Miracle-dz/UniFAR-FTUs huggingface dataset:
The Molecular Basis for Altered Cation Permeability in Hereditary Stomatocytic Human Red Blood Cells

Normal human RBCs have a very low basal permeability (leak) to cations, which is continuously corrected by the Na,K-ATPase. The leak is temperature-dependent, and this temperature dependence has been evaluated in the presence of inhibitors to exclude the activity of the Na,K-ATPase and NaK2Cl transporter. The severity of the RBC cation leak is altered in various conditions, most notably the hereditary stomatocytosis group of conditions. Pedigrees within this group have been classified into distinct phenotypes according to various factors, including the severity and temperature-dependence of the cation leak. As recent breakthroughs have provided more information regarding the molecular basis of hereditary stomatocytosis, it has become clear that these phenotypes elegantly segregate with distinct genetic backgrounds. The cryohydrocytosis phenotype, including South-east Asian Ovalocytosis, results from mutations in SLC4A1, and the very rare condition, stomatin-deficient cryohydrocytosis, is caused by mutations in SLC2A1. Mutations in RHAG cause the very leaky condition over-hydrated stomatocytosis, and mutations in ABCB6 result in familial pseudohyperkalemia. All of the above are large multi-spanning membrane proteins and the mutations may either modify the structure of these proteins, resulting in formation of a cation pore, or otherwise disrupt the membrane to allow unregulated cation movement across the membrane. More recently mutations have been found in two RBC cation channels, PIEZO1 and KCNN4, which result in dehydrated stomatocytosis. These mutations alter the activation and deactivation kinetics of these channels, leading to increased opening and allowing greater cation fluxes than in wild type.
Gaze-based rehearsal in children under 7: a developmental investigation of eye movements during a serial spatial memory task.

The emergence of strategic verbal rehearsal at around 7 years of age is widely considered a major milestone in descriptions of the development of short-term memory across childhood. Likewise, rehearsal is believed by many to be a crucial factor in explaining why memory improves with age. This apparent qualitative shift in mnemonic processes has also been characterized as a shift from passive visual to more active verbal mnemonic strategy use, but no investigation of the development of overt spatial rehearsal has informed this explanation. We measured serial spatial order reconstruction in adults and groups of children 5-7 years old and 8-11 years old, while recording their eye movements. Children, particularly the youngest children, overtly fixated late-list spatial positions longer than adults, suggesting that younger children are less likely to engage in covert rehearsal during stimulus presentation than older children and adults. However, during retention the youngest children overtly fixated more of the to-be-remembered sequences than any other group, which is inconsistent with the idea that children do nothing to try to remember. Altogether, these data are inconsistent with the notion that children under 7 do not engage in any attempts to remember. They are most consistent with proposals that children's style of remembering shifts around age 7 from reactive cue-driven methods to proactive, covert methods, which may include cumulative rehearsal.
Monte Carlo simulation of magnetic multilayered structures with giant magnetoresistance effects

Description of giant magnetoresistance effects in magnetic multilayered structures with the use of the anisotropic Heisenberg model for determination of magnetic properties of thin ferromagnetic films forming these structures is given. Monte Carlo simulations of magnetic properties for structures, which are constructed from two ferromagnetic films divided by nonmagnetic film, are carried out. The temperature and magnetic field dependencies are considered for ferromagnetic and antiferromagnetic configurations of these structures. The calculation of the magnetoresistance is carried out for different thicknesses of the ferromagnetic films. It was shown, that the obtained temperature dependence for the magnetoresistance is agreed very well with experimental results, measured for the magnetic multilayered structures similar to structures, which are considered in our investigations.
Reduction of effect of water absorption for metamaterial sensing applications

We investigate the effect of absorption coefficient of water, which is attached on split ring resonators (SRRs) surface, for a reflection spectrum in terahertz region. When the water is dropped on the SRRs surface, the resonant dip disappears owing to the relatively high value of the extinction coefficient of water in terahertz region. This makes it difficult to use the metamaterial as a highly sensitive device for water solutions. By inserting a thin dielectric film between the SRRs and water, we can remain the apparent resonant dip, and simultaneously observe the shift of the resonant frequency.
Weight Updating Methods for a Dynamic Weighted Fair Queuing (WFQ) Scheduler

This work aims to analyze different weight updating methods for a dynamic Weighted Fair Queuing (WFQ) scheduler providing Quality of Service (QoS) guarantees for the applications of the IST PHOENIX project and for new value-added services in general. Two weight updating methods are investigated in terms of granted delays to concerned service classes and buffer utilization of the related queues at a given IP interface. In particular, a novel weight updating based on the Knightly’s theory is proposed. Simulation results have demonstrated that a dynamic WFQ based on either of the weight updating methods can well support a proportional relative model of QoS in a Diff-Serv architecture in an IP-based Next Generation Network. The designed system is extremely simple and effective, and with low computational overhead by employing an innovative technique to evaluate the trend of the entering traffic aggregates, in order to trigger a scheduler’s weights updating only when needed.
The Acute Salivary Ghrelin Response to a Psychosocial Stress Is Enhanced in Symptomatic Patients with Bulimia Nervosa: A Pilot Study

Background: Stress is a precipitating factor for both binge eating and bulimia nervosa (BN); however, the biological mechanisms through which it may trigger binge eating are poorly understood. There is evidence that the adrenal hormone cortisol and the gastric peptide ghrelin might be involved in stress-induced food ingestion. We hypothesized that symptomatic patients with BN might disclose deranged responses of ghrelin and/or cortisol to stressors and that this could be related to their binge-eating behaviour. Methods: Here we investigated salivary cortisol and ghrelin responses to the Trier Social Stress Test (TSST) in 10 women with acute BN and 10 age-matched healthy females. Eating-related psychopathology and behaviours were assessed by self-report measures. Results: No significant differences emerged between bulimic patients and healthy controls in the pre-stress salivary levels of both cortisol and ghrelin. The BN patients displayed normal cortisol but enhanced ghrelin responses to TSST. No significant correlations emerged between stress-induced salivary hormone changes and self-report measures of binge eating. Conclusion: To our knowledge, this is the first study showing deranged salivary ghrelin reactivity to a psychosocial stressor in symptomatic patients with BN. The extent to which this could contribute to the binge-eating behaviour of BN subjects awaits clarification.
Retinal Arteriolar Morphometry Based on Full Width at Half Maximum Analysis of Spectral-Domain Optical Coherence Tomography Images

Objectives In this study, we develop a microdensitometry method using full width at half maximum (FWHM) analysis of the retinal vascular structure in a spectral-domain optical coherence tomography (SD-OCT) image and present the application of this method in the morphometry of arteriolar changes during hypertension. Methods Two raters using manual and FWHM methods measured retinal vessel outer and lumen diameters in SD-OCT images. Inter-rater reproducibility was measured using coefficients of variation (CV), intraclass correlation coefficient and a Bland-Altman plot. OCT images from forty-three eyes of 43 hypertensive patients and 40 eyes of 40 controls were analyzed using an FWHM approach; wall thickness, wall cross-sectional area (WCSA) and wall to lumen ratio (WLR) were subsequently calculated. Results Mean difference in inter-rater agreement ranged from -2.713 to 2.658 μm when using a manual method, and ranged from -0.008 to 0.131 μm when using a FWHM approach. The inter-rater CVs were significantly less for the FWHM approach versus the manual method (P < 0.05). Compared with controls, the wall thickness, WCSA and WLR of retinal arterioles were increased in the hypertensive patients, particular in diabetic hypertensive patients. Conclusions The microdensitometry method using a FWHM algorithm markedly improved inter-rater reproducibility of arteriolar morphometric analysis, and SD-OCT may represent a promising noninvasive method for in vivo arteriolar morphometry.
Proteomic identification of synaptic caspase substrates

The dismantling and elimination of excess neurons and their connections (pruning) is essential for brain development and may be aberrantly reactivated in some neurodegenerative diseases. Growing evidence implicates caspase‐mediated apoptotic and nonapoptotic cascades in the dysfunction and death of neurons in neurodegenerative disorders such as Alzheimer's, Parkinson, and Huntington's diseases. It is the cleaved caspase substrates that are the effectors of synapse elimination. However, their identities, specific cleavage sites, and functional consequences of cleavage are largely unknown. An important gap in our knowledge is a comprehensive catalog of synapse‐specific or synapse‐enriched caspase targets. Traditional biochemical approaches have revealed only a small number of neuronal caspase targets. Instead, we utilized a gel‐based proteomics approach to enable the first global analysis of caspase‐mediated cleavage events in mammalian brain synapses, employing both an in vitro system with recombinant activated caspases and an in vivo model of ethanol‐induced neuronal apoptosis. Of the more than 70 putative cleavage substrates that were identified, 22 were previously known caspase substrates. Among the novel targets identified and validated by Western blot were the proton pump ATPase subunit ATP6V1B2 and the N‐ethylmaleimide‐sensitive fusion protein (NSF). Our work represents the first comprehensive, proteome‐wide screen for proteolytic targets of caspases in neuronal synapses. Our discoveries will have significance for both furthering basic understanding of roles of caspases in synaptic plasticity and synaptic loss in neurodegeneration, and on a more immediately practical level, may provide candidate biomarkers for measuring synapse loss in human disease states.
Modelling seedling growth rates of 18 temperate arable weed species as a function of the environment and plant traits.

BACKGROUND AND AIMS The early growth rate of seedlings in the exponential phase is an important eco- physiological trait in crop/weed competition models based on assessments of relative weed green area. An understanding of the role of various plant traits in determining early growth rate may also be useful for identifying contrasting weed strategies for establishment before canopy closure. METHODS The response of seedling relative growth rate (RGR) to the environment was measured in outdoor sand beds in the autumn and the spring for 18 temperate annual weed species and two crops. Seedling growth was modelled using thermal time and effective day-degrees (combining the effect of temperature and radiation). The contribution of various plant traits in determining variability in RGR was investigated using regression analysis. KEY RESULTS The effective day-degree model was more effective for describing early weed growth than thermal time. Variability in RGR measured in the autumn was largely determined by differences between the species in net assimilation rate (NAR), whereas in the spring leaf area ratio (LAR) played a larger part. There were differences between the broadleaf and grass species in the relative contribution of NAR and LAR to RGR in both seasons. RGR in the spring was negatively correlated with initial seedling size. CONCLUSIONS The parameters derived in this study can be used to calibrate empirical models of crop yield loss based on relative weed green area to different growing seasons and assessment dates. The grass weeds, which tended to have large seeds, had a higher investment in roots in the seedling stage, potentially making them more competitive later in the season when resources become limiting.
Incremental model-to-text transformation

Model-driven engineering (MDE) promotes the use of abstractions to simplify the development of complex software systems. Through several model management tasks (e.g., model verification, re-factoring, model transformation), many software development tasks can be automated. For example, model-to-text transformations (M2T) are used to realize textual development artefacts (e.g., documentation, configuration scripts, code, etc.) from underlying source models. Despite the importance of M2T transformation, contemporary M2T languages lack support for developing transformations that scale. As MDE is applied to systems of increasing size and complexity, a lack of scalable M2T transformations and other model management tasks hinders industrial adoption. This is largely due to the fact that model management tools do not support efficient propagation of changes from models to other development artefacts. As such, the re-synchronisation of generated textual artefacts with underlying system models can take considerably large amount of time to execute due to redundant re-computations. This thesis investigates scalability in the context of M2T transformation, and proposes two novel techniques that enable efficient incremental change propagation from models to generated textual artefacts. In contrast to existing incremental M2T transformation technique, which relies on model differencing, our techniques employ fundamentally different approaches to incremental change propagation: they use a form of runtime analysis that identifies the impact of source model changes on generated textual artefacts. The structures produced by this runtime analysis, are used to perform efficient incremental transformations (scalable transformations). This claim is supported by the results of empirical evaluation which shows that the techniques proposed in this thesis can be used to attain an average reduction of 60% in transformation execution time compared to non-incremental (batch) transformation.
Nearly second-order optimality of online joint detection and estimation via one-sample update schemes

Sequential hypothesis test and change-point detection when the distribution parameters are unknown is a fundamental problem in statistics and machine learning. We show that for such problems, detection procedures based on sequential likelihood ratios with simple onesample update estimates such as online mirror descent are nearly second-order optimal. This means that the upper bound for the algorithm performance meets the lower bound asymptotically up to a log-log factor in the false-alarm rate when it tends to zero. This is a blessing, since although the generalized likelihood ratio (GLR) statistics are optimal theoretically, but they cannot be computed recursively, and their exact computation usually requires infinite memory of historical data. We prove the nearly second-order optimality by making a connection between sequential change-point detection and online convex optimization and leveraging the logarithmic regret bound property of online mirror descent algorithm. Numerical examples validate our theory.
Multidimensional Family Therapy Reduces Self-Reported Criminality Among Adolescents With a Cannabis Use Disorder

Multidimensional family therapy (MDFT) is an established treatment program for youth displaying multiproblem behavior. We examined whether MDFT decreased criminal offending among cannabis abusing adolescents, as compared with individual psychotherapy (IP). In a Western European randomized controlled trial comparing MDFT with IP, a sample of 169 adolescents with a cannabis disorder completed self-reports on criminal offending. Half indicated they had committed one or more criminal offenses in the 90 days before the baseline assessment. Follow-up assessments were at 6 and 12 months after randomization. The proportion of adolescents reporting nondelinquency increased during the study period, most so in the MDFT condition. In addition, MDFT lowered the number of violent offenses more than IP. This difference was not seen for property crimes. In cannabis abusing adolescents, MDFT is an effective treatment to prevent and reduce criminal offending. MDFT outperforms IP for violent crimes.
Channel estimation in full-dimensional massive MIMO system using one training symbol

In this paper, an algorithm is proposed to estimate thousands of channel parameters in massive multiple-input multiple-output (MIMO) systems with a three-dimensional antenna array, using only one training symbol while assuming no knowledge of path number, noise power, path gain and direction-of-arrival (DOA) statistics. Furthermore, the received data structure is exploited to give a low-complexity DOA acquisition scheme with closed-form computations. Simulation results show excellent performance of the proposed algorithm in both channel estimation and DOA acquisition.
The effects of repeated Toll-like receptors 2 and 4 stimulation in COPD alveolar macrophages

Background COPD is a progressive inflammatory airway disease characterized by increased numbers of alveolar macrophages in the lungs. Bacterial colonization of the lungs is a common feature in COPD and can promote inflammation through continual and repeated Toll-like receptor (TLR) stimulation. We have studied the response of COPD alveolar macrophages to repetitive stimulation with TLR2 and TLR4 ligands. We investigated the effect of sequential stimulation with different ligands to determine whether this results in tolerance or amplification of the immune response. Methods We stimulated alveolar macrophages from COPD patients (n=9) and smokers (n=8) with the TLR4 agonist lipopolysaccharide (LPS) or the TLR2 agonist Pam3CSK4 for 24 hours before restimulating again for 24 hours. Cytokine protein release and gene expression were investigated. Results Repetitive stimulation of COPD and smokers macrophages with LPS for both 24-hour periods caused a reduction in tumor necrosis factor α, CCL5, and IL-10 production compared to cells that were not exposed initially to LPS. IL-6 and CXCL8 production were not significantly altered following repetitive LPS stimulation. The same pattern was observed for repeated stimulation with Pam3CSK4. Using COPD macrophages, LPS followed by Pam3CSK4 stimulation increased the levels of all cytokines compared to media followed by Pam3CSK4. Conclusion TLR tolerance in COPD alveolar macrophages occurs after repetitive stimulation with the same TLR ligand, but this only occurs for selected cytokines. CXCL8 production is not reduced after repetitive TLR stimulation with the same ligand; this may be an important mechanism for the increased CXCL8 levels that have been observed in COPD. We showed that TLR4 stimulation followed by TLR2 stimulation does not cause tolerance, but enhances cytokine production. This may be a relevant mechanism by which bacteria cause excessive inflammation in COPD patients.
On the challenges of learning with inference networks on sparse, high-dimensional data

We study parameter estimation in Nonlinear Factor Analysis (NFA) where the generative model is parameterized by a deep neural network. Recent work has focused on learning such models using inference (or recognition) networks; we identify a crucial problem when modeling large, sparse, high-dimensional datasets -- underfitting. We study the extent of underfitting, highlighting that its severity increases with the sparsity of the data. We propose methods to tackle it via iterative optimization inspired by stochastic variational inference \citep{hoffman2013stochastic} and improvements in the sparse data representation used for inference. The proposed techniques drastically improve the ability of these powerful models to fit sparse data, achieving state-of-the-art results on a benchmark text-count dataset and excellent results on the task of top-N recommendation.
Adaptive duty cycling in sensor networks via Continuous Time Markov Chain modelling

The dynamic and unpredictable nature of energy harvesting sources that are used in wireless sensor networks necessitates the need for adaptive duty cycling techniques. Such adaptive control allows sensor nodes to achieve energy-neutrality, whereby both energy supply and demand are balanced. This paper proposes a framework enabling an adaptive duty cycling scheme for sensor networks that takes into account the operating duty cycle of the node, and application-level QoS requirements. We model the system as a Continuous Time Markov Chain (CTMC), and derive analytical expressions for key QoS metrics - such as latency, loss probability and power consumption. We then formulate and solve the optimal operating duty cycle as a non-linear optimization problem, using latency and loss probability as the constraints. Simulation results show that a Markovian duty cycling scheme can outperform periodic duty cycling schemes.
Complete and Time-Optimal Path-Constrained Trajectory Planning With Torque and Velocity Constraints: Theory and Applications

Time optimality and completeness are desired properties for robot trajectory planning, which ensure that robotic systems work reliably and efficiently. However, there is great difficulty to design planning algorithms that simultaneously guarantee time optimality and completeness. In this paper, a complete, time-optimal and real-time algorithm for path-constrained trajectory planning is proposed under both torque and velocity constraints. The time optimality and completeness of the proposed algorithm are theoretically proven with rigorous mathematical analysis. Moreover, the proposed approach is general and applicable on different robotic systems, and two examples are given on a two-link manipulator and an active-caster-based omnidirectional wheeled mobile robot. Comparative simulation and experimental results are provided to demonstrate the superior performance of the proposed algorithm.
Influence of Modified MF Resin on Surface Crack of Reconsitituted Bamboo

In order to decrease the surface crack of reconstituted bamboo,glycol and acrylamide were used to modify the melamine resin for bamboo impregnation.The results showed that when the weight of acrylamide was added to 6%of the resin and the mole ratio of glycol to formaldehyde was 1.5:1,the crack of reconstituted bamboo decreased.The lower viscosity of resin,the better its wettability on the surface of material to be impregnated,that resulted to small contact angle and faster impregnation speed.