Miracle-dz/UniFAR-FTUs huggingface dataset:
Physiological adaptation to high irradiance in duckweeds depends on light habitat niche and is ecotype and species-specific

Duckweeds are free-floating aquatic organisms with species ranging from 2 mm-10 mm, where each plant is a single leaflike structure. Recognized as an emerging food crop, their fast growth rates offer potential for cultivation in closed systemsHowever the majority of available duckweed clones lack information regarding habitat origin and physiology. We describe a novel UK collection derived from low light (dLL) or high light (dHL) habitats and profiled for growth, photosynthesis and photoprotection (Non Photochemical Quenching, NPQ) responses. Multiple ecotypes of three Lemna species and one ecotype of Spirodela polyrhiza, were grown under low light (LL:100 μmol m-2 s-1) and high light (HL:350 μmol m-2 s-1). We found species and ecotypic variation in photosynthesis acclimation. Duckweeds grown under HL exhibited lower growth rate, biomass, chlorophyll and quantum yield of photosynthesis. In HL-compared to LL, carotenoid de-epoxidation state and NPQ were higher whilst photosystem II efficiency (ϕPSII) and chla:b ratios were unchanged. Interestingly dLL plants showed relatively stronger acclimation to HL compared to dHL plants: These ecotypes achieved faster growth in HL: by area and colony gain, higher carotenoid levels and less degradation of chlorophyll. We conclude that adaptation to local habitat among ecotypes strongly affects performance under controlled conditions.
Th17 cells target the metabolic miR-142-5p-SDHC/SDHD axis promoting invasiveness and progression of cervical cancers

During cervical carcinogenesis, T-helper (Th)-17 cells accumulate in the peripheral blood and tumor tissues of cancer patients. We previously demonstrated that Th17 cells are associated with therapy resistance as well as cervical cancer metastases and relapse, however, the underlying Th17-driven mechanisms supporting cervical cancer progression are not fully understood as yet. In this study, we found that Th17 cells promote migration and invasion of cervical cancer cells in 2D cultures and 3D spheroids. We demonstrated that Th17 cells induced the expression of miR-142-5p in cervical cancer cells supporting their migration and invasiveness. As the responsible mechanism, we identified the subunits C and D of the succinate dehydrogenase (SDH) complex as new targets of miR-142-5p and provided evidence that Th17 cells reduced the expression of SDHC and SDHD that was dependent on miR-142-5p. Functional downstream analysis with inhibitors of miR-142-5p and siRNA knock down of SDHC and SDHD revealed that Th17-induced miR-142-5p-mediated reduced expression of SDHC and SDHD was responsible for enhanced migration and invasion of cervical cancer cells. Consistently, cervical cancer patients exhibited high levels of succinate in their serum associated with lymph node metastases and diminished expression of SDHD in patients′ biopsies significantly correlated with increased numbers of Th17 cells, advanced tumor stage and lymph node metastases. Correspondingly, a combination of weak or negative SDHD expression and a ratio of Th17/CD4+ T cells > 43.90 % in situ was associated with reduced recurrence free survival. In summary, we unraveled a novel molecular mechanism by which Th17 cells promote cervical cancer progression and suggest evaluation of Th17 cells as a potential target for immunotherapy in cervical cancer.
Task-specific invariant representation in auditory cortex

Categorical sensory representations are critical for many behaviors, including speech perception. In the auditory system, categorical information is thought to arise hierarchically, becoming increasingly prominent in higher order cortical regions. The neural mechanisms that support this robust and flexible computation remain poorly understood. Here, we studied sound representations in primary and non-primary auditory cortex while animals engaged in a challenging sound discrimination task. Population-level decoding of simultaneously recorded single neurons revealed that task engagement caused categorical sound representations to emerge in non-primary auditory cortex. In primary auditory cortex, task engagement caused a general enhancement of sound decoding that was not specific to task-relevant categories. These findings are consistent with mixed selectivity models of neural disentanglement, in which early sensory regions build an overcomplete representation of the world and allow neurons in downstream brain regions to flexibly and selectively read out behaviorally relevant, categorical information.
Two birds with one stone: human SIRPα nanobodies for functional modulation and in vivo imaging of myeloid cells

Signal-regulatory protein α (SIRPα) expressed by myeloid cells is of particular interest for therapeutic strategies targeting the interaction between SIRPα and the "don’t eat me" ligand CD47 and as a marker to monitor macrophage infiltration into tumor lesions. To address both approaches, we developed a set of novel human SIRPα (hSIRPα)-specific nanobodies (Nbs). We identified three high-affinity Nbs targeting the hSIRPα/hCD47 interface, thereby enhancing antibody-dependent cellular phagocytosis (ADCP). For non-invasive in vivo imaging, we chose S36 Nb as a non-modulating binder. By quantitative positron emission tomography (PET) in novel hSIRPα/hCD47 knock-in (KI) mice, we demonstrated the applicability of 64Cu-hSIRPα-S36 Nb to visualize tumor infiltration of myeloid cells. We envision that the hSIRPα-Nbs presented in this study have potential as versatile probes, including novel myeloid-specific checkpoint inhibitors for combinatorial treatment approaches and for in vivo stratification and monitoring of individual responses during cancer immunotherapies.
Loss of Tumour Suppressor TMEM127 Drives RET-mediated Transformation Through Disrupted Membrane Dynamics

Internalization from the cell membrane and endosomal trafficking of receptor tyrosine kinases (RTK) are important regulators of signaling in normal cells that can frequently be disrupted in cancer. The adrenal tumour pheochromocytoma (PCC) can be caused by activating mutations of the RET receptor tyrosine kinase, or inactivation of TMEM127, a transmembrane tumour suppressor implicated in trafficking of endosomal cargos. However, the role of aberrant receptor trafficking in PCC is not well understood. Here, we show that loss of TMEM127 causes wildtype RET protein accumulation on the cell surface, where increased receptor density facilitates constitutive ligand-independent activity and downstream signaling, driving cell proliferation. Loss of TMEM127 altered normal cell membrane organization and recruitment and stabilization of membrane protein complexes, impaired assembly, and maturation of clathrin coated pits, and reduced internalization and degradation of cell surface RET. In addition to RTKs, TMEM127 depletion also promoted surface accumulation of several other transmembrane proteins, suggesting it may cause global defects in surface protein activity and function. Together, our data identify TMEM127 as an important determinant of membrane organization including membrane protein diffusability, and protein complex assembly and provide a novel paradigm for oncogenesis in PCC where altered membrane dynamics promotes cell surface accumulation and constitutive activity of growth factor receptors to drive aberrant signaling and promote transformation.
A posteriori error for elasticity equations - Asymptotic expansion for Stokes problem

This thesis contains two main parts:The first part concerning the discontinuous Galerkin method for the timedependentlinear elasticity problem. In this part, we have derived the a posteriorierror bounds for semi-discrete and fully discrete formulation, by makinguse of the SE reconstruction technique which allows to estimate the errorbound for time-dependent problem through the error estimation of the ascociatedstationary elasticity problem. Then to derive the error bound for thestationary problem, we have presented two methods to obtain two different aposteriori bounds, by L2 duality technique and via energy norm. For fully discretescheme, we make use of the backward-Euler scheme and an appropiatespace-time reconstruction which has the zero-mean value in time.The second part concerning the derivation of an asymptotic expansionfor the solution of Stokes resolvent problem with a small perturbation of thedomain. In this work, we have applied the potential theory, boundary integralequation method and geometric properties of perturbed boundary. Thederivation is rigorous, and this method allows to derive high-order terms inasymptotic expansion. Also, it can be used for many other boundary valueproblems, whenever a suitable potential theory is available.
Engineering magnetic heterostructures to obtain large spin Hall efficiency for spin-orbit torque devices

By sandwiching a CoFeB ferromagnetic layer between Ta and Pt heavy metals with an opposite spin Hall angle, spin currents of the same polarity are transmitted from both interfaces of the Ta/CoFeB/Pt trilayer to the CoFeB layer simultaneously. Here, we investigated the spin-orbit torque, magnetization dynamics, and interface spin transmission efficiency of the trilayer heterostructure by spin-torque ferromagnetic resonance. A large effective spin Hall angle, substantially larger than both Ta and Pt, was obtained in the Ta/CoFeB/Pt stack. The thickness-dependence study showed that with the reducing of CoFeB thickness, Gilbert damping enhances by spin pumping and spin Hall angle increases by the spin Hall effect and the Rashba effect. Furthermore, the spin transparency derived from effective spin mixing conductance was 0.63 ± 0.07 and 0.48 ± 0.02 at the CoFeB/Pt and Ta/CoFeB interfaces, respectively. Hence, the spin Hall angle could be further enhanced by improving the spin transmission efficiency at the interface. Our method of increasing spin-orbit torque through stack engineering would have potential applications in domain wall racetrack memory, logic gates, and magnetic tunnel junctions.By sandwiching a CoFeB ferromagnetic layer between Ta and Pt heavy metals with an opposite spin Hall angle, spin currents of the same polarity are transmitted from both interfaces of the Ta/CoFeB/Pt trilayer to the CoFeB layer simultaneously. Here, we investigated the spin-orbit torque, magnetization dynamics, and interface spin transmission efficiency of the trilayer heterostructure by spin-torque ferromagnetic resonance. A large effective spin Hall angle, substantially larger than both Ta and Pt, was obtained in the Ta/CoFeB/Pt stack. The thickness-dependence study showed that with the reducing of CoFeB thickness, Gilbert damping enhances by spin pumping and spin Hall angle increases by the spin Hall effect and the Rashba effect. Furthermore, the spin transparency derived from effective spin mixing conductance was 0.63 ± 0.07 and 0.48 ± 0.02 at the CoFeB/Pt and Ta/CoFeB interfaces, respectively. Hence, the spin Hall angle could be further enhanced by improving the spin transmission efficiency at the int.
Closed-form variance swap prices under general affine GARCH models and their continuous-time limits

Fully explicit closed-form expressions are developed for the fair strike prices of discrete-time variance swaps under general affine GARCH type models that have been risk-neutralized with a family of variance dependent pricing kernels. The methodology relies on solving differential recursions for the coefficients of the joint cumulant generating function of the log price and the conditional variance processes. An alternative derivation is provided in the case of Gaussian innovations. Using standard assumptions on the asymptotic behavior of the GARCH parameters as the sampling frequency increases, the diffusion limit of a Gaussian GARCH model is derived and the convergence of the variance swap prices to its continuous-time limit is further investigated. Numerical examples on the term structure of the variance swap rates and on the convergence results are also presented.
A DESCRIPTIVE STUDY TO ASSESS COMPETENCIES OF STAFF NURSES RELATED TO FLUID AND ELECTROLYTE IMBALANCES.

assess Three of and The collected from 120 selected probability purposive using and The study revealed that maximum staff below average level of knowledge and in terms of interpretation, 44.17% staff nurses had below average level of interpretation. A moderate positive correlation (0.47) was found between knowledge and interpretation. Professional qualification had significant relationship with interpretation (at p< 0.05). Maximum deficit in terms of knowledge and interpretation was in risk for developing hypernatremia (87.5%) and nursing intervention for hyponatremia (89.17%) respectively. It was concluded that maximum deficit was in the areas of electrolyte imbalances. Therefore, an In-Service Education was conducted for the staff nurses of the selected hospital.
Learning Graph-Level Representations with Gated Recurrent Neural Networks

Recently a variety of methods have been developed to encode graphs into low-dimensional vectors that can be easily exploited by machine learning algorithms. The majority of these methods start by embedding the graph nodes into a low-dimensional vector space, followed by using some scheme to aggregate the node embeddings. In this work, we develop a new approach to learn graph-level representations, which includes a combination of unsupervised and supervised learning components. We start by learning a set of global node representations in an unsupervised fashion, followed by a strategy to map the graph nodes into sequences of node-neighbor pairs. Gated recurrent neural network (RNN) units are modiﬁed to accommodate both the node representations as well as their neighborhood information. Experiments on standard graph classiﬁcation benchmarks demonstrate that our proposed approach achieves superior or comparable performance relative to the state-of-the-art algorithms in terms of convergence speed and classiﬁcation accuracy. We further illustrate the effectiveness of the different components used by our approach.
On the Equivalence of Maximum Reaction Time and Maximum Data Age for Cause-Effect Chains

Real-time systems require a formal guarantee of timing-constraints, not only for individual tasks but also for data-propagation. The timing behavior of data-propagation paths in a given system is typically described by its maximum reaction time and its maximum data age. This paper shows that they are equivalent. To reach this conclusion, partitioned job chains are introduced, which consist of one immediate forward and one immediate backward job chain. Such partitioned job chains are proven to describe maximum reaction time and maximum data age in a universal manner. This universal description does not only show the equivalence of maximum reaction time and maximum data age, but can also be exploited to speed up the computation of such significantly. In particular, the speed-up for synthesized task sets based on automotive benchmarks can be up to 1600. Since only very few non-restrictive assumptions are made, the equivalence of maximum data age and maximum reaction time holds for almost any scheduling mechanism and even for tasks which do not adhere to the typical periodic or sporadic task model. This observation is supported by a simulation of a ROS2 navigation system.
Quasi-periodic pulsations in solar and stellar flares : an overview of recent results

Quasi-periodic pulsations (QPPs) appear to be a common feature observed in the light curves of both solar and stellar flares. However, their quasi-periodic nature, along with the facts that they can be small in amplitude and short-lived, make QPPs difficult to unequivocally detect. In this paper, we test the strengths and limitations of state-of-the-art methods for detecting QPPs using a series of hare-and-hounds exercises. The hare simulated a set of flares, both with and without QPPs of a variety of forms, while the hounds attempted to detect QPPs in blind tests. We use the results of these exercises to create a blueprint for anyone who wishes to detect QPPs in real solar and stellar data. We present eight, clear recommendations to be kept in mind for future QPP detections, with the plethora of solar and stellar flare data from new and future satellites. These recommendations address the key pitfalls in QPP detection, including detrending, trimming data, accounting for coloured noise, detecting stationary-period QPPs, detecting QPP with non-stationary periods, and ensuring detections are robust and false detections are minimized. We find that QPPs can be detected reliably and robustly by a variety of methods, which are clearly identified and described, if the appropriate care and due diligence is taken.
Parallel Restarted SGD for Non-Convex Optimization with Faster Convergence and Less Communication

In distributed training of deep neural networks, parallel mini-batch SGD is widely used to speed up the training process by using multiple workers. It uses multiple workers to sample local stochastic gradient in parallel, aggregates all gradients in a single server to obtain the average, and update each worker's local model using a SGD update with the averaged gradient. Ideally, parallel mini-batch SGD can achieve a linear speed-up of the training time (with respect to the number of workers) compared with SGD over a single worker. However, such linear scalability in practice is significantly limited by the growing demand for gradient communication as more workers are involved. Model averaging, which periodically averages individual models trained over parallel workers, is another common practice used for distributed training of deep neural networks since (Zinkevich et al. 2010) (McDonald, Hall, and Mann 2010). Compared with parallel mini-batch SGD, the communication overhead of model averaging is significantly reduced. Impressively, tremendous experimental works have verified that model averaging can still achieve a good speed-up of the training time as long as the averaging interval is carefully controlled. However, it remains a mystery in theory why such a simple heuristic works so well. This paper provides a thorough and rigorous theoretical study on why model averaging can work as well as parallel mini-batch SGD with significantly less communication overhead.
Prepared for submission to JHEP 2 D Seiberg-like dualities with an adjoint matter

We consider the analogue of Kutasov-Schwimmer-Seiberg duality for twodimensionalN = (2, 2) U(k) gauge theory with one adjoint X with the superpotential TrX l+1 and with fundamental and anti-fundamental chiral multiplets. We give the evidences for the proposed dualities by analytically proving that the elliptic genus of dual pair coincides with each other. For some of the dual pairs flowing to the superconformal field theory, we show the nonperturbative truncation of the chiral ring. For the theory with one adjoint and Nf > k fundamental fields, we argue the theory exhibits mass gap. ar X iv :1 70 2. 00 23 5v 3 [ he pth ] 2 1 Ju l 2 01 7
Phenomenological modelling of particulate emissions in direct injection spark ignition engines for driving cycle simulations

In order to achieve local air purity, particulate emissions from gasoline engines are meanwhile subject to statutory regulations. Due to the increasing complexity of the test procedures, away from standardized driving cycles to the point of real driving emissions, an almost infinite number of characteristics of the driving profile is possible. Simulation methods can help to reduce engine-out particle emissions by means of engine design and application, particularly at an early stage of development and by transferring the results to different variants. For this purpose, a new phenomenological simulation model is developed within this work that allows the determination of the particle raw emissions of gasoline engines based on physical principles. It is applicable both at stationary operating conditions and in transient driving cycles. Not only the particle number values are calculated, but also the particle mass and size distribution. This is achieved by the coupling of a 0D/1D engine process simulation, a new quasi-dimensional multi-zone model for the consideration of different soot particle formation causes and a stochastic soot model. Particle emissions from injector deposits, inhomogeneous gaseous mixture preparation and remaining fuel wall film are taken into consideration, as they contribute to the overall emissions by a different proportion. The applied gas-phase boundary conditions of the multi-zone structure are based on these formation processes. In zones that arise from inhomogeneous mixture preparation, the reaction of the air-fuel mixture under sub-stoichiometric conditions is calculated, whereas pyrolysis reactions are considered in zones with remaining liquid fuel film. The calculation of the remaining injector film mass considers film formation and evaporation. The formation of liquid wall films is calculated by the coupling of a newly developed spray model, a spray-wall interaction model and an evaporation model. The - within the framework of this work developed - homogenization sub-model calculates the in-cylinder mixing process over time by considering charge motion, spray-charge interaction and mixing time, enabling the estimation of the statistical equivalence ratio distribution function. The creation of the final simulation framework is accompanied by an engine measurement program and additional 3D-CFD simulations to evaluate the different sub-models. Due to its physically based structure, the model can take into account changes in thermodynamic boundary conditions and engine actuator settings after an engine-specific calibration at a couple of selected operating points. The final simulation framework is successfully evaluated by test bench measurements of the engine operating map, a variation of engine actuator settings and transient driving profiles. Thereby, the model can be applied in the future engine development in order to further reduce engine-emitted soot particle emissions.
From Research to Practice: Using Assessment and Early Intervention to Improve Student Success in Introductory Statistics

ABSTRACT In this study we used an assessment tool evaluated in a previous study to identify students who were at-risk of not being successful in our introductory statistics course. We then required these students to attend peer tutoring, early in the semester, as an intervention. While we saw a significant increase in student success for all students in this study compared with the previous study, the at-risk students who completed the required tutoring had a significantly higher increase in success than their peers.
Antecedents of Intention to Use E-Learning

This study investigates the effect of Attitude toward e-learning, Facility condition, and Personalization on intention to use e-learning. theory of plan behaviour is used to understand the antecedents of intention to use e-learning. Using academic staffs as a research object and SEM-PLS, we conclude that Attitude toward e-learning and Personalization have a positive relationship with intention to use e-learning. However, Facility condition has a negative effect on intention to use e-learning. This study partially contributes to theory of plan behaviour. This finding also could be used by university to formulate e-learning concept by considering the significant antecedents in this study