Miracle-dz/UniFAR-FTUs huggingface dataset:
Tansley Review No. 112

I. INTRODUCTION 360 II. PHOTOINHIBITION AND ACTIVE OXYGEN 360 III. OXYGEN AS AN ELECTRON ACCEPTOR 362 1. Oxygen‘poises’electron transport and carbon assimilation 362 2. The role of oxygen in ATP synthesis 364 3. How fast is O2reduction at PSI? 364 4. Chloroplastic processing of H2O2 366 IV. REDOX REGULATION OF PHOTOSYNTHETIC METABOLISM 368 1. The thioredoxin system 368 2. Manipulating the expression of thiol-regulated enzymes 369 3. Modifying sensitivity to thiol regulation 369 V. PHOTORESPIRATION 369 1. The pathway and its genetic manipulation 369 2. Engineering plants that photorespire less? 371 3. Is photorespiration important in energy dissipation? 372 4. Production and processing of photorespiratory H2O2 373 5. Catalase and foliar H2O2levels 374 6. Catalase and non-photorespiratory H2O2generation 375 VI. RESPIRATION 376 1. ‘Photosynthetic’respiration 376 2. AOS in the mitochondrion 376 3. AOX: regulation and significance to photosynthesis 377 VII. PHOTOSYNTHESIS AND REDOX SIGNAL TRANSDUCTION 378 1. The need for sensors, signals and transducers 378 2. Signal transduction at the local level 378 3. Remote signalling and responses leading to acclimation of photosynthesis? 379 4. Interactions between AOS, NO., and antioxidants 380 VIII. CONCLUSIONS 380 Acknowledgements 381 References 381 The gradual but huge increase in atmospheric O2 concentration that followed the evolution of oxygenic photosynthesis is one consequence that marks this event as one of the most significant in the earth's history. The high redox potential of the O2/water couple makes it an extremely powerful electron sink that enables energy to be transduced in respiration. In addition to the tetravalent interconversion of O2 and water, there exist a plethora of reactions that involve the partial reduction of O2 or photodynamic energy transfer to produce active oxygen species (AOS). All these redox reactions have become integrated during evolution into the aerobic photosynthetic cell. This review considers photosynthesis as a whole-cell process, in which O2 and AOS are involved in reactions at both photosystems, enzyme regulation in the chloroplast stroma, photorespiration, and mitochondrial electron transport in the light. In addition, oxidants and antioxidants are discussed as metabolic indicators of redox status, acting as sensors and signal molecules leading to acclimatory responses. Our aim throughout is to assess the insights gained from the application of mutagenesis and transformation techniques to studies of the role of O2 and related redox components in the integrated regulation of photosynthesis.
Prognostic Significance of VEGF, VEGF Receptors, and Microvessel Density in Diffuse Large B Cell Lymphoma Treated with Anthracycline-Based Chemotherapy.

Vascular endothelial growth factor (VEGF)-mediated signaling has at least two potential roles in diffuse large B cell lymphoma: potentiation of angiogenesis, and potentiation of proliferation and/or survival induced by autocrine VEGF receptor-mediated signaling by lymphoma cells. We have recently shown that diffuse large B cell lymphomas expressing high levels of VEGF protein also express high levels of VEGF receptors 1 and 2. We have now assessed a larger multi-institutional cohort of patients with de novo diffuse large B cell lymphoma treated with anthracycline-based therapy to address whether either tumor vascularity as assessed by microvessel density counting, or expression of VEGF protein and its receptors as assessed by immunohistochemistry, contribute to patient outcomes. Increased tumor vascularity (Figure 1a, b, c -- increasing microvessel density) is associated with poor overall survival (p=0.047), and is independent of the international prognostic index (Figure 1d). In contrast, high expression of VEGFR1 by lymphoma cells is associated with improved overall survival (p=0.044) and a trend toward improved progression-free survival (p=0.054). High expression of VEGF by lymphoma cells is also associated with a trend toward improved overall survival (p=0.089). The concordant trend toward improved survival with increased lymphoma cell expression of VEGF and its receptor VEGFR1, together with their coordinate overexpression in a subset of diffuse large B cell lymphomas (p=0.00025, chi value 21.5), suggests the presence of an autocrine VEGF-VEGFR1-mediated feedback loop in these lymphomas. Indeed the combination of high VEGF and VEGFR1 protein expression identifies a subgroup of patients with improved overall (p=0.003) and progression-free (p=0.026) survival; these findings are independent of the international prognostic index (Figure 2). The presence of improved survival in a cohort of patients whose lymphomas potentially depend on autocrine signaling via VEGFR1 suggests that dependence on this pathway may render patients susceptible to the effects of anthracycline-based therapy; indeed, downregulation of VEGF expression at the mRNA level by anthracyclines and other chemotherapeutic agents has been demonstrated in a variety of non-hematolymphoid neoplasms. The relative importance of autocrine VEGF-mediated signaling versus vascularity should certainly be taken into account in clinical trials of anti-VEGF/VEGF receptor therapy in diffuse large B cell lymphoma. [Figure][1] Figure ! [Figure][1] Figure [1]: pending:yes
Do AGN really suppress star formation?

Abstract Active galactic nuclei (AGN) are believed to regulate star formation inside their host galaxies through “AGN feedback”. We summarise our on-going study of luminous AGN (z ∼ 0.2−3; LAGN,bol 1043 erg s−1), which is designed to search for observational signatures of feedback by combining observed star-formation rate (SFR) measurements from statistical samples with cosmological model predictions. Using the EAGLE hydrodynamical cosmological simulations, in combination with our Herschel + ALMA surveys, we show that – even in the presence of AGN feedback – we do not necessarily expect to see any relationships between average galaxy-wide SFRs and instantaneous AGN luminosities. We caution that the correlation with stellar mass for both SFR and AGN luminosity can contribute to apparent observed positive trends between these two quantities. On the other hand, the EAGLE simulations, which reproduce our observations, predict that a signature of AGN feedback can be seen in the wide specific SFR distributions of all massive galaxies (not just AGN hosts). Overall, whilst we can not rule out that AGN have an immediate small-scale impact on in-situ star-formation, all of our results are consistent with a feedback model where galaxy-wide in-situ star formation is not rapidly suppressed by AGN, but where the feedback likely acts over a longer timescale than a single AGN episode.
Short communication Effects of suction-dredging for cockles on non-target fauna in the Wadden Sea

Suction dredging for cockles removes large cockles from tidal flats and may also cause mortality of non-target fauna and make the habitat less suitable for some species. This study examines whether suction dredging for cockles on tidal flats of the Dutch Wadden Sea had affected densities of non-target fauna, directly after fishing and one year later. Densities of non-target fauna in two randomly chosen undredged locations were compared to densities at the surrounding heavily commercially dredged area. A significant negative effect of cockle dredging on densities of 0-group Macoma balthica was observed and this effect persisted one year after dredging. The dredged area appeared to be less suitable for settlement of mussels Mytilus edulis. No significant effects of dredging on the mudsnail Hydrobia ulvae and on 0 and 1-group C. edule were found. For the mobile young Macoma balthica it seems unlikely that the effect found after one year was still due to the mortality caused by dredging and this suggests that the habitat was less suitable as a consequence of dredging. Thus, even in the highly dynamic ecosystem of the Wadden Sea, effects of bottom disturbance by cockle dredging may persist after one year. D 2003 Elsevier B.V. All rights reserved.
Variation and Correlation of Morpho-Agronomic Traits and Biochemical Contents (Protein and Ã'-Odap) in Turkish Grass Pea (Lathyrus sativus L.) Landrac

Fifty-one landraces and one released variety ‘Gurbuz-2001’ of grass pea (Lathyrus sativus L.) collected from Turkey and grown in Samsun-Turkey condition were characterized by morpho-agronomic traits, protein and neurotoxic ȕ-ODAP (ȕ-N-oxalyl-L-Į,ȕ-diaminopropionic acid) contents in seed. High variation was observed among landraces regarding all the studied traits. ȕ-ODAP content was determined by using capillary zone electrophoresis (CZE) and extreme levels of ȕ-ODAP were 1.40 and 3.05 mg g-1 in among the landraces (average 1.96 mg g-1). Also the correlations between traits were investigated and, the strongest positive correlations were between main stem number and leaflet length and, between seed yield per plant and pod number per plant. Seed yield significantly (p< 0.01) and positively correlated with most of the investigated traits. Correlations between ȕ-ODAP content and other traits were negative or no significant. In general, many of the landraces were superior than released variety ‘Gurbuz-2001’ with their seed yield, protein content and ȕ-ODAP content
Gravitational wave probes of axionlike particles

We have recently shown that axions and axion-like particles (ALPs) may emit an observable stochastic gravitational wave (GW) background when they begin to oscillate in the early universe. In this note, we identify the regions of ALP parameter space which may be probed by future GW detectors, including ground- and space-based interferometers and pulsar timing arrays. Interestingly, these experiments have the ability to probe axions from the bottom up, i.e. in the very weakly coupled regime which is otherwise unconstrained. Furthermore, we discuss the effects of finite dark photon mass and kinetic mixing on the mechanism, as well as the (in)sensitivity to couplings of the axion to Standard Model fields. We conclude that realistic axion and ALP scenarios may indeed be probed by GW experiments in the future, and provide signal templates for further studies.
IRS-Enhanced OFDMA: Joint Resource Allocation and Passive Beamforming Optimization

Intelligent reflecting surface (IRS) is an emerging technique to enhance the wireless communication spectral efficiency with low hardware and energy cost. In this letter, we consider the integration of IRS to an orthogonal frequency division multiple access (OFDMA) based multiuser downlink communication system, and study the pertinent joint optimization of the IRS reflection coefficients and OFDMA time-frequency resource block as well as power allocations to maximize the users’ common (minimum) rate. Specifically, due to the lack of frequency-selective passive beamforming capability at the IRS, only one set of reflection coefficients can be designed for adapting to a large number of channels of multiple users over different frequency sub-bands. To tackle this difficulty, we propose a novel dynamic passive beamforming scheme where the IRS reflection coefficients are dynamically adjusted over different time slots within each channel coherence block to create artificial time-varying channels and select only a subset of the users to be simultaneously served in each time slot, thus achieving a higher passive beamforming gain. Although the formulated optimization problem is non-convex, we propose an efficient algorithm to obtain a high-quality suboptimal solution to it. Numerical results show that the proposed scheme significantly improves the system common rate over the setup without IRS and that with random IRS reflection coefficients. Moreover, our proposed dynamic passive beamforming outperforms the fixed passive beamforming which employs a common set of reflection coefficients in each channel coherence block, by more flexibly balancing between passive beamforming and multiuser diversity gains.
Second-order Lovelock gravity from entanglement in conformal field theories

Holographic entanglement entropy and the first law of thermodynamics are believed to decode the gravity theory in the bulk. In particular, assuming the Ryu-Takayanagi (RT)\cite{ryu-takayanagi} formula holds for ball-shaped regions on the boundary around CFT vacuum states implies\cite{Nonlinear-Faulkner} a bulk gravity theory equivalent to Einstein gravity through second-order perturbations. In this paper, we show that the same assumptions can also give rise to second-order Lovelock gravity. Specifically, we generalize the procedure in \cite{Nonlinear-Faulkner} to show that the arguments there also hold for Lovelock gravity by proving through second-order perturbation theory, the entropy calculated using the Wald formula\cite{Wald_noether} in Lovelock also obeys an area law (at least up to second order). Since the equations for second-order perturbations of Lovelock gravity are different in general from the second-order perturbation of the Einstein-Hilbert action, our work shows that the holographic area law cannot determine a unique bulk theory even for second-order perturbations assuming only RT on ball-shaped regions. It is anticipated that RT on all subregions is expected to encode the full non-linear Einstein equations on asymptotically AdS spacetimes.
AVIATOR: Morphological object reconstruction in 3D

Reconstructing 3D distributions from their 2D projections is a ubiquitous problem in various scientific fields, particularly so in observational astronomy. In this work, we present a new approach to solving this problem: a Vienna inverse-Abel-transform based object reconstruction algorithm AVIATOR. The reconstruction that it performs is based on the assumption that the distribution along the line of sight is similar to the distribution in the plane of projection, which requires a morphological analysis of the structures in the projected image. The output of the AVIATOR algorithm is an estimate of the 3D distribution in the form of a reconstruction volume that is calculated without the problematic requirements that commonly occur in other reconstruction methods such as symmetry in the plane of projection or modelling of radial profiles. We demonstrate the robustness of the technique to different geometries, density profiles, and noise by applying the AVIATOR algorithm to several model objects. In addition, the algorithm is applied to real data: We reconstruct the density and temperature distributions of two dense molecular cloud cores and find that they are in excellent agreement with profiles reported in the literature. The AVIATOR algorithm is thus capable of reconstructing 3D distributions of physical quantities consistently using an intuitive set of assumptions.
Semi-Supervised Learning of Bearing Anomaly Detection via Deep Variational Autoencoders

Most of the data-driven approaches applied to bearing fault diagnosis up to date are established in the supervised learning paradigm, which usually requires a large set of labeled data collected a priori. In practical applications, however, obtaining accurate labels based on real-time bearing conditions can be far more challenging than simply collecting a huge amount of unlabeled data using various sensors. In this paper, we thus propose a semi-supervised learning approach for bearing anomaly detection using variational autoencoder (VAE) based deep generative models, which allows for effective utilization of dataset when only a small subset of data have labels. Finally, a series of experiments is performed using both the Case Western Reserve University (CWRU) bearing dataset and the University of Cincinnati's Center for Intelligent Maintenance Systems (IMS) dataset. The experimental results demonstrate that the proposed semi-supervised learning scheme greatly outperforms two mainstream semi-supervised learning approaches and a baseline supervised convolutional neural network approach, with the overall accuracy improvement ranging between 3% to 30% using different proportions of labeled samples.
Learning Adversarial MDPs with Bandit Feedback and Unknown Transition

A.1. Updating Occupancy Measure This subsection explains how to implement the update defined in Eq. (7) efficiently. We use almost the same approach as in (Rosenberg & Mansour, 2019a) with the only difference being the choice of confidence set. We provide details of the modification here for completeness. It has been shown in (Rosenberg & Mansour, 2019a) that Eq. (7) can be decomposed into two steps: (1) compute q̃t+1(x, a, x′) = q̂t(x, a, x′) exp{−η̂̀t(x, a)} for any (x, a, x′), which is the optimal solution of the unconstrained problem; (2) compute the projection step: q̂t+1 = argmin q∈∆(Pi) D(q ‖ q̃t+1), (11)
EDAS: Efficient and Differentiable Architecture Search

Transferrable neural architecture search can be viewed as a binary optimization problem where a single optimal path should be selected among candidate paths in each edge within the repeated cell block of the directed a cyclic graph form. Recently, the field of differentiable architecture search attempts to relax the search problem continuously using a one-shot network that combines all the candidate paths in search space. However, when the one-shot network is pruned to the model in the discrete architecture space by the derivation algorithm, performance is significantly degraded to an almost random estimator. To reduce the quantization error from the heavy use of relaxation, we only sample a single edge to relax the corresponding variable and clamp variables in the other edges to zero or one. By this method, there is no performance drop after pruning the one-shot network by derivation algorithm, due to the preservation of the discrete nature of optimization variables during the search. Furthermore, the minimization of relaxation degree allows searching in a deeper network to discover better performance with remarkable search cost reduction (0.125 GPU days) compared to previous methods. By adding several regularization methods that help explore within the search space, we could obtain the network with notable performances on CIFAR-10, CIFAR-100, and ImageNet.
Cross-lingual Pre-training Based Transfer for Zero-shot Neural Machine Translation

Transfer learning between different language pairs has shown its effectiveness for Neural Machine Translation (NMT) in low-resource scenario. However, existing transfer methods involving a common target language are far from success in the extreme scenario of zero-shot translation, due to the language space mismatch problem between transferor (the parent model) and transferee (the child model) on the source side. To address this challenge, we propose an effective transfer learning approach based on cross-lingual pre-training. Our key idea is to make all source languages share the same feature space and thus enable a smooth transition for zero-shot translation. To this end, we introduce one monolingual pre-training method and two bilingual pre-training methods to obtain a universal encoder for different languages. Once the universal encoder is constructed, the parent model built on such encoder is trained with large-scale annotated data and then directly applied in zero-shot translation scenario. Experiments on two public datasets show that our approach significantly outperforms strong pivot-based baseline and various multilingual NMT approaches.
Recovering from Biased Data: Can Fairness Constraints Improve Accuracy?

Multiple fairness constraints have been proposed in the literature, motivated by a range of concerns about how demographic groups might be treated unfairly by machine learning classifiers. In this work we consider a different motivation; learning from biased training data. We posit several ways in which training data may be biased, including having a more noisy or negatively biased labeling process on members of a disadvantaged group, or a decreased prevalence of positive or negative examples from the disadvantaged group, or both. Given such biased training data, Empirical Risk Minimization (ERM) may produce a classifier that not only is biased but also has suboptimal accuracy on the true data distribution. We examine the ability of fairness-constrained ERM to correct this problem. In particular, we find that the Equal Opportunity fairness constraint (Hardt, Price, and Srebro 2016) combined with ERM will provably recover the Bayes Optimal Classifier under a range of bias models. We also consider other recovery methods including reweighting the training data, Equalized Odds, and Demographic Parity. These theoretical results provide additional motivation for considering fairness interventions even if an actor cares primarily about accuracy.
See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Data

Mental disorders such as depression and anxiety have been increasing at alarming rates in the worldwide population. Notably, the major depressive disorder has become a common problem among higher education students, aggravated, and maybe even occasioned, by the academic pressures they must face. While the reasons for this alarming situation remain unclear (although widely investigated), the student already facing this problem must receive treatment. To that, it is first necessary to screen the symptoms. The traditional way for that is relying on clinical consultations or answering questionnaires. However, nowadays, the data shared at social media is a ubiquitous source that can be used to detect the depression symptoms even when the student is not able to afford or search for professional care. Previous works have already relied on social media data to detect depression on the general population, usually focusing on either posted images or texts or relying on metadata. In this work, we focus on detecting the severity of the depression symptoms in higher education students, by comparing deep learning to feature engineering models induced from both the pictures and their captions posted on Instagram. The experimental results show that students presenting a BDI score higher or equal than 20 can be detected with 0.92 of recall and 0.69 of precision in the best case, reached by a fusion model. Our findings show the potential of large-scale depression screening, which could shed light upon students at-risk.
Learning to Super Resolve Intensity Images From Events

An event camera detects per-pixel intensity difference and produces asynchronous event stream with low latency, high dynamic range, and low power consumption. As a trade-off, the event camera has low spatial resolution. We propose an end-to-end network to reconstruct high resolution, high dynamic range (HDR) images directly from the event stream. We evaluate our algorithm on both simulated and real-world sequences and verify that it captures fine details of a scene and outperforms the combination of the state-of-the-art event to image algorithms with the state-of-the-art super resolution schemes in many quantitative measures by large margins. We further extend our method by using the active sensor pixel (APS) frames or reconstructing images iteratively.