Changing climate and growing human impacts are resulting in globally rising temperatures and the widespread loss of habitats. How species will adapt to these changes is not well understood. The Northern Goshawk (Accipiter gentilis) can be found across the Holarctic but is coming under more intense pressure in many places. Studies of recent populations in Finland and Denmark have shown a marked decline in body size of Northern Goshawks over the past century. Here we investigate long-term changes to Norwegian populations of Northern Goshawk by including material from the Middle Ages and Viking Age. We measured 240 skeletons of modern Northern Goshawks from Norway, Sweden, Denmark and Finland, and 89 Medieval Goshawk bones. Our results show that Norwegian and Swedish female Goshawks have decreased in size over the past century, whilst males showed little decline. Medieval female Goshawks were larger than contemporary females. The Viking Age specimens showed little difference to modern populations but appeared smaller than specimens from the Medieval period. A decline in forest habitats and a concomitant shift towards smaller prey likely drove a shift to smaller body size in Northern Goshawks. Our study shows that significant body size changes in birds can occur over relatively short time spans in response to environmental factors, and that these effects can sometimes differ between sexes.

Autoimmunity is the break of tolerance to self-antigens that leads to organ-specific or systemic diseases characterized by the presence of pathogenic autoreactive antibodies (AAb) produced by plasmablast and/or plasma cells. AAb are prevalent in the general population and not systematically associated with clinical symptoms. In contrast, in some individuals, these AAb are pathogenic and drive the development of signs and symptoms of antibody-mediated autoimmune diseases (AbAID). AAb production, isotype profiles, and glycosylations are promoted by pro-inflammatory triggers linked to genetic, environmental, and hormonal parameters. Recent evidence supports a role for pathogenic AAb of the IgE isotype in a number of AbAID. Autoreactive IgE can drive the activation of mast cells, basophils and other types of FcεRI-bearing cells and may play a role in promoting autoantibody production and other pro-inflammatory pathways. In this review, we discuss the current knowledge on the pathogenicity of autoreactive IgE in AbAID and their status as therapeutic targets. We also highlight unresolved issues including the need for assays that reproducibly quantify IgE AAbs, to validate their diagnostic and prognostic value, and to further study their pathophysiological contributions to AbAID.

A document by Anindya Ghosh. Click on the document to view its contents.

SARS-CoV-2 and its ever-emerging variants, are spread from host-to-host via expelled respiratory aerosols and saliva droplets. Knowing the number of virions which are exhaled by a person requires precise measurements of the size, count, velocity and trajectory of the virus-laden particles that are ejected directly from the mouth. These measurements are achieved in 3D, at 15000 images/second, and are applied when speaking, yelling, and coughing. In this study 33 events have been analysed by post-processing ~500000 images. Using these data, the flow rate of SARS-CoV-2 virions have been evaluated. At high concentrations, 10^7 virions/mL, it is found that 136 to 231 virions are ejected during a single cough, where the virion flow rate peak is capable of reaching 32 virions within a millisecond. This peak can reach tens of virions/ms when yelling, but reduced to only a few virions/ms when speaking. At medium concentrations, ~10^5 virions/mL, those results are hundreds of times lower. The total number of virions that are ejected when yelling at 110db, instead of speaking at 85db, increases by two to three fold. From the measured data analysed in this article, the flow rate of other diseases such as influenza, tuberculosis or measles, can also be estimated. As these data are openly accessible, they can be used by modellers for the simulation of saliva droplet transport and evaporation, allowing to further advance our understanding of airborne pathogen transmission.

We know little about the relationships that matter to emerging adults. This preregistered report elaborates our plan to survey 200 emerging adults (approximately half male) to better understand the role of significant others. First, we plan to ask emerging adults to list individuals who matter and we will code the open-ended text for relationship type. Second, we will use an adapted version of Schneck’s (2009) Mattering Scale to assess mattering among five common close relationships (i.e., mother figure, father figure, partner, best friend, sibling). We predict that mattering of partner and best friend will be highest, mothers will matter more than fathers, and siblings will matter least. We hypothesize failing to mention a relationship in the open-ended portion will be related to lower mattering of that relationship. Finally, we anticipate better well being and less loneliness when the list of those who matter is longer and when significant others matter more.

Animal migration impacts organismal health and disease transmission: migrants are simultaneously exposed to parasites and able to reduce infection at the population and individual levels. However, these dynamics are difficult to study; empirical studies reveal disparate results while existing theory makes assumptions that simplify natural complexity. Here, we systematically review empirical studies of migration and infection across taxa, highlighting key gaps in our understanding. Next, we develop a unified evolutionary framework incorporating different mechanisms of parasite-migration interactions while accounting for ecological complexity that goes beyond previous theory. Our framework generates diverse migration-infection patterns paralleling those seen in empirical systems, including partial and differential migration. Finally, we generate predictions about which mechanisms dominate which empirical systems to guide future studies. Our framework provides an overarching understanding of selective pressures shaping migration patterns in the context of animal health and disease, which is critical for predicting how environmental change may threaten migration.

Biodiversity is diminishing at alarming rates due to multiple anthropogenic drivers. To mitigate these drivers, their impacts must be quantified accurately and comparably across drivers. To enable that, we present a generally applicable framework introducing fundamental principles of ecological impact quantification, including the quantification of interactions between multiple drivers. The framework contrasts biodiversity variables in impacted against those in unimpacted or other reference situations, while accounting for their temporal dynamics through modelling. Properly accounting for temporal dynamics reduces biases in impact quantification and comparison. The framework addresses key questions around ecological impacts in global change science, namely, how to compare impacts under temporal dynamics across stressors, how to account for stressor interactions in such comparisons, and how to compare the success of management actions over time.

A Case report of atypical preeclampsia with severity criteria for hydatidiform complete mole.

The analysis of tourist accommodation bookings provides valid information for the management of these establishments. The objective of this work is to analyze the performance of different Machine Learning techniques for the prediction of booking cancellations, as well as to analyze possible patterns in the study data. For this purpose, the following supervised learning methods are used: Multilayer Perceptron Neural Network, Radial Basis Function Neural Network, Decision Tree, Random Forest, AdaBoost and XgBoost, analyzing the performance of these techniques. The dataset used corresponds to the bookings of a resort hotel and a city hotel located in Portugal. As a result, the study compares the classification methods applied and identifies those with better performance, proving that Machine Learning techniques generate reliable forecasts for the management of the tourism industry.

Relative and absolute intensity-based protein quantification across cell lines, tissue atlases, and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity, and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation, and quantitation workflows.

DNA damage occurs throughout life from a variety of sources, and it is imperative to repair damage in a timely manner to maintain genome stability. Thus, DNA repair mechanisms are a fundamental part of life. Nucleotide Excision Repair (NER) plays an important role in the removal of bulky DNA adducts, such as cyclobutane pyrimidine dimers (CPDs) from ultraviolet (UV) light or DNA crosslinking damage from platinum-based chemotherapeutics, such as cisplatin. A main component for the NER pathway is transcription factor IIH (TFIIH), a multifunctional, 10-subunit protein complex with crucial roles in both transcription and NER. In transcription, TFIIH is a component of the pre-initiation complex (PIC) and is important for promoter opening and the phosphorylation of RNA Polymerase II (RNA Pol II). During repair, TFIIH is important for DNA unwinding, recruitment of downstream repair factors, and verification of the bulky lesion. Several different disease states can arise from mutations within subunits of the TFIIH complex. Most strikingly are Xeroderma Pigmentosum (XP), XP combined with Cockayne Syndrome (CS), and Trichothiodystrophy (TTD). Here, we summarize the recruitment and functions of TFIIH in the two NER subpathways, global genomic (GG-NER) and transcription-coupled NER (TC-NER). We will also discuss how TFIIH’s roles in the two subpathways lead to different genetic disorders.

Robotic concrete extrusion is a novel additive manufacturing process (3D concrete printing) and is part of a continuously digitally controlled value chain. According to the state of the art, concrete is considered to be an isotropic material due to the manufacturing process. However, for the additive manufacturing process, the isotropic approach hast to be reconsidered due to the layered structure. It can be assumed that due to the layered structure, the material properties vary depending on the deposition direction and the geometry of the layers. The aim of the work was to record the material-technical characteristics of extruded elements manufactured according to standards in comparison with concrete recipes. Process-related influences on the mechanical parameters of additively manufactured concrete elements were examined and evaluated in more detail. Based on the findings obtained, the dimensioning, design and measurement of components can be carried out and thus guidelines for components can be derived. With these derived guidelines, the material utilization and economic efficiency can be improved.

Background: Due to the recency of the postbiotic field, there are no head-to-head postbiotic studies investigating its biotherapeutic potential for atopic dermatitis (AD). No network meta-analysis (NMA) has yet been conducted to synthesize relevant studies to compare postbiotic interventions for AD. Objective: To assess the comparative efficacy and safety of postbiotic strains for treating pediatric AD. Methodology: This is an NMA of randomized controlled studies that evaluate postbiotics in treating pediatric AD. Systematic search of databases and registers from inception to November 30, 2022. Three authors independently performed the search, screening, appraisal using the Cochrane risk of bias tool version 2 and data extraction. Data analysis was done using STATA14 software. Results: There were 9 studies that evaluated 8 postbiotic preparations. Lactobacillus rhamnosus IDCC 3201 (LR) ranked highest in the efficacy outcome. Compared to placebo, LR may be effective in reducing symptoms of atopic dermatitis both in the main analysis (SMD -0.53, 95%CI -1.02 to -0.04) and sensitivity analysis involving studies that used SCORAD (MD -5.52, 95% CI -10.46 to -0.58), based on low-certainty evidence. Based on moderate-certainty evidence, LR probably does not increase the risk of adverse events (RR 0.97, 95% CI 0.79 to 1.21). Although Lactobacillus paracasei GM080 (LP2) ranked highest in the safety outcome, it may not reduce AD symptoms compared to placebo (SMD -0.03, 95% CI -0.37 to 0.32) based on low-certainty evidence. Conclusion: LR showed significant benefits for children with AD, based on low-certainty evidence. Further investigation on LR is recommended.

The ease with which mobile money is used to facilitate cross-border payments presents a global threat to law enforcement in the fight against laundering and terrorist financing. This paper aims to use machine learning classifiers to predict transactions flagged as a fraud in mobile money transfers. Data for this paper came from real-time transactions that stimulate a  well-known mobile transfer fraud scheme. This paper uses logistic regression as the baseline model and compares it with ensembles and gradient descent models. The results indicate that the established logistic regression model did not perform too poorly compared to the other models. The random forest classifier had the most outstanding performance among all measures. The amount of money transferred was the top feature to predict money laundering transactions through mobile money transfers. These findings suggest that more research is needed to improve the logistic regression model. The random forest classifier should be further explored as a potential tool for law enforcement and financial institutions to detect money laundering activities in mobile money transfers.

Lyme Neuroborreliosis: A case reportBabak Sayad1, Arefeh Babazadeh2, Mohammad Barary3, Rezvan Hosseinzadeh4, Soheil Ebrahimpour2, Zeinab Mohseni Afshar1, *Clinical Research Development Center, Imam Reza Hospital, Kermanshah University of Medical Sciences, Kermanshah, IranInfectious Diseases and Tropical Medicine Research Center, Health Research Institute, Babol University of Medical Sciences, Babol, IranStudent Research Committee, Virtual School of Medical Education and Management, Shahid Beheshti University of Medical Sciences, Tehran, IranStudent Research Committee, Babol University of Medical Sciences, Babol, Iran

The extended phenotype of helical burrowing behavior in animals has evolved independently many times since first appearing after the Cambrian explosion (~540 million years ago). A number of hypotheses have been proposed to explain the evolution of helical burrowing in certain taxa, but no study has searched for a general explanation encompassing all taxa. We reviewed helical burrowing in both extant and extinct animals and from the trace fossil record and compiled from the literature 10 possible hypotheses for why animals construct helical burrows, including our own ideas. Of these, six were post-construction hypotheses—-benefits to the creator or offspring, realized after burrow construction—-and four were construction hypotheses reflecting direct benefits to the creator during construction. We examined the fit of these hypotheses to a total of 21 extant taxa and ichnotaxa representing 59–184 spp. Only two hypotheses—-antipredator, biomechanical advantage—-could not be rejected for any species (possible in 100% of spp.), but six of the hypotheses could not be rejected for most species (possible in 86–100 % of spp.): microclimate buffer, reduced falling sediment (soil), anticrowding, vertical patch, and the above two hypotheses. Four of these six were construction hypotheses, raising the possibility that helical burrowing might have evolved without providing post-construction benefits. Our analysis showed that increased drainage, deposit feeding, microbial farming, and offspring escape could not explain helical burrowing behavior in the majority of taxa (5–48%). Overall, the evidence does not support a general explanation for the evolution of helical burrowing in animals. The function and evolution of the helix as an extended phenotype would seem, at least in some cases, to provide different advantages for different taxa. Although direct tests of many of the hypotheses would be difficult, we nevertheless offer ways to test some of the hypotheses for selected taxa.

This article reports and analyzes the results of protein complex model accuracy estimation by our methods (DeepUMQA3 and GraphGPSM) in the 15 th Critical Assessment of techniques for protein Structure Prediction (CASP15). The new deep learning-based multimeric complex model accuracy estimation methods are proposed based on the ensemble of three level features coupling with deep residual/graph neural networks. For the input multimeric complex model, we describe it from three levels: overall complex features, intra-monomer features, and inter-monomer features. We designed an overall ultrafast shape recognition (USR) to characterize the relationship between local residues and the overall complex topology, and an inter-monomer USR to characterize the relationship between the residues of one monomer and the topology of other monomers. On the 39 complex targets of CASP15, DeepUMQA3 (Group name: GuijunLab-RocketX) ranked first in the assessment of interface residue accuracy. The Pearson correlation coefficient (PCC) between the interface residues lDDT predicted by DeepUMQA3 and the real lDDT is 0.570, and DeepUMQA3 achieved the highest PCC on 29 out of 39 targets. GraphGPSM (Group name: GuijunLab-PAthreader) had a TM-score PCC>0.9 on 14 targets, showing a good ability to estimate the overall fold accuracy.

The human predictor team PEZYFoldings got third place with GDT-TS (First place with the Assessor’s formulae) in the single-domain category and tenth place in the multimer category in CASP15. In this paper, I describe the exact method used by PEZYFoldings in competitions. As AlphaFold2 and AlphaFold-Multimer, developed by DeepMind, are state-of-the-art structure prediction tools, it was assumed that enhancing the input and output of the tools was an effective strategy to obtain the highest accuracy for structure prediction. Therefore, I used additional tools and databases to collect evolutionarily related sequences and introduced a deep-learning-based model in the refinement step. In addition to these modifications, manual interventions were performed to address various tasks. Detailed analyses were performed after the competition to identify the main contributors to performance. Comparing the number of evolutionarily related sequences I used with those of the other teams that provided AlphaFold2’s baseline predictions revealed that an extensive sequence similarity search was one of the main contributors. The impact of the refinement model was minimal (p <0.05 for the TM score). In addition, I noticed that I had gained large Z-scores with the subunits of H1137, for which I performed manual domain parsing considering the interfaces between the subunits. This finding implies that the manual intervention contributed to my performance. The prediction performance was low when I could not identify the evolutionarily related sequences. T1130 is an example; however, other teams can model better structures. Based on the discussions from the CASP15 conference, the two teams that ranked higher than PEZYFoldings had some hits for T1130. This may be because T1130 is a eukaryotic protein, whereas the additional databases used were mainly from metagenomic sequences, which primarily consist of prokaryotic proteins. These results highlight the opportunities for improvement in 1) multimer prediction, 2) building larger and more diverse databases, and 3) developing tools to predict structures from primary sequences alone. In addition, transferring the manual intervention process to automation is a future concern.