Miracle-dz/UniFAR-FTUs huggingface dataset:
Association of the APOA-5 Genetic Variant rs662799 with Metabolic Changes after an Intervention for 9 Months with a Low-Calorie Diet with a Mediterranean Profile

In cross-sectional studies, the genetic variant rs662799 of the APOA5 gene is associated with high serum triglyceride concentrations, and in some studies, the effect of short-term dietary interventions has been evaluated. The aim of the present investigation was to evaluate the role of this genetic variant in metabolic changes after the consumption of a low-calorie diet with a Mediterranean pattern for 9 months. A population of 269 Caucasian obese patients was recruited. Adiposity and biochemical parameters were measured at the beginning (basal level) and after 3 and 9 months of the dietary intervention. The rs662799 genotype was assessed with a dominant analysis (TT vs. CT + CC). The APOA5 variant distribution was: 88.1% (n = 237) (TT), 11.5% (n = 31) (TC) and 0.4% (n = 1) (CC). There were significant differences only in triglyceride levels at all times of the study between the genotype groups. After 3 and 9 months of dietary intervention, the following parameters improved in both genotype groups: adiposity parameters, systolic pressure, total cholesterol, LDL cholesterol, leptin, adiponectin and the leptin/adiponectin ratio. The intervention significantly decreased insulin levels, HOMA-IR and triglyceride levels in non-C allele carriers (Delta 9 months TT vs. TC + CC). i.e., insulin levels (delta: −3.8 + 0.3 UI/L vs. −1.2 + 0.2 UI/L; p = 0.02), HOMA-IR levels (delta: −1.2 + 0.2 units vs. −0.3 + 0.1 units; p = 0.02), triglyceride levels (delta: −19.3 + 4.2 mg/dL vs. −4.2 + 3.0 mg/dL; p = 0.02). In conclusion, non-C allele carriers of rs662799 of the APOA5 gene showed a decrease of triglyceride, insulin and HOMA-IR levels after consuming a low-calorie diet with a Mediterranean pattern; we did not observe this effect in C allele carriers, despite a significant weight loss.
In Vitro Phytobiological Investigation of Bioactive Secondary Metabolites from the Malus domestica-Derived Endophytic Fungus Aspergillus tubingensis Strain AN103

Endophytic fungi including black aspergilli have the potential to synthesize multiple bioactive secondary metabolites. Therefore, the search for active metabolites from endophytic fungi against pathogenic microbes has become a necessity for alternative and promising strategies. In this study, 25 endophytic fungal isolates associated with Malus domestica were isolated, grown, and fermented on a solid rice medium. Subsequently, their ethyl acetate crude extracts were pretested for biological activity. One endophytic fungal isolate demonstrated the highest activity and was chosen for further investigation. Based on its phenotypic, ITS ribosomal gene sequences, and phylogenetic characterization, this isolate was identified as Aspergillus tubingensis strain AN103 with the accession number (KR184138). Chemical investigations of its fermented cultures yielded four compounds: Pyranonigrin A (1), Fonsecin (2), TMC 256 A1 (3), and Asperazine (4). Furthermore, 1H-NMR, HPLC, and LC-MS were performed for the identification and structure elucidation of these metabolites. The isolated pure compounds showed moderate-to-potent antibacterial activities against Pseudomonas aeruginosa and Escherichia coli (MIC value ranged from 31 and 121 to 14.5 and 58.3 μg/mL), respectively; in addition, the time–kill kinetics for the highly sensitive bacteria against isolated compounds was also investigated. The antifungal activity results show that (3) and (4) had the maximum effect against Fusarium solani and A. niger with inhibition zones of 16.40 ± 0.55 and 16.20 ± 0.20 mm, respectively, and (2) had the best effect against Candida albicans, with an inhibition zone of 17.8 ± 1.35 mm. Moreover, in a cytotoxicity assay against mouse lymphoma cell line L5178Y, (4) exhibited moderate cytotoxicity (49% inhibition), whereas (1–3) reported weak cytotoxicity (15, 26, and 19% inhibition), respectively. Our results reveal that these compounds might be useful to develop potential cytotoxic and antimicrobial drugs and an alternative source for various medical and pharmaceutical fields.
Cloud Computing-Based Analyses to Predict Vehicle Driving Shockwave for Active Safe Driving in Intelligent Transportation System

In the cloud computing era, the analyses of various types of big data gathered from the Internet of Vehicles, the Internet of Things, and smart sensors/devices achieve convenient services. Some famous applications include vehicle traffic loading on roads (from Google MAP), video sharing for fans group (from Facebook or Youtube), and so on. However, to achieve real-time active safe driving (RT-ASD) under unstable driving in high-threat areas on roads becomes an open issue in cloud computing-based intelligent transportation system. This paper proposes a predictive backward shockwave analysis approach (PSA) to achieve the RT-ASD based on the analyses of macroscopic traffic shockwave (PSA_MA) and microscopic car-following (PSA_mi). The PSA contributes in several aspects to active safe driving: 1) predicting and analyzing high threat of backward shockwaves from the gathered big data of the driving state information vehicles; 2) informing the analyzed threat messages to the vehicles in high-threat areas via the 3-Tier hierarchical cloud computing mechanism; 3) reducing the driving threat certainly; and 4) PSA_MA and PSA_mi can be applied for achieving active safe driving in autonomous self-driving vehicles and human-driving vehicles. The numerical results show that the PSA outperforms in approaches to relative error rate prediction, the accuracy of the backward shockwave determination, average vehicle velocity, average travel time, number of goodput vehicles, time-to-collision, and distance-to-collision.
Review of Metasurfaces and Metadevices: Advantages of Different Materials and Fabrications

Flat optics, metasurfaces, metalenses, and related materials promise novel on-demand light modulation within ultrathin layers at wavelength scale, enabling a plethora of next-generation optical devices, also known as metadevices. Metadevices designed with different materials have been proposed and demonstrated for different applications, and the mass production of metadevices is necessary for metadevices to enter the consumer electronics market. However, metadevice manufacturing processes are mainly based on electron beam lithography, which exhibits low productivity and high costs for mass production. Therefore, processes compatible with standard complementary metal–oxide–semiconductor manufacturing techniques that feature high productivity, such as i-line stepper and nanoimprint lithography, have received considerable attention. This paper provides a review of current metasurfaces and metadevices with a focus on materials and manufacturing processes. We also provide an analysis of the relationship between the aspect ratio and efficiency of different materials.
Multi-Microgrids Operation With Interruptible Loads in Local Energy and Reserve Markets

In the evolution of the power systems, a particular case is the presence of a number of microgrids (MGs) operated with mutual interconnection, but without connection to the main distribution system. The interconnected MGs form a structure in which the overall system operation and resource scheduling can be determined by considering centralized or decentralized approaches. This article introduces local energy and reserve markets (LERMs) in which the MG managers (MGMs) can meet their required energy and reserve with optimal scheduling of their resources, besides competing with the other MGs. To model such decision-making framework for MGMs, a bilevel optimization approach is developed in which the MGMs’ problem is modeled as the upper level problem and the LERMs clearing problem is modeled as the lower level problem. This model is transformed into a mathematical programming with equilibrium constraints (MPEC) using the primal-dual transformation. Then, the resulting MPEC for each MG is replaced with its Karush–Kuhn–Tucker conditions, obtaining an equilibrium problem with equilibrium constraints (EPEC) model. The nonlinear terms of the model are linearized through different approaches. Finally, the EPEC model is transformed into a mixed-integer linear problem considering the objective function of all MGMs. The model is applied to a test system with three interconnected MGs. Moreover, the sensitivity of the results to the probability of calling reserve is investigated.
2022 roadmap on low temperature electrochemical CO2 reduction

Electrochemical CO2 reduction (CO2R) is an attractive option for storing renewable electricity and for the sustainable production of valuable chemicals and fuels. In this roadmap, we review recent progress in fundamental understanding, catalyst development, and in engineering and scale-up. We discuss the outstanding challenges towards commercialization of electrochemical CO2R technology: energy efficiencies, selectivities, low current densities, and stability. We highlight the opportunities in establishing rigorous standards for benchmarking performance, advances in in operando characterization, the discovery of new materials towards high value products, the investigation of phenomena across multiple-length scales and the application of data science towards doing so. We hope that this collective perspective sparks new research activities that ultimately bring us a step closer towards establishing a low- or zero-emission carbon cycle.
Research on Multi-View 3D Reconstruction Technology Based on SFM

Multi-view 3D reconstruction technology is used to restore a 3D model of practical value or required objects from a group of images. This paper designs and implements a set of multi-view 3D reconstruction technology, adopts the fusion method of SIFT and SURF feature-point extraction results, increases the number of feature points, adds proportional constraints to improve the robustness of feature-point matching, and uses RANSAC to eliminate false matching. In the sparse reconstruction stage, the traditional incremental SFM algorithm takes a long time, but the accuracy is high; the traditional global SFM algorithm is fast, but its accuracy is low; aiming at the disadvantages of traditional SFM algorithm, this paper proposes a hybrid SFM algorithm, which avoids the problem of the long time consumption of incremental SFM and the problem of the low precision and poor robustness of global SFM; finally, the MVS algorithm of depth-map fusion is used to complete the dense reconstruction of objects, and the related algorithms are used to complete the surface reconstruction, which makes the reconstruction model more realistic.
Interactive Inflammatory Bowel Disease Biologics Decision Aid Does Not Improve Patient Outcomes Over Static Education: Results From a Randomized Trial

INTRODUCTION: To support shared decision-making (SDM) between patients and providers surrounding biologic treatments, we created IBD&me (ibdandme.org)—a freely available, unbranded, interactive decision aid. We performed a multicenter comparative effectiveness trial comparing the impact of IBD&me on SDM vs a biologics fact sheet developed by the Crohn's & Colitis Foundation. METHODS: We enrolled patients with inflammatory bowel disease (IBD) being seen at a clinic within IBD Qorus—a multicenter adult IBD learning health system—between March 5, 2019, and May 14, 2021. Eligible patients included those with recent IBD-related symptoms who reported that they wanted to discuss biologics with their provider during their upcoming visit. Patients were randomized 1:1 using stratified block randomization and received an e-mail 1 week before their visit inviting them to review either IBD&me or a fact sheet. The primary outcome was patient perception of SDM as measured by the 9-Item SDM Questionnaire (0–100 scale; higher = better); the Student t test was used to compare outcomes between arms. RESULTS: Overall, 152 patients were randomized (biologics fact sheet 75, IBD&me 77); most patients had Crohn's disease (66.4%) and were biologic-experienced (82.9%). No differences were seen between groups regarding SDM (fact sheet 72.6 ± 25.6, IBD&me 75.0 ± 20.8; P = .57). Most patients stated they would be likely to recommend the fact sheet (79.6%) or IBD&me (84.9%; P = .48) to another patient with IBD. DISCUSSION: No differences in outcomes were seen between IBD&me and the biologics fact sheet in this comparative effectiveness study; patients reported high satisfaction with both resources. Further study, particularly among biologic naïve patients, is needed to determine the utility of interactive components to IBD decision aids.
A super-capacitor based energy storage for quick variation in stand-alone PV systems

PHOTOVOLTAIC (PV) system is one of the most prominent energy sources, producing electricity directly from sunlight. In additionally, it is easy to install and is supported financially by many governments as part of their strategy to reduce CO2 gas emissions, and to achieve their agreed set of reduction targets by 2020. In the meantime, researchers have been working on the PV system to make it more efficient, easy to maintain, reliable to use and cost effective. In the stand-alone PV system, a battery is required. This is due to the fluctuating nature of the output energy delivered by the PV arrays owing to the weather conditions and the unpredictable behaviour of uses with regard to the consumption of energy. During the hours of sunshine, the PV system is directly feeding the load and any surplus electrical energy is stored in the battery at a constant current. During the night, or during a period of low solar irradiation, the energy is supplied to the load from the battery. However, the stand-alone PV system is designed to provide an acceptable balance between reliability and cost, which is a major challenge to the designer owing to the approaches used to size the PV arrays and the battery bank. As a result, the unpredictable, quick daily changes on the PV output is not dependable. Moreover, battery performance, length of life and energy efficiency depends on the rate at which it is discharged. Therefore, it is essential to use other methods to deal with any quick variation in energy. In this thesis, a super capacitor is used to solve this problem, as it can deal with the fast-changing weather, or a rapid variation in the energy requirements of the customer. A critical evaluation with in-depth analysis of the placement and the implementation for the super-capacitor in the PV standalone system has been carried out. The results show, super-capacitor capacitance and the converter efficiency affect the delivered load energy. However, the bi-directional topology performs better than uni-directional under the same conditions. Finally, a further improvement of the system at component level, has been developed through an energy recovery snubber for the switching transition and achieved a recovery of energy for the resistive load, 94.44% for the turn on transition and 92.86% for the turn off transition. Moreover, for the inductive load, 78.33% and 97.33% of energy has been recovered for the turn on and for the turn off transition respectively.
The Subject Construction and Role Mental Model Construction of Erotic Movies Based on Lacan's Desire Theory

Jacques Lacan is a famous French psychoanalyst, but his influence has long gone beyond the scope of psychoanalysis and has affected almost all fields of “human science. ” Film and psychoanalysis coincided almost simultaneously and influenced each other. But with the development of film, researchers found that film studies in the period of classic film and film semiotics have come to an end. It has certain theoretical value to explore the construction of the subject and the mental model of the erotic film based on Lacan's desire theory. Method. The body in the film and video described in this paper includes both the material body as an aesthetic object and the spiritual body with aesthetic consciousness. The so-called artistic presentation of the body element means that the beauty of the body form displayed by the actor's body in the film video is perceived by the audience, and the desire to stare is captured by the audience, thereby completing the audience's entire body composed of eyes, body, and mind. The body is not only an aesthetic object but also an aesthetic subject, and the human cognitive system is not just a closed brain. Because the nervous system, body, and environment are constantly changing and interacting, true cognition is a unified system of all three. The main part of this paper uses Lacan's psychoanalytic theory to deeply analyze the relationship between the characters shown in the film. From the cognitive impairment of the self to the failure of communication between characters, the language of uncertainty entangled by desire is removed, and the behavior and facial expressions of the movie characters are used as the analysis basis to fully interpret the inner world of the characters. This includes love, hatred, pain, struggle, and many other contradictory emotions. In addition, this paper also analyzes the metaphor of the relationship between the characters from the perspective of semiotics and discusses the description of the relationship between the characters from the lens language from the perspective of cinematography. The special meaning conveyed by the recurring elements in the film is emphasized, and the correlation between the pictures is explained from the less mentioned photographic aspect of the film. Results/Discussion. Looking at Lacan's theory, the subject, the other, and desire are linked by the subject spirit, which is the core of the connection between the three, and only through the resistance and struggle of the subject spirit can the illusion of the subject be broken. Bewilderment and the suppression of the other and desire finally become the self. And the embodiment of this true self is particularly prominent in film art. Lacan's purpose is not merely to dissolve the subject but to seek a stand in the midst of destruction, pursuing the ultimate transcendence of man's desire for the external other and internal desire.
Four-Criterion-Optimization-Based Coordination Motion Control of Dual-Arm Robots

In order to address the coordination constraints and physical constraints subjected to the dual-arm robot simultaneously, a novel four-criterion-optimization coordination motion (FCOCM) scheme is proposed, which combines four optimization criteria, namely, the repetitive motion planning (RMP), minimum velocity norm (MVN), maximize manipulability (MM), and infinity-norm velocity minimization (INVM). The scheme can remedy discontinuity in the INVM scheme, eliminate joint angular drift phenomenon, prevent the high joint angular velocity and MM, thereby improving the motion efficiency and ensuring safety and accuracy in the process of tasks. Besides, this scheme also considers real-time trajectory feedback, satisfies physical constraints, and ensures the joint angular velocity is zero at the end of tasks. Furthermore, the improved FCOCM scheme is solved by a novel power-exponent-type variable-parameter recurrent neural network (PET-VPNN) model proposed in this article. A novel Sinh-tunable type activation function which achieves better convergence performance is also proposed. Simulations and experiment are presented to verify the superiority of the proposed coordination motion control method. This research is of great significance for the coordination motion control of dual-arm robots in complex path planning tasks.
Ensemble Machine Learning Model for Accurate Air Pollution Detection Using Commercial Gas Sensors

This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advantage and become key components of IoT devices in atmospheric condition monitoring. However, their native coarse resolution and poor selectivity limit their performance. Thus, we adopted recurrent neural network (RNN) models to extract the time-series concentration data characteristics and improve the detection accuracy. Firstly, four types of RNN models, LSTM and GRU, Bi-LSTM, and Bi-GRU, were optimized to define the best-performance single weak models for CO, O3, and NO2 gases, respectively. Next, ensemble models which integrate multiple single weak models with a dynamic model were defined and trained. The testing results show that the ensemble models perform better than the single weak models. Further, a retraining procedure was proposed to make the ensemble model more flexible to adapt to environmental conditions. The significantly improved determination coefficients show that the retraining helps the ensemble models maintain long-term stable sensing performance in an atmospheric environment. The result can serve as an essential reference for the applications of IoT devices with commercial gas sensors in environment condition monitoring.
Biclonal Diffuse Large B-cell Lymphoma Commonly Characterized by Partial Trisomy 18q Involving MALT1 and BCL2

A 68-year-old man was admitted because of a left shoulder mass and swollen right testis. Pathological examinations indicated a diagnosis of diffuse large B-cell lymphoma (DLBCL) with the CD20+BCL6+MUM1+BCL2+CD10-MYC- phenotype in both lesions. G-banding of soft tissue showed 47,XY,+18, whereas testicular cells showed 47,X,+X,-Y,der(4)t(4;18)(p15;? ),del(5)(q? ),+13. Fluorescence in situ hybridization detected additional MALT1 and BCL2 signals in both lesions. Southern blot demonstrated different IGH rearrangements between the soft tissue and testis. The patient was diagnosed with biclonal DLBCL with different karyotypes but similar immunophenotypes. Partial trisomy 18q involving MALT1 and BCL2 may be commonly involved in the pathogenesis of this biclonal DLBCL.
Travel-Time Prediction of Bus Journey With Multiple Bus Trips

Accurate travel-time prediction of public transport is essential for reliable journey planning in urban transportation systems. However, existing studies on bus travel-/arrival-time prediction often focus only on improving the prediction accuracy of a single bus trip. This is inadequate in modern public transportation systems, where a bus journey usually consists of multiple bus trips. In this paper, we investigate the problem of travel-time prediction for bus journeys that takes into account a passenger’s riding time on multiple bus trips, and also his/her waiting time at transfer points (interchange stations or bus stops). A novel framework is proposed to separately predict the riding and waiting time of a given journey from multiple datasets (i.e., historical bus trajectories, bus route, and road network), and combining the results to form the final travel-time prediction. We empirically determine the impact factors of bus riding times and develop a long short-term memory model that can accurately predict the riding time of each segment of the bus lines/routes. We also demonstrate that the waiting time at transfer points significantly impacts the total journey travel time, and estimating the waiting time is non-trivial as we cannot assume a fixed distribution waiting time. In order to accurately predict the waiting time, we introduce a novel interval-based historical average method that can efficiently address the correlation and sensitivity issues in waiting time prediction. Experiments on real-world data show that the proposed method notably outperforms six baseline approaches for all the scenarios considered.