Current opportunities and challenges in developing hydro-climatic services in the Himalayas: report of pump priming project November 2019

The India-UK Water Centre (IUKWC) promotes cooperation and collaboration between the complementary priorities of NERC-MoES water security research. This report assesses the significant issues for hydro-climatic modelling and service development in the mountain regions of northern India. It is the main output from an IUKWC Pump Priming Project that ran from March to August 2018 and has been produced by an author team of climate scientist, hydrologists and glaciologist from India and the UK. It is found that although state-ofthe-art weather forecasting, climate, hydrological and glacier models are being used there are still substantial prediction uncertainties on all prediction timescales. There is a lack of detailed understanding of regional meteorological and hydrological processes, which results in potential misrepresentation of them in the models. Large-scale drivers of regional climate variability in the region have been identified but questions remain about their relevance on different timescales, their interaction, and their representation in global weather forecasting and climate models. Improving short-term predictions and climate change projections requires more meteorological, hydrological and glaciological observations in the Himalayas, improvements in data sharing, as well as additional efforts to integrate meteorological and hydrological modelling. There is also a need for improved communication of predictions to users, which should include their uncertainties. The report is intended for workshop participants, India-UK Water Centre Open Network members and stakeholders

fMRI evidence of ‘mirror’ responses to geometric shapes

Mirror neurons may be a genetic adaptation for social interaction [1]. Alternatively, the associative hypothesis [2], [3] proposes that the development of mirror neurons is driven by sensorimotor learning, and that, given suitable experience, mirror neurons will respond to any stimulus. This hypothesis was tested using fMRI adaptation to index populations of cells with mirror properties. After sensorimotor training, where geometric shapes were paired with hand actions, BOLD response was measured while human participants experienced runs of events in which shape observation alternated with action execution or observation. Adaptation from shapes to action execution, and critically, observation, occurred in ventral premotor cortex (PMv) and inferior parietal lobule (IPL). Adaptation from shapes to execution indicates that neuronal populations responding to the shapes had motor properties, while adaptation to observation demonstrates that these populations had mirror properties. These results indicate that sensorimotor training induced populations of cells with mirror properties in PMv and IPL to respond to the observation of arbitrary shapes. They suggest that the mirror system has not been shaped by evolution to respond in a mirror fashion to biological actions; instead, its development is mediated by stimulus-general processes of learning within a system adapted for visuomotor control

All peak coordinates for the main effect of adaptation in Session 2, at <i>p</i><0.001 uncorrected, with a cluster extent of four voxels.

*<p>Indicates significant at <i>p</i><0.05 FWE corrected for search volume. BA, Brodmann Area.</p

All peak coordinates for the main effect of adaptation in Session 1, at <i>p</i><0.001 uncorrected, with a cluster extent of four voxels.

*<p>Indicates significant at <i>p</i><0.05 FWE corrected for search volume. BA, Brodmann Area.</p

Schematic representation of the fMRI adaptation logic.

<p>Note that although reference is made to mirror <i>neurons</i>, fMRI data is driven by populations of neurons. Purple ovals denote populations of sensory neurons encoding visual properties of stimuli; blue ovals denote populations of motor neurons responsible for action execution. a) Before training, motor neurons are activated by observation of actions (top) but not by observation of shapes (bottom). These cells are therefore mirror neurons b) Training where participants respond to each arbitrary geometric shape with a distinctive action establishes novel excitatory links (broken arrow) between neurons encoding sensory properties of each shapes and motor neurons encoding the trained action. c) Session 1. Adaptation from shape observation to action execution (signified by paler flash on right), and vice versa, shows that, as a result of training, the shapes activate neuronal populations with motor properties. d) Session 2. Adaptation from shape observation to action observation, and vice versa, shows that shape and action observation activates common neuronal populations; i.e. cells with <i>mirror</i> properties. Session 2 adaptation would not have occurred if experimental training had linked visual neurons with i) purely motor neurons, ii) canonical neurons, or iii) logically related mirror neurons. The training must have linked neurons encoding the sensory properties of the geometric shapes with neurons that were already encoding both sensory and motor properties of action, i.e. congruent mirror neurons.</p

Statistical parametric maps (SPM) of the main effects of adaptation in Session 1.

<p>The SPM is thresholded for display at <i>p</i><0.001 uncorrected with a cluster extent of 4 voxels. Results are rendered upon the smoothed average brain provided in SPM8.</p

Schematic illustration of the experimental design.

<p>a) An example set of trained shape-response mappings. The relationship between shape and action type was held constant for a given participant throughout training, but was varied between participants. b) Illustration of a sequence of trials in Session 1. Observation of shapes alternated with execution of actions. A trial was ‘trained’ if preceded by an event type with which it had been paired during training, and ‘untrained’ if preceded by a different event type. c) Illustration of a sequence of trials in Session 2. In this session, observation of shapes alternated with observation of actions, and ‘trained’ trials were those in which, if training induced <i>mirror</i> representations to respond to arbitrary shapes, both the preceding and current events should activate the same motor representation. See also <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0051934#pone-0051934-t001" target="_blank">Table 1</a>.</p

An example of 16 trials in a block, categorised according to their Transition Type and Trial Type with respect to the previous trial.

<p>These categorisations represent a participant who had been trained with hexagon-splay fingers and triangle-make fist mappings.</p

The Time Machine framework: monitoring and prediction of biodiversity loss

Transdisciplinary solutions are needed to achieve the sustainability of ecosystem services for future generations. We propose a framework to identify the causes of ecosystem function loss and to forecast the future of ecosystem services under different climate and pollution scenarios. The framework (i) applies an artificial intelligence (AI) time-series analysis to identify relationships among environmental change, biodiversity dynamics and ecosystem functions; (ii) validates relationships between loss of biodiversity and environmental change in fabricated ecosystems; and (iii) forecasts the likely future of ecosystem services and their socioeconomic impact under different pollution and climate scenarios. We illustrate the framework by applying it to watersheds, and provide system-level approaches that enable natural capital restoration by associating multidecadal biodiversity changes to chemical pollution.</p

Toward generalizable prediction of antibody thermostability using machine learning on sequence and structure features

ABSTRACTOver the last three decades, the appeal for monoclonal antibodies (mAbs) as therapeutics has been steadily increasing as evident with FDA’s recent landmark approval of the 100th mAb. Unlike mAbs that bind to single targets, multispecific biologics (msAbs) have garnered particular interest owing to the advantage of engaging distinct targets. One important modular component of msAbs is the single-chain variable fragment (scFv). Despite the exquisite specificity and affinity of these scFv modules, their relatively poor thermostability often hampers their development as a potential therapeutic drug. In recent years, engineering antibody sequences to enhance their stability by mutations has gained considerable momentum. As experimental methods for antibody engineering are time-intensive, laborious and expensive, computational methods serve as a fast and inexpensive alternative to conventional routes. In this work, we show two machine learning approaches – one with pre-trained language models (PTLM) capturing functional effects of sequence variation, and second, a supervised convolutional neural network (CNN) trained with Rosetta energetic features – to better classify thermostable scFv variants from sequence. Both of these models are trained over temperature-specific data (TS50 measurements) derived from multiple libraries of scFv sequences. On out-of-distribution (refers to the fact that the out-of-distribution sequnes are blind to the algorithm) sequences, we show that a sufficiently simple CNN model performs better than general pre-trained language models trained on diverse protein sequences (average Spearman correlation coefficient, [Formula: see text], of 0.4 as opposed to 0.15). On the other hand, an antibody-specific language model performs comparatively better than the CNN model on the same task ([Formula: see text] 0.52). Further, we demonstrate that for an independent mAb with available thermal melting temperatures for 20 experimentally characterized thermostable mutations, these models trained on TS50 data could identify 18 residue positions and 5 identical amino-acid mutations showing remarkable generalizability. Our results suggest that such models can be broadly applicable for improving the biological characteristics of antibodies. Further, transferring such models for alternative physicochemical properties of scFvs can have potential applications in optimizing large-scale production and delivery of mAbs or bsAbs