bluuebunny/crossref_metadata_2025_split huggingface dataset:
ProtGPT2 is a deep unsupervised language model for protein design
Authors: Ferruz Noelia , Schmidt Steffen , Höcker Birte 
Published: July 2022

AbstractProtein design aims to build novel proteins customized for specific purposes, thereby holding the potential to tackle many environmental and biomedical problems. Recent progress in Transformer-based architectures has enabled the implementation of language models capable of generating text with human-like capabilities. Here, motivated by this success, we describe ProtGPT2, a language model trained on the protein space that generates de novo protein sequences following the principles of natural ones. The generated proteins display natural amino acid propensities, while disorder predictions indicate that 88% of ProtGPT2-generated proteins are globular, in line with natural sequences. Sensitive sequence searches in protein databases show that ProtGPT2 sequences are distantly related to natural ones, and similarity networks further demonstrate that ProtGPT2 is sampling unexplored regions of protein space. AlphaFold prediction of ProtGPT2-sequences yields well-folded non-idealized structures with embodiments and large loops and reveals topologies not captured in current structure databases. ProtGPT2 generates sequences in a matter of seconds and is freely available.
Actin nano-architecture of phagocytic podosomes
Authors: Herron J. Cody , Hu Shiqiong , Watanabe Takashi , Nogueira Ana T. , Liu Bei , Kern Megan E. , Aaron Jesse , Taylor Aaron , Pablo Michael , Chew Teng-Leong , Elston Timothy C. , Hahn Klaus M. 
Published: July 2022

AbstractPodosomes are actin-enriched adhesion structures important for multiple cellular processes, including migration, bone remodeling, and phagocytosis. Here, we characterize the structure and organization of phagocytic podosomes using interferometric photoactivated localization microscopy, a super-resolution microscopy technique capable of 15–20 nm resolution, together with structured illumination microscopy and localization-based super-resolution microscopy. Phagocytic podosomes are observed during frustrated phagocytosis, a model in which cells attempt to engulf micropatterned IgG antibodies. For circular patterns, this results in regular arrays of podosomes with well-defined geometry. Using persistent homology, we develop a pipeline for semi-automatic identification and measurement of podosome features. These studies reveal an hourglass shape of the podosome actin core, a protruding knob at the bottom of the core, and two actin networks extending from the core. Additionally, the distributions of paxillin, talin, myosin II, α-actinin, cortactin, and microtubules relative to actin are characterized.
The Cryo-EM structures of two amphibian antimicrobial cross-β amyloid fibrils
Authors: Bücker Robert , Seuring Carolin , Cazey Cornelia , Veith Katharina , García-Alai Maria , Grünewald Kay , Landau Meytal 
Published: July 2022

AbstractThe amyloid-antimicrobial link hypothesis is based on antimicrobial properties found in human amyloids involved in neurodegenerative and systemic diseases, along with amyloidal structural properties found in antimicrobial peptides (AMPs). Supporting this hypothesis, we here determined the fibril structure of two AMPs from amphibians, uperin 3.5 and aurein 3.3, by cryogenic electron microscopy (cryo-EM), revealing amyloid cross-β fibrils of mated β-sheets at atomic resolution. Uperin 3.5 formed a 3-blade symmetrical propeller of nine peptides per fibril layer including tight β-sheet interfaces. This cross-β cryo-EM structure complements the cross-α fibril conformation previously determined by crystallography, substantiating a secondary structure switch mechanism of uperin 3.5. The aurein 3.3 arrangement consisted of six peptides per fibril layer, all showing kinked β-sheets allowing a rounded compactness of the fibril. The kinked β-sheets are similar to LARKS (Low-complexity, Amyloid-like, Reversible, Kinked Segments) found in human functional amyloids.
Ports’ criticality in international trade and global supply-chains
Authors: Verschuur J. , Koks E. E. , Hall J. W. 
Published: July 2022

AbstractWe quantify the criticality of the world’s 1300 most important ports for global supply chains by predicting the allocation of trade flows on the global maritime transport network, which we link to a global supply-chain database to evaluate the importance of ports for the economy. We find that 50% of global trade in value terms is maritime, with low-income countries and small islands being 1.5 and 2.0 times more reliant on their ports compared to the global average. The five largest ports globally handle goods that embody >1.4% of global output, while 40 ports add >10% of domestic output of the economies they serve, predominantly small islands. We identify critical cross-border infrastructure dependencies for some landlocked and island countries that rely on specific ports outside their jurisdiction. Our results pave the way for developing new strategies to enhance the resilience and sustainability of port infrastructure and maritime trade.
Contextuality in infinite one-dimensional translation-invariant local Hamiltonians
Authors: Yang Kaiyan , Zeng Xiao , Luo Yujing , Yang Guowu , Shu Lan , Navascués Miguel , Wang Zizhu 
Published: July 2022

AbstractIn recent years there has been a growing interest in treating many-body systems as Bell scenarios, where lattice sites play the role of distant parties and only near-neighbor statistics are accessible. We investigate contextuality arising from three Bell scenarios in infinite, translation-invariant 1D models: nearest-neighbor with two dichotomic observables per site; nearest- and next-to-nearest neighbor with two dichotomic observables per site, and nearest-neighbor with three dichotomic observables per site. For the first scenario, we give strong evidence that it cannot exhibit contextuality, not even in non-signaling physical theories beyond quantum mechanics. For the second one, we identify several low-dimensional models that reach the ultimate quantum limits, paving the way for self-testing ground states of quantum many-body systems. For the last scenario, which generalizes the Heisenberg model, we give strong evidence that, in order to exhibit contextuality, the dimension of the local quantum system must be at least 3.
Akkermansia muciniphila phospholipid induces homeostatic immune responses
Authors: Bae Munhyung , Cassilly Chelsi D. , Liu Xiaoxi , Park Sung-Moo , Tusi Betsabeh Khoramian , Chen Xiangjun , Kwon Jaeyoung , Filipčík Pavel , Bolze Andrew S. , Liu Zehua , Vlamakis Hera , Graham Daniel B. , Buhrlage Sara J. , Xavier Ramnik J. , Clardy Jon 
Published: July 2022

AbstractMultiple studies have established associations between human gut bacteria and host physiology, but determining the molecular mechanisms underlying these associations has been challenging1–3. Akkermansia muciniphila has been robustly associated with positive systemic effects on host metabolism, favourable outcomes to checkpoint blockade in cancer immunotherapy and homeostatic immunity4–7. Here we report the identification of a lipid from A. muciniphila’s cell membrane that recapitulates the immunomodulatory activity of A. muciniphila in cell-based assays8. The isolated immunogen, a diacyl phosphatidylethanolamine with two branched chains (a15:0-i15:0 PE), was characterized through both spectroscopic analysis and chemical synthesis. The immunogenic activity of a15:0-i15:0 PE has a highly restricted structure–activity relationship, and its immune signalling requires an unexpected toll-like receptor TLR2–TLR1 heterodimer9,10. Certain features of the phospholipid’s activity are worth noting: it is significantly less potent than known natural and synthetic TLR2 agonists; it preferentially induces some inflammatory cytokines but not others; and, at low doses (1% of EC50) it resets activation thresholds and responses for immune signalling. Identifying both the molecule and an equipotent synthetic analogue, its non-canonical TLR2–TLR1 signalling pathway, its immunomodulatory selectivity and its low-dose immunoregulatory effects provide a molecular mechanism for a model of A. muciniphila’s ability to set immunological tone and its varied roles in health and disease.
Cortical feedback loops bind distributed representations of working memory
Authors: Voitov Ivan , Mrsic-Flogel Thomas D. 
Published: July 2022

AbstractWorking memory—the brain’s ability to internalize information and use it flexibly to guide behaviour—is an essential component of cognition. Although activity related to working memory has been observed in several brain regions1–3, how neural populations actually represent working memory4–7 and the mechanisms by which this activity is maintained8–12 remain unclear13–15. Here we describe the neural implementation of visual working memory in mice alternating between a delayed non-match-to-sample task and a simple discrimination task that does not require working memory but has identical stimulus, movement and reward statistics. Transient optogenetic inactivations revealed that distributed areas of the neocortex were required selectively for the maintenance of working memory. Population activity in visual area AM and premotor area M2 during the delay period was dominated by orderly low-dimensional dynamics16,17 that were, however, independent of working memory. Instead, working memory representations were embedded in high-dimensional population activity, present in both cortical areas, persisted throughout the inter-stimulus delay period, and predicted behavioural responses during the working memory task. To test whether the distributed nature of working memory was dependent on reciprocal interactions between cortical regions18–20, we silenced one cortical area (AM or M2) while recording the feedback it received from the other. Transient inactivation of either area led to the selective disruption of inter-areal communication of working memory. Therefore, reciprocally interconnected cortical areas maintain bound high-dimensional representations of working memory.
Community established best practice recommendations for tephra studies—from collection through analysis
Authors: Wallace Kristi L. , Bursik Marcus I. , Kuehn Stephen , Kurbatov Andrei V. , Abbott Peter , Bonadonna Costanza , Cashman Katharine , Davies Siwan M. , Jensen Britta , Lane Christine , Plunkett Gill , Smith Victoria C. , Tomlinson Emma , Thordarsson Thor , Walker J. Douglas 
Published: July 2022

AbstractTephra is a unique volcanic product with an unparalleled role in understanding past eruptions, long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, high-resolution time-stratigraphic markers across a range of sedimentary settings and thus are used in numerous disciplines (e.g., volcanology, climate science, archaeology). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that inhibits data integration across geographic regions and disciplines. We present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to guide future investigators and to ensure that sufficient data are gathered for interoperability. Recommendations include standardized field and laboratory data collection, reporting and correlation guidance. These are organized as tabulated lists of key metadata with their definition and purpose. They are system independent and usable for template, tool, and database development. This standardized framework promotes consistent documentation and archiving, fosters interdisciplinary communication, and improves effectiveness of data sharing among diverse communities of researchers.
Standard metadata for 3D microscopy
Authors: Ropelewski Alexander J. , Rizzo Megan A. , Swedlow Jason R. , Huisken Jan , Osten Pavel , Khanjani Neda , Weiss Kurt , Bakalov Vesselina , Engle Michelle , Gridley Lauren , Krzyzanowski Michelle , Madden Tom , Maiese Deborah , Mandal Meisha , Waterfield Justin , Williams David , Hamilton Carol M. , Huggins Wayne 
Published: July 2022

AbstractRecent advances in fluorescence microscopy techniques and tissue clearing, labeling, and staining provide unprecedented opportunities to investigate brain structure and function. These experiments’ images make it possible to catalog brain cell types and define their location, morphology, and connectivity in a native context, leading to a better understanding of normal development and disease etiology. Consistent annotation of metadata is needed to provide the context necessary to understand, reuse, and integrate these data. This report describes an effort to establish metadata standards for three-dimensional (3D) microscopy datasets for use by the Brain Research through Advancing Innovative Neurotechnologies® (BRAIN) Initiative and the neuroscience research community. These standards were built on existing efforts and developed with input from the brain microscopy community to promote adoption. The resulting 3D Microscopy Metadata Standards (3D-MMS) includes 91 fields organized into seven categories: Contributors, Funders, Publication, Instrument, Dataset, Specimen, and Image. Adoption of these metadata standards will ensure that investigators receive credit for their work, promote data reuse, facilitate downstream analysis of shared data, and encourage collaboration.
Factors associated with stroke associated pneumonia among adult stroke patients admitted to university of Gondar hospital, Northwest Ethiopia
Authors: Assefa Messay , Tadesse Abilo , Adane Aynishet , Yimer Mekonnen , Tadesse Melaku 
Published: July 2022

AbstractStroke is the major cause of disability and death in sub-Saharan African countries. The presence and severity of complications play a major role in the outcome of stroke. Stroke associated pneumonia is often noticed post stroke infection that has been linked to an increased risk of hospital mortality, a longer hospital stay and higher healthcare expenses. Report on details of stroke-associated pneumonia has never been documented in countries of sub-Saharan Africa. This study aimed to determine the incidence and risk factors of stroke-associated pneumonia among adult stroke patients in hospital settings, Northwest Ethiopia. The study was undertaken at a stroke care unit, University of Gondar hospital between January 1, 2020 and December 31, 2020. A convenience sampling method was used to recruit study subjects. Relevant clinical history was taken, focused physical examination was done, and brain imaging (CT scan or MRI) was performed to settle the diagnosis of stroke. A modified Centre for Disease Control and Prevention (CDC) criteria was used to diagnose stroke-associated pneumonia. All patients with stroke-associated pneumonia were treated according to the 2016 Infectious Diseases Society of America/American Thoracic Society Clinical Practice Guidelines. The Data were cleaned in Epi Info version 4.6.0.2, and analyzed using SPSS version 26. Variables associated with stroke-associated pneumonia were computed using logistic regression analysis. P value < 0.05 was considered to declare statistical significance. The study comprised a total of 325 adult stroke patients. The mean age of study subjects was 65.2 years (SD ± 15.7). The most prevalent type of stroke was ischemic stroke, which accounted for 68% of all cases. Hemiparesis (94%), facial palsy (87%), and swallowing disturbance (51%) were the frequently noticed neurological findings. Stroke-associated pneumonia complicated 116/325 (36%) of stroke patients. Multi-variate regression analysis revealed that patients who were elderly (age > 75 years) (AOR = 3.910, CI 1.181–12.936, P = 0.026), had swallowing disturbance (AOR = 4.656, CI 2.356–9.202, P-value < 0.001), epileptic seizures (AOR = 2.678, 95% CI 1.253–5.721, P-value < 0.001) and moderate to severe stroke (NIHSS score = 16–21) (AOR = 5.994, 95% CI 2.043–17.585, P-value < 0.001) were at risk of developing stroke-associated pneumonia. SAP was a substantial medical complication among stroke patients. Early identification and prompt intervention measures for the identified risk factors might address the burden of SAP.
Structural, electrical, and magnetic properties of nano Sr1−XLaXFe12O19 (X = 0.2–0.8)
Authors: Baba Basha D. , Suresh Kumar N. , Chandra Babu Naidu K. , Ranjith Kumar G. 
Published: July 2022

AbstractThe current work is mainly devoted to the synthesis, structural, electrical, and magnetic characterization of Sr1−XLaXFe12O19 (X = 0.2–0.8) (SLFO) nanoparticles synthesized via the hydrothermal technique. The hexagonal peaks were determined using X-ray diffraction analysis. The obtained results indicated that the lattice constants were noted to be increasing from 0.58801 to 0.58825 nm (a = b), and 2.30309 to 2.30341 nm (c) with increase of in ‘X’. The morphological studies ensured that the grains as well as nanoparticles of SLFO acquired almost spherical shape. The optical properties were investigated using FTIR and UV–Visible spectra. The optical bandgap (Eg) of SLFO was found to be increasing from 1.866 to 2.118 eV with increase of dopant content. The electrical properties of SLFO were studied in detail as a function of temperature, and frequency. In addition, the dielectric modulus, and impedance spectroscopy analysis was carried out to describe the space charge polarization, and electric conduction mechanism, respectively. The hysteresis loop (M–H curves) of SLFO revealed the decrease of magnetization from 36.34 to 7.17 emu/g with increase in ‘X’.
Pupal size as a proxy for fat content in laboratory-reared and field-collected Drosophila species
Authors: Enriquez Thomas , Lievens Victoria , Nieberding Caroline M. , Visser Bertanne 
Published: July 2022

AbstractIn arthropods, larger individuals tend to have more fat reserves, but data for many taxa are still missing. For the vinegar fly Drosophila melanogaster, only few studies have provided experimental data linking body size to fat content. This is rather surprising considering the widespread use of D. melanogaster as a model system in biology. Here, we hypothesized that fat content in D. melanogaster is positively correlated with body size. To test this, we manipulated the developmental environment of D. melanogaster by decreasing food availability. We then measured pupal size and quantified fat content of laboratory-reared D. melanogaster. We subsequently measured pupal size and fat content of several field-caught Drosophila species. Starvation, crowding, and reduced nutrient content led to smaller laboratory-reared pupae that contained less fat. Pupal size was indeed found to be positively correlated with fat content. The same correlation was found for field-caught Drosophila pupae belonging to different species. As fat reserves are often strongly linked to fitness in insects, further knowledge on the relationship between body size and fat content can provide important information for studies on insect ecology and physiology.
Left ventricular active strain energy density is a promising new measure of systolic function
Authors: MacIver David H. , Agger Peter , Rodrigues Jonathan C. L. , Zhang Henggui 
Published: July 2022

AbstractThe left ventricular ejection fraction does not accurately predict exercise capacity or symptom severity and has a limited role in predicting prognosis in heart failure. A better method of assessing ventricular performance is needed to aid understanding of the pathophysiological mechanisms and guide management in conditions such as heart failure. In this study, we propose two novel measures to quantify myocardial performance, the global longitudinal active strain energy (GLASE) and its density (GLASED) and compare them to existing measures in normal and diseased left ventricles. GLASED calculates the work done per unit volume of muscle (energy density) by combining information from myocardial strain and wall stress (contractile force per unit cross sectional area). Magnetic resonance images were obtained from 183 individuals forming four cohorts (normal, hypertension, dilated cardiomyopathy, and cardiac amyloidosis). GLASE and GLASED were compared with the standard ejection fraction, the corrected ejection fraction, myocardial strains, stroke work and myocardial forces. Myocardial shortening was decreased in all disease cohorts. Longitudinal stress was normal in hypertension, increased in dilated cardiomyopathy and severely decreased in amyloid heart disease. GLASE was increased in hypertension. GLASED was mildly reduced in hypertension (1.39 ± 0.65 kJ/m3), moderately reduced in dilated cardiomyopathy (0.86 ± 0.45 kJ/m3) and severely reduced in amyloid heart disease (0.42 ± 0.28 kJ/m3) compared to the control cohort (1.94 ± 0.49 kJ/m3). GLASED progressively decreased in the hypertension, dilated cardiomyopathy and cardiac amyloid cohorts indicating that mechanical work done and systolic performance is severely reduced in cardiac amyloid despite the relatively preserved ejection fraction. GLASED provides a new technique for assessing left ventricular myocardial health and contractile function.
Applying machine learning and predictive modeling to retention and viral suppression in South African HIV treatment cohorts
Authors: Maskew Mhairi , Sharpey-Schafer Kieran , De Voux Lucien , Crompton Thomas , Bor Jacob , Rennick Marcus , Chirowodza Admire , Miot Jacqui , Molefi Seithati , Onaga Chuka , Majuba Pappie , Sanne Ian , Pisa Pedro 
Published: July 2022

AbstractHIV treatment programs face challenges in identifying patients at risk for loss-to-follow-up and uncontrolled viremia. We applied predictive machine learning algorithms to anonymised, patient-level HIV programmatic data from two districts in South Africa, 2016–2018. We developed patient risk scores for two outcomes: (1) visit attendance ≤ 28 days of the next scheduled clinic visit and (2) suppression of the next HIV viral load (VL). Demographic, clinical, behavioral and laboratory data were investigated in multiple models as predictor variables of attending the next scheduled visit and VL results at the next test. Three classification algorithms (logistical regression, random forest and AdaBoost) were evaluated for building predictive models. Data were randomly sampled on a 70/30 split into a training and test set. The training set included a balanced set of positive and negative examples from which the classification algorithm could learn. The predictor variable data from the unseen test set were given to the model, and each predicted outcome was scored against known outcomes. Finally, we estimated performance metrics for each model in terms of sensitivity, specificity, positive and negative predictive value and area under the curve (AUC). In total, 445,636 patients were included in the retention model and 363,977 in the VL model. The predictive metric (AUC) ranged from 0.69 for attendance at the next scheduled visit to 0.76 for VL suppression, suggesting that the model correctly classified whether a scheduled visit would be attended in 2 of 3 patients and whether the VL result at the next test would be suppressed in approximately 3 of 4 patients. Variables that were important predictors of both outcomes included prior late visits, number of prior VL tests, time since their last visit, number of visits on their current regimen, age, and treatment duration. For retention, the number of visits at the current facility and the details of the next appointment date were also predictors, while for VL suppression, other predictors included the range of the previous VL value. Machine learning can identify HIV patients at risk for disengagement and unsuppressed VL. Predictive modeling can improve the targeting of interventions through differentiated models of care before patients disengage from treatment programmes, increasing cost-effectiveness and improving patient outcomes.