Miracle-dz/UniFAR-FTUs huggingface dataset:
Demand and Capacity Balancing Technology Based on Multi-agent Reinforcement Learning

To effectively solve Demand and Capacity Balancing (DCB) in large-scale and high-density scenarios through the Ground Delay Program (GDP) in the pre-tactical stage, a sequential decision-making framework based on a time window is proposed. On this basis, the problem is transformed into Markov Decision Process (MDP) based on local observation, and then Multi-Agent Reinforcement Learning (MARL) method is adopted. Each flight is regarded as an independent agent to decide whether to implement GDP according to its local state observation. By designing the reward function in multiple combinations, a Mixed Competition and Cooperation (MCC) mode considering fairness is formed among agents. To improve the efficiency of MARL, we use the double Q-Learning Network (DQN), experience replay technology, adaptive ϵ-greedy strategy and Decentralized Training with Decentralized Execution (DTDE) framework. The experimental results show that the training process of the MARL method is convergent, efficient and stable. Compared with the Computer-Assisted Slot Allocation (CASA) method used in the actual operation, the number of flight delays and the average delay time is reduced by 33.7% and 36.7% respectively.
Collection, particle sizing and detection of airborne viruses

Viruses that affect humans, animals and plants are often dispersed and transmitted through airborne routes of infection. Due to current technological deficiencies, accurate determination of the presence of airborne viruses is challenging. This shortcoming limits our ability to evaluate the actual threat arising from inhalation or other relevant contact with aerosolized viruses. To improve our understanding of the mechanisms of airborne transmission of viruses, air sampling technologies that can detect the presence of aerosolized viruses, effectively collect them and maintain their viability, and determine their distribution in aerosol particles, are needed. The latest developments in sampling and detection methodologies for airborne viruses, their limitations, factors that can affect their performance and current research needs, are discussed in this review. Much more work is needed on the establishment of standard air sampling methods and their performance requirements. Sampling devices that can collect a wide size range of virus‐containing aerosols and maintain the viability of the collected viruses are needed. Ideally, the devices would be portable and technology‐enabled for on‐the‐spot detection and rapid identification of the viruses. Broad understanding of the airborne transmission of viruses is of seminal importance for the establishment of better infection control strategies.
Volumes of brain structures in captive wild-type and laboratory rats: 7T magnetic resonance in vivo automatic atlas-based study

Selective breeding of laboratory rats resulted in changes of their behavior. Concomitantly, the albino strains developed vision related pathologies. These alterations certainly occurred on the background of modifications in brain morphology. The aim of the study was to assess and compare volumes of major structures in brains of wild-captive, laboratory albino and laboratory pigmented rats. High resolution T2-weighted images of brains of adult male Warsaw Wild Captive Pisula-Stryjek rats (WWCPS, a model of wild type), laboratory pigmented (Brown Norway strain, BN) and albino rats (Wistar strain, WI) were obtained with a 7T small animal-dedicated magnetic resonance tomograph. Volume quantification of whole brains and 50 brain structures within each brain were performed with the digital Schwarz rat brain atlas and a custom-made MATLAB/SPM8 scripts. Brain volumes were scaled to body mass, whereas volumes of brain structures were normalized to individual brain volumes. Normalized brain volume was similar in WWCPS and BN, but lower in WI. Normalized neocortex volume was smaller in both laboratory strains than in WWCPS and the visual cortex was smaller in albino WI rats than in WWCPS and BN. Relative volumes of phylogenetically older structures, such as hippocampus, amygdala, nucleus accumbens and olfactory nuclei, also displayed certain strain-related differences. The present data shows that selective breeding of laboratory rats markedly affected brain morphology, the neocortex being most significantly altered. In particular, albino rats display reduced volume of the visual cortex, possibly related to retinal degeneration and the development of blindness.
The Cardioprotective Signaling Activity of Activated Protein C in Heart Failure and Ischemic Heart Diseases

Activated protein C (APC) is a vitamin-K dependent plasma serine protease, which functions as a natural anticoagulant to downregulate thrombin generation in the clotting cascade. APC also modulates cellular homeostasis by exhibiting potent cytoprotective and anti-inflammatory signaling activities. The beneficial cytoprotective effects of APC have been extensively studied and confirmed in a number of preclinical disease and injury models including sepsis, type-1 diabetes and various ischemia/reperfusion diseases. It is now well-known that APC modulates downstream cell signaling networks and transcriptome profiles when it binds to the endothelial protein C receptor (EPCR) to activate protease-activated receptor 1 (PAR1) on various cell types. However, despite much progress, details of the downstream signaling mechanism of APC and its crosstalk with other signaling networks are far from being fully understood. In this review, we focus on the cardioprotective properties of APC in ischemic heart disease and heart failure with a special emphasis on recent discoveries related to the modulatory effect of APC on AMP-activated protein kinase (AMPK), PI3K/AKT, and mTORC1 signaling pathways. The cytoprotective properties of APC might provide a novel strategy for future therapies in cardiac diseases.
Discerning the spatio-temporal disease patterns of surgically induced OA mouse models

Osteoarthritis (OA) is the most common cause of disability in ageing societies, with no effective therapies available to date. Two preclinical models are widely used to validate novel OA interventions (MCL-MM and DMM). Our aim is to discern disease dynamics in these models to provide a clear timeline in which various pathological changes occur. OA was surgically induced in mice by destabilisation of the medial meniscus. Analysis of OA progression revealed that the intensity and duration of chondrocyte loss and cartilage lesion formation were significantly different in MCL-MM vs DMM. Firstly, apoptosis was seen prior to week two and was narrowly restricted to the weight bearing area. Four weeks post injury the magnitude of apoptosis led to a 40–60% reduction of chondrocytes in the non-calcified zone. Secondly, the progression of cell loss preceded the structural changes of the cartilage spatio-temporally. Lastly, while proteoglycan loss was similar in both models, collagen type II degradation only occurred more prominently in MCL-MM. Dynamics of chondrocyte loss and lesion formation in preclinical models has important implications for validating new therapeutic strategies. Our work could be helpful in assessing the feasibility and expected response of the DMM- and the MCL-MM models to chondrocyte mediated therapies.
IMPACT OF DIETARY SUPPLEMENTATION OF PREBIOTICS ON THE GROWTH PERFORMANCE AND IMMUNITY IN BROILERS FED LOW PROTEIN DIETS

The current study was conducted to find out the influence of feeding broilers on low protein diets supplemented with prebiotic. Growth performance, carcass traits, muscle cholesterol, triglycerides, chemical composition, blood parameters and immune response of broilers were evaluated. A total number of 68 birds’ oneday old broiler chicks (Ross 308) randomly distributed into 4 equal groups each of 17 chicks. The first group (control) was fed the basal (100%NRC CP) diet free from prebiotic. The other three groups (T2, T3 and T4) were fed on low protein diets (95, 90 & 85% of NRC requirements, respectively) and supplemented with prebiotic at level of 0.1%. The results showed that, birds in the second group had significantly higher live body weight, feed conversion, and dressing percentages. Supplementation of prebiotic had no significant effects on relative weight of liver, gizzard and immune organs. Reduction in the relative weight of abdominal fat pad in all prebiotic treated groups. There were significant reduction (P<0.05) in the meat cholesterol, triglycerides and fat mass of broiler breast and thigh, while protein content was significantly (P<0.05) increased in all prebiotic treated groups. A numerical increased in antibody titre in birds in the second and third groups. Birds in the second group had the best economic feed efficiency. It could be concluded that adding prebiotic to broiler diets low in protein (95, 90 and 85% of NRC) has a beneficial effect on growth parameters, carcass traits, economic value in addition to healthy and nutritious poultry products for consumers.
Prediction of Protein Metal Binding Sites Using Deep Neural Networks

Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three‐dimensional structure by folding show which vital function is fulfilled. The prediction of metal‐binding in proteins will be considered as a step‐in function assignment for new proteins, which helps to obtain functional proteins in genomic studies, is critical to protein function annotation and drug discovery. Computational predictions made by using machine learning methods from the data obtained from amino acid sequences are widely used in the protein metal‐binding and various bioinformatics fields. In this work, we present three different deep learning architectures for prediction of metal‐binding of Histidines (HIS) and Cysteines (CYS) amino acids. These architectures are as follows: 2D Convolutional Neural Network, Long‐Short Term Memory and Recurrent Neural Network. Their comparison is carried out on the three different sets of attributes derived from a public dataset of protein sequences. These three sets of features extracted from the protein sequence were obtained using the PAM scoring matrix, protein composition server, and binary representation methods. The results show that a better performance for prediction of protein metal‐ binding sites is obtained through Convolutional Neural Network architecture.
EFFECT OF VITAMIN E AND THYROXINE ON SOME BIOCHEMICAL AND PHYSIOLOGICAL MARKERS OF LAYING HENS (ISA BROWN STRAIN).

The present work was conducted to evaluate the effect of dietary two levels of vitamin E (250 and 500 mg/Kg diet) with/or without single dose of thyroid hormone Thyroxin (0.25 mg/Kg diet), on the biochemical and physiological markers of Laying hens (ISA Brown Strain). A total of 120, 29 weeks-old layer, were housed in 120 pens and assigned to 6 diets groups, first group (control, receiving basal diet), the second and third groups received basal diet supplemented with vit-E 250 and 500 mg/kg diet respectively, the forth group kept on the basal diet supplemented with a single level of thyroid hormone 0.25mg/kg diet; the fifth and sixth groups received basal diet supplemented with the same level of thyroid hormone combined with the two levels of vit-E/kg diet. The study was accomplished and still for 8 weeks. Finally, dietary vit-E in second level and thyroid hormone improved physiological and antioxidant parameters and decreased lipid peroxidation as confirmed from lowering in malonic dialdehyde (MDA) and enhanced GPx activity, B and Gamma globulin. Moreover, it increased HDL-C, decreased triglycerides, LDL-c and risk ratio TC/HDL-C in laying hens (ISA Brown Strain). Combining both of thyroxine and V. E had a more pronounced effect than adding each solely in comparison to the control group.
Tree-ring isotopes suggest atmospheric drying limits temperature–growth responses of treeline bristlecone pine

Abstract Altitudinally separated bristlecone pine populations in the White Mountains (California, USA) exhibit differential climate–growth responses as temperature and tree-water relations change with altitude. These populations provide a natural experiment to explore the ecophysiological adaptations of this unique tree species to the twentieth century climate variability. We developed absolutely dated annual ring-width chronologies, and cellulose stable carbon and oxygen isotope chronologies from bristlecone pine growing at the treeline (~3500 m) and ~200 m below for the period AD 1710–2010. These chronologies were interpreted in terms of ecophysiological adaptations to climate variability with a dual-isotope model and a leaf gas exchange model. Ring widths show positive tree growth anomalies at treeline and consistent slower growth below treeline in relation to the twentieth century warming and associated atmospheric drying until the 1980s. Growth rates of both populations declined during and after the 1980s when growing-season temperature and atmospheric vapour pressure deficit continued to increase. Our model-based interpretations of the cellulose stable isotopes indicate that positive treeline growth anomalies prior to the 1980s were related to increased stomatal conductance and leaf-level transpiration and photosynthesis. Reduced growth since the 1980s occurred with a shift to more conservative leaf gas exchange in both the treeline and below-treeline populations, whereas leaf-level photosynthesis continued to increase in response to rising atmospheric CO2 concentrations. Our results suggest that warming-induced atmospheric drying confounds positive growth responses of apparent temperature-limited bristlecone pine populations at treeline. In addition, the observed ecophysiological responses of attitudinally separated bristlecone pine populations illustrate the sensitivity of conifers to climate change.
Advances in management of periprosthetic joint infections: an historical prospective study.

OBJECTIVE The purpose of our study is to assess the incidence of prosthetic joint infection (PJI) after total Knee arthroplasty (TKA), total Hip arthroplasty (THA) and total Shoulder arthroplasty (TSA), to identify risk factors, determine the microbial spectrum and management's outcome. PATIENTS AND METHODS A case-control, retrospective observational study was performed analyzing patients who developed a PJI after TKA, THA, and TSA from 2000 to 2017 at our hospital. The patient's risk profile was defined extracting from clinical records the following data: sex, age, BMI, type of implant, comorbidity, year of surgery, year of infection, previous intra-articular injection, microbial isolation, medical and surgical management outcome. We include in the "control group" for each "case" at least 3 patients who didn't have a PJI after TJA. RESULTS 28 patients met all inclusion and exclusion criteria. Comparing the "cases" with "controls" demographics parameters, medical comorbidities and previous intra-articular injection were not associated with an increased risk of PJI. Comparing the "early/delayed group" with "late group", BMI was associated with an increased risk of early/delayed PJI, while demographics parameters, medical comorbidities, and previous intra-articular injection did not significantly increase the risk of PJI. Logistic regression showed that for each BMI unit there was a 20-fold increased risk of early prosthetic infection (OR 1.19, IC 1.03-1.38, p=0.01). Staphylococci were isolated most frequently from pre-operative and intra-operative cultures. Two-stage arthroplasty exchange and surgical debridement resulted in the most performed surgical treatment with a success rate of 88 and 87%. CONCLUSIONS Obesity is a risk factor for "early/delayed infection" of TJA. Two-stage arthroplasty exchange, debridement, antibiotics, and implant retention in patients are treatments with a high rate of success in terms of reinfection.
Effect of the Oxidative Phosphorylation Uncoupler Para-Nitrophenol on the Activated Sludge Community Structure and Performance of a Submerged Membrane Bioreactor

In this work, the metabolic uncoupler para-nitrophenol (pNP) was applied to suppress excess sludge production and to investigate its effects on the system’s performance and activated sludge community structure. The COD removal efficiency decreased from 99.0% to 89.5% prior to and after pNP addition, respectively. Application of pNP transiently reduced NH4+-N, NO3−-N and NO2−-N removal efficiencies, suggesting partial inhibition of both nitrifying and denitrifying activity. However, no changes in the relative abundance of the nitrifying bacteria occurred. Phosphorus removal efficiency was sharply reduced after pNP addition, as the consequence of hydrolysis of stored cell reserves. Tetrasphaera, a key polyphosphate accumulating organism, was also affected by the addition of pNP, a fact that highly influenced system’s ability to remove phosphorus. A drastic drop in Soluble Microbial Products (SMP) and Extracellular Polymeric Substances (EPS) was also detected shortly after the introduction of the uncoupler. On the other hand, MBR’s physicochemical parameters were restored to initial values a week after the addition of pNP. Moreover, remarkable changes in beta-diversity were noted after pNP addition. An increase of Bacteroidetes, Gammaproteobacteria and Firmicutes over Actinobacteria and Alphaproteobacteria was also observed after pNP addition.
Tracking of Quantized Signals Based on Online Kernel Regression

Kernel-based approaches have achieved noticeable success as non-parametric regression methods under the framework of stochastic optimization. However, most of the kernel-based methods in the literature are not suitable to track sequentially streamed quantized data samples from dynamic environments. This shortcoming occurs mainly for two reasons: first, their poor versatility in tracking variables that may change unpredictably over time, primarily because of their lack of flexibility when choosing a functional cost that best suits the associated regression problem; second, their indifference to the smoothness of the underlying physical signal generating those samples. This work introduces a novel algorithm constituted by an online regression problem that accounts for these two drawbacks and a stochastic proximal method that exploits its structure. In addition, we provide tracking guarantees by analyzing the dynamic regret of our algorithm. Finally, we present some experimental results that support our theoretical analysis and show that our algorithm has a favorable performance compared to the state-of-the-art.
Positive Predictive Value for Colorectal Lesions at CT Colonography: Analysis of Factors Impacting Results in a Large Screening Cohort.

OBJECTIVE The purpose of this study is to evaluate factors affecting the positive predictive value (PPV) for detecting colorectal lesions at CT colonography (CTC), using optical colonoscopy (OC) as the reference standard for concordance. MATERIALS AND METHODS Consecutive CTC studies from a single screening program interpreted as positive for at least one detected colorectal lesion 6 mm or larger and sent for subsequent OC were analyzed according to per-polyp and per-patient results. Univariable and multivariable analysis of multiple input factors was performed. RESULTS Of 1650 studies (median patient age, 59.7 years; 877 men and 773 women) with 2688 total CTC-detected lesions 6 mm or larger, the overall PPVs were 88.8% (2386/2688) by polyp and 90.8% (1499/1650) by patient. The by-polyp PPV was significantly higher for polypoid (91.2%; 1793/1965) versus flat or nonpolypoid (79.4%; 459/578) lesions (p < 0.0001). Overall per-patient PPVs were 72.3% (1193/1650) for any neoplasia 6 mm or larger and 38.8% (641/1650) for advanced neoplasia. PPVs for advanced neoplasia increased by CTC Reporting and Data System category: 5.8% (45/781) for C2, 67.1% (511/762) for C3, and 79.4% (85/107) for C4. PPVs for cancer also increased by CTC Reporting and Data System category: 0% (0/781) for C2, 2.2% (17/762) for C3, and 52.3% (56/107) for C4. On multivariable regression analysis, polyp morphologic type (flat vs polypoid) and diagnostic confidence were the strongest predictors of CTC-OC concordance. CTC PPV results are somewhat underestimated because 28.8% (87/302) of CTC-OC-discordant results were categorized as likely OC false-negatives at consensus review. CONCLUSION Concordance between CTC and OC is high for relevant colorectal polyps and masses. Unlike stool-based tests that provide only a binary positive or negative result, CTC can specify the nature of the positive findings, resulting in much greater specificity and risk stratification for patient management decisions.
Impairments in error processing and their association with ADHD symptoms in individuals born preterm

Preterm birth is associated with heightened risk for attention-deficit/hyperactivity disorder (ADHD)-like symptoms and neurocognitive impairments, including impairments in performance monitoring. Here, we investigate the cognitive and neurophysiological processes from a performance-monitoring task in preterm-born adolescents and examine whether these processes in preterm-born adolescents reflect identical neurophysiological impairments to those observed in term-born adolescents with ADHD. We compared 186 preterm-born individuals to 69 term-born individuals with ADHD and 135 term-born controls on cognitive-performance measures and event-related potentials (ERPs) of conflict monitoring (N2) and error processing (ERN, Pe) from a flanker task. Preterm-born adolescents demonstrated reduced N2, ERN and Pe amplitudes, compared to controls, and similar ERN and Pe impairments to term-born adolescents with ADHD. While ADHD symptoms correlated with ERN amplitude at FCz among the preterm-born, ERN amplitude at Fz, N2 and Pe amplitude were not associated with ADHD symptoms. Preterm-born individuals show impairments on neurophysiological indices of conflict monitoring (N2) and error processing (ERN and Pe). Early neurophysiological error processing may be a marker underlying the processes linked to the increased risk for ADHD among preterm-born individuals. Error detection processes are malleable and potential targets for non-pharmacological interventions. Preterm-born individuals are likely to benefit from early interventions.
Investigation of the Transitions for Coplanar Waveguide to Terahertz Spoof Surface Plasmon Polariton Waveguides

In this article, we present the investigation and design of high-performance transitions from coplanar waveguide (CPW) to single-conductor, corrugated waveguides (WGs), namely, terahertz spoof surface plasmon polariton (SSPP) WGs. In contrast to the previous studies in the literature on CPW-to-SSPP WG transitions, we propose a novel methodology to examine the relationship between the guided wavenumber and momentum matching, which is based on analytical calculations, together with the effects of flaring grounds on transition performance. This novel methodology allows us to design a low-loss transition circuit (TC) for any given CPW and SSPP dimensions, which is demonstrated for the first time in the literature. We verify the proposed methodology by the design, fabrication, and measurement of several TCs. The measurement results show that the insertion losses of the proposed TCs can be as low as −1.2, −1, and −2.4 dB at 0.25, 0.275, and 0.3 THz, respectively, which brings a significant improvement of 0.6–2.3 dB in the 0.25–0.3 THz band, compared with the previously reported studies in the literature for the terahertz band.