Miracle-dz/UniFAR-FTUs huggingface dataset:
Impulsivity across reactive, proactive and cognitive domains in Parkinson's disease on dopaminergic medication: Evidence for multiple domain impairment

Impulse control disorders (ICD) may occur in Parkinson’s disease (PD) although it remains to be understood if such deficits may occur even in the absence of a formal ICD diagnosis. Moreover, studies addressing simultaneously distinct neurobehavioral domains, such as cognitive, proactive and reactive motor impulsivity, are still lacking. Here, we aimed to investigate if reactive, proactive and cognitive impulsivity involving risk taking are concomitantly affected in medicated PD patients, and whether deficits were dependent on response strategies, such as speed accuracy tradeoffs, or the proportion of omission vs. commission errors. We assessed three different impulsivity domains in a sample of 21 PD patients and 13 matched controls. We found impaired impulsivity in both reactive (p = 0.042) and cognitive domains (p = 0.015) for the PD patients, irrespective of response strategy. For the latter, effect sizes were larger for the actions related with reward processing (p = 0.017, dCohen = 0.9). In the proactive impulsivity task, PD patients showed significantly increased number of omissions (p = 0.041), a response strategy which was associated with preserved number of commission errors. Moreover, the number of premature and proactive response errors were correlated with disease stage. Our findings suggest that PD ON medication is characterized compared to healthy controls by impairment across several impulsivity domains, which is moderated in the proactive domain by the response strategy.
Targeting mTOR with MLN0128 Overcomes Rapamycin and Chemoresistant Primary Effusion Lymphoma

Primary effusion lymphoma (PEL) is an aggressive and incurable malignancy, which is usually characterized by lymphomatous effusions in body cavities without tumor masses. PEL has no established treatment and a poor prognosis, with a median survival time shorter than 6 months. PEL usually develops in the context of immunosuppression, such as HIV infection or post-organ transplantation. The optimal treatment for PEL has not been established, as PEL is generally resistant to traditional chemotherapy. The molecular drivers for PEL are still unknown; however, PEL displays a constitutively active mammalian target of rapamycin (mTOR) pathway, which is critical for metabolic and cell survival mechanisms. Therefore, the evaluation of novel agents targeting the mTOR pathway could be clinically relevant for the treatment of PEL. ABSTRACT Primary effusion lymphoma (PEL) is caused by Kaposi’s sarcoma-associated herpesvirus (KSHV). PEL has a highly active mTOR pathway, which makes mTOR a potential therapeutic target. MLN0128 is an ATP-competitive inhibitor of mTOR that has entered clinical trials for solid tumors. Our results demonstrated that MLN0128 has a greater effect on inhibiting proliferation than the allosteric mTOR inhibitor rapamycin. MLN0128 has ∼30 nM 50% inhibitory concentration (IC50) across several PEL cell lines, including PEL that is resistant to conventional chemotherapy. MLN0128 induced apoptosis in PEL, whereas rapamycin induced G1 arrest, consistent with a different mechanism of action. MLN0128 inhibited phosphorylation of mTOR complex 1 and 2 targets, while rapamycin only partially inhibited mTOR complex 1 targets. PEL xenograft mouse models treated with MLN0128 showed reduced effusion volumes in comparison to the vehicle-treated group. Rapamycin-resistant (RR) clones with an IC50 for rapamycin 10 times higher than the parental IC50 emerged consistently after rapamycin exposure as a result of transcriptional adaptation. MLN0128 was nevertheless capable of inducing apoptosis in these RR clones. Our results suggest that MLN0128 might offer a new approach to the treatment of chemotherapy-resistant PEL. IMPORTANCE Primary effusion lymphoma (PEL) is an aggressive and incurable malignancy, which is usually characterized by lymphomatous effusions in body cavities without tumor masses. PEL has no established treatment and a poor prognosis, with a median survival time shorter than 6 months. PEL usually develops in the context of immunosuppression, such as HIV infection or post-organ transplantation. The optimal treatment for PEL has not been established, as PEL is generally resistant to traditional chemotherapy. The molecular drivers for PEL are still unknown; however, PEL displays a constitutively active mammalian target of rapamycin (mTOR) pathway, which is critical for metabolic and cell survival mechanisms. Therefore, the evaluation of novel agents targeting the mTOR pathway could be clinically relevant for the treatment of PEL.
Quantitative insights into the cyanobacterial cell economy

Phototrophic microorganisms are promising resources for green biotechnology. Compared to heterotrophic microorganisms, however, the cellular economy of phototrophic growth is still insufficiently understood. We provide a quantitative analysis of light-limited, light-saturated, and light-inhibited growth of the cyanobacterium Synechocystis sp. PCC 6803 using a reproducible cultivation setup. We report key physiological parameters, including growth rate, cell size, and photosynthetic activity over a wide range of light intensities. Intracellular proteins were quantified to monitor proteome allocation as a function of growth rate. Among other physiological adaptations, we identify an upregulation of the translational machinery and downregulation of light harvesting components with increasing light intensity and growth rate. The resulting growth laws are discussed in the context of a coarse-grained model of phototrophic growth and available data obtained by a comprehensive literature search. Our insights into quantitative aspects of cyanobacterial adaptations to different growth rates have implications to understand and optimize photosynthetic productivity.
Methane Pyrolysis with a Molten Cu–Bi Alloy Catalyst

Current methods of hydrogen production from methane generate more than 5 kg of CO2 for every 1 kg of hydrogen. Methane pyrolysis on conventional solid heterogeneous catalysts produces hydrogen without CO2, but the carbon coproduct poisons the catalyst. This can be avoided by using a molten metal alloy catalyst. We present here a study of methane pyrolysis using mixtures of molten Cu–Bi alloys as the catalyst. We find that molten Cu–Bi is an active catalyst, even though pure molten Bi and Cu are not. Surface tension measurements and constant-temperature ab initio molecular dynamics simulations indicate that the surface is enriched in Bi and that the catalytic activity is correlated with the concentration of Bi at the surface. Bader charge analysis indicates that bismuth donates charge to copper. In the most stable configuration of dissociated methane on these liquid surfaces, CH3 binds to a bismuth surface atom and H to Cu. The energy barriers for the dissociative adsorption of methane, calculated using th.
Graphene-Like Porous ZnO/Graphene Oxide Nanosheets for High-Performance Acetone Vapor Detection

In order to obtain acetone sensor with excellent sensitivity, selectivity, and rapid response/recovery speed, graphene-like ZnO/graphene oxide (GO) nanosheets were synthesized using the wet-chemical method with an additional calcining treatment. The GO was utilized as both the template to form the two-dimensional (2-D) nanosheets and the sensitizer to enhance the sensing properties. Sensing performances of ZnO/GO nanocomposites were studied with acetone as a target gas. The response value could reach 94 to 100 ppm acetone vapor and the recovery time could reach 4 s. The excellent sensing properties were ascribed to the synergistic effects between ZnO nanosheets and GO, which included a unique 2-D structure, large specific surface area, suitable particle size, and abundant in-plane mesopores, which contributed to the advance of novel acetone vapor sensors and could provide some references to the synthesis of 2-D graphene-like metals oxide nanosheets.
Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures

The progress of technology on energy and IoT fields has led to an increasingly complicated electric environment in low-voltage local microgrid, along with the extensions of electric vehicle, micro-generation, and local storage. It is required to establish a home energy management system (HEMS) to efficiently integrate and manage household energy micro-generation, consumption and storage, in order to realize decentralized local energy systems at the community level. Domestic power demand prediction is of great importance for establishing HEMS on realizing load balancing as well as other smart energy solutions with the support of IoT techniques. Artificial neural networks with various network types (e.g., DNN, LSTM/GRU based RNN) and other configurations are widely utilized on energy predictions. However, the selection of network configuration for each research is generally a case by case study achieved through empirical or enumerative approaches. Moreover, the commonly utilized network initialization methods assign parameter values based on random numbers, which cause diversity on model performance, including learning efficiency, forecast accuracy, etc. In this paper, an evolutionary ensemble neural network pool (EENNP) method is proposed to achieve a population of well-performing networks with proper combinations of configuration and initialization automatically. In the experimental study, power demand predictions of multiple households are explored in three application scenarios: optimizing potential network configuration set, forecasting single household power demand, and refilling missing data. The impacts of evolutionary parameters on model performance are investigated. The experimental results illustrate that the proposed method achieves better solutions on the considered scenarios. The optimized potential network configuration set using EENNP achieves a similar result to manual optimization. The results of household demand prediction and missing data refilling perform better than the naïve and simple predictors.
Global offshore wind energy deployment: a geo-clustering approach

ABSTRACT The availability of wind resources around the world makes it an interesting alternative for clean energy. Offshore wind energy with considerable evolution in technology and investment becomes a sustainable source of renewable energy in the future. Technical feasibility and geographical constraints, demand for energy and the stability of regions for long-term investments and development of wind parks are critical factors in the estimation of offshore wind potential. This paper provides a geo-clustering approach to offshore wind energy to find hotspots around the world. A clustering approach based on neural network is applied to find the segments and the profiles of them. The segmentation is performed based on geographic location, total wind capacity in shallow, transitional and deep water, length of coastline, demand, investment leverage, and risk.
Layer-by-Layer Nano-assembly: A Powerful Tool for Optical Fiber Sensing Applications

The ability to tune the composition of nanostructured thin films is a hot topic for the design of functional coatings with advanced properties for sensing applications. The control of the structure at the nanoscale level enables an improvement of intrinsic properties (optical, chemical or physical) in comparison with the traditional bulk materials. In this sense, among all the known nanofabrication techniques, the layer-by-layer (LbL) nano-assembly method is a flexible, easily-scalable and versatile approach which makes possible precise control of the coating thickness, composition and structure. The development of sensitive nanocoatings has shown an exceptional growth in optical fiber sensing applications due to their self-assembling ability with oppositely charged components in order to obtain a multilayer structure. This nanoassembly technique is a powerful tool for the incorporation of a wide variety of species (polyelectrolytes, metal/metal oxide nanoparticles, hybrid particles, luminescent materials, dyes or biomolecules) in the resultant multilayer structure for the design of high-performance optical fiber sensors. In this work we present a review of applications related to optical fiber sensors based on advanced LbL coatings in two related research areas of great interest for the scientific community, namely chemical sensing (pH, gases and volatile organic compounds detection) as well as biological/biochemical sensing (proteins, immunoglobulins, antibodies or DNA detection).
Risk of massive blood product requirement in cardiac surgery

Abstract Cardiac surgery under cardiopulmonary bypass (CPB) accounts for most consumption of red blood cells (RBCs). Identifying risk factors for massive red blood cell transfusion (MRT) in cardiac surgery may help to reduce this consumption. We retrospectively analyzed 8238 patients who underwent valve surgery and/or coronary artery bypass grafting (CABG) under CPB at 2 major heart centers in China. Uni- and multivariate logistic regression was carried out to assess whether risk factors for MRT (defined as receiving at least 4 units RBCs) varied with type of cardiac surgery. A total of 1691 patients (21%) received at least 4 units RBCs (6.77 ± 4.78 units per person). This MRT group consumed 70% of the total units of allogeneic RBCs in the study. MRT incidence was 2-fold higher among patients undergoing CABG with or without valve surgery than among patients undergoing valve surgery alone. Multivariate logistic analysis identified the following MRT risk factors common to valve surgery alone, CABG alone, and their combination: female sex, older age, renal dysfunction, lower body mass index, lower preoperative hemoglobin, and longer CPB. Several independent MRT risk factors were also identified specific to valve surgery: active endocarditis, nonatrial fibrillation, smaller left atrium diameter, abnormal international normalized ratio, and repeat surgery. Different types of cardiac surgery share several, but not all, MRT risk factors. This study may help guide the prediction and management of patients at higher MRT risk.
Rheumatoid factor as predictor of response to treatment with anti-TNF alpha drugs in patients with rheumatoid arthritis

Abstract We determined whether rheumatoid factor (RF) and anti-cyclic citrullinated peptide antibody (ACPA) can predict remission or severe disability in rheumatoid arthritis (RA) patients treated with anti-tumor necrosis factor (TNF) alpha drugs. We performed a cohort study based on the clinical data from a referral center for the treatment of RA in Bogotá, Colombia, were included patients aged ≥18 years with diagnosis of RA with an active disease and for whom a treatment scheme was begun with anti-TNF alpha medication, with a minimum follow-up time of 12 months. Disease activity of Rheumatoid Arthritis was assessed through measurement of RF, ACPA, disease activity score (DAS28), and health assessment questionnaire (HAQ). We calculated the incidence rates (IRs) for remission and severe disability. We also calculated the incidence rate ratio (IRR) for each outcome by adjusting for possible confounders using the Poisson regression method. The hypothesis was tested with a P value of <. 05. Statistical analysis was performed in Stata 15. We included 400 patients receiving an anti-TNF alpha agent. Median age was 60 years, and 322 patients were women (80.5%). RF was positive in 357 patients (89%), ACPA in 348 patients (87%), and co-positivity in 324 patients (81%). Median follow-up was 41 months (range, 12–79 months). The IR for remission was 23 per 100 person-years in RF-negative patients and 16 per 100 person-years in RF-positive patients. The adjusted IRR (age sex, treatment, and ACPA) was 1.51 (95%CI, 1.05–2.18). The IR for severe disability was 10.8 per 100 person-years in the RF-positive cohort and 2.3 per 100 person-years in the RF-negative cohort. The IRR adjusted for these factors was 4.37 (95%CI, 1.6–12). Co-positivity had a similar behavior to RF. No differences were recorded in the rates of remission or disability in ACPA-positive and ACPA-negative patients. Our findings suggest that remission is less frequent and severe disability more frequent in RF-positive patients treated with anti-TNF alpha agents than in RF-negative patients.
Exogenous Application of Phytohormones Promotes Growth and Regulates Expression of Wood Formation-Related Genes in Populus simonii × P. nigra

Although phytohormones are known to be important signal molecules involved in wood formation, their roles are still largely unclear. Here, Populus simonii × P. nigra seedlings were treated with different concentrations of exogenous phytohormones, indole-3-acetic acid (IAA), gibberellin (GA3), and brassinosteroid (BR), and the effects of phytohormones on growth were investigated. Next, 27 genes with known roles in wood formation were selected for qPCR analysis to determine tissue-specificity and timing of responses to phytohormone treatments. Compared to the control, most IAA, GA3, and BR concentrations significantly increased seedling height. Meanwhile, IAA induced significant seedling stem diameter and cellulose content increases that peaked at 3 and 30 mg·L−1, respectively. Significant increase in cellulose content was also observed in seedlings treated with 100 mg·L−1 GA3. Neither stem diameter nor cellulose content of seedlings were affected by BR treatment significantly, although slight effects were observed. Anatomical measurements demonstrated improved xylem, but not phloem, development in IAA- and BR-treated seedlings. Most gene expression patterns induced by IAA, GA3, and BR differed among tissues. Many IAA response genes were also regulated by GA3, while BR-induced transcription was weaker and slower in Populus than for IAA and GA3. These results reveal the roles played by phytohormones in plant growth and lay the foundation for exploring molecular regulatory mechanisms of wood formation in Populus.
Molecular Simulation of the Adsorption and Diffusion in Cylindrical Nanopores: Effect of Shape and Fluid–Solid Interactions

We report on molecular simulations of model fluids composed of three tangentially bonded Lennard-Jones interaction sites with three distinct morphologies: a flexible “pearl-necklace” chain, a rigid “stiff” linear configuration, and an equilateral rigid triangular ring. The adsorption of these three models in cylindrical pores of diameters 1, 2, and 3 nm and with varying solid–fluid strength was determined by direct molecular dynamics simulations, where a sample pore was placed in contact with a bulk fluid. Adsorption isotherms of Type I, V, and H1 were obtained depending on the choice of pore size and solid–fluid strength. Additionally, the bulk-phase equilibria, the nematic order parameter of the adsorbed phase, and the self-diffusion coefficient in the direction of the pore axis were examined. It was found that both the molecular shape and the surface attractions play a decisive role in the shape of the adsorption isotherm. In general, the ring molecules showed a larger adsorption, while the fully flexible model showed the smallest adsorption. Morphology and surface strength were found to have a lesser effect on the diffusion of the molecules. An exceptional high adsorption and diffusion, suggesting an enhanced permeability, was observed for the linear stiff molecules in ultraconfinement, which was ascribed to a phase transition of the adsorbed fluid into a nematic liquid crystal.
Where There Is Fire There Is SMOKE: A Scalable Edge Computing Framework for Early Fire Detection

A Cyber-Physical Social System (CPSS) tightly integrates computer systems with the physical world and human activities. In this article, a three-level CPSS for early fire detection is presented to assist public authorities to promptly identify and act on emergency situations. At the bottom level, the system’s architecture involves IoT nodes enabled with sensing and forest monitoring capabilities. Additionally, in this level, the crowd sensing paradigm is exploited to aggregate environmental information collected by end user devices present in the area of interest. Since the IoT nodes suffer from limited computational energy resources, an Edge Computing Infrastructure, at the middle level, facilitates the offloaded data processing regarding possible fire incidents. At the top level, a decision-making service deployed on Cloud nodes integrates data from various sources, including users’ information on social media, and evaluates the situation criticality. In our work, a dynamic resource scaling mechanism for the Edge Computing Infrastructure is designed to address the demanding Quality of Service (QoS) requirements of this IoT-enabled time and mission critical application. The experimental results indicate that the vertical and horizontal scaling on the Edge Computing layer is beneficial for both the performance and the energy consumption of the IoT nodes.
Enhanced Broadband RF Differential Rectifier Integrated with Archimedean Spiral Antenna for Wireless Energy Harvesting Applications

This work addresses the design and implementation of a broadband differential rectifier (DR) combined with an Archimedean spiral dipole antenna (ASDA) for wireless power harvesting at low incident power densities below 200 μW/cm2. The proposed design exhibits an improved RF-DC conversion efficiency over a wide frequency range from 1.2 to 5 GHz. This frequency band is associated with several wireless communication services, for instance, ISM, WLAN, 5G, LTE, and GPS applications. The receiving planar ASDA exhibits circular polarization and has an average measured gain of 4.5 dBi from 1.2 to 5 GHz. To enable a wide operating bandwidth, the rectifier circuit is constituted by two architectures, designated A and B. Each scheme is designed to harvest power efficiently across a specific bandwidth. The optimal performance of both rectifiers are obtained using the nonlinear harmonic-balance simulations. The antenna–rectifier integration yields a compact rectenna with a high-efficiency performance over the intended bandwidth from 1.2 to 5 GHz for an input power of 9 dBm and terminal load resistance of 1 kΩ. The total measured RF-DC conversion efficiency is maintained above 30% across the entire frequency range with a peak value of 61% achieved at 1.2 GHz. In comparison with similar architectures, the proposed rectenna maintains a stable output efficiency despite the wide fluctuations in the input frequency and also has a minimum footprint size (58 × 55 mm2).