Miracle-dz/UniFAR-FTUs huggingface dataset:
Developmental transcriptomes predict adult social behaviors in the socially flexible sweat bee, Lasioglossum baleicum

Natural variation can provide important insights into the genetic and environmental factors that shape social behavior and its evolution. The sweat bee, Lasioglossum baleicum, is a socially flexible bee capable of producing both solitary and eusocial nests. We demonstrate that within a single nesting aggregation, soil temperatures are a strong predictor of the social structure of nests. Sites with warmer temperatures in the spring have a higher frequency of social nests than cooler sites, perhaps because warmer temperatures provide a longer reproductive window for those nests. To identify the molecular correlates of this behavioral variation, we generated a de novo genome assembly for L. baleicum, and we used transcriptomic profiling to compare adults and developing offspring from eusocial and solitary nests. We find that adult, reproductive females have similar expression profiles regardless of social structure in the nest, but that there are strong differences between reproductive females and workers from social nests. We also find substantial differences in the transcriptomic profiles of stage-matched pupae from warmer, social-biased sites compared to cooler, solitary-biased sites. These transcriptional differences are strongly predictive of adult reproductive state, suggesting that the developmental environment may set the stage for adult behaviors in L. baleicum. Together, our results help to characterize the molecular mechanisms shaping variation in social behavior and highlight a potential role of environmental tuning during development as a factor shaping adult behavior and physiology in this socially flexible bee.
Maternal allergic sensitization affects the T cell modulatory capacity and molecular cargo of milk derived extracellular vesicles

Background Human milk extracellular vesicles (EVs) affect various cell types in the gastrointestinal tract, including T cells, and play a role in the development of the newborn’s immune system by delivering specific molecular cargo to target cells. Although maternal allergic sensitization alters the composition of milk, it is unknown whether this impacts the function of milk EVs. Therefore, we analyzed the T cell modulatory capacity and compared the protein and miRNA cargoes of EVs from milk of allergic and non-allergic mothers. Methods EVs were isolated from human milk from allergic and non-allergic donors by differential centrifugation, density gradient floatation and size exclusion chromatography. Functional modulation of primary human CD4+ T cells by EVs was assessed in vitro. Proteomic analysis and small RNA sequencing was performed on milk EVs to evaluate protein and miRNA abundance and to identify cellular targets of this EV cargo in relevant T cell signaling pathways. Results T cell proliferation, activation and cytokine production were suppressed in the presence of milk EVs. Remarkably, milk EVs from allergic mothers inhibited T cell activation to a lesser extent than EVs from non-allergic mothers. Integrative multi-omics analysis identified EV cargo of which the cellular targets could be linked to differential modulation of T cell activation-associated processes . Conclusions Milk EVs from non-allergic mothers are stronger inhibitors of T cell activation compared to milk EVs from allergic mothers. This altered functionality might be linked to changes in miRNA and protein cargo that modulate T cell signaling pathways in an integrative manner.
Palaeoproteomic investigation of an ancient human skeleton with abnormal deposition of dental calculus

Detailed investigation of extremely severe pathological conditions in ancient human skeletons is important as it could shed light on the breadth of potential interactions between humans and disease etiologies in the past. Here, we applied palaeoproteomics to investigate an ancient human skeletal individual with severe oral pathology, focusing our research on bacterial pathogenic factors and host defense response. This female skeleton, from the Okhotsk period (i.e., 5th–13th century) of Northern Japan, poses relevant amounts of abnormal dental calculus deposition and exhibits oral dysfunction due to severe periodontal disease. A shotgun mass-spectrometry analysis identified 81 human proteins and 15 bacterial proteins from the calculus of the subject. We identified two pathogenic or bioinvasive proteins originating from two of the three “red complex” bacteria, the core species associated with severe periodontal disease in modern humans, as well as two additional bioinvasive proteins of periodontal-associated bacteria. Moreover, we discovered defense response system-associated human proteins, although their proportion was mostly similar to those reported in ancient and modern human individuals with lower calculus deposition. These results suggest that the bacterial etiology was similar and the host defense response was not necessarily more intense in ancient individuals with significant amounts of abnormal dental calculus deposition.
Degeneracy in negative feedback (NFBL) and incoherent feedforward (IFFL) loops: Adaptation and resonance

Degeneracy in dynamic models refers to these situations where multiple combinations of parameter values produce identical patterns for the observable variable. In this paper we investigate the presence of degeneracy and the biological and dynamic mechanism that give rise to ti in adaptive and resonant/phasonant patterns generated by two qualitatively different circuit mechanisms: non-linear feedback loop (NFBL) and incoherent feedback loop (IFFL). We use minimal models of these circuit types together with analytical calculations, regular perturbation analysis, phase-plane analysis and numerical simulations. We identify the principles underlying the emergence of degeneracy in NFBLs and IFFLs and the the persistence of the degeneracy structure in the response of these adaptive systems to oscillatory inputs at arbitrary frequencies. Our results have implication for the understanding of adaptive systems and for the development of methods for circuit and dynamical systems reconstruction based on experimental or observational data.
Non-monotonic Relationship between Mutation Rate and Speed of adaptation under Antibiotic Exposure in Escherichia coli

The mutation is a fundamental source of biological evolution that create genetic variation in populations. Mutations can create new advantageous traits, but also potentially interfere with pre-existing organismal functions. Therefore, organisms may have evolved their mutation rates to appropriate levels to maintain or improve their fitness. In this study, we aimed to experimentally quantify the relationship between mutation rate and the speed of antibiotic resistance evolution. We conducted experimental evolution using twelve Escherichia coli mutator strains with increased mutation rates and five antibiotics. Our results showed that the highest mutation rate did not necessarily lead to the highest speed of adaptation, indicating a non-monotonic relationship between the speed of drug resistance evolution and mutation rate as expected. Moreover, this relationship was observed to differ among drugs, with significant differences in peak size observed between bacteriostatic and bactericidal antibiotics. We also successfully reproduced the mutation-rate dependence of the speed of adaptation using numerical simulation of a population dynamics model. These findings offer significant insights into the mutation rate’s evolution concomitant with the development of antibiotic resistance.
A federated query between neXtProt and OrthoDB retrieves 44 uncharacterized human proteins highly expressed in the brain and conserved in Drosophila melanogaster

To help researchers identify uncharacterized human genes that can be investigated using Drosophila melanogaster as a model organism, a new query federated between neXtProt and OrthoDB has been added on the neXtProt platform (NXQ_00300). The output of this query shows that there are 44 uncharacterized genes highly expressed in the human brain for which a homolog is found in Drosophila melanogaster
Specifying credal sets with probabilistic answer set programming

Probabilistic Answer Set Programming offers an intuitive and powerful declarative language to represent uncertainty about combinatorial structures. Remark-ably, under the credal semantics, such programs can specify any infinitely monotone Choquet Capacity in an intuitive way. Yet, one might be interested in specifying more general credal sets. We examine how probabilistic answer set programs can be extended to represent more general credal sets with constructs that allow for imprecise probability values. We characterize the credal sets that can be captured with various languages, and discuss the expressivity and complexity added by the use of imprecision in probabilistic constructs.
BiodivBERT: a Pre-Trained Language Model for the Biodiversity Domain

Information Extraction in the Life Sciences is getting increasing attention due to the constantly growing amount of data and text. The advancements of deep learning models further accelerate this development. However, applying these models to domain-specific data is crucial as applied domains often require different entity type extractions than general ones. This paper introduces BiodivBERT, the first pretrained language model for the biodiversity domain. We constructed two pre-training corpora (abstracts and abstracts + full text) based on a keyword search strategy from two leading publishers in the Life Sciences. In addition, we fine-tuned BiodivBERT on two downstream tasks, i.e., Named Entity Recognition (NER) and Relation Extraction (RE), using various state-of-the-art benchmarks. The results show that BiodivBERT outperforms the state-of-the-art approaches. Moreover, we discuss a potential application of BiodivBERT for ontology auto-population. We publicly release data and code for both pre-training and fine-tuning.
Predicting missing annotations in Gene Ontology with Knowledge Graph Embeddings and True Path Rule

Gene Ontology (GO) and its Annotations (GOA) provide a controlled and evolving vocabulary for gene products and gene functions widely used in molecular biology. GO & GOA are updated and maintained both automatically from biological publications and manually by curators. These knowledge bases however are often incomplete for two reasons: 1) Research in biological domain itself is still ongoing; 2) The amount of experimental evidence might not be yet sufficient to validate annotations. In this paper, we address the gap in evidence between gene products and their annotations by making link predictions using Knowledge Graph Embedding (KGE) methods. Through the application of the True Path Rule (TPR) in the training stage of KGE, we were able to improve the performance of traditional KGE methods. We report two experimental scenarios with GO and GO Chicken Annotation datasets to show the contribution of embedding TPR to prediction accuracy.
Learning calibrated belief functions from conformal predictions

We consider the problem of supervised classification. We focus on the problem of calibrating the classifier’s outputs. We show that the p-values provided by Inductive Conformal Prediction (ICP) can be interpreted as a possibility distribution over the set of classes. This allows us to use ICP to compute a predictive belief function which is calibrated by construction. We also propose a learning method which provides p-values in a simpler and faster way, by making use of a multi-output regression model. Results obtained on the Cifar10 and Digits data sets show that our approach is comparable to standard ICP in terms of accuracy and calibration, while offering a reduced complexity and avoiding the use of a calibration set.
OntoClue, a framework to compare vector-based approaches for document relatedness using the RELISH corpus

The continuous increase of biomedical scholarly publications makes it challenging to construct document recommendation algorithms to navigate through literature, an important feature for researchers to keep up with relevant publications. Understanding semantic relatedness and similarity between two documents could improve document recommendations. The objective of this study is performing a comparative analysis of vector-based approaches to assess document similarity in the RELISH corpus. Here we present our approach to compare five different techniques to generate vectors representing the text in the documents. These techniques employ a combination of various Natural Language Processing frameworks such as Word2Vec, Doc2Vec, dictionary-based Named Entity Recognition as well as state-of-the-art models based on BERT
ObjectGlobe: Open Distributed Query Processing Services on the Internet.

Today, there is a vast discrepancy between data publication and data processing capabilities on the Internet. Virtually anybody can publish data, e.g., in HTMLor XML-format. Consequently, a wide range of (different quality) data sources exist on the Internet, ranging from personal documents to real estate offers to product catalogs, to name just a few. In a way, the Internet could be viewed as a large distributed database. However, today’s web sites are merely “dumb” page servers which are only capable to send the data sitting behind a particular URL/URI. At best, web sites can process local queries if they are backed by a database system and a query interface is published via, e.g., a forms interface. But true distributed query processing plans as enabled by homogeneous distributed databases with interacting distributed subplans are not supported. Our goal is to create a query processing server that can be deployed throughout the Internet. These query servers can then be used in a federation to execute truly distributed query processing plans composed of completely unrelated query processing services which are offered on the Internet in an open market. These services could be specialized on providing data, resources for the query execution itself (CPU power, storage area) or functions which can be embedded in the execution. Such an open system could vastly ease the interaction in business-to-business and business-to-customer applications like shopping portals, electronic marketplaces or virtual enterprises. For example, somebody could search for real estate offers which fulfill some constraints with regard to the building, its location and the corresponding ambient data. The respective query should then access data from several commercial realtor databases, a geographical information system and a server with global ambient data. Additionally, the query should use a ranking function specialized for real estate data and provided in the form of mobile code by a third party specialized in that particular business area.
Query Refinement in Similarity Retrieval Systems.

In many applications, users specify target values for certain attributes/features without requiring exact matches to these values in return. Instead, the result is typically a ranked list of the top k objects that best match the specified feature values. User subjectivity is an important aspect of such queries, i.e., which objects are relevant to the user and which are not depends on the perception of the user. Due to the subjective nature of similarity-based retrieval, the answers returned by the system to a user query often do not satisfy the user’s information need right away; either because the weights and the distance functions associated with the features do not accurately capture the user’s perception or because the specified target values do not fully capture her information need or both. The most commonly used technique to overcome this problem is query refinement. In this technique, the user provides to the system some feedback on the “relevance” of the answers to the user’s query. The system then analyzes the feedback, refines the query (i.e., modifies the weights, distance functions, target values etc.) evaluates it and returns the new results. In this paper, we provide an overview of the techniques used to construct the refined query based on the feedback from the user as well as the techniques to evaluate the refined query efficiently. We present experimental results demonstrating the effectiveness of the techniques discussed in the paper.
COMPARISON OF TONSILLECTOMY WITH HARMONIC SCALPEL AND CONVENTIONAL DISSECTION TONSILLECTOMY IN THE MANAGEMENT OF PATIENTS WITH CHRONIC TONSILLITIS

Background: Tonsillectomy remains one of the most common childhood surgeries with an increase in several surgeries being done to treat OSA or sleep-disordered breathing. Aim: The study aims to compare the operating time, intraoperative blood loss, postoperative pain, the time needed to return to a normal diet, and activity in harmonic scalpel and dissection type of tonsillectomy. Materials and Methods: A prospective study was conducted at the ENT department of Pushpagiri Institute of Medical Sciences and Research Centre, Thiruvalla, Kerala, for March 2015 to September 2016. A total of 44 patients were included in the study of which 18 patients underwent tonsillectomy with harmonic scalpel whereas the rest underwent cold dissection method. Before the procedure, a thorough history, ENT examination, and the necessary investigations were completed. Informed consent was obtained from all patients. Result: Out of 24 cases of dissection, 15 (62.5%) were males and 9 (37.5%) were females. Whereas in the case of a harmonic scalpel, both males and females were equal (10 each), and there is no significant difference. Only in the case of postoperative day 5 showed a significant difference in the pain when the methods were different. It had an average pain of 3.42 in the dissection method and 2.10 in the harmonic scalpel method. In all other cases the post-operative at 6 hours, day 1, day 3, and day 7, there were no significant differences in pain severity between the two methods. Conclusion: We conclude that harmonic scalpel tonsillectomy significantly reduces intraoperative bleeding and reduction in pain scores on POD 5 and overall pain scores and early return to normal activity.
Understanding the physical and social environmental determinants of road traffic injury in South Africa

Abstract Road traffic injuries (RTIs) are a major public health challenge, accounting for significant injury, economic and psycho-social burden to societies across the world. While decreases are projected for many high-income countries (HICs) over the next decade or so, staggering increases in the burden of mortality and morbidity are forecast for low- and middle-income countries (LMICs). The unique contextual influences on RTIs in LMICs are, however, not well understood. Conceptual frameworks applied mostly to HICs also do not provide adequate recognition of the unique contextual influences of LMICs. Accordingly, the research in this thesis adopts a predominantly geographical approach to incorporate a large range of physical and social environmental effects, and which are aggregated at different spatial and spatial-temporal scales to understand the contextual influences to road traffic injuries (RTIs) in the South African (S.A) setting. In this regard, four studies are presented; these include: a geographical epidemiology and risk analysis at the district council level and for time, space and population aggregations; an integrated spatialtemporal analysis at the province-week level; a fine-scale geographical analysis at the police area level; and a small area analysis at the suburb level for the city of Durban. In addition to important effects relating to alcohol and travel exposure, findings have shown most environmental influences on RTIs in S.A to be development-related, including effects relating to social and area deprivation, violence and crime, and rurality. With the exception of rurality, the above effects showed a positive association with the occurrence of RTIs in S.A. The findings have implications for alignment and possible integration of road safety policies and practices with other developmental policies in the country. In addition, this research has shown that geographical approaches may provide a useful analytical framework for understanding the complexity and interacting influences within broader systems-based approaches; and especially those of the contextual environment that are particularly relevant for LMIC settings.
Photodynamic Therapy for Choroidal Neovascularization

Photodynamic therapy (PDT) is a treatment modality designed to selectively occlude ocular choroidal neovascular tissue. The therapy is a 2-step process, consisting initially of an injection of the photosensitizer verteporfin, followed 15 minutes later by laser treatment to the targeted sites of neovascularization in the retina. The laser treatment selectively damages the vascular endothelium. Patients may be re-treated if leakage from choroidal neovascularization (CNV) persists.
Iterated Deep Q-Network: Efficient Learning of Bellman Iterations for Deep Reinforcement Learning

Value-based reinforcement learning methods strive to obtain accurate approximations of optimal action-value functions. Notoriously, these methods heavily rely on the application of the optimal Bellman operator, which needs to be approximated from samples. Most approaches consider only a single Bellman iteration, which limits their power. In this paper, we introduce iterated Deep Q-Network (iDQN), a new DQN-based algorithm that incorporates several consecutive Bellman iterations into the training loss. iDQN leverages the online network of DQN to build a target for a second online network, which in turn serves as a target for a third online network, and so forth, thereby taking into account future Bellman iterations. While using the same number of gradient steps, iDQN allows for better learning of the Bellman iterations than DQN. After providing some theoretical guarantees, we evaluate iDQN against relevant baselines on 54 Atari 2600 games to showcase its benefit in terms of approximation error and performance. iDQN outperforms DQN while being orthogonal to more advanced DQN-based approaches.
Automatic Language Identification Using a Segment-based Approach 1

A segment-based Automatic Language Identi cation (ALI) system has been developed. The system was designed around a formal probabilistic framework. This framework forms the basis for investigating the ALI approach proposed by House and Neuburg which utilizes phonotactic constraints of languages. The system incorporates di erent components which model the phonotactic, prosodic, and acoustic properties of the di erent languages used in the system. The system was trained and tested using the OGI Multi-Language Telephone Speech Corpus. An overall system performance of 47.7% was achieved in identifying the language of test utterances.