second 37.80° ± 7.84°, ICC = 0.59). Conclusions Smartphones can be used to reliably measure leg segments and joint angles during aquatic gait, providing a simpler method for obtaining these measurements and enabling the wider use of aquatic motion analysis in clinical practice and research.
Molecular interactions of trichoderma β-1,4-glucosidase (ThBglT12) with mycelial cell wall components of phytopathogenic Macrophomina phaseolina

Abstract Efficacy of a β-1,4-glucosidase from Trichoderma harzianum T12 (ThBglT12) in disrupting the cell wall of the phytopathogenic fungus M. phaseolina (Macrophomina phaseolina) was studied, as the underlying molecular mechanisms of cell wall recognition remains elusive. In this study, the binding location identified by a consensus of residues predicted by COACH tool, blind docking, and multiple sequence alignment revealed that molecular recognition by ThBglT12 occurred through interactions between the α-1,3-glucan, β-1,3-glucan, β-1,3/1,4-glucan, and chitin components of M. phaseolina, with corresponding binding energies of −7.4, −7.6, −7.5 and −7.8 kcal/mol. The residue consensus verified the participation of Glu172, Tyr304, Trp345, Glu373, Glu430, and Trp431 in the active site pocket of ThBglT12 to bind the ligands, of which Trp345 was the common interacting residue. Root mean square deviation (RMSD), root mean square fluctuation (RMSF), total energy, and minimum distance calculation from molecular dynamics (MD) simulation further confirmed the stability and the closeness of the binding ligands into the ThBglT12 active site pocket. The h-bond occupancy by Glu373 and Trp431 instated the role of the nucleophile for substrate recognition and specificity, crucial for cleaving the β-1,4 linkage. Further investigation showed that the proximity of Glu373 to the anomeric carbon of β-1,3/1,4-glucan (3.5 Å) and chitin (5.5 Å) indicates the nucleophiles’ readiness to form enzyme-substrate intermediates. Plus, the neighboring water molecule appeared to be correctly positioned and oriented towards the anomeric carbon to hydrolyze the β-1,3/1,4-glucan and chitin, in less than 4.0 Å. In a nutshell, the study verified that the ThBglT12 is a good alternative fungicide to inhibit the growth of M. phaseolina. Communicated by Ramaswamy H. Sarma
Healthcare personnel’s attitude and coverage about tetanus vaccination in Turkey: a multicenter study

ABSTRACT The tetanus vaccine is not routinely given to Turkish adults. Protective tetanus immunity decreases with age. Health-care personnel (HCPs), who are role models in the field of health, are a target group in order to achieve a higher rate of tetanus vaccination in the community. This study was designed to evaluate attitudes and coverage regarding tetanus vaccination among a large sample of Turkish HCPs. This cross-sectional epidemiologic study was conducted from July to August 2019. A questionnaire was sent to HCPs using social media. Of the 10,644 HCPs included in the study, 65% were female. Overall, the tetanus vaccination coverage (TVC) among HCPs was 78.5% (95% CI: 77.7%-79.3%). TVC was significantly higher among physicians [83.4% (95% CI: 82%-84.6%); p < . 001] compared with all other HCPs except nurses. Older age (≥40 years) and length of professional experience were significantly correlated with TVC. Of the 8353 HCPs who received tetanus vaccines during their lifetime, 73.03% received tetanus vaccination in the past 10 years. The self-vaccination rate for protection against tetanus was 13.1%. Acute injuries (25.42%) and pregnancy (23.9%) were the most common reasons for having the tetanus vaccine. One-third (33.7%) of HCPs did not have information about whether pregnant women could receive tetanus vaccinations. This survey study provided excellent baseline information about HCPs’ coverage rates and attitudes regarding tetanus vaccination. The present results suggested that tetanus boosters for HCPs should be established as soon as possible, and revealed that the HCPs younger than 30 years with relatively less professional experience and all other HCPs except nurses and physicians should be identified as the target population for future intervention programs.
Ensemble Conformalized Quantile Regression for Probabilistic Time Series Forecasting

This article presents a novel probabilistic forecasting method called ensemble conformalized quantile regression (EnCQR). EnCQR constructs distribution-free and approximately marginally valid prediction intervals (PIs), which are suitable for nonstationary and heteroscedastic time series data. EnCQR can be applied on top of a generic forecasting model, including deep learning architectures. EnCQR exploits a bootstrap ensemble estimator, which enables the use of conformal predictors for time series by removing the requirement of data exchangeability. The ensemble learners are implemented as generic machine learning algorithms performing quantile regression (QR), which allow the length of the PIs to adapt to local variability in the data. In the experiments, we predict time series characterized by a different amount of heteroscedasticity. The results demonstrate that EnCQR outperforms models based only on QR or conformal prediction (CP), and it provides sharper, more informative, and valid PIs.
cosFormer: Rethinking Softmax in Attention

Transformer has shown great successes in natural language processing, computer vision, and audio processing. As one of its core components, the softmax attention helps to capture long-range dependencies yet prohibits its scale-up due to the quadratic space and time complexity to the sequence length. Kernel methods are often adopted to reduce the complexity by approximating the softmax operator. Nevertheless, due to the approximation errors, their performances vary in different tasks/corpus and suffer crucial performance drops when compared with the vanilla softmax attention. In this paper, we propose a linear transformer called cosFormer that can achieve comparable or better accuracy to the vanilla transformer in both casual and cross attentions. cosFormer is based on two key properties of softmax attention: i). non-negativeness of the attention matrix; ii). a non-linear re-weighting scheme that can concentrate the distribution of the attention matrix. As its linear substitute, cosFormer fulfills these properties with a linear operator and a cosine-based distance re-weighting mechanism. Extensive experiments on language modeling and text understanding tasks demonstrate the effectiveness of our method. We further examine our method on long sequences and achieve state-of-the-art performance on the Long-Range Arena benchmark. The source code is available at https://github.com/OpenNLPLab/cosFormer.
Efficient Classification of Local Problems in Regular Trees

We give practical, efficient algorithms that automatically determine the distributed round complexity of a given locally checkable graph problem, in two settings. We present one algorithm for unrooted regular trees and another algorithm for rooted regular trees. The algorithms take the description of a locally checkable labeling problem as input, and the running time is polynomial in the size of the problem description. The algorithms decide if the problem is solvable in 𝑂 ( log 𝑛 ) rounds. If not, it is known that the complexity has to be Θ ( 𝑛 1 / 𝑘 ) for some 𝑘 = 1 , 2 , . , and in this case the algorithms also output the right value of the exponent 𝑘 . In rooted trees in the 𝑂 ( log 𝑛 ) case we can then further determine the exact complexity class by using algorithms from prior work; for unrooted trees the more fine-grained classification in the 𝑂 ( log 𝑛 ) region remains an open question.
UV Counterpart of an X-Ray Ultrafast Outflow in IRAS 17020+4544

We report on the discovery of a UV absorption counterpart of a low-ionization X-ray ultrafast outflow (UFO) in the narrow-line Seyfert 1 galaxy IRAS 17020+4544. This UV signature of the UFO is seen as a narrow and blueshifted Lyα absorption feature in the far-UV spectrum, taken with the Cosmic Origins Spectrograph (COS) on the Hubble Space Telescope (HST). The Lyα feature is found to be outflowing with a velocity of −23,430 km s−1 (0.078 c). We carry out high-resolution UV spectroscopy and photoionization modeling to study the UFO that is seen in the HTS/COS spectrum. The results of our modeling show that the UV UFO corresponds to a low-ionization, low-velocity component of the X-ray UFO found previously with XMM-Newton’s Reflection Grating Spectrometer. The other higher-velocity and higher-ionization components of the X-ray UFOs are not significantly detected in the HST/COS spectrum, consistent with predictions of our photoionization calculations. The multiple ionization and velocity components of the UFOs in IRAS 17020+4544 suggest a scenario where a powerful primary UFO entrains and shocks the ambient medium, resulting in formation of weaker secondary UFO components, such as the one found in the UV band.
Delay-adaptive step-sizes for asynchronous learning

In scalable machine learning systems, model training is often parallelized over multiple nodes that run without tight synchronization. Most analysis results for the related asynchronous algorithms use an upper bound on the information delays in the system to determine learning rates. Not only are such bounds hard to obtain in advance, but they also result in unnecessarily slow convergence. In this paper, we show that it is possible to use learning rates that depend on the actual time-varying delays in the system. We develop general convergence results for delay-adaptive asynchronous iterations and specialize these to proximal incremental gradient descent and block-coordinate descent algorithms. For each of these methods, we demonstrate how delays can be measured on-line, present delay-adaptive step-size policies, and illustrate their theoretical and practical advantages over the state-of-the-art.
Point Cloud Generation with Continuous Conditioning

Generative models can be used to synthesize 3D objects of high quality and diversity. However, there is typically no control over the properties of the generated object.This paper proposes a novel generative adversarial network (GAN) setup that generates 3D point cloud shapes conditioned on a continuous parameter. In an exemplary application, we use this to guide the generative process to create a 3D object with a custom-fit shape. We formulate this generation process in a multi-task setting by using the concept of auxiliary classifier GANs. Further, we propose to sample the generator label input for training from a kernel density estimation (KDE) of the dataset. Our ablations show that this leads to significant performance increase in regions with few samples. Extensive quantitative and qualitative experiments show that we gain explicit control over the object dimensions while maintaining good generation quality and diversity.
Phase Aberration Robust Beamformer for Planewave US Using Self-Supervised Learning

Ultrasound (US) is widely used for clinical imaging applications thanks to its real-time and non-invasive nature. However, its lesion detectability is often limited in many applications due to the phase aberration artefact caused by variations in the speed of sound (SoS) within body parts. To address this, here we propose a novel self-supervised 3D CNN that enables phase aberration robust plane-wave imaging. Instead of aiming at estimating the SoS distribution as in conventional methods, our approach is unique in that the network is trained in a self-supervised manner to robustly generate a high-quality image from various phase aberrated images by modeling the variation in the speed of sound as stochastic. Experimental results using real measurements from tissue-mimicking phantom and \textit{in vivo} scans confirmed that the proposed method can significantly reduce the phase aberration artifacts and improve the visual quality of deep scans.
Plasmoid-fed Prominence Formation (PF2) During Flux Rope Eruption

We report a new, plasmoid-fed scenario for the formation of an eruptive prominence (PF2), involving reconnection and condensation. We use grid-adaptive resistive two-and-a-half-dimensional magnetohydrodynamic simulations in a chromosphere-to-corona setup to resolve this plasmoid-fed scenario. We study a preexisting flux rope (FR) in the low corona that suddenly erupts due to catastrophe, which also drives a fast shock above the erupting FR. A current sheet (CS) forms underneath the erupting FR, with chromospheric matter squeezed into it. The plasmoid instability occurs and multiple magnetic islands appear in the CS once the Lundquist number reaches ∼3.5 × 104. The remnant chromospheric matter in the CS is then transferred to the FR by these newly formed magnetic islands. The dense and cool mass transported by the islands accumulates in the bottom of the FR, thereby forming a prominence during the eruption phase. More coronal plasma continuously condenses into the prominence due to the thermal instability as the FR rises. Due to the fine structure brought in by the PF2 process, the model naturally forms filament threads, aligned above the polarity inversion line. Synthetic views at our resolution of 15 km show many details that may be verified in future high-resolution observations.
A Developmentally-Inspired Examination of Shape versus Texture Bias in Machines

Early in development, children learn to extend novel category labels to objects with the same shape, a phenomenon known as the shape bias. Inspired by these findings, Geirhos et al. (2019) examined whether deep neural networks show a shape or texture bias by constructing images with conflicting shape and texture cues. They found that convolutional neural networks strongly preferred to classify familiar objects based on texture as opposed to shape, suggesting a texture bias. However, there are a number of differences between how the networks were tested in this study versus how children are typically tested. In this work, we re-examine the inductive biases of neural networks by adapting the stimuli and procedure from Geirhos et al. (2019) to more closely follow the developmental paradigm and test on a wide range of pre-trained neural networks. Across three experiments, we find that deep neural networks exhibit a preference for shape rather than texture when tested under conditions that more closely replicate the developmental procedure.
The deepest problem: some perspectives on quantum gravity

Quantum gravity is likely the deepest problem facing current physics. While traditionally associated with short distance nonrenormalizability, it is evident that the long distance problem of unitarity, arising at high energies with black hole formation, is more profound. This reveals a conflict between foundational principles of quantum field theory: those of quantum mechanics, relativity, and locality. Difficulties modifying quantum mechanics suggest a"quantum-first"approach, with other principles as mathematical properties of a quantum space of states. A challenge is how to describe locality, in terms of Hilbert space structure. Perturbative gravity gives clues, with structure apparently different than in field theory. The mathematical structure of subsystems plausibly supplants conventional locality and plays a foundational role in the theory. This view differs from one of spacetime"emerging"from another quantum system. If a black hole behaves as a subsystem, a"black hole theorem"says that unitarity requires interactions with its environment depending on its state, or more drastic phenomena. Minimal interactions can be parameterized, in an effective approach; they could arise from wormholes or other fundamental dynamics. These or other near-horizon modifications potentially alter electromagnetic or gravitational signatures of this strong gravity region, now being probed in a new era of observation; it is important to seek observational clues for or constraints on such scenarios. One may also investigate quantum gravity via its S-matrix. New perturbative structure has been discovered there, but the harder question again goes beyond to the nonperturbative regime. Long-distance behavior of amplitudes indicates novel analytic behavior; further exploration may provide important clues. Other key questions regard quantum description of cosmologies, and of associated observables.