Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between Late-Eating Pattern and Higher Consumption of Ultra-Processed Food among Italian Adults: Findings from the INHES Study

Late eating is reportedly associated with adverse metabolic health, possibly through poor diet quality. We tested the hypothesis that meal timing could also be linked to food processing, an independent predictor of health outcomes. We analysed data on 8688 Italians (aged > 19years) from the Italian Nutrition & HEalth Survey (INHES) established in 2010–2013 throughout Italy. Dietary data were collected through a single 24 h dietary recall, and the NOVA classification was used to categorize foods according to increasing levels of processing: (1) minimally processed foods (e.g., fruits); (2) culinary ingredients (e.g., butter); (3) processed foods (e.g., canned fish); (4) ultra-processed foods (UPFs; e.g., carbonated drinks, processed meat). We then calculated the proportion (%) of each NOVA group on the total weight of food eaten (g/d) by creating a weight ratio. Subjects were classified as early or late eaters based on the population’s median timing for breakfast, lunch and dinner. In multivariable-adjusted regression models, late eaters reported a lower intake of minimally processed food (β = −1.23; 95% CI −1.75 to −0.71), a higher intake of UPF (β = 0.93; 0.60 to 1.25) and reduced adherence to a Mediterranean Diet (β = −0.07; −0.12 to −0.03) as compared to early eaters. Future studies are warranted to examine whether increased UPF consumption may underpin the associations of late eating with adverse metabolic health reported in prior cohorts.
Efficient embeddings and simulations for hypercubic networks

Part I of this thesis studies the relative computational strengths of the shuffle-exchange graph and the butterfly network, the most common representatives of the two main classes of hypercube-derived networks, demonstrating new structural similarities and establishing the computational equivalence of the two networks. The butterfly network can simulate a certain class of structured hypercube algorithms as efficiently as the shuffle-exchange graph can. We show that each network is able to efficiently simulate an arbitrary computation on the other. In terms of what a network with a given number of nodes can compute in a given number of steps, all of these networks are equivalent up to constant factors. The simulation does not involve a direct embedding of either network into the other; it does not lead to the immediate translation of results that deal with the combinatorial structures of the two networks rather than with algorithmic issues. However, many structural results for one of the networks do in fact have analogues for the other. We give an improved lower bound on dilation for embeddings of the X-tree network into the shuffle-exchange graph, bringing it nearer to the optimal result known for the butterfly network. We consider recent results in the VLSI packaging of deBruijn graph networks, and show that a recently developed packaging scheme can be made to yield the same optimal behavior in terms of the number of off-chip connections needed that is easily achievable for the butterfly network. In Part II, we study the problems of dynamic tree embedding and dynamic allocation of distributed memory resources. For either the butterfly network or the hypercube, we give algorithms that achieve small constant dilation and maximum load O(M/N + log N) with high probability. We also give an improved algorithm for the hypercube that achieves constant dilation and maximum load O(M/N + 1) with high probability. Finally, we prove a lower bound of $\Omega$($\sqrt{\log N}$) on dilation for deterministic embedding algorithms. We consider the problem of dynamic allocation of distributed memory resources. We give a fully on-line algorithm for this distributed dynamic allocation problem that guarantees 100% usage of the available space while achieving slowdown O(M/N + 1). (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.) (Abstract shortened with permission of school.)
Animals in Fashion: Portrayal of Animal Biodiversity on Children’s Clothing

While cultural products such as clothes are usually not designed with an educational goal in mind, they may still raise biodiversity awareness. This study explored the portrayal of animal biodiversity on children’s clothing marketed by three major clothing retailers in the Netherlands. Findings showed that although nonhuman animals were a common theme, diversity was quite low. The portrayal was centered on mammals, in particular exotic and domestic species, and a gender binary was uncovered, restricting animals such as dinosaurs to boys’ clothes and butterflies to girls’ clothes. Moreover, portrayals were often highly simplified and anthropomorphic, which reduced recognizability. The results show that children’s clothes currently do not offer the balanced and iconic depiction of animal biodiversity needed for broadening people’s perceptions. To achieve a more extensive representation that can help connect people with biodiversity, a shift in ideas will be required of what animals are suitable to portray.
SOLUTION OF GENERALIZED BACKWARD STOCHASTIC DIFFERENTIAL EQUATIONS WITH JUMPS

In generalized complete probability space with right continous filtration,existence and uniqueness of the solution of BSDE drived by Brownian motion and Piosson point process are considered.To p integrable parameters ( p ≥2),if p =2,under local Lipschitzian condition or p ≥2,under Lipschitzian condition,the solution exists and is unique.
Finding patterns of atoll morphometrics at a range of spatial scales

Small Island Nations, often comprised of Atolls, are at considerable risk of climate change impacts from sea-level rise to coral acidification to increasing cyclone intensity; understanding how they will change in the coming century is vital for climate mitigation and resiliency. However, the morphology of atolls are not well quantified or summarized. In this work, we calculate modern atoll morphometrics on a global scale including 3,786 motu and 593 reef flats on 154 atolls. Temporal composites of Landsat imagery are created for 4 years (2015–2018), and are classified into motu, reef flat, open water/lagoon via unsupervised classification. Morphometrics, including widths, lengths, and area, are computed for each motu and reef flat of the atoll in Python, creating a database of atolls and their associated morphometrics. Consistency in automated processing of atoll morphometrics removes spatial and user bias, enabling a better understanding of geographic patterns of atoll area and other morphometrics. Distinct differences in the distribution of motu are seen on a regional scale in French Polynesia, while globally, wider reef flats and larger motu are found closest to the equator. Globally, there is a consistent reef flat width in front of large motu (>1 km length) of 184 ± 93 m (n = 724). Our atoll morphometric database creates a baseline of current atoll characteristics that can be expanded upon in the future and used for evaluating temporal changes to atoll islands.
`Rapid learning' techniques for discrete event systems

One of the approaches that have been followed in the study of discrete event systems (DES) is based on the analysis of state trajectories (sample paths) of these systems when analytical techniques fail to provide closed-form solutions or adequate approximations. The theory of perturbation analysis is founded on the fact that observing the behavior of a DES under one set of design or control parameters often allows one to infer its behavior under a different set of such parameters. This strongly suggests the potential for 'learning' about the global behavior of a system by simply observing one sample path. In turn, this implies the potential for adjusting various critical parameters on line so as to continuously improve performance and react to unexpected changes. The term 'rapid learning' is used to capture the idea that this global behavior can be inferred from a single sample path without having to resort to time-consuming and prohibitively costly 'trial-and-error' techniques (e.g. repetitive simulation). The article overviews the rapid learning framework based on stochastic timed automata as models of DES. The 'constructability problem', fundamental to this framework, is formulated, and two specific approaches to its solution are reviewed and compared. Some applications of rapid learning techniques from areas such as manufacturing and communication networks are briefly described. Some developments promising an alternative approach to solving combinatorially hard design and optimization problems are also discussed. < >
Guided Deep Learning Manifold Linearization of Porous Media Flow Equations

Integrated reservoir studies for performance prediction and decision-making processes are computationally expensive. In this paper, we develop a novel linearization approach to reduce the computational burden of intensive reservoir simulation execution. We achieve this by introducing two novel components: (1) augment the state-space to yield a bi-linear system, and (2) an autoencoder based on a deep neural network to linearize physics reservoir equations in a reduced manifold employing a Koopman operator. Recognizing that reservoir simulators execute expensive Newton-Raphson iterations after each timestep to solve the nonlinearities of the physical model, we propose "lifting" the physics to a more amenable manifold where the model behaves close to a linear system, similar to the Koopman theory, thus avoiding the iteration step. We use autoencoder deep neural networks with specific loss functions and structure to transform the nonlinear equation and frame it as a bilinear system with constant matrices over time. In such a way, it forces the states (pressures and saturations) to evolve in time by simple matrix multiplications in the lifted manifold. We also adopt a "guided" training approach: our training process is performed in three steps: we initially train the autoencoder, then we use a "conventional" MOR (Dynamic Mode Decomposition) as an initializer for the final full training when we use reservoir knowledge to improve and to lead the results to physically meaningful output. Many simulation studies exhibit extremely nonlinear and multi-scale behavior, which can be difficult to model and control. Koopman operators can be shown to represent any dynamical system through linear dynamics. We applied this new framework to a two-dimensional two-phase (oil and water) reservoir subject to a waterflooding plan with three wells (one injector and two producers) with speed ups around 100 times faster and accuracy in the order of 1-3 percent on the pressure and saturations predictions. It is worthwhile noting that this method is a non-intrusive data-driven method since it does not need access to the reservoir simulation internal structure; thus, it is easily applied to commercial reservoir simulators and is also extendable to other studies. In addition, an extra benefit of this framework is to enable the plethora of well-developed tools for MOR of linear systems. This is the first work that utilizes the Koopman operator for linearizing the system with controls to the author's knowledge. As with any ROM method, this can be directly applied to a well-control optimization problem and well-placement studies with low computational cost in the prediction step and good accuracy.
Pathophysiology of Cav1.3 L-type calcium channels in the heart

Ca2+ plays a crucial role in excitation-contraction coupling in cardiac myocytes. Dysfunctional Ca2+ regulation alters the force of contraction and causes cardiac arrhythmias. Ca2+ entry into cardiomyocytes is mediated mainly through L-type Ca2+ channels, leading to the subsequent Ca2+ release from the sarcoplasmic reticulum. L-type Ca2+ channels are composed of the conventional Cav1.2, ubiquitously expressed in all heart chambers, and the developmentally regulated Cav1.3, exclusively expressed in the atria, sinoatrial node, and atrioventricular node in the adult heart. As such, Cav1.3 is implicated in the pathogenesis of sinoatrial and atrioventricular node dysfunction as well as atrial fibrillation. More recently, Cav1.3 de novo expression was suggested in heart failure. Here, we review the functional role, expression levels, and regulation of Cav1.3 in the heart, including in the context of cardiac diseases. We believe that the elucidation of the functional and molecular pathways regulating Cav1.3 in the heart will assist in developing novel targeted therapeutic interventions for the aforementioned arrhythmias.
Comprehensive prognostic modeling of locoregional recurrence after radiotherapy for patients with locoregionally advanced hypopharyngeal squamous cell carcinoma

Purpose To propose and evaluate a comprehensive modeling approach combing radiomics, dosiomics and clinical components, for more accurate prediction of locoregional recurrence risk after radiotherapy for patients with locoregionally advanced HPSCC. Materials and methods Clinical data of 77 HPSCC patients were retrospectively investigated, whose median follow-up duration was 23.27 (4.83-81.40) months. From the planning CT and dose distribution, 1321 radiomics and dosiomics features were extracted respectively from planning gross tumor volume (PGTV) region each patient. After stability test, feature dimension was further reduced by Principal Component Analysis (PCA), yielding Radiomic and Dosiomic Principal Components (RPCs and DPCs) respectively. Multiple Cox regression models were constructed using various combinations of RPC, DPC and clinical variables as the predictors. Akaike information criterion (AIC) and C-index were used to evaluate the performance of Cox regression models. Results PCA was performed on 338 radiomic and 873 dosiomic features that were tested as stable (ICC1 > 0.7 and ICC2 > 0.95), yielding 5 RPCs and DPCs respectively. Three comprehensive features (RPC0, P<0.01, DPC0, P<0.01 and DPC3, P<0.05) were found to be significant in the individual Radiomic or Dosiomic Cox regression models. The model combining the above features and clinical variable (total stage IVB) provided best risk stratification of locoregional recurrence (C-index, 0.815; 95%CI, 0.770-0.859) and prevailing balance between predictive accuracy and complexity (AIC, 143.65) than any other investigated models using either single factors or two combined components. Conclusion This study provided quantitative tools and additional evidence for the personalized treatment selection and protocol optimization for HPSCC, a relatively rare cancer. By combining complementary information from radiomics, dosiomics, and clinical variables, the proposed comprehensive model provided more accurate prediction of locoregional recurrence risk after radiotherapy.
Performance Analysis of a Cooperative MAC Protocol of Wireless Ad Hoc Networks

ABSTRACT Different medium access control (MAC) layer protocols, for example, IEEE 802.11 series and others are used in wireless local area networks. They have limitation in handling bulk data transfer applications, like video-on-demand, videoconference, etc. To avoid this problem a cooperative MAC protocol environment has been introduced, which enables the MAC protocol of a node to use its nearby nodes MAC protocol as and when required. We have found on various occasions that specified cooperative MAC establishes cooperative transmissions to send the specified data to the destination. In this paper we propose cooperative MAC priority (CoopMACPri) protocol which exploits the advantages of priority value given by the upper layers for selection of different paths to nodes running heterogeneous applications in a wireless ad hoc network environment. The CoopMACPri protocol improves the system throughput and minimizes energy consumption. Using a Markov chain model, we developed a model to analyse the performance of CoopMACPri protocol; and also derived closed-form expression of saturated system throughput and energy consumption. Performance evaluations validate the accuracy of the theoretical analysis, and also show that the performance of CoopMACPri protocol varies with the number of nodes. We observed that the simulation results and analysis reflects the effectiveness of the proposed protocol as per the specifications.
Improvement of AERIT/qRetrievals and Their Validation at Anmyeon-Do, South Korea

AbstractAn Atmospheric Emitted Radiance Interferometer (AERI), which measures downwelling radiances, has been in operation at Anmyeon-do, South Korea, since June 2010. Temperature and moisture (T/q) profiles with high temporal and vertical resolution can be retrieved from the measured AERI spectrum through the retrieval algorithm AERIPROF. In this work, AERIPROF has been optimized to improve the retrieval performance: 1) a bias spectrum was computed from the coincident radiosondes during the field experiments at Anmyeon-do and 2) regression coefficients were obtained from local radiosondes and associated simulated spectral radiances. An evaluation was performed in the lower troposphere (<700 hPa) with the radiosondes on clear-sky days during the field experiments at Anmyeon-do. The optimized statistical regression results in an improvement of ~0.6 K for temperature and ~0.6 g kg−1 for the mixing ratio on average, in comparison to the original statistical regression. In addition, the optimized AERI T/q ret.
Low Threshold Current and Polarization-Stabilized 795 nm Vertical-Cavity Surface-Emitting Lasers

Low threshold current and polarization-stabilized 795 nm vertical-cavity surface-emitting lasers (VCSELs) are fabricated by integrating a surface grating of high polarization selectivity and high reflectivity. The rigorous coupled-wave analysis method is used to design the surface grating. For the devices with a grating period of 500 nm, a grating depth of ~150 nm, and a diameter of the surface grating region of 5 μm, a threshold current of 0.4 mA and an orthogonal polarization suppression ratio (OPSR) of 19.56 dB are obtained. The emission wavelength of 795 nm of a single transverse mode VCSEL is achieved at a temperature of 85 °C under an injection current of 0.9 mA. In addition, experiments demonstrate that the threshold and output power also depended on the size of the grating region.
Dermoscopic image segmentation combining pigment separation and deep learning networks

Dermoscopic image segmentation aims to detect damaged skin areas. It is difficult to detect skin lesions accurately due to factors such as the method of image acquisition, the characteristics of the lesion, and the texture of the skin. This paper proposes a dermatoscopic image segmentation algorithm that combines the pigment separation algorithm with the Segnet network model. First, pigment separation is performed on the dermatoscopic images to obtain the corresponding melanin and hemosiderin images. Afterwards, the melanin and hemosiderin images are converted to single-channel grayscale maps and merged with the original images to produce image data with a channel number of 5. The channel-expanded images are then segmented using Segnet deep neural network. Experimental results on the ISIC-2018 dermoscopy image dataset show that the proposed algorithm achieves better segmentation results in terms of accuracy, sensitivity, and specificity.
Differences in gut bacterial community composition between modern and slower-growing broiler breeder lines: Implications of growth selection on microbiome composition

In the last century broiler chicken lines have undergone an extensive breeding regime aimed primarily at growth and high meat yield. It is not known if breeding has also resulted in a change to the broiler breeder’s associated gut microbiota. Here we compared the gut microbiota of 37-week-old commercial Cobb breeding dams with dams from a broiler Legacy line which has not undergone selection since 1986. The dams from both lines were kept together in the same shed under the same management protocol from day of hatch to avoid additional confounders. We chose this age to allow significant bacterial exchange, thus avoiding exposure dependent artifacts and so that we could compare dams at the same developmental state of adulthood and peak laying performance. Significant differences in the composition of the cecum bacterial communities were found. Bacteria of the genus Akkermansia, implicated in mucin degradation and associated with host metabolic health, accounted for 4.98% ± 5.04% of the Cobb cecum community, but were mostly absent from the ceca of the Legacy line dams. Inversely, Legacy dams had higher levels of Clostridiales, Lactobacillales and Aeromonadales. These results show that breeding has resulted in a change in the gut microbiota composition, likely by changing the physiological conditions in the mucosa. It remains unclear if changes in gut microbiota composition are a part of the mechanism affecting growth or are a secondary result of other physiological changes accelerating growth. Therefore, the identification of these changes opens the door to further targeted research.
Epigallocatechin gallate alleviates high-fat diet-induced hepatic lipotoxicity by targeting mitochondrial ROS-mediated ferroptosis

Background: Non-alcoholic fatty liver disease (NAFLD) is a chronic advanced liver disease that is highly related to metabolic disorders and induced by a high-fat diet (HFD). Recently, epigallocatechin gallate (EGCG) has been regarded as a protective bioactive polyphenol in green tea that has the ability to protect against non-alcoholic fatty liver disease, but the molecular mechanism remains poorly deciphered. Ferroptosis plays a vital role in the progression of non-alcoholic fatty liver disease, but experimental evidence of ferroptosis inhibition by epigallocatechin gallate is limited. Hence, our study aimed to investigate the effect and mechanisms of epigallocatechin gallate on hepatic ferroptosis to mitigate hepatic injury in high-fat diet-fed mice. Methods: Fifty male C57BL/6 mice were fed either a standard chow diet (SCD), a high-fat diet, or a high-fat diet and administered epigallocatechin gallate or ferrostatin-1 (a ferroptosis-specific inhibitor) for 12 weeks. Liver injury, lipid accumulation, hepatic steatosis, oxidative stress, iron overload, and ferroptosis marker proteins were examined. In vitro, steatotic L-02 cells were used to explore the underlying mechanism. Results: In our research, we found that epigallocatechin gallate notably alleviated liver injury and lipid accumulation, oxidative stress, hepatic steatosis, decreased iron overload and inhibited ferroptosis in a high-fat diet-induced murine model of non-alcoholic fatty liver disease. In vitro experiments, using ferrostatin-1 and a mitochondrial reactive oxygen species (MtROS) scavenger (Mito-TEMPO), we found that epigallocatechin gallate remarkably alleviated oxidative stress and inhibited ferroptosis by reducing the level of mitochondrial reactive oxygen species in steatotic L-02 cells. Conclusion: Taken together, our results revealed that epigallocatechin gallate may exert protective effects on hepatic lipotoxicity by inhibiting mitochondrial reactive oxygen species-mediated hepatic ferroptosis. Findings from our study provide new insight into prevention and treatment strategies for non-alcoholic fatty liver disease pathological processes.