Miracle-dz/UniFAR-FTUs huggingface dataset:
Moving beyond MARCO

The use of imaging systems in protein crystallisation means that the experimental setups no longer require manual inspection to determine the outcome of the trials. However, it leads to the problem of how best to find images which contain useful information about the crystallisation experiments. The adoption of a deeplearning approach in 2018 enabled a four-class machine classification system of the images to exceed human accuracy for the first time. Underpinning this was the creation of a labelled training set which came from a consortium of several different laboratories. The MARCO classification model does not have the same accuracy on local data as it does on images from the original test set; this can be somewhat mitigated by retraining the ML model and including local images. We have characterized the image data used in the original MARCO model, and performed extensive experiments to identify training settings most likely to enhance the local performance of a MARCO-dataset based ML classification model.
Controlling periodic long-range signalling to drive a morphogenetic transition

Cells use signal relay to transmit information across tissue scales. However, the production of information carried by signal relay remains poorly characterised. To determine how the coding features of signal relay are generated, we used the classic system for long-range signalling: the periodic cAMP waves that drive Dictyostelium collective migration. Combining imaging and optogenetic perturbation of cell signalling states, we find that migration is triggered by an increase in wave frequency generated at the signalling centre. Wave frequency is regulated by cAMP wave circulation, which organises the long-range signal. To determine the mechanisms modulating wave circulation, we combined mathematical modelling, the general theory of excitable media and mechanical perturbations to test competing models. Models in which cell density and spatial patterning modulate the wave frequency cannot explain the temporal evolution of signalling waves. Instead, our evidence leads to a model where wave circulation increases the ability for cell to relay the signal, causing further increase in the circulation rate. This implies that information driving a morphogenetic transition gradually unfolds due to positive feedback between cell signalling state and signalling pattern. This demonstrates how the coding properties of a long-range signal arise from self-organised wave dynamics at the signalling centre.
A moonlighting function of Atg11 in spindle positioning

Autophagy is an evolutionarily conserved degradation mechanism of misfolded proteins and damaged organelles which helps maintain cellular homeostasis. Besides its canonical function, proteins of the autophagy pathway also participate in other cellular processes including chromosome congression. Here, we perform a comprehensive screen of autophagy mutants in yeast and found that cells lacking an autophagy-related protein Atg11 have poor mitotic stability of linear and circular chromosomes. atg11Δ cells grow slowly at 37°C and accumulate at metaphase with aberrant astral microtubules (aMTs) along with mispositioned and misaligned spindles. Deletion of the spindle positioning checkpoint (SPOC) factor Bub2, but not the spindle assembly checkpoint (SAC) component Mad2, could improve the growth of the atg11Δ cells. Strikingly, the spindle pole body (SPB) localization pattern of an SPB component Spc72 is found to be randomized in atg11Δ cells resulting in inverted SPB inheritance. Atg11 is transiently localized to SPBs and interacts both physically and genetically with Spc72. Taken together, our study uncovers a moonlighting function of Atg11 participating between spatially separated processes, the cell cycle and autophagy, essential for proper spindle positioning and asymmetric SPB inheritance. Our findings, therefore, highlight one of the many failsafe mechanisms likely in place to ensure asymmetric cell division.
Mapping of the bs5 and bs6 non-race-specific recessive resistances against bacterial spot of pepper

Bacterial spot caused by Xanthomonas euvesicatoria is a major disease of pepper (Capsicum annuum L.) in warm and humid production environments. Use of genetically resistant cultivars is an effective approach to manage bacterial spot. Two recessive resistance genes, bs5 and bs6, confer non-race-specific resistance against bacterial spot. The objective of our study was to map these two loci in the pepper genome. We used a genotyping-by-sequencing approach to initially map the position of the two resistances. Segregant populations for bs5 and bs6 were developed by crossing susceptible Early CalWonder (ECW) with near-isogenic lines ECW50R (bs5 introgression) or ECW60R (bs6 introgression). Following fine-mapping, bs5 was delimited to a ~535 Kbp interval on chromosome 3, and bs6 to a ~666 Kbp interval in chromosome 6 of pepper. We also identified 14 and 8 candidate resistance genes for bs5 and bs6, respectively, based on predicted protein coding polymorphisms between ECW and the corresponding resistant parent. Mapping of bs5 and bs6 will facilitate their use in breeding programs through marker-assisted selection and is also a crucial step towards understanding the mechanisms of resistance. Key message Two recessive bacterial spot resistance genes were mapped in the pepper genome, which will facilitate their advancement in commercial pepper for management of all races of Xanthomonas euvesicatoria.
Gene panel design for spatial transcriptomics with prioritized gene sets

A fundamental limitation of the emerging single-cell spatial transcriptomics (sc-ST) technologies is their panel size. Being based on fluorescence in situ hybridization, an sc-ST dataset can profile only a pre-determined panel of a few hundred genes. This often forces biologists to build panels from only the marker genes of different cell types and forgo other genes of interest, e.g., genes encoding ligand-receptor complexes or genes in specific pathways. We propose scGIST– a deep neural network that designs sc-ST panels through constrained feature selection. On four datasets, scGIST outperformed alternative methods in terms of cell type detection accuracy. Moreover, unlike other methods, scGIST allows genes of interest to be prioritized for inclusion in the panel while staying within the its size constraint. We demonstrate through diverse use cases that scGIST includes large fractions of prioritized genes without compromising cell type prediction efficacy making it a valuable addition to sc-ST’s algorithmic toolbox.
Concurrent measurement of perfusion parameters related to small blood and lymphatic vessels in the human brain using dynamic dual-spin-echo perfusion (DDSEP) MRI

PURPOSE Accumulating evidence from recent studies has indicated the importance of studying the interaction between the microvascular and lymphatic systems in the brain. To date, most imaging methods can only measure blood or lymphatic vessels separately, such as dynamic-susceptibility-contrast (DSC) MRI for blood vessels and DSC MRI in the CSF (cDSC MRI) for lymphatic vessels. An approach that can measure both blood and lymphatic vessels in a single scan will offer the advantages such as halved scan time and contrast dosage. Based on previous works on DSC and cDSC MRI, this study proposes an MRI approach for concurrent measurement of perfusion parameters related to small blood and lymphatic vessels in the brain within one single scan. METHODS Bloch simulations were performed to optimize a dual-echo sequence for the measurement of gadolinium(Gd)-induced blood and CSF signal changes using a short and a long echo time, respectively. MRI experiments were performed in healthy subjects to evaluate the dual-echo approach by comparing it with existing separate methods. RESULTS The proposed method showed consistent results in human brains as previous studies using separate methods. Signal changes from small blood vessels occurred faster than lymphatic vessels after intravenous Gd-injection. CONCLUSION Gd-induced signal changes in blood and CSF can be detected simultaneously in healthy subjects with the proposed sequence. To the best of our knowledge, this may be the first study in which the temporal difference in Gd-induced signal changes from small blood and lymphatic vessels after intravenous Gd-injection was measured in the same human subjects.
A genome-wide CRISPR screen implicates plasma membrane asymmetry in exogenous C6-ceramide toxicity

The bioactive sphingolipid ceramide impacts diverse cellular processes (e.g., apoptosis and cell proliferation) through its effects on membrane dynamics and intracellular signalling pathways. The dysregulation of ceramide metabolism has been implicated in cancer evasion of apoptosis and targeting ceramide metabolism has potential therapeutic benefits as a strategy to kill cancer cells and slow tumor growth. However, the mechanisms of cancer cell resistance to ceramide-mediated cell death are vastly intertwined and incompletely understood. To shed light on this mystery, we performed a genome wide CRISPR-Cas9 screen to systematically identify regulators of cancer resistance to the soluble short chain ceramide, C6 ceramide (C6-Cer). Our results reveal a complex landscape of genetic modifiers of C6-Cer toxicity, including genes associated with ceramide and sphingolipid metabolism, vesicular trafficking, and membrane biology. Furthermore, we find that loss of the phospholipid flippase subunit TMEM30A impairs the plasma membrane trafficking of its binding partner the P4-type ATPase ATP11B, and depletion of TMEM30A or ATP11B disrupts plasma membrane asymmetry and promotes resistance to C6-Cer toxicity independent of alterations in C6-Cer uptake. Together, our findings provide a resource of genetic modifiers of C6-Cer toxicity and reveal an unexpected role of plasma membrane asymmetry in C6-Cer induced cell death.
Distinct replay signatures for prospective decision-making and memory preservation

Theories of neural replay propose that it supports a range of functions, most prominently planning and memory consolidation. Here, we test the hypothesis that distinct signatures of replay in the same task are related to model-based decisionmaking (‘planning’) and memory preservation. We designed a reward learning task wherein participants utilized structure knowledge for model-based evaluation, while at the same time had to maintain knowledge of two independent and randomly alternating task environments. Using magnetoencephalography (MEG) and multivariate analysis, we first identified temporally compressed sequential reactivation, or replay, both prior to choice and following reward feedback. Before choice, prospective replay strength was enhanced for the current task-relevant environment when a model-based planning strategy was beneficial. Following reward receipt, and consistent with a memory preservation role, replay for the alternative distal task environment was enhanced as a function of decreasing recency of experience with that environment. Critically, these planning and memory preservation relationships were selective to pre-choice and post-feedback periods. Our results provide new support for key theoretical proposals regarding the functional role of replay and demonstrate that the relative strength of planning and memory-related signals are modulated by on-going computational and task demands. Significance statement The sequential neural reactivation of prior experience, known as replay, is considered to be an important mechanism for both future planning and preserving memories of the past. Whether, and how, replay supports both of these functions remains unknown. Here, in humans, we found that prior to a choice, rapid replay of potential future paths was enhanced when planning was more beneficial. By contrast, after choice feedback, when no future actions are imminent, we found evidence for a memory preservation signal evident in enhanced replay of paths that had been visited less in the recent past. The results demonstrate that distinct replay signatures, expressed at different times, relate to two dissociable cognitive functions.
Road to MOND: A novel perspective

Accepting that galactic mass discrepancies are due to modified dynamics, I show why it is specifically the MOND paradigm that is pointed to cogently. MOND is thus discussed here as a special case of a larger class of modified dynamics theories whereby galactic systems with large mass discrepancies are described by scale-invariant dynamics. This is a novel presentation that uses more recent, after-the-fact insights and data (largely predicted beforehand by MOND). Starting from a purist set of tenets, I follow the path that leads specifically to the MOND basic tenets. The main signposts are: (i) Space-time scale invariance underlies the dynamics of systems with large mass discrepancies. (ii) In these dynamics, $G$ must be replaced by a single "scale-invariant" gravitational constant, Q0 (in MOND, Q0=A0=Ga0, where a0 is MOND's acceleration constant). (iii) Universality of free fall points to the constant q0=Q0/G as the boundary between the G-controlled, standard dynamics, and the Q0-controlled, scale-invariant dynamics (in MOND, q0=a0). (iv) Data clinches the case for q0 being an acceleration (MOND).
Assessing Turbulence Strength via Lyaponuv Exponents

In this paper we study the link between `turbulence strength' in a flow and the leading Lyaponuv exponent that characterize it. To this end we use two approaches. The first, analytical, considers the truncated convection equations in 2-dimensions with three (Lorenz model) and six components and study their leading Lyaponuv exponent as a function of the Rayleigh number. For the second approach we analyze fifteen time series of measurements taken by a plane flying at constant height in the upper troposphere. For each of these time series we estimate the leading Lyaponuv exponent which we then correlate with the structure constant for the temperature.
Learning the sound inventory of a complex vocal skill via an intrinsic reward

Reinforcement learning (RL) is thought to underlie the acquisition of vocal skills like birdsong and speech, where sounding like one’s “tutor” is rewarding. Yet, we find that the standard actor-critic RL model of birdsong learning fails to account for juvenile zebra finches’ efficient learning of an inventory of multiple syllables. But when we replace a single actor with multiple independent actors that jointly maximize a common intrinsic reward, then birds’ empirical learning trajectories are accurately reproduced. Importantly, the influence of each actor (syllable) on the magnitude of global reward is competitively determined by its acoustic similarity to target syllables. This leads to each actor matching the target it is closest to, and occasionally, to the competitive exclusion of an actor from the learning process (i.e., the learned song). We propose that a competitive-cooperative multi-actor (MARL) algorithm is key for the efficient learning of the action inventory of a complex skill.
Not getting in too deep: A practical deep learning approach to routine crystallisation image classification

Using a relatively small training set of ∼16 thousand images from macromolecular crystallisation experiments, we compare classification results obtained with four of the most widely-used convolutional deep-learning network architectures that can be implemented without the need for extensive computational resources. We show that the classifiers have different strengths that can be combined to provide an ensemble classifier achieving a classification accuracy comparable to that obtained by a large consortium initiative. We use eight classes to effectively rank the experimental outcomes, thereby providing detailed information that can be used with routine crystallography experiments to automatically identify crystal formation for drug discovery and pave the way for further exploration of the relationship between crystal formation and crystallisation conditions. Author summary X-ray crystallography can provide structural information on the molecules that play important roles in cell function and allow interactions vital for drug design to be elucidated. However, the technique requires the molecules to be crystallised and obtaining sufficiently high-quality crystals can require hundreds of experiments under different conditions. Robots have been designed to perform the microscopic experiments with imaging systems to record the results over time but automated classification of all these images is becoming essential. In this work we compare different classifiers that can be used without the need for vast computational resources and show that an ensemble classifier which combines the different strengths of four deep-learning networks is able to correctly classify the experimental results into one of eight different categories 94% of the time.
TRAF6 modulates PD-L1 expression through YAP1-TFCP2 signaling in melanoma

Background Immunotherapy represented by the programmed death-1 (PD-1)/ligand 1 (PD-L1) monoclonal antibodies has led tumor treatment into a new era. However, the low overall response rate and high incidence of drug resistance largely damage the clinical benefits of existing immune checkpoint therapies. Recent studies correlate the response to PD-1/PD-L1 blockade with PD-L1 expression levels in tumor cells. Hence, identifying molecular targets and pathways controlling PD-L1 protein expression and stability in tumor cells is a major priority. Methods In this study, we first performed a Stress and Proteostasis CRISPR interference library-based screening to identify PD-L1 positive modulators. We then used in vitro and in vivo assays to investigate the biological function and mechanism of TRAF6 and its downstream YAP1/TFCP2 signaling in malignant melanoma. Results Here, we identified TRAF6 as a critical regulator of PD-L1 in melanoma cells. Suppression of TRAF6 expression down-regulates PD-L1 expression on the membrane surface of melanoma cells. We also found that PD-L1 protein abundance is regulated by YAP1/TFCP2 transcriptional complex. TRAF6 stabilizes YAP1 by K63 poly-ubiquitination modification, subsequently promoting the formation of YAP1/TFCP2 and PD-L1 transcription. Furthermore, inhibition of TRAF6 by Bortezomib enhanced cytolytic activity of CD8+ T cells by reduction of endogenous PD-L1. Notably, Bortezomib enhances anti-tumor immunity to an extent that is comparable to anti-PD-1 mAb therapies with no obvious toxicity. Conclusions These findings uncover a novel molecular mechanism for regulating PD-L1 protein abundance by a E3 ligase in cancer cells and reveal the potential of using TRAF6 inhibitors to stimulate internal anti-tumor immunological effect for TRAF6-PD-L1 overexpressing cancers.
Simple Neurofeedback via Machine Learning: Challenges in real time multivariate assessment of meditation state

Attaining proficiency in meditation is difficult, especially without feedback since the mind may be easily distracted with thoughts and only long term efforts see any impact. Self-regulation would be much more effective if provided real time assessment and this can be achieved through EEG neurofeedback. Therefore, this work proposes a scheme for assessing meditation-like state in real time from short EEG segments, using low computational settings. Signal processing techniques are used to extract features from long term meditation practitioners’ multichannel EEG data. An autoencoder model is then trained on these features such that the model can be run in real time. Its reconstruction errors or its latent variables are used to provide non typical feedback parameters which are used to establish an objective measure of meditation ability. Our approach is optimised to have lightweight architectures handling small blocks of data and can be conveniently used on low density EEG acquisition systems as it requires only a few channels. However, our experimental results suggest that the meditation state has substantial overlap even in terms of multivariate EEG features and show prominent temporal dynamics, both of which are not captured using simple one class algorithms. Being an extremely flexible one-class model, we have described multiple improvements to the proposed autoencoder model to address the above issues and develop simple yet high precision neurofeedback protocols.
IgG3 subclass antibodies recognize antigenically drifted influenza viruses and SARS-CoV-2 variants through efficient bivalent binding

The constant domains of antibodies are important for effector functions, but less is known about how they can affect binding and neutralization of viruses. Here we evaluated a panel of human influenza virus monoclonal antibodies (mAbs) expressed as IgG1, IgG2 or IgG3. We found that many influenza virus-specific mAbs have altered binding and neutralization capacity depending on the IgG subclass encoded, and that these differences result from unique bivalency capacities of the subclasses. Importantly, subclass differences in antibody binding and neutralization were greatest when the affinity for the target antigen was reduced through antigenic mismatch. We found that antibodies expressed as IgG3 bound and neutralized antigenically drifted influenza viruses more effectively. We obtained similar results using a panel of SARS-CoV-2-specific mAbs and the antigenically advanced B.1. 351 strain of SARS-CoV-2. We found that a licensed therapeutic mAb retained neutralization breadth against SARS-CoV-2 variants when expressed as IgG3, but not IgG1. These data highlight that IgG subclasses are not only important for fine-tuning effector functionality, but also for binding and neutralization of antigenically drifted viruses. Significance Influenza viruses and coronaviruses undergo continuous change, successfully evading human antibodies elicited from prior infections or vaccinations. It is important to identify features that allow antibodies to bind with increased breadth. Here we examined the effect that different IgG subclasses have on monoclonal antibody binding and neutralization. We show that IgG subclass is a determinant of antibody breadth, with IgG3 affording increased neutralization of antigenically drifted variants of influenza virus and SARS-CoV-2. Future studies should evaluate IgG3 therapeutic antibodies and vaccination strategies or adjuvants that may skew antibody responses toward broadly reactive isotypes.
Cones and cone pathways remain functional in advanced retinal degeneration

Most defects causing retinal degeneration in retinitis pigmentosa (RP) are rod-specific mutations, but the subsequent degeneration of cones, which produces loss of daylight vision and high-acuity perception, is the most debilitating feature of the disease. To understand better why cones degenerate and how cone vision might be restored, we have made the first single-cell recordings of light responses from degenerating cones and retinal interneurons after most rods have died and cones have lost their outer-segment disk membranes and synaptic pedicles. We show that degenerating cones have functional cyclic-nucleotide-gated channels and can continue to give light responses, apparently produced by opsin localized either to small areas of organized membrane near the ciliary axoneme or distributed throughout the inner segment. Light responses of second-order horizontal and bipolar cells are smaller and less sensitive but surprisingly similar to those of normal retina. Furthermore, retinal output as reflected in responses of ganglion cells is largely intact at cone-mediated light levels. Together, these findings show that cones and their retinal pathways can remain functional even as degeneration is progressing, an encouraging result for future research aimed at enhancing the light sensitivity of residual cones to restore vision in patients with genetically inherited retinal degeneration.