Getting the bugs out of AI: Advancing ecological research on arthropods through computer vision.

Deep learning for computer vision has shown promising results in the field of entomology, however, there still remains untapped potential. Deep learning performance is enabled primarily by large quantities of annotated data which, outside of rare circumstances, are limited in ecological studies. Currently, to utilize deep learning systems, ecologists undergo extensive data collection efforts, or limit their problem to niche tasks. These solutions do not scale to region agnostic models. However, there are solutions that employ data augmentation, simulators, generative models, and self-supervised learning that can supplement limited labelled data. Here, we highlight the success of deep learning for computer vision within entomology, discuss data collection efforts, provide methodologies for optimizing learning from limited annotations, and conclude with practical guidelines for how to achieve a foundation model for entomology capable of accessible automated ecological monitoring on a global scale.

Clinical ApoA-IV amyloid is associated with fibrillogenic signal sequence.

Apolipoprotein A-IV amyloidosis is an uncommon form of the disease normally resulting in renal and cardiac dysfunction. ApoA-IV amyloidosis was identified in 16 patients attending the National Amyloidosis Centre and in eight clinical samples received for histology review. Unexpectedly, proteomics identified the presence of ApoA-IV signal sequence residues (p.18-43 to p.20-43) in 16/24 trypsin-digested amyloid deposits but in only 1/266 non-ApoA-IV amyloid samples examined. These additional signal residues were also detected in the cardiac sample from the Swedish patient in which ApoA-IV amyloid was first described, and in plasma from a single cardiac ApoA-IV amyloidosis patient. The most common signal-containing peptide observed in ApoA-IV amyloid, p.20-43, and to a far lesser extent the N-terminal peptide, p.21-43, were fibrillogenic in vitro at physiological pH, generating Congo red-positive fibrils. The addition of a single signal-derived alanine residue to the N-terminus has resulted in markedly increased fibrillogenesis. If this effect translates to the mature circulating protein in vivo, then the presence of signal may result in preferential deposition as amyloid, perhaps acting as seed for the main circulating native form of the protein; it may also influence other ApoA-IV-associated pathologies. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

Comparative study of the stabilities of synthetic in vitro and natural ex vivo transthyretin amyloid fibrils.

Systemic amyloidosis caused by extracellular deposition of insoluble fibrils derived from the pathological aggregation of circulating proteins, such as transthyretin, is a severe and usually fatal condition. Elucidation of the molecular pathogenic mechanism of the disease and discovery of effective therapies still represents a challenging medical issue. The in vitro preparation of amyloid fibrils that exhibit structural and biochemical properties closely similar to those of natural fibrils is central to improving our understanding of the biophysical basis of amyloid formation in vivo and may offer an important tool for drug discovery. Here, we compared the morphology and thermodynamic stability of natural transthyretin fibrils with those of fibrils generated in vitro either using the common acidification procedure or primed by limited selective cleavage by plasmin. The free energies for fibril formation were -12.36, -8.10, and -10.61 kcal mol-1, respectively. The fibrils generated via plasmin cleavage were more stable than those prepared at low pH and were thermodynamically and morphologically similar to natural fibrils extracted from human amyloidotic tissue. Determination of thermodynamic stability is an important tool that is complementary to other methods of structural comparison between ex vivo fibrils and fibrils generated in vitro Our finding that fibrils created via an in vitro amyloidogenic pathway are structurally similar to ex vivo human amyloid fibrils does not necessarily establish that the fibrillogenic pathway is the same for both, but it narrows the current knowledge gap between in vitro models and in vivo pathophysiology.

Clinical Amyloid Typing by Proteomics: Performance Evaluation and Data Sharing Between Two Centres.

Amyloidosis is a relatively rare human disease caused by the deposition of abnormal protein fibres in the extracellular space of various tissues, impairing their normal function. Proteomic analysis of patients' biopsies, developed by Dogan and colleagues at the Mayo Clinic, has become crucial for clinical diagnosis and for identifying the amyloid type. Currently, the proteomic approach is routinely used at National Amyloidosis Centre (NAC, London, UK) and Istituto di Tecnologie Biomediche-Consiglio Nazionale delle Ricerche (ITB-CNR, Milan, Italy). Both centres are members of the European Proteomics Amyloid Network (EPAN), which was established with the aim of sharing and discussing best practice in the application of amyloid proteomics. One of the EPAN's activities was to evaluate the quality and the confidence of the results achieved using different software and algorithms for protein identification. In this paper, we report the comparison of proteomics results obtained by sharing NAC proteomics data with the ITB-CNR centre. Mass spectrometric raw data were analysed using different software platforms including Mascot, Scaffold, Proteome Discoverer, Sequest and bespoke algorithms developed for an accurate and immediate amyloid protein identification. Our study showed a high concordance of the obtained results, suggesting a good accuracy of the different bioinformatics tools used in the respective centres. In conclusion, inter-centre data exchange is a worthwhile approach for testing and validating the performance of software platforms and the accuracy of results, and is particularly important where the proteomics data contribute to a clinical diagnosis.

Diagnostic amyloid proteomics: experience of the UK National Amyloidosis Centre.

Systemic amyloidosis is a serious disease which is caused when normal circulating proteins misfold and aggregate extracellularly as insoluble fibrillary deposits throughout the body. This commonly results in cardiac, renal and neurological damage. The tissue target, progression and outcome of the disease depends on the type of protein forming the fibril deposit, and its correct identification is central to determining therapy. Proteomics is now used routinely in our centre to type amyloid; over the past 7 years we have examined over 2000 clinical samples. Proteomics results are linked directly to our patient database using a simple algorithm to automatically highlight the most likely amyloidogenic protein. Whilst the approach has proved very successful, we have encountered a number of challenges, including poor sample recovery, limited enzymatic digestion, the presence of multiple amyloidogenic proteins and the identification of pathogenic variants. Our proteomics procedures and approaches to resolving difficult issues are outlined.

Plasminogen activation triggers transthyretin amyloidogenesis in vitro.

Systemic amyloidosis is a usually fatal disease caused by extracellular accumulation of abnormal protein fibers, amyloid fibrils, derived by misfolding and aggregation of soluble globular plasma protein precursors. Both WT and genetic variants of the normal plasma protein transthyretin (TTR) form amyloid, but neither the misfolding leading to fibrillogenesis nor the anatomical localization of TTR amyloid deposition are understood. We have previously shown that, under physiological conditions, trypsin cleaves human TTR in a mechano-enzymatic mechanism that generates abundant amyloid fibrils in vitro In sharp contrast, the widely used in vitro model of denaturation and aggregation of TTR by prolonged exposure to pH 4.0 yields almost no clearly defined amyloid fibrils. However, the exclusive duodenal location of trypsin means that this enzyme cannot contribute to systemic extracellular TTR amyloid deposition in vivo Here, we therefore conducted a bioinformatics search for systemically active tryptic proteases with appropriate tissue distribution, which unexpectedly identified plasmin as the leading candidate. We confirmed that plasmin, just as trypsin, selectively cleaves human TTR between residues 48 and 49 under physiological conditions in vitro Truncated and full-length protomers are then released from the native homotetramer and rapidly aggregate into abundant fibrils indistinguishable from ex vivo TTR amyloid. Our findings suggest that physiological fibrinolysis is likely to play a critical role in TTR amyloid formation in vivo Identification of this surprising intersection between two hitherto unrelated pathways opens new avenues for elucidating the mechanisms of TTR amyloidosis, for seeking susceptibility risk factors, and for therapeutic innovation.

Diagnosis, pathogenesis and outcome in leucocyte chemotactic factor 2 (ALECT2) amyloidosis.

Introduction: Renal biopsy series from North America suggest that leucocyte chemotactic factor 2 (ALECT2) amyloid is the third most common type of renal amyloid. We report the first case series from a European Centre of prevalence, clinical presentation and diagnostic findings in ALECT2 amyloidosis and report long-term patient and renal outcomes for the first time. Methods: We studied the clinical features, diagnostic investigations and the outcome of all patients with ALECT2 amyloidosis followed systematically at the UK National Amyloidosis Centre (NAC) between 1994 and 2015. Results: Twenty-four patients, all non-Caucasian, were diagnosed with ALECT2 amyloidosis representing 1.3% of all patients referred to the NAC with biopsy-proved renal amyloid. Diagnosis was made at median age of 62 years, usually from renal histology; immunohistochemical staining was definitive for ALECT2 fibril type. Median estimated glomerular filtration rate (GFR) at diagnosis was 33 mL/min/1.73 m2 and median proteinuria was 0.5 g/24 h. Hepatic amyloid was evident on serum amyloid P component (SAP) scintigraphy in 11/24 cases but was not associated with significant derangement of liver function. No patient had evidence of cardiac amyloidosis or amyloid neuropathy. Median follow-up was 4.8 (range 0.5-15.2) years, during which four patients died and four progressed to end-stage renal disease. The mean rate of GFR loss was 4.2 (range 0.5-9.6) mL/min/year and median estimated renal survival from diagnosis was 8.2 years. Serial SAP scans revealed little or no change in total body amyloid burden. Conclusions: ALECT2 amyloidosis is a relatively benign type of renal amyloid, associated with a slow GFR decline, which is reliably diagnosed on renal histology. Neither the molecular basis nor the factors underlying the apparent restriction of ALECT2 amyloidosis to non-Caucasian populations have been determined.

Proteolytic cleavage of Ser52Pro variant transthyretin triggers its amyloid fibrillogenesis.

The Ser52Pro variant of transthyretin (TTR) produces aggressive, highly penetrant, autosomal-dominant systemic amyloidosis in persons heterozygous for the causative mutation. Together with a minor quantity of full-length wild-type and variant TTR, the main component of the ex vivo fibrils was the residue 49-127 fragment of the TTR variant, the portion of the TTR sequence that previously has been reported to be the principal constituent of type A, cardiac amyloid fibrils formed from wild-type TTR and other TTR variants [Bergstrom J, et al. (2005) J Pathol 206(2):224-232]. This specific truncation of Ser52Pro TTR was generated readily in vitro by limited proteolysis. In physiological conditions and under agitation the residue 49-127 proteolytic fragment rapidly and completely self-aggregates into typical amyloid fibrils. The remarkable susceptibility to such cleavage is likely caused by localized destabilization of the ß-turn linking strands C and D caused by loss of the wild-type hydrogen-bonding network between the side chains of residues Ser52, Glu54, Ser50, and a water molecule, as revealed by the high-resolution crystallographic structure of Ser52Pro TTR. We thus provide a structural basis for the recently hypothesized, crucial pathogenic role of proteolytic cleavage in TTR amyloid fibrillogenesis. Binding of the natural ligands thyroxine or retinol-binding protein (RBP) by Ser52Pro variant TTR stabilizes the native tetrameric assembly, but neither protected the variant from proteolysis. However, binding of RBP, but not thyroxine, inhibited subsequent fibrillogenesis.

Antibodies to human serum amyloid P component eliminate visceral amyloid deposits.

Accumulation of amyloid fibrils in the viscera and connective tissues causes systemic amyloidosis, which is responsible for about one in a thousand deaths in developed countries. Localized amyloid can also have serious consequences; for example, cerebral amyloid angiopathy is an important cause of haemorrhagic stroke. The clinical presentations of amyloidosis are extremely diverse and the diagnosis is rarely made before significant organ damage is present. There is therefore a major unmet need for therapy that safely promotes the clearance of established amyloid deposits. Over 20 different amyloid fibril proteins are responsible for different forms of clinically significant amyloidosis and treatments that substantially reduce the abundance of the respective amyloid fibril precursor proteins can arrest amyloid accumulation. Unfortunately, control of fibril-protein production is not possible in some forms of amyloidosis and in others it is often slow and hazardous. There is no therapy that directly targets amyloid deposits for enhanced clearance. However, all amyloid deposits contain the normal, non-fibrillar plasma glycoprotein, serum amyloid P component (SAP). Here we show that administration of anti-human-SAP antibodies to mice with amyloid deposits containing human SAP triggers a potent, complement-dependent, macrophage-derived giant cell reaction that swiftly removes massive visceral amyloid deposits without adverse effects. Anti-SAP-antibody treatment is clinically feasible because circulating human SAP can be depleted in patients by the bis-d-proline compound CPHPC, thereby enabling injected anti-SAP antibodies to reach residual SAP in the amyloid deposits. The unprecedented capacity of this novel combined therapy to eliminate amyloid deposits should be applicable to all forms of systemic and local amyloidosis.

Robotic pilot study for analysing spasticity: clinical data versus healthy controls.

BACKGROUND: Spasticity is a motor disorder that causes significant disability and impairs function. There are no definitive parameters that assess spasticity and there is no universally accepted definition. Spasticity evaluation is important in determining stages of recovery. It can determine treatment effectiveness as well as how treatment should proceed. This paper presents a novel cross sectional robotic pilot study for the primary purpose of assessment. The system collects force and position data to quantify spasticity through similar motions of the Modified Ashworth Scale (MAS) assessment in the Sagittal plane. Validity of the system is determined based on its ability to measure velocity dependent resistance. METHODS: Forty individuals with Acquired Brain Injury (ABI) and 45 healthy individuals participated in a robotic pilot study. A linear regression model was applied to determine the effect an ABI has on force data obtained through the robotic system in an effort to validate it. Parameters from the model were compared for both groups. Two techniques were performed in an attempt to classify between healthy and patients. Dynamic Time Warping (DTW) with k-nearest neighbour (KNN) classification is compared to a time-series algorithm using position and force data in a linear discriminant analysis (LDA). RESULTS: The system is capable of detecting a velocity dependent resistance (p<0.05). Differences were found between healthy individuals and those with MAS 0 who are considered to be healthy. DTW with KNN is shown to improve classification between healthy and patients by approximately 20 % compared to that of an LDA. CONCLUSIONS: Quantitative methods of spasticity evaluation demonstrate that differences can be observed between healthy individuals and those with MAS of 0 who are often clinically considered to be healthy. Exploiting the time-series nature of the collected data demonstrates that position and force together are an accurate predictor of patient health.

Structure, folding dynamics, and amyloidogenesis of D76N ß2-microglobulin: roles of shear flow, hydrophobic surfaces, and α-crystallin.

Systemic amyloidosis is a fatal disease caused by misfolding of native globular proteins, which then aggregate extracellularly as insoluble fibrils, damaging the structure and function of affected organs. The formation of amyloid fibrils in vivo is poorly understood. We recently identified the first naturally occurring structural variant, D76N, of human ß2-microglobulin (ß2m), the ubiquitous light chain of class I major histocompatibility antigens, as the amyloid fibril protein in a family with a new phenotype of late onset fatal hereditary systemic amyloidosis. Here we show that, uniquely, D76N ß2m readily forms amyloid fibrils in vitro under physiological extracellular conditions. The globular native fold transition to the fibrillar state is primed by exposure to a hydrophobic-hydrophilic interface under physiological intensity shear flow. Wild type ß2m is recruited by the variant into amyloid fibrils in vitro but is absent from amyloid deposited in vivo. This may be because, as we show here, such recruitment is inhibited by chaperone activity. Our results suggest general mechanistic principles of in vivo amyloid fibrillogenesis by globular proteins, a previously obscure process. Elucidation of this crucial causative event in clinical amyloidosis should also help to explain the hitherto mysterious timing and location of amyloid deposition.

PROTAX-GPU: a scalable probabilistic taxonomic classification system for DNA barcodes.

DNA-based identification is vital for classifying biological specimens, yet methods to quantify the uncertainty of sequence-based taxonomic assignments are scarce. Challenges arise from noisy reference databases, including mislabelled entries and missing taxa. PROTAX addresses these issues with a probabilistic approach to taxonomic classification, advancing on methods that rely solely on sequence similarity. It provides calibrated probabilistic assignments to a partially populated taxonomic hierarchy, accounting for taxa that lack references and incorrect taxonomic annotation. While effective on smaller scales, global application of PROTAX necessitates substantially larger reference libraries, a goal previously hindered by computational barriers. We introduce PROTAX-GPU, a scalable algorithm capable of leveraging the global Barcode of Life Data System (>14 million specimens) as a reference database. Using graphics processing units (GPU) to accelerate similarity and nearest-neighbour operations and the JAX library for Python integration, we achieve over a 1000 × speedup compared with the central processing unit (CPU)-based implementation without compromising PROTAX's key benefits. PROTAX-GPU marks a significant stride towards real-time DNA barcoding, enabling quicker and more efficient species identification in environmental assessments. This capability opens up new avenues for real-time monitoring and analysis of biodiversity, advancing our ability to understand and respond to ecological dynamics. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Degradation versus fibrillogenesis, two alternative pathways modulated by seeds and glycosaminoglycans.

The mechanism that converts native human transthyretin into amyloid fibrils in vivo is still a debated and controversial issue. Commonly, non-physiological conditions of pH, temperature, or organic solvents are used in in vitro models of fibrillogenesis of globular proteins. Transthyretin amyloid formation can be achieved under physiological conditions through a mechano-enzymatic mechanism involving specific serine proteases such as trypsin or plasmin. Here, we investigate S52P and L111M transthyretin variants, both causing a severe form of systemic amyloidosis mostly targeting the heart at a relatively young age with heterogeneous phenotype among patients. Our studies on thermodynamics show that both proteins are significantly less stable than other amyloidogenic variants. However, despite a similar thermodynamic stability, L111M variant seems to have enhanced susceptibility to cleavage and a lower tendency to form fibrils than S52P in the presence of specific proteases and biomechanical forces. Heparin strongly enhances the fibrillogenic capacity of L111M transthyretin, but has no effect on the S52P variant. Fibrillar seeds similarly affect the fibrillogenesis of both proteins, with a stronger effect on the L111M variant. According to our model of mechano-enzymatic fibrillogenesis, both full-length and truncated monomers, released after the first cleavage, can enter into fibrillogenesis or degradation pathways. Our findings show that the kinetics of the two processes can be affected by several factors, such as intrinsic amyloidogenicity due to the specific mutations, environmental factors including heparin and fibrillar seeds that significantly accelerate the fibrillogenic pathway.

Improving the Barrier and Mechanical Properties of Paper Used for Packing Applications with Renewable Hydrophobic Coatings Derived from Camelina Oil.

This study looked at using modified camelina oil to develop sustainable coatings that could replace those derived from petroleum-based materials for use in packaging and other industrial sectors. Solvent-free synthesis of maleic anhydride grafted camelina oil (MCO) was carried out at two different temperatures (200 and 230 °C) to obtain sustainable hydrophobic coating materials for paper substrates. Maleic anhydride grafting of camelina oil was confirmed with attenuated total reflectance-Fourier transform infrared and NMR spectroscopic techniques, and up to 16% grafting of maleic anhydride was achieved, as determined by the titration method. MCO, obtained at different reaction temperatures, was coated onto cellulosic paper and evaluated for its hydrophobicity, mechanical, oxygen, and water vapor barrier properties. Scanning electron microscopy indicated the homogeneous dispersion of coating material onto the paper substrate. MCO-coated papers (MCO-200C paper and MCO-230C paper) provided a water contact angle of above 90° which indicates that the modified oil was working as a hydrophobic coating. Water vapor permeability (WVP) testing of coated papers revealed a reduction in WVP of up to 94% in comparison to the uncoated paper. Moreover, an improved oxygen barrier property was also observed for paper coated with both types of MCO. Analysis of the mechanical properties showed a greater than 70% retention of tensile strength and up to a five-fold increase in elongation at break of coated versus uncoated papers. Overall, the results show that camelina oil, a renewable resource, can be modified to produce environmentally friendly hydrophobic coating materials with improved mechanical and water vapor barrier properties that can serve as a potential coating material in the packaging industry. The results of this research could find applications in the huge paper packaging industries, specially in food packaging.

Multimodal Artificial Intelligence in Medicine.

Traditional medical Artificial Intelligence models, approved for clinical use, restrict themselves to single-modal data e.g. images only, limiting their applicability in the complex, multimodal environment of medical diagnosis and treatment. Multimodal Transformer Models in healthcare can effectively process and interpret diverse data forms such as text, images, and structured data. They have demonstrated impressive performance on standard benchmarks like USLME question banks and continue to improve with scale. However, the adoption of these advanced AI models is not without challenges. While multimodal deep learning models like Transformers offer promising advancements in healthcare, their integration requires careful consideration of the accompanying ethical and environmental challenges.

Trapping of palindromic ligands within native transthyretin prevents amyloid formation.

Transthyretin (TTR) amyloidosis is a fatal disease for which new therapeutic approaches are urgently needed. We have designed two palindromic ligands, 2,2'-(4,4'-(heptane-1,7-diylbis(oxy))bis(3,5-dichloro-4,1-phenylene)) bis(azanediyl)dibenzoic acid (mds84) and 2,2'-(4,4'-(undecane-1,11-diylbis(oxy))bis(3,5-dichloro-4,1-phenylene)) bis(azanediyl)dibenzoic acid (4ajm15), that are rapidly bound by native wild-type TTR in whole serum and even more avidly by amyloidogenic TTR variants. One to one stoichiometry, demonstrable in solution and by MS, was confirmed by X-ray crystallographic analysis showing simultaneous occupation of both T4 binding sites in each tetrameric TTR molecule by the pair of ligand head groups. Ligand binding by native TTR was irreversible under physiological conditions, and it stabilized the tetrameric assembly and inhibited amyloidogenic aggregation more potently than other known ligands. These superstabilizers are orally bioavailable and exhibit low inhibitory activity against cyclooxygenase (COX). They offer a promising platform for development of drugs to treat and prevent TTR amyloidosis.

The mental health impacts of the COVID-19 pandemic among individuals with depressive, anxiety, and stressor-related disorders: A scoping review.

OBJECTIVE: A scoping review of studies published in the first year of the COVID-19 pandemic focused on individuals with pre-existing symptoms of depression, anxiety, and specified stressor-related disorders, with the objective of mapping the research conducted. ELIGIBILITY CRITERIA: (1) direct study of individuals with pre-existing depressive, anxiety, and/or specified stressor-related (i.e., posttraumatic stress, acute stress) disorders/issues; (2) focus on mental health-related pandemic effects, and; (3) direct study of mental health symptoms related to depression, anxiety, or psychological distress. SOURCES OF EVIDENCE: Database-specific subject headings and natural language keywords were searched in Medline, Embase, APA PsycInfo, and Cumulative Index to Nursing & Allied Health Literature (CINAHL) up to March 3, 2021. Review of potentially relevant studies was conducted by two independent reviewers and proceeded in two stages: (1) title and abstract review, and; (2) full paper review. DATA CHARTING: Study details (i.e., location, design and methodology, sample or population, outcome measures, and key findings) were extracted from included studies by one reviewer and confirmed by the Principal Investigator. RESULTS: 66 relevant articles from 26 countries were identified. Most studies adopted a cross-sectional design and were conducted via online survey. About half relied on general population samples, with the remainder assessing special populations, primarily mental health patients. The most commonly reported pre-existing category of disorders or symptoms was depression, followed closely by anxiety. Most studies included depressive and anxiety symptoms as outcome measures and demonstrated increased vulnerability to mental health symptoms among individuals with a pre-existing mental health issue. CONCLUSION: These findings suggest that improved mental health supports are needed during the pandemic and point to future research needs, including reviews of other diagnostic categories and reviews of research published in subsequent years of the pandemic.

Federated learning and differential privacy for medical image analysis.

The artificial intelligence revolution has been spurred forward by the availability of large-scale datasets. In contrast, the paucity of large-scale medical datasets hinders the application of machine learning in healthcare. The lack of publicly available multi-centric and diverse datasets mainly stems from confidentiality and privacy concerns around sharing medical data. To demonstrate a feasible path forward in medical image imaging, we conduct a case study of applying a differentially private federated learning framework for analysis of histopathology images, the largest and perhaps most complex medical images. We study the effects of IID and non-IID distributions along with the number of healthcare providers, i.e., hospitals and clinics, and the individual dataset sizes, using The Cancer Genome Atlas (TCGA) dataset, a public repository, to simulate a distributed environment. We empirically compare the performance of private, distributed training to conventional training and demonstrate that distributed training can achieve similar performance with strong privacy guarantees. We also study the effect of different source domains for histopathology images by evaluating the performance using external validation. Our work indicates that differentially private federated learning is a viable and reliable framework for the collaborative development of machine learning models in medical image analysis.