Rate Of Discovery Research Articles

Recognisable Neuroradiological Findings in Five Neurogenetic Disorders.

The rate of discovery and increased understanding of genetic causes for neurodevelopmental disorders has peaked over the past decade. It is well recognised that some genes show marked variability in neuroradiological phenotypes, and inversely, some radiological phenotypes are associated with several different genetic conditions. However, some readily recognisable brain magnetic resonance imaging (MRI) patterns, especially in the context of corresponding associated clinical findings, should prompt consideration of a pathogenic variant in a specific gene or gene pathway. As these conditions can often prove challenging to diagnose, a clinical suspicion of a specific disorder may be invaluable to guide and interpret genetic testing. This review focuses on five neurogenetic syndromes with recognisable brain findings that radiologists, paediatric neurologists, geneticists, and other specialists involved in neurodevelopmental disorders should be able to recognise in order to pinpoint the gene or gene groups involved and delve into their molecular mechanisms. The comprehensively reviewed conditions include DDX3X-related neurodevelopmental disorder, Van Maldergem syndrome, NMDAR-related disorders, EML1-associated disorder and ARFGEF2-related periventricular nodular heterotopia with microcephaly.

Discovery of British and Irish bryophytes 1. Trends in the detection and reporting of new species, 1538–2021

ABSTRACT Introduction We examine the discovery of British and Irish bryophytes, based on a database of their first records. For 1140 taxa in the current (2021) checklist we have compiled information on the place and date of the first record, the finder and the author and place of its publication. The full database is posted on the Zenodo repository. Here we analyse changes over time in the attributes (e.g. size) of newly discovered species, their location and the main finders. Methods We checked first records reported in bryological publications, extracting the relevant details for our database. Most are supported by herbarium specimens. To check on these we have relied mainly on the expertise of others, examining only a few specimens ourselves. Key results Unlike flowering plants, very few bryophytes were known until ca. 1690 and the first to be discovered tended to be widespread, large species with frequent sporophytes, growing in lowland England. After Dillenius’s Historia muscorum (1741) there was a major trough in activity, extending to the 1780s. Thereafter there was a surge in records from the Highlands of Scotland (from 1790) and western Ireland (from 1809). The flood of new moss records in the early nineteenth century culminated in the 1850s, with William Wilson making an outstanding contribution. New liverwort records increased later in the century and peaked between 1900 and 1909. There followed a further trough for both groups from 1920 to 1949. Since then the rate of discovery has remained steady at about 2.8 species per year.

A yeast cell cycle pulse generator model shows consistency with multiple oscillatory and checkpoint mutant datasets

Modeling biological systems holds great promise for speeding up the rate of discovery in systems biology by predicting experimental outcomes and suggesting targeted interventions. However, this process is dogged by an identifiability issue, in which network models and their parameters are not sufficiently constrained by coarse and noisy data to ensure unique solutions. In this work, we evaluated the capability of a simplified yeast cell-cycle network model to reproduce multiple observed transcriptomic behaviors under genomic mutations. We matched time-series data from both cycling and checkpoint arrested cells to model predictions using an asynchronous multi-level Boolean approach. We showed that this single network model, despite its simplicity, is capable of exhibiting dynamical behavior similar to the datasets in most cases, and we demonstrated the drop in severity of the identifiability issue that results from matching multiple datasets.

Covalent docking and molecular dynamics simulations reveal the specificity-shifting mutations Ala237Arg and Ala237Lys in TEM beta-lactamase.

The rate of modern drug discovery using experimental screening methods still lags behind the rate at which pathogens mutate, underscoring the need for fast and accurate predictive simulations of protein evolution. Multidrug-resistant bacteria evade our defenses by expressing a series of proteins, the most famous of which is the 29-kilodalton enzyme, TEM β-lactamase. Considering these challenges, we applied a covalent docking heuristic to measure the effects of all possible alanine 237 substitutions in TEM due to this codon's importance for catalysis and effects on the binding affinities of commercially-available β-lactam compounds. In addition to the usual mutations that reduce substrate binding due to steric hindrance, we identified two distinctive specificity-shifting TEM mutations, Ala237Arg and Ala237Lys, and their respective modes of action. Notably, we discovered and verified through minimum inhibitory concentration assays that, while these mutations and their bulkier side chains lead to steric clashes that curtail ampicillin binding, these same groups foster salt bridges with the negatively-charged side-chain of the cephalosporin cefixime, widely used in the clinic to treat multi-resistant bacterial infections. To measure the stability of these unexpected interactions, we used molecular dynamics simulations and found the binding modes to be stable despite the application of biasing forces. Finally, we found that both TEM mutants also bind strongly to other drugs containing negatively-charged R-groups, such as carumonam and ceftibuten. As with cefixime, this increased binding affinity stems from a salt bridge between the compounds' negative moieties and the positively-charged side chain of the arginine or lysine, suggesting a shared mechanism. In addition to reaffirming the power of using simulations as molecular microscopes, our results can guide the rational design of next-generation β-lactam antibiotics and bring the community closer to retaking the lead against the recurrent threat of multidrug-resistant pathogens.

Regulatory mutants of Serratia with modulated antibiotic production due to intergenic transposon insertions

Antibiotic resistance is an increasing problem, exacerbated by global dissemination of drug resistance genes under selection pressure. Moreover, the rate of new antibiotic discovery declined over previous decades and so there is a growing need for new antibiotic discovery and a deeper appreciation of the various genetic and physiological factors that influence antibiotic biosynthesis. The enterobacterium Serratia sp. ATCC 39006 (Serratia) is a useful model for studies on the biosynthesis and regulation of bioactive secondary metabolites, including two antibiotics - a carbapenem and prodigiosin. Both compounds are tightly regulated in response to various physiological and environmental signals, including quorum sensing. We have identified novel regulators of antibiotic production after random transposon mutagenesis. Multiple regulatory mutations mapped to a small locus defining an intergenic region (IGR). These IGR insertion mutants display elevated production of both the carbapenem and prodigiosin antibiotics and they also exhibit reduced motility, confirming pleiotropic impacts. Exploiting phage transduction, we constructed double mutants with 15 known Serratia regulators and showed that the IGR mutant phenotype was epistatic over several of these. Further analysis suggested the presence of a putative gene encoded within the IGR locus, which may play an impactful role in the intricate regulatory network. Functional characterisation of the IGR region and its physiological impacts in both the modulation of antibiotic production and in wider pleiotropy were dissected. Mechanistic understanding of the regulatory mechanisms involved may prove exploitable in enhancing controlled antibiotic hyperproduction.

Dung beetles (Coleoptera: Scarabaeidae: Scarabaeinae) of Northeast India – Patterns and gaps in discovery

True dung beetles belonging to the sub-family Scarabaeinae, a group with over 6000 species known worldwide, play an important role in biological pest control, soil fertilization, and several other ecosystem services. At present, the Northeast India biogeographic region, the meeting point of two world biodiversity hotspots, has reports of 206 species of Scarabaeinae. State-wise species reports from this group indicated that certain states like Meghalaya and Arunachal Pradesh have relatively higher richness compared to other states like Nagaland and Mizoram. The species discovery curve of Northeast Indian Scarabaeinae indicated two distinct periods of descriptions in 1931 and 2000, apart from which the rate of discoveries in the region has been slow. Body size and range of the species were weak indicators of the date of description and pattern of species discovery in the region. Mapping of the type localities of the species suggests that most of the discoveries were around major cities with easier accessibility, suggesting gaps in the systematic studies conducted in the region. Incomplete distribution information, especially of ecologically important taxa like dung beetles leads to several challenges regarding the lack of clarity on their ecology and conservation status. Our results thereby stress on the need for systematic taxonomic inventorying and long-term monitoring of ecologically important, yet underexplored taxa such as Scarabaeinae in the region.

Seen only once: an evolutionarily distinct species of Toadlet (Uperoleia: Myobatrachidae) from the Wessel Islands of northern Australia.

There is a high rate of recent species discovery in remote regions of northern Australia, especially for amphibians and reptiles. The Wessel Islands, located in the northeastern corner of the Northern Territory, has recently been identified as a region of high species and phylogenetic endemism based on samples collected during the sole reptile and amphibian survey there in 1993. Using a phylogenetic approach, we describe a new, evolutionarily distinct species of Uperoleia endemic to the Wessel Islands. This description is based on three specimens, one female and two juveniles, which represent the only confirmed vouchers of the species. Due to the low number of specimens, this new species cannot be diagnosed morphologically from other closely related Uperoleia, and nothing is currently known about the mating call or basic biology. The discovery of this species provides further evidence for the islands importance as an area of endemism and identifies an urgent need for further surveys to document the unique biological diversity of the Wessel Islands.

Estimating vertebrate biodiversity using the tempo of taxonomy – a view from Hubbert’s peak

Abstract Reservoirs of natural resources are finite and, with increasing exploitation, production typically increases, reaches a maximum (Hubbert’s peak) and then declines. Similarly, species are the currency of biodiversity, and recognized numbers are dependent upon successful discovery. Since 1758, taxonomists have exploited a shrinking reservoir of as-yet-unnamed vertebrate taxa such that rates of species description at first rose, reached a peak and then declined. Since about 1950, increases in research funding and technological advances have fostered a renewed increase in rates of discovery that continues today. Many attempts to estimate global biodiversity are forecasts from data on past rates of description. Here we show that rates of discovery of new vertebrate taxa have been dependent upon the size (richness) of the taxonomic pool under consideration and the intensity of ‘sampling’ effected by taxonomists in their efforts to discover new forms. Because neither the current number of as-yet-to-be-described taxa nor future amounts of taxonomic efforts can be known a priori, attempts to produce an accurate estimate of total global biodiversity based on past rates of discovery are largely unconstrained.

NAFLD-Associated HCC: Progress and Opportunities.

Due to an increase in the obesity-associated metabolic syndrome of epidemic proportions, nonalcoholic fatty liver disease (NAFLD) is becoming a leading cause of hepatocellular carcinoma (HCC) in western countries. This presents added challenges, as NAFLD-associated HCC tends to present at an advanced stage in older patients with co-morbidities. Their prognosis is generally poor with the benefits of standard therapies less certain. The pathogenesis of NAFLD-associated HCC is multifactorial and not well understood, although the risk of HCC developing undoubtedly increases as NAFLD progresses to steatohepatitis and cirrhosis. Recent advances in our understanding of the drivers of NAFLD and HCC will hopefully lead to the development of clinically relevant biomarkers, tools and strategies to aid the identification of high-risk patients, inform preventive measures, and introduction of better tolerated targeted therapies. Lifestyle modification and chemoprevention with drugs such as anti-platelets, statins and anti-diabetics are being evaluated for HCC prevention. The landmark IMBrave150 study introducing the combination of atezolizumab and bevacizumab has recently transformed the landscape of systemic therapies in HCC, with follow-up analyses and real-world data for patients with NAFLD-associated HCC eagerly anticipated. While responses may vary in ways not yet appreciated, the rate of discovery and progress suggests imminent change and opportunities.

Methodological assessment of uncertainty in forecasting hydrocarbon reserves of the North Varieganskoye field (Western Siberia)

Relevance. The assessment of uncertainties for the calculation of hydrocarbon reserves is a timely topic. The quality of hydrocarbon reserves is declining as old oil fields are depleted. Rates of discovery are in decline since most of the territories prospectively rich in hydrocarbon resources have already been explored. Newly discovered fields are classified as small and medium in terms of the amount of hydrocarbon reserves and difficult in terms of the quality of reserves. Purpose of the research is to determine important reservoir properties that have the greatest impact on the variability of the initial hydrocarbon reserves using sensitivity analysis and conducting a probabilistic hydrocarbon reserves assessment by the Monte Carlo method. Methods of research. The probabilistic hydrocarbon reserves assessment was carried out by the Monte Carlo method with the corresponding frequency curves of the probability distribution of volumetric parameters, which were performed using the Oracle Crystal Ball application. The sensitivity analysis of volumetric parameters that have the greatest impact on the amount of the initial hydrocarbons reserves was performed using the Oracle Crystal Ball application as well. Results and their application. An assessment of uncertainties and risks is necessary both for newly discovered fields in order to negate the risk of drilling unproductive wells and mature fields for a targeted program of geological and technological measures (GTM). Conclusions. The formation of bedded sand bodies of the Vasyugan (SE11, SE12, SE13) and Tyumen (SE2) suites took place in various sedimentation conditions, which could not but affect the quality of the reservoir of the studied formations. The analysis of the sensitivity of parameters has shown that different parameters affect the amount of hydrocarbon reserves in places SE11, SE12, SE13 and place SE2 .

Abstract OT-07-01: Guardant360® related clinical outcomes in patients who share medical records-breast cancer (GRECO-B)

Abstract Background: Cell free circulating tumor DNA (ctDNA) is a proven viable alternative to tissue molecular profiling with high sensitivity, specificity, and positive predictive value. However, there is limited data on the overall outcomes of patients following ctDNA testing. Guardant360® Related Clinical Outcomes in Patients who Share Medical Records - Breast Cancer (GRECO-Breast) is a novel, patient-centric approach to evaluate clinical data and outcomes. This real-world prospective observational study intends to assess the health outcomes of patients with advanced breast cancer who have undergone Guardant360 ctDNA testing by consenting patients through siteless methodologies to share their medical records and imaging studies documenting their clinical history. Trial design: This is an observational study to be conducted in the United States. Patients with a diagnosis of breast cancer and a completed Guardant360 test will consent to share their medical records. Patients will be recruited by telephone or email to determine interest in the study. Should patients be interested in contributing to this study, they will be asked to consent via an electronic signature platform (e-signature) and electronic informed consent form (e-consent). Enrolled participants will be required to sign an electronic medical records release form for data review and verification by the study team. A healthcare information management organization will abstract participant demographics, cancer-related therapies, and clinical outcomes directly from source data for final analysis. Eligibility criteria: Prospective participants will be adults (age 18+) with a diagnosis of breast cancer with a completed Guardant360 test who consent to be enrolled in the study. Endpoints: Primary endpoint: To measure event free survival (EFS, composite endpoint of overall survival, progression events, and subjects lost to follow-up) stratified by treatment and genomic biomarker. Secondary endpoints: 1) Assess the rate of biomarker discovery compared to tissue genotyping results, when available; 2) Time to Next Treatment (TTNT); 3) Real-world Time to Tumor Progression (rwTTP); defined and documented either clinically or radiologically 4) Real-world Overall Survival (rwOS); defined as either clinically recorded death or secondary validated sources; 5) Record completeness of data and data quality. Statistical methods: Descriptive statistics will be used to characterize the patient population. These will include frequency distributions, cross tabulations, and summary measures such as means, standard deviations, and ranges. Graphical displays will be employed, where appropriate, such as box plots, scatter plots, and survival curves for time-to-event endpoints. Present accrual and target accrual: GRECO-Breast opened for enrollment in June 2020 with a goal of enrolling up to 300 participants. Contact information: For more information, please visit: https://clinicaltrials.gov/ct2/show/NCT04436393 Citation Format: Aaron Fuchs, Matthew Muia, Vicky Ho, Kristi Ashton, Naveen Kumar, Enrique Marino, Junhua Yu, Kathryn Lang, Victoria M Raymond. Guardant360® related clinical outcomes in patients who share medical records-breast cancer (GRECO-B) [abstract]. In: Proceedings of the 2020 San Antonio Breast Cancer Virtual Symposium; 2020 Dec 8-11; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2021;81(4 Suppl):Abstract nr OT-07-01.

Abstract 133: Micro-environment on demand (MOD): A versatile technology for cognate T-cell receptor - neoantigen discovery

Abstract NCI estimates that cancer will be the leading cause of death in 2030, worldwide. Checkpoint inhibitors and adoptive cell therapies (ACTs) cost up to ~$2 million/patient and have shown durable responses in a few patients while many patients experience life-threatening side-effects. So, there is a critical need to identify cost-effective therapies to treat cancer with minimal to no side-effects. Deep mapping of cancer antigens and their cognate immune cells is one approach that has the potential for a more universal/targeted treatment. However, systematic methods do not exist to identify cancer antigens and their cognate immune cells functionally and link phenotypic readouts to antigen and immune cell DNA sequences. Here we present a single cell handling and analysis platform based on Printed Droplet Microfluidics, a new droplet microfluidic technology that enables the precise construction of cell-cell interaction experiments with single cell resolution, followed by functional and genomic analysis. At the core of the technology is a fluorescence-activated flow sorter repurposed to deterministically sort and merge droplets containing single cells, beads, reagents, and drugs, collect and assay merged droplets and resort assay-positive droplets for downstream genomic analyses. The selectivity of the sorting/merging technology enables both the construction of experiments with up to one million precise cell combinations and the performance of flexible multi-step molecular biology workflows. We will show proof-of-concept for the MOD platform that integrates three technologies after fluorescently labeled single-cells are encapsulated in droplets - droplet-based cell-sorting and merging, single-cell-based cytokine assays and selective sequencing of T-cell receptor variable regions based on cytokine signals. The technology has the potential to rapidly accelerate the rate of immunotherapy discovery by enabling a larger number of experiments to be performed with higher precision, more modes of analysis, and less starting material than competing bulk technologies. Citation Format: Maithreyan Srinivasan, Justin Madrigal, Nathan Schoepp, Russell Cole. Micro-environment on demand (MOD): A versatile technology for cognate T-cell receptor - neoantigen discovery [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 133.

Adaptive machine learning for efficient materials design

Abstract

Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction

The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material’s structure and functionality is an important step in the process, such that viable polymorphs for a given chemical composition need to be identified. Machine-learning-based surrogate models have the potential to accelerate the search for polymorphs that target specific applications. Herein, we report a readily generalizable active-learning (AL) accelerated algorithm for identification of electrochemically stable iridium oxide polymorphs of IrO2 and IrO3. The search is coupled to a subsequent analysis of the electrochemical stability of the discovered structures for the acidic oxygen evolution reaction (OER). Structural candidates are generated by identifying all 956 structurally unique AB2 and AB3 prototypes in existing materials databases (more than 38000). Next, using an active learning approach, we find 196 IrO2 polymorphs within the thermodynamic amorphous synthesizability limit and reaffirm the global stability of the rutile structure. We find 75 synthesizable IrO3 polymorphs and report a previously unknown FeF3-type structure as the most stable, termed α-IrO3. To test the algorithms performance, we compare to a random search of the candidate space and report at least a 2-fold increase in the rate of discovery. Additionally, the AL approach can acquire the most stable polymorphs of IrO2 and IrO3 with fewer than 30 density functional theory optimizations. Analysis of the structural properties of the discovered polymorphs reveals that octahedral local coordination environments are preferred for nearly all low-energy structures. Subsequent Pourbaix Ir–H2O analysis shows that α-IrO3 is the globally stable solid phase under acidic OER conditions and supersedes the stability of rutile IrO2. Calculation of theoretical OER surface activities reveal ideal weaker binding of the OER intermediates on α-IrO3 than on any other considered iridium oxide. We emphasize that the proposed AL algorithm can be easily generalized to search for any binary metal oxide structure with a defined stoichiometry.

High‐Throughput Characterization of Perovskite Solar Cells for Rapid Combinatorial Screening

To discover the ideal perovskite material for solar cell application, a large parameter space (composition, surrounding condition, fabrication technique, etc.) must first be explored. Therefore, screening this parameter space using a rapid combinatorial screening approach can drastically speed up the rate of discovery. During the last decade, these discoveries and optimization processes of perovskite materials have been achieved using simple lab‐scale deposition techniques and characterization methods, resulting in a substantial time‐consuming process, slowing the rate of progress in the field of photovoltaics. Thus, the benefits of developing fully automated, high‐throughput characterization techniques become apparent. Herein, a high‐throughput solar cell testing system that enables parallel, real‐time, and comprehensive measurements is detailed, allowing for 16 solar cells to be characterized simultaneously. The importance of measurement reproducibility, condition verification, and structured data postprocessing is shown.

Wheat pathogen Zymoseptoria tritici N-myristoyltransferase inhibitors: on-target antifungal activity and an unusual metabolic defense mechanism.

Zymoseptoria tritici is the causative agent of Septoria tritici blotch (STB), which costs billions of dollars annually to major wheat-producing countries in terms of both fungicide use and crop loss. Agricultural pathogenic fungi have acquired resistance to most commercially available fungicide classes, and the rate of discovery and development of new fungicides has stalled, demanding new approaches and insights. Here we investigate a potential mechanism of targeting an important wheat pathogen Z. tritici via inhibition of N-myristoyltransferase (NMT). We characterize Z. tritici NMT biochemically for the first time, profile the in vivo Z. tritici myristoylated proteome and identify and validate the first Z. tritici NMT inhibitors. Proteomic investigation of the downstream effects of NMT inhibition identified an unusual and novel mechanism of defense against chemical toxicity in Z. tritici through the application of comparative bioinformatics to deconvolute function from the previously largely unannotated Z. tritici proteome. Research into novel fungicidal modes-of-action is essential to satisfy an urgent unmet need for novel fungicide targets, and we anticipate that this study will serve as a useful proteomics and bioinformatics resource for researchers studying Z. tritici.

T-psi-C: user friendly database of tRNA sequences and structures.

tRNAs have been widely studied for their role as genetic code decoders in the ribosome during translation, but have recently received new attention due to the discovery of novel roles beyond decoding, often in connection with human diseases. Yet, existing tRNA databases have not been updated for more than a decade, so they do not contain this new functional information and have not kept pace with the rate of discovery in this field. Therefore, a regularly updated database that contains information about newly discovered characteristics of tRNA molecules and can be regularly updated is strongly needed. Here, we report the creation of the T-psi-C database (http://tpsic.igcz.poznan.pl), an up-to-date collection of tRNA sequences that contains data obtained from high-throughput tRNA sequencing, e.g. all isoacceptors and isodecoders for human HEK293 cells. This database also contains 3D tRNA structures obtained from Protein Data Bank and generated using homology modeling. The T-psi-C database can be continuously updated by any member of the scientific community, and contains its own application programming interface (API), which allows users to retrieve or upload data in JSON format. Altogether, T-psi-C is user-friendly, easy to develop and an up-to-date source of knowledge about tRNAs.

Underwater wireless optical communication multi-tokens random backoff neighbor discovery method.

The cooperation of multiple pieces of equipment can greatly improve the efficiency of work when performing tasks underwater. Networking communication is a crucial technology for underwater cooperation. Wireless optical communication technology has many advantages in underwater networking communication. Neighbor discovery is a key part of networking between nodes, and it is required to establish links quickly. This paper combines the token mechanism with the random backoff mechanism and proposes a multi-tokens random backoff (MTRB) neighbor discovery method for underwater wireless optical communication. Simulation and analysis of the MTRB method show that the method has high neighbor discovery efficiency. When the parameters are selected properly, the rate of neighbor discovery by the MTRB method is increased by about 50% compared with the single token and the random backoff algorithm.

The Butterflies (Lepidoptera) of an Isolated Island: Monhegan, Maine

Repeated surveys of the butterflies of Monhegan Island, ME, from 1998 to 2018 confirmed the presence of at least 40 species. This total represents more than 42% of the state's recorded species—a high proportion given the island's small size and isolation. The rate of discovery indicates that, based on observations accumulated over time, about 50 species could be observed on Monhegan, including large migratory taxa as well as a variety of habitat-limited sedentary residents. The absence of several mainland species can be attributed to limited habitat and lack of some plant species. Records show changes in the butterfly fauna over recent decades; further changes may be expected because of climate warming, habitat modification, and stochastic events.

Heuristic Approach to Temporal Assignments of Spatial Grid Points for Vegetation Monitoring

In this research, vegetation trends are studied to give valuable information toward effective land use in the East African region, based on the normalized difference vegetation index (NDVI). Previously, testing procedures controlling the rate of false discoveries were used to detect areas with significant changes based on square regions of land. This article improves the assignment of grid points (pixels) to regions by formulating the spatial problem as a multidimensional temporal assignment problem. Lagrangian relaxation is applied to the problem allowing reformulation as a dynamic programming problem. A recursive heuristic approach with a penalty/reward function for pixel reassignment is proposed. This combined methodology not only controls an overall measure of combined directional false discoveries and nondirectional false discoveries, but make them as powerful as possible by adequately capturing spatial dependency present in the data. A larger number of regions are detected, while maintaining control of the mdFDR under certain assumptions.