Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ups and Downs of Training RoBERTa-based models on Smaller Datasets for Translation Tasks from Classical Chinese into Modern Standard Mandarin and Modern English

The paper presents an investigation into the effectiveness of pre-trained language models, Siku-RoBERTa and RoBERTa, for Classical Chinese to Modern Standard Mandarin and Classical Chinese to English translation tasks. The English translation model resulted in unsatisfactory performance due to the small dataset, while the Modern Standard Mandarin model gave reasonable results.
Findings of the CoCo4MT 2023 Shared Task on Corpus Construction for Machine Translation

This paper provides an overview of the first shared task on choosing beneficial instances for machine translation, conducted as part of the CoCo4MT 2023 Workshop at MTSummit. This shared task was motivated by the need to make the data annotation process for machine translation more efficient, particularly for low-resource languages for which collecting human translations may be difficult or expensive. The task involved developing methods for selecting the most beneficial instances for training a machine translation system without access to an existing parallel dataset in the target language, such that the best selected instances can then be manually translated. Two teams participated in the shared task, namely the Williams team and the AST team. Submissions were evaluated by training a machine translation model on each submission’s chosen instances, and comparing their performance with the chRF++ score. The system that ranked first is by the Williams team, that finds representative instances by clustering the training data.
Fibrillar adhesion dynamics govern the timescales of nuclear mechano-response via the vimentin cytoskeleton

The cell nucleus is continuously exposed to external signals, of both chemical and mechanical nature. To ensure proper cellular response, cells need to regulate not only the transmission of these signals, but also their timing and duration. Such timescale regulation is well described for fluctuating chemical signals, but if and how it applies to mechanical signals reaching the nucleus is still unknown. Here we demonstrate that the formation of fibrillar adhesions locks the nucleus in a mechanically deformed conformation, setting the mechanical response timescale to that of fibrillar adhesion remodelling (∼1 hour). This process encompasses both mechanical deformation and associated mechanotransduction (such as via YAP), in response to both increased and decreased mechanical stimulation. The underlying mechanism is the anchoring of the vimentin cytoskeleton to fibrillar adhesions and the extracellular matrix through plectin 1f, which maintains nuclear deformation. Our results reveal a mechanism to regulate the timescale of mechanical adaptation, effectively setting a low pass filter to mechanotransduction.
Prefrontal cortex signals value category while basal ganglia represent learned values in value learning

Value learning can be variable for objects with the exact same reward but the underlying neural mechanism for such variability is not known. We have addressed this question by recording single-unit activity in the prefrontal cortex (PFC) and substantia nigra reticulata (SNr), two key nodes in cortex-basal ganglia circuitry with crucial roles in value learning, as macaque monkeys learned to associate novel objects with either high or low rewards. Estimating the trial by trial learned values based on choice performance, revealed stark differences between learned values across objects with the same reward outcome. Importantly, while PFC neurons rapidly learned to differentiate objects based on their value category, the firing in SNr correlated with the variability in learned values within a value category. Our results suggest that the variation in objects’ learned values is more likely to be a readout of SNr firing while PFC may provide a top-down teaching signal to basal ganglia to demarcate the value categories.
Telomere-to-telomere assembly by preserving contained reads

Automated telomere-to-telomere (T2T) de novo assembly of diploid and polyploid genomes remains a formidable task. A string graph is a commonly used assembly graph representation in the overlap-based algorithms. The string graph formulation employs graph simplification heuristics, which drastically reduce the count of vertices and edges. One of these heuristics involves removing the reads contained in longer reads. However, this procedure is not guaranteed to be safe. In practice, it occasionally introduces gaps in the assembly by removing all reads that cover one or more genome intervals. The factors contributing to such gaps remain poorly understood. In this work, we mathematically derived the frequency of observing a gap near a germline and a somatic heterozygous variant locus. Our analysis shows that (i) an assembly gap due to contained read deletion is an order of magnitude more frequent in Oxford Nanopore reads than PacBio HiFi reads due to differences in their read-length distributions, and (ii) this frequency decreases with an increase in the sequencing depth. Drawing cues from these observations, we addressed the weakness of the string graph formulation by developing the RAFT assembly algorithm. RAFT addresses the issue of contained reads by fragmenting reads and producing a more uniform readlength distribution. The algorithm retains spanned repeats in the reads during the fragmentation. We empirically demonstrate that RAFT significantly reduces the number of gaps using simulated datasets. Using real Oxford Nanopore and PacBio HiFi datasets of the HG002 human genome, we achieved a twofold increase in the contig NG50 and the number of haplotype-resolved T2T contigs compared to Hifiasm.
Ribosome Decision Graphs for the Representation of Eukaryotic RNA Translation Complexity

The application of ribosome profiling has revealed an unexpected abundance of translation in addition to that responsible for the synthesis of previously annotated protein-coding regions. Multiple short sequences have been found to be translated within single RNA molecules, both within annotated protein-coding and non-coding regions. The biological significance of this translation is a matter of intensive investigation. However, current schematic or annotation-based representations of mRNA translation generally do not account for the apparent multitude of translated regions within the same molecules. They also do not take into account the stochasticity of the process that allows alternative translations of the same RNA molecules by different ribosomes. There is a need for formal representations of mRNA complexity that would enable the analysis of quantitative information on translation and more accurate models for predicting the phenotypic effects of genetic variants affecting translation. To address this, we developed a conceptually novel abstraction that we term Ribosome Decision Graphs (RDGs). RDGs represent translation as multiple ribosome paths through untranslated and translated mRNA segments. We termed the later ‘translons’. Non-deterministic events, such as initiation, re-initiation, selenocysteine insertion or ribosomal frameshifting are then represented as branching points. This representation allows for an adequate representation of eukaryotic translation complexity and focuses on locations critical for translation regulation. We show how RDGs can be used for depicting translated regions, analysis of genetic variation and quantitative genome-wide data on translation for characterisation of regulatory modulators of translation.
STARQ: Domain-Invariant Brainstem Nuclei Segmentation and Signal Quantification

Brainstem nuclei are hard to distinguish due to very few distinctive features which makes detecting them with high accuracy extremely difficult. We introduce StARQ that builds on SeBRe, a deep learning-based framework to segment regions of interest. StARQ provides new functionalities for automated segmentation of brainstem nuclei at high granularity, and quantification of underlying neural features such as axonal tracings, and synaptic punctae. StARQ will serve as a toolbox for generalized brainstem analysis, enabling reliable high-throughput computational analysis with open-source models.
Methods for Implementing Integrated Step-Selection Functions with Incomplete Data

Integrated step-selection analyses (iSSAs) are versatile and powerful frameworks for studying habitat and movement preferences of tracked animals. iSSAs utilize integrated step-selection functions (iSSFs) to model movements in discrete time, and thus, require animal location data that are regularly spaced in time. However, many realworld datasets are incomplete due to tracking devices failing to locate an individual at one or more scheduled times, leading to slight irregularities in the duration between consecutive animal locations. To address this issue, researchers typically only consider bursts of regular data (i.e., sequences of locations that are equally spaced in time), thereby reducing the number of observations used to model movement and habitat selection. We reassess this practice and explore four alternative approaches that account for temporal irregularity resulting from missing data. Using a simulation study, we compare these alternatives to a baseline approach where temporal irregularity is ignored and demonstrate the potential improvements in model performance that can be gained by leveraging these additional data.
miRNA-mediated inhibition of an actomyosin network in hippocampal pyramidal neurons restricts sociability in adult male mice

Social deficits are frequently observed in patients suffering from neurodevelopmental disorders, but the molecular mechanisms regulating sociability are still poorly understood. We recently reported that the loss of the microRNA cluster miR-379-410 leads to hypersocial behavior and anxiety in mice. Here, we show that ablating miR-379-410 in excitatory neurons of the postnatal mouse hippocampus recapitulates hypersociability, but not anxiety. At the cellular level, miR-379-410 loss in excitatory neurons leads to increased excitatory synaptic transmission and upregulation of an actomyosin gene network. Re-expression of three cluster miRNAs, as well as pharmacological inhibition of the actomyosin activator ROCK, was sufficient to reinstate normal sociability in miR-379-410 knockout mice. Several actomyosin genes and miR-379-410 family members were reciprocally dysregulated in isogenic human iPSC-derived neurons harboring a deletion present in Williams-Beuren-Syndrome patients, which are characterized by hypersocial behavior. Together, our results unveil a novel microRNA-actomyosin pathway involved in the control of sociability.
Digital Health Taxonomy

Taxonomies have been an invaluable tool in helping researchers structuring and categorizing terms and concepts of various fields. The same principles apply to the digital transformation in the healthcare industry, where a growing plethora of terms and concepts are not well-defined and cause confusion. A taxonomy can be used to organize and classify these terms and concepts, providing a coherent vision and establishing a knowledge base for research and implementation of digital health transformation. In this work, the authors conducted a literature review and proposed a taxonomy to organize and classify terms, concepts, technologies, and systems associated with the digitalization of healthcare services. The purpose was to establish a coherent set of concepts and clear definitions, creating a robust knowledge base and a well-defined conceptual framework for this rapidly evolving field.
No Root Store Left Behind

When a root certificate authority (CA) in the Web PKI misbehaves, primary root-store operators such as Mozilla and Google respond by distrusting that CA. However, full distrust is often too broad, so root stores often implement partial distrust of roots, such as only accepting a root for a subset of domains. Unfortunately, derivative root stores (e.g., Debian and Android) that mirror decisions made by primary root stores are often out-of-date and cannot implement partial distrust, leaving TLS applications vulnerable. We propose augmenting root stores with per-certificate programs called General Certificate Constraints (GCCs) that precisely control the trust of root certificates. We propose that primary root-store operators write GCCs and distribute them, along with routine root certificate additions and removals, to all root stores in the Web PKI. To justify our arguments, we review specific instances of CA certificate mis-issuance over the last decade that resulted in partial distrust of roots that derivative root stores were unable to precisely mirror. We also review prior work that illustrates the alarming lag between primary and derivative root stores. We discuss preliminary designs for GCC deployment and how GCCs could enable pre-emptive restrictions on CA power.
Physical Deployability Matters

While many network research papers address issues of deployability, with a few exceptions, this has been limited to protocol compatibility or switch-resource constraints, such as flow table sizes. We argue that good network designs must also consider the costs and complexities of deploying the design within the constraints of the physical environment in a datacenter: physical deployability. Traditional metrics of network "goodness" do not account for these costs and constraints, which might explain why some otherwise attractive designs have not been deployed in real-world datacenters.
Lightweb: Private web browsing without all the baggage

This paper proposes lightweb, a new system for private browsing. A lightweb client can browse a web of text-based pages without revealing to anyone---not the network, not the servers hosting the pages---which pages it is reading. Unlike Tor and other anonymizing web proxies, which are inherently vulnerable to traffic-analysis attacks, lightweb's design protects against traffic-analysis attacks by design. While lightweb is expensive in relative terms (hundreds of core-seconds of server computation per page load), we show with microbenchmarks that the total system cost can be inexpensive in absolute terms (comparable to the cost of a Netflix membership). This paper does not present a polished system, but instead aims to spark discussion on radical approaches to a privacy-first web.
SAFETY CULTURE IN OBSTETRIC NURSES' CLINICAL PRACTICE

Objective: to identify the safety culture dimensions that influence obstetric nurses’ clinical practice. Method: an observational, analytical and cross-sectional study, carried out from September 2018 to May 2019 at two maternity hospitals integrated in a hospital center in the Central region of Portugal. A total of 70 obstetric nurses answered the Hospital Survey on Patient Safety Culture. The data were submitted to descriptive and inferential analysis. The dimensions were considered strong when they presented scores ≥ 75.0% of positive answers, and deemed as problematic with scores ≤ 50.0%. Results: the “Teamwork within units” dimension was the one that obtained scores above 75.0%. The “Supervisor expectations”, “Feedback and communication about errors”, “Communication openness”, “Organizational learning” and “Hospital handoffs and transitions” dimensions obtained scores between 50.0% and 65.0%. The “Non-punitive response to error”, “Frequency of events reported”, “Management support for patient safety” and “Staffing” dimensions had scores below 40.0%. Conclusion
Exploring Collaborative Filtering Algorithms in MOOCs Recommender Systems: A Comprehensive Overview

Nowadays, Massive Open Online Courses (MOOCs) have revolutionized traditional education, making it more accessible to millions of learners. These courses allow students to enroll and learn from a variety of subjects offered by prestigious universities worldwide. However, the vast amount of information available in MOOCs can make it challenging for students to choose courses that meet their needs and interests, leading to high dropout and low completion rates. To address this issue, Recommender Systems (RS) can be used to personalize the learning process in order to improve the learners' engagement and the completion rates. This overview examines papers published between 2018 and 2022 in the Scopus database and available in the IEEEXplore, ACM Digital Library, and Science Direct repositories to present the state-of-the-art of MOOCs recommender systems based on collaborative filtering algorithms. Through a qualitative analysis, we examine the use of collaborative filtering algorithms in MOOCs recommender systems and highlight the challenges of this technique, including cold start and sparsity problems, as well as the recent techniques used in combination with this algorithm to alleviate these challenges.
Dinitrogen fixation in coral reef ecosystems facing climate change

Tropical coral reefs are among the most productive and diverse ecosystems, despite being surrounded by ocean waters where nutrients are in short supply. Benthic dinitrogen (N2) fixation is a significant internal source of “new” nitrogen (N) in reef ecosystems, but related information appears to be sparse. Here, we review the current state (and gaps) of knowledge on N2 fixation associated with coral reef organisms and their ecosystems. By summarizing the existing literature, we show that benthic N2 fixation is an omnipresent process in tropical reef environments. Highest N2 fixation rates are detected in reef-associated cyanobacterial mats and seagrass meadows, clearly showing the significance of these functional groups, if present, to the input of new N in reef ecosystems. Nonetheless, key benthic organisms such as hard corals also importantly contribute to benthic N2 fixation in the reef. Given the usually high coral coverage of healthy reef systems, these results indicate that benthic symbiotic associations may be more important than previously thought. In fact, mutualisms between carbon (C) and N2 fixers have likely evolved that may enable reef communities to mitigate N limitation. We then explore the potential effects of the increasing human interferences on the process of benthic reef N2 fixation via changes in diazotrophic populations, enzymatic activities or availability of benthic substrates favourable to these microorganisms. Current knowledge indicates positive effects of ocean acidification, warming and deoxygenation and negative effects of increased ultraviolet radiation on the amount of N fixed in coral reefs. Eutrophication may either boost or suppress N2 fixation, depending on the nutrient becoming limiting. Since N2 fixation appears to play a fundamental role in nutrientlimited reef ecosystems, these assumptions need to be expanded and confirmed by future research efforts addressing the knowledge gaps identified in this review. This chapter has been published as: Cardini U, Bednarz VN, Foster RA, Wild C (2014) Benthic N2 fixation in coral reefs and the potential effects of human-induced environmental change. Ecol Evol 4:1706-1727
Processing of Synthetic Aperture Radar (SAR) Images

Synthetic aperture radar provides broad-area imaging at high resolutions, which is used in applications such as environmental monitoring, earth-resource mapping, and military systems. This book presents the tools required for the digital processing of synthetic aperture radar images. They are of three types: (a) the elements of physics, (b) mathematical models and (c) image processing methods adapted to particular applications.
A Fuzzy Deep Learning Network for Dynamic Mobile Malware Detection

Smartphones and tablets are nowadays targets of malicious writers, that are able to develop more and more aggressive malicious applications to exfiltrate information from mobile devices. The signature-based detection currently exploited in (commercial and free) mobile antimalware is not able to detect never seen threats, as a matter of fact, antimalware are able just to recognise a malware if its signature is stored into the antimalware repository. With this in mind, we propose a mobile malware detector. We consider a dynamic analysis, in particular, we extract system call traces from running applications that, once transformed into images, represent the input for a deep neuro-fuzzy model. The aim of the deep neuro-fuzzy model is to discern malware applications from legitimate ones. We evaluate the deep neuro-fuzzy model effectiveness by considering a dataset composed by 6817 (malware and trusted) real-world Android samples, by reaching a training accuracy of 0.95 and a testing accuracy equal to 0.9, with the aim to empirically demonstrate the effectiveness of the proposed deep neuro-fuzzy model in the Android malware detection task.
Hybrid Model of the Analytical Solution with the Permeance Network

Hybrid model based on permeance network and separation of variables method at the air-gap. This study was applied to an outer rotor permanent magnet synchronous motor which is often used in the electric vehicles. The dynamic study of this machine amounts to developing a set of mathematical equations to translate the physical phenomenon considered. We use the permeance network method to establish this system of equations which will be solved with the laws of Kirchhoff. The modeling of the air-gap is the most delicate part, it can be calculated by the finite element method with good precision but it has a drawback which is a significant time. On the other hand the analytical solution is less precise compared to the finite element method but it specifies a low calculation time. The main objective of this hybridization is to retain the advantages of each of these two methods and reduce their disadvantages. For the optimization of the studied motor we calculate the current sheet in the machine in order to solve the demagnetization problem of the poles which may be caused by a large armature current.
Utilizing Hydrophobic Surfaces for Microplastics Quantification and Detection in Water Reservoirs

Microplastic pollution is one of the most prevalent environmental issues facing society today. To combat this emerging issue, research continues to develop to detect these plastics, but current methods are time-consuming and often involve costly technology. By developing a more cost-effective and efficient method to quantify and detect microplastics, future pollution can be mitigated, which is the focus of this report. Multiple methodologies were considered: density separation, plankton nets, photoluminescence spectroscopy, and passive microplastics collection devices. After careful consideration of laboratory testing results, the requirement for portability, and the constraint of a 30-minute time limit, the chosen approach employs a hydrophobic surface in conjunction with a vortex chamber to accurately quantify and detect microplastics in water reservoirs. The vortex chamber used in laboratory testing was built with two galvanized steel basins of differing sizes. The smaller basin contains a plexiglass slide coated in the selected hydrophobic surface in conjunction with small mesh netting on the top of the basin allowing water to flow into the larger basin while detaining the microplastics inside the smaller basin. A sample of microplastics suspended in water was added to the system and a small submersible pump generated a vortex in the smaller basin to circulate the microplastics onto the slide. Experiments were first performed with Museum Wax as the chosen hydrophobic surface, but recovery rates were low, with an average of 25% of the microspheres. To improve recovery rates, a larger plexiglass slide was chosen with Ultra Clear sealant as the selected hydrophobic surface, increasing recovery rates to 60% for microspheres and 88% microfibers for a 10-minute trial. The chosen design has the capability to utilize rechargeable battery power on a research vessel as evidenced by laboratory testing conducted on Bayou Lafourche. Additionally, the chosen design facilitates the preservation of microplastics for further processing and research, while also enabling seamless imaging through the utilization of a transparent hydrophobic surface.