Miracle-dz/UniFAR-FTUs huggingface dataset:
m6A landscape is more pervasive when Trypanosoma brucei exits the cell cycle

N6-methyladenosine (m6A) is a mRNA modification with important roles in gene expression. In African trypanosomes, this post-transcriptional modification is detected in hundreds of transcripts and it affects the stability of the variant surface glycoprotein (VSG) transcript in the proliferating blood stream form. However, how m6A landscape varies across the life cycle remains poorly defined. Using full-length, non-fragmented RNA, we immunoprecipitated and sequenced m6A-modified transcripts across three life cycle stages of Trypanosoma brucei – slender (proliferative), stumpy (quiescent), and procyclic forms (proliferative). We found that 1037 transcripts are methylated in at least one of these three life cycle stages. While 21% of methylated transcripts are common in the three stages of the life cycle, globally each stage has a distinct methylome. Interestingly, 47% of methylated transcripts are detected in the quiescent stumpy form only, suggesting a critical role for m6A when parasites exit the cell cycle and prepare for transmission by the Tsetse fly. In this stage, we found that a significant proportion of methylated transcripts encodes for proteins involved in RNA metabolism, which is consistent with their reduced transcription and translation. Moreover, we found that not all major surface proteins are regulated by m6A, as procyclins are not methylated, and that, within the VSG repertoire, not all VSG transcripts are demethylated upon parasite differentiation to procyclic form. This study reveals that the m6A regulatory landscape is specific to each life cycle stage, becoming more pervasive as T. brucei exits the cell cycle. Summary African trypanosome parasites adapt to mammalian and insect hosts by adjusting gene expression, morphology, and metabolism. In this study, we focus on how N6-methyladenosine (m6A), a post-transcriptional modification, affects the parasite’s transcriptome throughout its differentiation from the mammalian host to the fly. We found that methylation is differentially regulated as the life cycle progresses, being particularly prevalent in the non-proliferative stumpy form, as more methylated transcripts are found at this insect-infective stage than in slender and procyclic forms. We further show that the not all parasite surface proteins are regulated by m6A and that the previously identified link between m6A methylation and the expression level of the major surface protein of bloodstream forms applies to the active variant surface glycoprotein, but not always to silent genes, suggesting two distinct regulatory mechanisms of (de)methylation.
Limited transmission of microbial species among coral reef fishes from the Great Barrier Reef, Australia

Reef fishes account for one-third of all extant marine fishes and exhibit enormous biodiversity within a highly interactive ecosystem. Yet relatively little is known about the diversity and evolution of microbial species (bacteria, viruses, and eukaryotes) associated with reef fish, even though this may provide valuable insights into the factors that shape microbial communities within vertebrate hosts as well as the extent and pattern of cross-species transmission. Through metatranscriptomic sequencing we characterised the viruses, bacteria, and single-celled eukaryotes from 128 reef fish species inhabiting Lizard Island and Orpheus Island on the Great Barrier Reef, Australia. We assessed whether microbial communities differed between islands that are separated by approximately 450 kilometres, and to what extent viruses were able to emerge in new hosts. Notably, despite strong ecological interactions in the reef environment, and the presence of the same families and subfamilies of viruses and bacteria on both islands, there was minimal evidence for the cross-species transmission of individual microorganisms among fish species. An exception was the high prevalence of the bacterial pathogen Photobacterium damselae among apparently healthy cardinalfishes from both islands, indicating that these fish species are natural reservoirs within the reef system. Overall, these data suggest that reef fishes have microbial-host associations that arose prior to the formation of the Great Barrier Reef, leading to strong host barriers to cross-species microbial transmission even within a highly interactive and species-rich environment.
Expecting the Unexpected: Infants Use Others’ Surprise to Revise Their Own Expectations

Abstract Human infants show systematic responses to events that violate their expectations. Can they also revise these expectations based on others’ expressions of surprise? Here we ask whether infants (N = 156, mean = 15.2 months, range: 12.0–18.0 months) can use an experimenter’s expression of surprise to revise their own expectations about statistically probable vs. improbable events. An experimenter sampled a ball from a box of red and white balls and briefly displayed either a surprised or an unsurprised expression at the outcome before revealing it to the infant. Following an unsurprised expression, the results were consistent with prior work; infants looked longer at a statistically improbable outcome than a probable outcome. Following a surprised expression, however, this standard pattern disappeared or was even reversed. These results suggest that even before infants can observe the unexpected events themselves, they can use others’ surprise to expect the unexpected. Starting early in life, human learners can leverage social information that signals others’ prediction error to update their own predictions.
REASONER: An Explainable Recommendation Dataset with Comprehensive Labeling Ground Truths

Explainable recommendation has attracted much attention from the industry and academic communities. It has shown great potential to improve the recommendation persuasiveness, informativeness and user satisfaction. In the past few years, while a lot of promising explainable recommender models have been proposed, the datasets used to evaluate them still suffer from several limitations, for example, the explanation ground truths are not labeled by the real users, the explanations are mostly single-modal and around only one aspect. To bridge these gaps, in this paper, we build a new explainable recommendation dataset, which, to our knowledge, is the ﬁrst contribution that provides a large amount of real user labeled multi-modal and multi-aspect explanation ground truths. In speciﬁc, we ﬁrstly develop a video recommendation platform, where a series of questions around the recommendation explainability are carefully designed. Then, we recruit about 3000 high-quality labelers with different backgrounds to use the system, and collect their behaviors and feedback to our questions. In this paper, we detail the construction process of our dataset and also provide extensive analysis on its characteristics. In addition, we develop a library, where many well-known explainable recommender models are implemented in a uniﬁed framework. Based on this library, we build several benchmarks for different explainable recommendation tasks. At last, we present many new opportunities brought by our dataset, which are expected to promote the ﬁeld of explainable recommendation. Our dataset, library and the related documents have been released at https://reasoner2023.github.
Universal Captioner: Inducing Content-Style Separation in Vision-and-Language Model Training

While captioning models have obtained compelling results in describing natural images, there is a growing effort to increase their capability of dealing with real-world concepts. In this paper, we address the task of generating fluent descriptions by training on a non-uniform combination of data sources, containing both human-and automatically-collected captions. To this end, we propose a model which induces a separation between content and descriptive style through the incorporation of stylistic parameters and keywords extracted from large-scale multi-modal models as pivotal data. In terms of visual features, our model avoids the need of object detectors and employs grid-like features together with a single objective of prompt language modeling. Experimen-tally, we consistently outperform existing methods in terms of caption quality and capability of describing out-of-domain concepts. Finally, our model obtains a new state of the art on both COCO and nocaps.
Fisheries gear and biological context drive fishing-related incidental mortality in Pacific salmon spawning migrations

Most fisheries inadvertently capture non-target fish species or populations. In addition, fish may interact with and escape fishing gears without being observed. Regardless of whether fish are released or escape, negative impacts from interacting with fishing gears (including stress and injury) may lead to fishing-related incidental mortality (FRIM). However, FRIM is typically difficult to observe and therefore is not easily or well quantified. Further, FRIM is contingent on many biological and environmental factors, which have received little consideration in fisheries management. Specifically, infectious disease is expected to mediate FRIM, but has been studied little in this context. Because of their life histories, Pacific salmon are both particularly vulnerable to FRIM and ideal model animals for its study. This dissertation sought to further the knowledge regarding FRIM in Pacific salmon during their freshwater spawning migrations by conducting in situ studies of capture methods and pertinent biological factors. Salmon received radio-frequency tags following exposure to fisheries gear so that migration survival could be determined. Quantitative PCR was performed on non-lethal biopsies to determine the presence and load of infectious agents, as well as immune system function. Sockeye salmon escaping or released from gillnets experienced elevated FRIM, slowed migration, and reduced spawning success. Although the impact of gears was consistent between years, the incidence of FRIM was reduced for mature fish. The high impact of severe injuries was indicated by low migration survival and the elevated expression of acute phase response genes. Mature Chinook salmon with infections of a blood-borne protozoan parasite experienced elevated mortality, regardless of a simulated fisheries gear exposure. My dissertation indicates that salmon interacting with gillnets in freshwater experience significant FRIM, but biological factors can override these impacts. Future research should seek to estimate the rate of gillnet escapes, identify gears that minimize physical damage, localize the time in migrations when maturity confers resilience, and use repeated sampling to better define the role of infectious agents in FRIM. Fisheries managers could reduce FRIM by prioritizing lower impact gears, promoting fisheries in locations where salmon are more mature and pathogens are sparse, and conducting regular monitoring of infectious agents.
Teachers’ Adoption of Information and Communication Technology in Foundation Phase Teaching: A Study of Selected Schools in the Eastern Cape in South Africa

Abstract This paper aimed to evaluate the teachers’ use of information and communication technology (ICT) as a means to improve teaching and learning of mathematics in disadvantaged contexts. Sixty teachers were selected from one district through convenience and stratified random sampling, and they responded to structured questionnaires. The predictors for teachers’ integration of ICT comprised, possession of a computer record of word problems and exercises on a computer, regularly using the Internet to download word problems videos, transcriptions and exercise samples, and regularly using computer games to create enthusiasm among learners when tackling word problems. The analysis of data was done using both descriptive and inferential statistics. The results showed a lack of ICT resources in the selected schools and a shortage of teachers skilled in ICT use. The research contributed to technology acceptance model’s (TAM) theoretical validity by recommending an integrative conceptual framework of system adoption.
Optimized C-Medoids Evidential Clustering to Evaluate and Explore Multi Feature Data Stream

- In artificial intelligence-related applications, for example, bio-clinical, bio-informatics, information grouping is a significant and complex assignment with various circumstances. Model-based bunching is the sensible and straightforwardness to depict and assess information which can be treated as non-vertical portrayal of social information. As a result of Barycentric space present in model grouping, keep up with and update the design of the bunch with various information focuses is as yet testing task for various items in bio-clinical social information. So that in this paper we propose and present A Novel Optimized Evidential C-Medoids (NOEC) which is connects with family o model-based bunching approach for update and closeness of clinical social information. We use Ant Colony Optimization way to deal with empower the administrations of comparability with various highlights for social update bunch clinical information. Play out our methodology on various bio-clinical related manufactured informational indexes. Test consequences of proposed approach give better and effective outcomes with examination of various boundaries as far as exactness and time with handling of clinical social informational indexes.
Assessing conservation status of calcareous grassland in two French military training areas in Natura 2000 context

Calcareous grasslands are habitats of high conservation value. These rich habitats are declining notably due to agriculture and habitat fragmentation. In France, calcareous grasslands may be found on Military Training Areas (MTA), which protect them from intensive agriculture and urban development. By analysing their �oristic structure and composition, the aim of this publication is to determine the conservation status of grassland on two French MTA, following a militarisation gradient. Vegetation characteristics appeared different according to the level of military activities. Military activities and their related management, favour rich and diversi�ed grasslands in the good conservation state. They also allow the creation of a mosaic of habitats within the grasslands with the presence of overgrown areas. Thus, MTAs appeared to present good potential for preserving the remaining calcareous grassland in France.
STUDY OF VIABLE BACTERIOPHAGES DURING ACTIVATION OF THE PSEUDOLYSOGENIC STATE OF PECTOBACTERIUM

Th e rare occurrence of viable bacteriophages is an issue in studying bacterial-phage systems of the phytopathogenic bacterium Pectobacterium carotovorum subsp. carotovorum (Pcc). Aim . To obtain viable bacteriophages by activating pseudolysogeny of the Pcc strains. Methods. To obtain bacteriophage F66, isogenic clones of Nal r mutants of strains Pcc 66A and Pcc M2-4 were used. To study the properties of the phage, ultracentrifugation of viral particles, electron microscopy, electrophoretic separation
TRAFFIC SIGN RECOGNITION USING DEEP LEARNING FOR AUTONOMOUS DRIVERLESS VEHICLES

Previously, innovative things like brilliant vehicles played a significant role. It is critical to grasp and understand the street indicators in order to foster clever automobiles for street security. The figure is based on the previous LeNet-5 CNN procurement. Gabor's presentation was used to present the approach, which was followed by the standard revolution and then the reconciliation of components. The Adams approach is the ideal tactic to adopt in this situation. Saturation and Shade The value of shade reflects the qualities of quick discovery and low light. With the help of German Vehicle Identification, the street recognisable proof technique is being tested. This project has an HTML and CSS UI framework, and Flask is used to deploy both the model and the framework.The UI framework collects the essential inputs to estimate water quality and feeds them to a machine learning model, which then displays the expected outcome on the page.
Benchmarking of Fast and Interpretable UF Machine Learning Potentials

Ab initio methods offer great promise for materials design, but they come with a hefty computational cost. Recent advances with machine learning interatomic potentials (MLIPs) have revolutionized molecular dynamic simulations by providing high accuracies similar to ab initio models but at much reduced computational cost. Our study evaluates the ultra-fast force fields (UF3) potential, employing linear regression with cubic B-spline basis for assessing effective two-and three-body potentials. On benchmarking, UF3 displays comparable precision to established models like GAP, MTP, NNP (Behler Parrinello), and qSNAP MLIPs, yet is significantly faster by two to three orders of magnitude. A distinct feature of UF3 is its capability to render visual representations of learned two-and three-body potentials, shedding light on potential gaps in the learning model. In refining UF3’s performance, a comprehensive sweep of the hyperparameter space was undertaken. While our current optimizations are concentrated on energies and forces, we are primed to broaden UF3’s evaluation spectrum, focusing on its applicability in critical areas of molecular dynamics simulations. The outcome of these investigations will not only enhance the predictability and usability of UF3 but also pave the way for its broader applications in advanced materials discovery and simulations.
Empirical Learning on Machine Learning Approaches for Discovery and Forecast of Coronavirus

- The new, fatal Coronavirus disease originated in the Wuhan city of China in December 2019 by spreading like a fire in different stages across the entire world. It has affected millions of people and has caused deaths in thousands worldwide. It is a requisite to find the infected people, classify them and take necessary precaution in earlier stages. Due to the increased spread of Coronavirus, limited polymerase chain reaction kits are available in the hospitals for undergoing testing for Coronavirus patients. It is more essential to develop artificial intelligence-based tools for automatic diagnostic and classification of Coronavirus epidemic. This paper aims to acquire knowledge about the disease epidemiology, prevention from spreading of Coronavirus Severe Acute Respiratory Syndrome, and suggests various algorithms of machine learning. The work classifies the images of Coronavirus obtained from X-Ray and computed tomography. Machine learning technologies have potential clinical applications for medical images.
An Agent-Based Model of Reddit Interactions and Moderation

—Among popular social media platforms, Reddit stands out for its decentralized approach to moderation and community management. Due to this and its community-based network structure, Reddit provides a unique environment for studying the diffusion of knowledge and beliefs over social media. While assortativity, polarization, and user behavior have been examined within empirical contexts, having the ability to model the impacts of different moderation policies and rules across communities could provide useful insights for limiting the spread of misinformation. In this work, we introduce an agent-based model of Reddit interactions and moderating actions. By simulating interactions at the user level and specifying user-specific attributes, our model allows practitioners to conduct experiments with various types of actors and moderators and study their potential impact on Reddit-facilitated discussions and information diffusion. Additionally, subreddit-specific attributes enable communities to have different standards and thresholds for user conduct. To validate this model, we rely on an empirical dataset of over 100K posts and 800K comments across three U.S. political events in addition to user surveys and studies.
Evaluating Directed Fuzzers: Are We Heading in the Right Direction?

Directed fuzzing recently has gained significant attention due to its ability to reconstruct proof-of-concept (PoC) test cases for target code such as buggy lines or functions. Surprisingly, however, there has been no in-depth study on the way to properly evaluate directed fuzzers despite much progress in the field. In this paper, we present the first systematic study on the evaluation of directed fuzzers. In particular, we analyze common pitfalls in evaluating directed fuzzers with extensive experiments on five state-of-the-art tools, which amount to 30 CPU-years of computational effort, in order to confirm that different choices made at each step of the evaluation process can significantly impact the results. For example, we find that a small change in the crash triage logic can substantially affect the measured performance of a directed fuzzer, while the majority of the papers we studied do not fully disclose their crash triage scripts. We argue that disclosing the whole evaluation process is essential for reproducing research and facilitating future work in the field of directed fuzzing. In addition, our study reveals that several common evaluation practices in the current directed fuzzing literature can mislead the overall assessments. Thus, we identify such mistakes in previous papers and propose guidelines for evaluating directed fuzzers.
Transformational Leadership and Safety Performance of Malaysia’s Small and Medium Manufacturing Firms

: The increasing accident trend in Malaysia indicates insufficient or ineffective safety performancewithin its industry. Majority of accidents at workplaces were derived from Small Medium-Size Enterprises (SMEs) sector which accounted at 80%. Safety behaviour is also found as the substantial factor towards the scenario. Therefore, the research objective is to investigate the relationship between transformational leadership towards workers’ safety participation, safety compliance and safety performance in SMEs manufacturing firms. A total of 157 respondents who are safety officers or human resource personnel from selected manufacturing SMEs had participated in this study. The analysis of data collection has been performed by statistical tool Partial-least-squares structural equation modelling (PLS-SEM) Smart PLS version 3.0. A significant effect has been outlined for transformational leadership towards safety participation, safety compliance and safety performance. This research concluded that an increase in transformational leadership of supervisors will improve the overall safety performance of SME at employee as well as organisational level.