The lexical-semantic issues in translating food taste adjectives into Chinese are examined in this article. Chinese food has a wide variety of colorful flavors. Correctly interpreting the adjectives that describe how food tastes is crucial. With tasty, simple-to-digest meals, it promotes and popu-larizes Chinese cuisine. Because, eating light, low-fat meals is crucial in today's world. Yet, due to poor translation and unclear menus, Chinese restaurants in CIS nations, particularly in Uzbeki-stan, are unpopular with customers. The biggest mistakes in Chinese restaurant menu are related to the incorrect translation of the taste adjectives of the food. This article aims to identify the most erroneously translated Chinese adjectives, analyze them linguistically, and determine the cause of translation errors. The translation ability of 60 Chinese language translators with HSK 4 level is analyzed on the basis of experiment. Recommendations for solving the problem studied in this ar-ticle have been developed.

Statistical machine translation (SMT) which was the dominant paradigm in machine translation (MT) research for nearly three decades has recently been superseded by the end-to-end deep learning approaches to MT. Although deep neural models produce state-of-the-art results in many translation tasks, they are found to under-perform on resource-poor scenarios. Despite some success, none of the present-day benchmarks that have tried to overcome this problem can be regarded as a universal solution to the problem of translation of many low-resource languages. In this work, we investigate the performance of phrase-based SMT (PB-SMT) and NMT on two rarely-tested low-resource language-pairs, English-to-Tamil and Hindi-to-Tamil, taking a specialised data domain (software localisation) into consideration. This paper demonstrates our findings including the identification of several issues of the current neural approaches to low-resource domain-specific text translation and rankings of our MT systems via a social media platform-based human evaluation scheme.

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.

Ribosome profiling and mass spectroscopy have identified canonical and noncanonical translation initiation codons (TICs) that are upstream of the main translation initiation site and used to translate oncogenic proteins. Here, we use a Kozak Similarity Score algorithm to find that nearly all of these TICs have flanking nucleotides closely matching the Kozak sequence. Remarkably, the nucleotides flanking alternative noncanonical TICs are frequently closer to the Kozak sequence than the nucleotides flanking TICs used to translate the gene’s main protein. Of note, the 5’ untranslated region (5‘UTR) of cancer-associated genes with an upstream TIC tend to be significantly longer than the same region in genes not associated with cancer. The presence of a longer than typical 5’UTR increases the likelihood of ribosome binding to upstream noncanonical TICs, and may be a distinguishing feature of a number of genes overexpressed in cancer. Noncanonical TICs that are located in the 5’UTR, although thought disadvantageous and suppressed by evolution, may translate oncogenic proteins because of their flanking nucleotides

Different bacterial species have dramatically different generation times, from 20-30 minutes in Escherichia coli to about two weeks in Mycobacterium leprae. The translation machinery in a cell needs to synthesize all proteins for a new cell in each generation. The three subprocesses of translation, i.e., initiation, elongation, and termination, are expected to be under stronger selection pressure to optimize in short-generation bacteria (SGB) such as Vibrio natriegens than in the long-generation Mycobacterium leprae. The initiation efficiency depends on the start codon decoded by the initiation tRNA, the optimal Shine-Dalgarno (SD) decoded by the anti-SD (aSD) sequence on small subunit rRNA, and the secondary structure that may embed the initiation signals and prevent them from being decoded. The elongation efficiency depends on the tRNA pool and codon usage. The termination efficiency in bacteria depends mainly on the nature of the stop codon and the nucleotide immediately downstream of the stop codon. By contrasting SGB with long-generation bacteria (LGB), we predict that 1) SGB to have more ribosome RNA operons to produce ribosomes, and more tRNA genes for carrying amino acid to ribosomes, 2) SGB to have a higher percentage of genes using AUG as start codon and UAA as stop codon than LGB, 3) SGB to exhibit better codon and anticodon adaptation than LGB, and 4) SGB to have weaker secondary structure near translation initiation signals than LGB. These differences between SGB and LGB should be more pronounced in highly expressed genes than lowly expressed genes. We present empirical evidence in support of these predictions.

Generative large language models (LLMs), e.g., ChatGPT, have demonstrated remarkable proficiency across several NLP tasks such as machine translation, question answering, text summarization, and natural language understanding. Recent research has shown that utilizing ChatGPT for assessing the quality of machine translation (MT) achieves state-of-the-art performance at the system level but performs poorly at the segment level. To further improve the performance of LLMs on MT quality assessment, we conducted an investigation into several prompting methods. Our results indicate that by combining Chain-of-Thoughts and Error Analysis, a new prompting method called Error Analysis Prompting, LLMs like ChatGPT can \textit{generate human-like MT evaluations at both the system and segment level}. Additionally, we discovered some limitations of ChatGPT as an MT evaluator, such as unstable scoring and biases when provided with multiple translations in a single query. Our findings aim to provide a preliminary experience for appropriately evaluating translation quality on ChatGPT while offering a variety of tricks in designing prompts for in-context learning. We anticipate that this report will shed new light on advancing the field of translation evaluation with LLMs by enhancing both the accuracy and reliability of metrics. The project can be found at https://github.com/Coldmist-Lu/ErrorAnalysis_Prompt.

Over the years, machine learning has emerged as a tool for automated translation and has been studied relentlessly for decades. RBMT, SMT, and NMT models have been used to achieve machine translation and the results have drastically improved from when research in this field first began. Although a few general-purpose translators such as Google Translate or Microsoft Translator have accurate translations compared to that of a human translator, many pieces of text containing highly technical terms or homonyms are often mistranslated completely. When considering the necessity and importance of translating technical patents from different domains, accuracy in translation is not something that can be compromised. This motivates the need to improve the performance of machine translation further. The scope of this paper covers three open-source machine translation models for the purpose of patent documentation translation from English to Japanese, evaluates their performance on patent data, and proposes a methodology that enabled us to improve one of the model’s BLEU score by 41.22%, achieving a BLEU score of 46.18.

One of the most pivotal challenges that learners may face, during foreign language learning, is building a reliable lexicon. Insufficient vocabulary knowledge may put serious obstacles in the foreign language learning process. Thus, students need to equip themselves with different strategies to cope with these difficulties. Translation equivalence (TE) is one of these strategies. Since the success or failure of any vocabulary learning strategy depends on two main factors: developing the learners’ vocabulary knowledge and the extent of difficulty or ease with which the learner acquires new words, this research aims at investigating the impact of this strategy on EFL learners’ vocabulary knowledge and word learnability. 258 Tunisian 9^th graders participated in this project. Two vocabulary recognition tests, one using translation equivalence and the other using only-English strategies, were used to test the hypotheses. Different statistical tests and techniques were employed to analyze data. Findings showed that TE has a positive impact on learners’ vocabulary knowledge and revealed that students, at this level of proficiency, learn vocabulary through translation better than any other strategy using only English.

Pharmacogenomics may improve patient care by guiding drug selection and dosing, however this requires prior knowledge of the pharmacogenomics of drugs commonly used in a specific setting. The aim of this study was to identify a preliminary set of pharmacogenetic variants important in Southern Africa. We describe co-morbidities in 3997 patients from Malawi, South Africa, and Zimbabwe. These patient cohorts were included in pharmacogenomic studies of anticoagulation, dyslipidemia, hypertension, HIV, and breast cancer. The 20 topmost prescribed drugs in this population were identified. Using literature, a list of pharmacogenes vital in disposition of the top 20 drugs was constructed leading to drug-gene pairs potentially informative in translation of pharmacogenomics. The most reported morbidity was hypertension (58.4%), making antihypertensives the most prescribed drugs, particularly amlodipine. Dyslipidemia occurred in 31.5% of the participants and statins were frequently prescribed cholesterol lowering drugs. HIV was reported in 20.3% of the study participants with lamivudine/stavudine/efavirenz being the most prescribed antiretroviral combination. Based on these data, pharmacogenes of immediate interest in Southern African populations include ABCB1, CYP2B6, CYP2C9, CYP2C19, CYP2D6, CYP3A4, CYP3A5, SLC22A1, SLCO1B1 and UGT1A1. Variants in these genes are a good starting point for pharmacogenomic translation programs in Southern Africa.

Studies over the past three years have substantially expanded the involvements of eIF3 in mRNA translation. It now appears that this multi-subunit complex is involved in every possible form of mRNA translation, controlling every step of protein synthesis from initiation to elongation, termination and quality control in positive as well as negative fashion. Through the study of eIF3, we are beginning to appreciate protein synthesis as a highly integrated process coordinating protein production with protein folding, subcellular targeting, and degradation. At the same time, eIF3 subunits appear to have specific functions that probably vary between different tissues and individual cells. Considering the broad functions of eIF3 in protein homeostasis, it comes as little surprise that eIF3 is increasingly implicated in major human diseases and first attempts at therapeutically targeting eIF3 have been undertaken. Much remains to be learned, however, about subunit- and tissue-specific functions of eIF3 in protein synthesis and disease and their regulation by environmental conditions and posttranslational modifications.

The investigation of membrane proteins, key constituents of cells, is hampered by the difficulty and complexity of their in vitro synthesis, of unpredictable yield. Cell-free synthesis is herein employed to unravel the impact of the expression construct on gene transcription and translation, without the complex regulatory mechanisms of cellular systems. Through the systematic design of plasmids in the immediacy of the start of the target gene, it was possible to identify translation initiation and the conformation of mRNA as the main factors governing the cell-free expression efficiency of the human voltage dependent anion channel (VDAC), a relevant membrane protein in drug-based therapy. A simple translation initiation model was developed to quantitatively assess the expression potential for the designed constructs. A scoring function is proposed that quantifies the feasibility of formation of the translation initiation complex through the ribosome-mRNA hybridization energy and the accessibility of the mRNA segment binding to the ribosome. The scoring function enables to optimize plasmid sequences and semi-quantitatively predict protein expression efficiencies. This scoring function is publicly available as webservice XenoExpressO at rna.tbi.univie.ac.at.

Exploiting the linguistic knowledge of the source language for neural machine translation (NMT) has recently achieved impressive performance on many large-scale language pairs. However, since the Turkish→English machine translation task is low-resource and the source-side Turkish is morphologically-rich, there are limited resources of bilingual corpora and linguistic information available to further improve the NMT performance. Focusing on the above issues, we propose a multi-source NMT approach that models the word feature in parallel to external linguistic features by using two separate encoders to explicitly incorporate linguistic knowledge into the NMT model. We extend the word embedding layer of the knowledge-based encoder to accommodate for each word’s linguistic annotations in the context. Moreover, we share all parameters across encoders to enhance the representation ability of the NMT model on the source language. Experimental results show that our proposed approach achieves substantial improvements of up to 2.4 and 1.1 BLEU scores in Turkish→English and English→Turkish machine translation tasks, respectively, which points to a promising way to utilize the source-side linguistic knowledge for the low-resource NMT.

Circular RNAs (circRNAs) are covalently closed RNAs that are present in all eukaryotes tested. CircRNAs are less abundantly expressed than mRNAs. Recent RNAseq analyses indicate that more than 800,000 circRNA isoforms exist in humans, which is more than twice the number of currently known mRNA isoforms. CircRNAs are generated through backsplicing that depends on pre-mRNA structures, which are influenced by intronic elements, for example primate-specific Alu elements, leading to species-specific circRNAs. CircRNAs are mostly cytosolic, stable and influence cells by sequestering miRNAs and RNA-binding proteins. We review the increasing evidence that circRNAs are translated into proteins using several cap-independent translational mechanisms, that include internal ribosomal entry sites, N6-methyladenosine RNA modification, adenosine to inosine RNA editing and interaction with the eIF4A3 component of the exon junction complex. CircRNA are translated under conditions that favor cap-independent translation, notably in cancer. CircRNA-encoded proteins are shorter than mRNA-encoded proteins and overall have a high propensity for phase separation. In summary, circRNA encoded proteins are translated under particular cellular conditions and contribute to proteome diversity and pathophysiology.

The Transformer architecture, while adept at capturing context through self-attention, falls short in encapsulating complex syntactic structures effectively. Addressing this gap, we introduce the Linguistic Structure through Graphical Interpretation with BERT (LSGIB) approach in Machine Translation (MT) frameworks. Combining the strengths of Graph Attention Network (GAT) and BERT, LSGIB intricately captures syntactic dependencies as explicit knowledge from the source language. This enhances the source language representation and aids in more accurate target language generation. Our empirical analysis leverages gold-standard syntax-annotated sentences and employs a Quality Estimation (QE) model. This approach enables us to assess translation improvements in terms of syntactic accuracy, extending beyond traditional BLEU score metrics. The LSGIB model demonstrates superior translation quality across diverse MT tasks, maintaining robust BLEU scores. Our study delves into the optimal sentence lengths benefiting from LSGIB and identifies which syntactic dependencies are more precisely captured. We observe that GAT's ability to learn specific dependency relations directly influences the translation quality of sentences with those relations. Additionally, we discover that incorporating syntactic structure into BERT's intermediate and lower layers offers a novel approach to modeling linguistic structure in source sentences.

According to data from the U.S FDA, the likelihood of a new drug progressing from Phase I clinical trials to market approval is only 8% (DiMasi et al., 2016). Furthermore, a significant attrition rate of 35% occurs during Phase II studies due to the failure to demonstrate clinical efficacy (Arrowsmith, 2011). These statistics underscore the urgent need to reassess the current research and development (R&D) paradigm. To address this challenge, a promising approach called reverse translational pharmacology (RTP) has emerged, aiming to enhance the predictive value of preclinical models used in biomedical research by leveraging animal models that spontaneously develop analogous diseases to humans (Schneider et al., 2018). The rationale behind RTP is that knowledge gained from clinical trials in canines can subsequently inform and guide human clinical trials, leading to improved outcomes for both species. Additionally, this approach capitalizes on the opportunity to stimulate veterinary drug development by leveraging pharmacokinetic (PK), efficacy, and safety data obtained from human clinical studies (Schneider et al., 2018). The application of reverse translational pharmacology holds immense potential for expediting drug development and advancing medical knowledge. By employing animal models that naturally develop diseases akin to those found in humans, researchers can enhance the predictive capacity of preclinical models, thereby mitigating the high attrition rates encountered in later stages of clinical trials. Moreover, the exchange of knowledge and data between human and veterinary medicine engenders a synergistic relationship, fostering progress in both domains (Schneider et al., 2018). As this innovative approach gains momentum, fostering collaborations between basic and clinician scientists, pharmaceutical companies, and veterinary professionals becomes increasingly imperative. By sharing resources, expertise, and findings, we can pave the way for groundbreaking discoveries and the development of safe and efficacious treatments for diverse diseases. Embracing reverse translational pharmacology offers an avenue to redefine the R&D paradigm, bringing us closer to a future where a higher proportion of drugs successfully traverse the arduous path from the laboratory to market approval, ultimately benefiting patients across species. In this brief commentary, we highlight four examples of diseases where reverse translational modeling has been used to support pharmaceutical research, including some applications developed in our research laboratory (SMART Pharmacology) at the Iowa State University College of Veterinary Medicine. In doing so, this report also seeks to provide a brief overview of the heuristic value of computational approaches in comparative research. To further illustrate the value of these in silico approaches, we deliberately chose to employ some features of ChatGPT to produce the present summary (OpenAI. (2023). GPT-3.5 [ChatGPT]. Retrieved from https://openai.com).

Insulin-degrading enzyme (IDE) is a highly conserved metalloprotease mainly localized in the cytosol. Although IDE can degrade insulin and some other low molecular weight substrates efficiently, its ubiquitous expression suggests additional functions supported by experimental findings, such as a role in stress responses, cellular protein homeostasis. Translation of full-length (Met1) IDE transcripts has reported to result in targeting to mitochondria but the role of IDE in this compartment is unknown. To obtain initial leads on the function of IDE in mitochondria, here we used a proximity biotinylation approach to identify proteins interacting with wild-type and protease-dead IDE targeted to the mitochondrial matrix. We find that mitochondrial IDE interacts strongly with mitochondrial ribosomes as well as with proteins of the respiratory chain. The mitochondrial interactomes of wild type and mutant IDE are highly similar and do not reveal any proteolytic IDE substrates. We speculate that IDE could adopt similar functions in mitochondria as in the cytosol, acting as a chaperone and contributing to protein homeostasis and stress responses.

This study aimed to translate and validate the Genomic Nursing Concept Inventory (GNCI) in Greek, analyze the genomic knowledge of nurses in Crete, Greece, and explore potential factors that could affect it. This cross-sectional study included 324 nurses from public hospitals and primary care units between July and December 2022. The GNCI is a 31-item questionnaire that evaluates genomic nursing concepts, but it has not been translated and validated in Greek. The translation process included independent forward translations, reconciliation, and back-translation. To evaluate internal consistency, Cronbach’s alpha was used, and Confirmatory Factor Analysis (CFA) was conducted to determine the fit of the model. Multiple linear regression analysis was performed to examine the associations between the demographic variables and GNCI scores. The internal consistency of the GNCI was acceptable (Cronbach’s alpha= 0.622). The CFA indicated that the one-factor model had an adequate fit (SRMR = 0.076, CD = 0.677, CFI = 0.744). The mean GNCI score was moderate (0.35±0.14). Regression analysis suggested that only gender could affect GNCI scores, with female nurses scoring higher than male nurses (p>0.001). In conclusion, the Greek version of the GNCI is a reliable tool for assessing genomic knowledge among Greek nurses.

To investigate the differences in cadium (Cd) uptake and translocation among edible rapes and select suitable rape varieties for safe production, a field experiment was carried out with 25 varieties of edible rapes on a farmland polluted with Cd in the central souothern part of Hunan Province. The growing characteristics and Cd contents in rape tissues were measured, and the amount of Cd removed was calculated. The results showed that the growth of 25 rape varieties was not inhibited without withering or inconsistent changes in leaf and so on. The Cd contents in stem-leaf and root of rapes were in the range 0.05-0.26mg·kg-1 and 0.04-0.26mg·kg-1, respectively. Basing on BCF, the stem-leaf showed a greater capacity for Cd transport than root. And the total Cd removed by rape were 0.002-0.016 mg·plant-1. Except for plant height, BCF of soil available Cd from soil to stem-leaf, TCF of Cd from root to stem-leaf and Cd taken away by rape, there was no significant difference among edible rape varieties. Although Cd contents in stem-leaf of 25 rape varieties does not exceed the national safety standards, planting “Lvjin 1”, “Guanyou Qingjing” and “Guanyou brassica” not only reduced soil pollution but also allowed the production of safe leafy rape.

Recent advancements in neural language models, particularly attention-based architectures like the Transformer, have substantially surpassed traditional methods in various natural language processing tasks. These models adeptly generate nuanced token representations by considering contextual relations within a sequence. However, augmenting these models with explicit syntactic knowledge, such as part of speech tags, has been found to remarkably bolster their effectiveness, especially under constrained data scenarios. This study introduces the Linguistic Enhanced Transformer (LET), which integrates multiple syntactic features, showing a notable increase in translation accuracy, evidenced by an improvement of up to 1.99 BLEU points on subsets of the WMT '14 English-German dataset. Furthermore, this paper demonstrates that enriching BERT models with syntax-aware embeddings enhances their performance on several GLUE benchmark tasks.

Resilience has been defined as one's competence to maintain a mental health state and overall well-being when undergoing grave stress or facing significant adversities. Numerous resili-ence-investigating research tools were developed over the years, with the Connor-Davidson Re-silience Scale (CD-RISC), a self-rated tool presenting valuable psychometric properties, remaining one of the most prominent. We aimed to translate and validate the brief CD-RISC-10 in a convenient sample of 584 nurses in Greece's secondary and tertiary health care system. We conducted a con-firmatory factor analysis and known-groups validity and estimated the reliability of the CD-RISC-10. Our confirmatory factor analysis revealed that the scale had a unifactorial structure since all the model fit indices were very good. Moreover, the reliability of the CD-RISC-10 was very good since Cronbach's alpha was 0.924 and McDonald's omega was 0.925. Therefore, the Greek version of the CD-RISC-10 confirmed the factor structure of the original one and had very good validity and reliability.

This manuscript addresses the phenomenon of masked priming in bilingual individuals. We employed a lexical decision task with masked translation priming, which serves as a valuable tool for elucidating the orthographic and lexical processes involved in the initial stages of reading. Our study builds upon previous research conducted on monolingual masked priming, which consistently demonstrates shifts in the response time (RT) distributions when comparing related and unrelated primes. Within the framework of a diffusion model, we implemented two theoretical positions. First, we posited that translation priming operates at the orthographic level, resulting in enhanced efficiency during the encoding process. Second, we explored the possibility that translation priming operates at the semantic level, influencing the accumulation of evidence during the lexical decision task. Our findings indicate that translation priming elicits outcomes similar to those observed in monolingual priming paradigms. Specifically, we observed that translation priming facilitation is manifested as shifts in the RT distributions. We interpret these findings as suggesting that the benefits derived from the encoding process are not specific to the accessed lexicon following brief stimulus presentation.

Similarly to his special theory of relativity, Einstein's special principle of relativity extends beyond the limits currently known to it. Up to now, the principle has not been suspected to have the potential to translate laws. However, the principle holds an inherent capacity to translate physical laws and, in so doing, speeds up our understanding and control of physical reality. Such a translation of laws leads to the faster discovery of other laws, such as the Laws of Electronic Motion, characterizing the motion of electrons around the atomic nucleus, and the Third Law of Biological Resistance, facilitating the successful control of drug resistance in medicine. The ability of the special principle of relativity to translate laws shines forth once the said principle is duly interpreted. This interpretation exposes a parallelism between experimental frames of reference and justifies the applicability of the laws of one frame of reference in other parallel frames of reference. This process opens the door to our faster discovery of the laws governing a multiplicity of frames parallel to a frame we already know the laws of. In practice, the interpretation of Einstein's special principle of relativity speeds up our understanding of the workings of physical reality and strengthens our capacity to subdue matter.

Malnutrition in children is a global health problem, particularly in developing countries. The effects of an insufficient supply of nutrients on body composition and physiological functions may have implications for drug disposition and ultimately affect the clinical outcome in this vulnerable population. Physiologically based pharmacokinetic (PBPK) modeling can be used to predict the effect of malnutrition as it links physiological changes to pharmacokinetic (PK) consequences. However, the absence of detailed information on body composition and the limited availability of controlled clinical trials in malnourished children complicates the establishment and evaluation of a generic PBPK model in this population. In this manuscript we describe the creation of physiologically-based bridge to a malnourished pediatric population, by combining information on a) the differences in body composition between healthy and malnourished adults and b) the differences in physiology between healthy adults and children. Model performance was confirmed using clinical reference data. This study presents a physiologically-based translational framework for prediction of drug disposition in malnourished children. The model is readily applicable for dose recommendation strategies to address the urgent medicinal needs of this vulnerable population.

Cyclin dependent kinase 1 (CDK1) has been primarily identified as a key cell cycle regulator in both mitosis and meiosis. Recently, an extramitotic function of CDK1 emerged when evidence was found that CDK1 is involved in many cellular events that are essential for cell proliferation and survival. In this review we summarize the involvement of active CDK1 in the initiation and elongation steps of protein synthesis in eukaryotes. During its activation CDK1 influences the initiation of protein synthesis, promotes the activity of specific translational initiation factors and affects the functioning of a subset of elongation factors. Our review provides insights into gene expression regulation during the transcriptionally silent cell cycle/M-phase and describes quantitative and qualitative translational changes based on the extramitotic role of the cell cycle master regulator CDK1, to optimize temporal synthesis of proteins to sustain division-related processes: mitosis and cytokinesis.

The cellular stress response corresponds to the molecular changes that cell undergoes in response to various environmental stimuli. It induces drastic changes in the regulation of gene expression, at transcriptional and post-transcriptional levels. Actually, translation is strongly affected with a blockade of the classical cap-dependent mechanism, whereas alternative mechanisms are activated to support translation of specific mRNAs. One of the major mechanisms involved in stress-activated translation is the internal ribosome entry site (IRES)-driven initiation. IRESs, first discovered in viral mRNAs, are present in cellular mRNAs coding for master regulators of cell responses, whose expression must be tightly controlled. IRESs allow translation of these mRNAs in response to different stresses, including DNA damage, amino-acid starvation, hypoxia or endoplasmic reticulum stress, as well as to physiological stimuli such as cell differentiation or synapse network formation. Importantly, cellular mRNA IRESs are regulated by IRES trans-acting factor (ITAFs), exerting their action by at least nine different mechanisms. This review presents an update of the reported ITAFs regulating cellular mRNA translation and of the different mechanisms allowing them to control translation initiation in specific conditions. The impact of ITAFs on coordinated expression of mRNA families and consequences in cell physiology and diseases are also highlighted.

Codon bias analysis of SARS-CoV-2 reveals suboptimal adaptation for translation in human cells it infects. The detailed examination of the codons preferentially used by SARS-CoV-2 shows a strong preference for LysAAA, GlnCAA, GluGAA, and Arg AGA infrequently used in human genes. In the absence of an adapted tRNA pool, efficient decoding of these codons requires a 5-methoxycarbonylmethyl-2-thiouridine (mcm5s2) modification at the U34 wobble position of the corresponding tRNAs (tLysUUU; tGlnUUG; tGluUUC; tArgUCU). The optimal translation of SARS-CoV-2 open reading frames (ORFs) may therefore require several adjustments to the host's translation machinery, enabling the highly biased viral genome to achieve a more favorable "Ready-to-Translate" state in human cells. Experimental approaches based on LC-MS/MS quantification of tRNA modifications and on alteration of enzymatic tRNA modification pathways provide strong evidence to support the hypothesis that SARS-CoV-2 induces U34 tRNA modifications and relies on these modifications for its lifecycle. The conclusions emphasize the need for future studies on the evolution of SARS-CoV-2 codon bias and its ability to alter the host tRNA pool through the manipulation of RNA modifications.

Traditional African vegetables (TAV) are an important component of an ecologically sustainable approach to addressing food and nutrition security in Africa (countries?). This review aims to identify the scope of the knowledge translation literature that exists across multiple aspects of the TAV value chain and to identify gaps in knowledge translation research. A scoping review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) was conducted. The Population, Concept, and Context (PCC) framework was used to guide the development of inclusion and exclusion criteria for this review. Web of Science, Scopus, Science direct and ABI/Inform databases were searched to identify relevant studies which were then screened using the Covidence online software to select studies and extract information for analysis and reporting. A total of 182 studies/articles were selected and examined. The most significant knowledge documented about TAVs is their vast genetic diversity contributing to their rich nutritional and medicinal properties, their ability to withstand harsh environmental conditions such as drought, pests, and diseases as well as the effects of various post-handling techniques on nutrient content of TAVs with the production phase of TAVs value chain being the most studied. This review found that although there is a breadth of knowledge created about TAVs, there is still little documentation of transferring and utilizing of this knowledge across the TAVs value chain. This review identified that more research is needed to document strategies and practices for the transfer and utilisation of knowledge to ensure it is utilized to enhance nutritional outcomes across Africa.

Polyploidy, a prevalent event in plant evolution, drives phenotypic diversification and speciation. While transcriptional changes and regulation in polyploids have been extensively studied, the translational level impact remains largely unexplored. To address this gap, we conducted a com-parative transcriptomic and translatomic analysis of cotton leaves from allopolyploid species G. hirsutum (AD1) and G. barbadense (AD2) relative to their model A-genome and D-genome diploid progenitors. Our data revealed that while allopolyploidization significantly affects the transcrip-tional landscape, its impact on translation was relatively modest, evidenced by a narrower ex-pression range and fewer expression changes in ribosome-protected fragments than in mRNA levels. Allopolyploid-specific changes commonly identified in both AD1 and AD2 were observed in 7,393 genes at either transcriptional or translational levels. Interestingly, the majority of translational changes exhibited concordant down-regulation in both ribosome-protected fragments and mRNA, particularly associated with terpenoid synthesis and metabolism. Regarding translational efficiency (TE), at least one-fifth of cotton genes exhibit translational level regulation, with a general trend of more down-regulation than up-regulation of TE. The magnitude of translational regulation was slightly reduced in allopolyploids compared to diploids, and allopolyploidy tends to have a more profound impact on genes and functional associations with ultra-low TE. Moreover, we demonstrated a reduced extent of homeolog expression biases during translation compared to transcription. In conclusion, our study provides insights into the regulatory consequences of allopolyploidy post-transcription, contributing to a comprehensive understanding of regulatory mechanisms of duplicated gene expression evolution.

Optoacoustic imaging is an emerging technology that holds great promise for improving general clinical practice since it offers a unique combination of optical absorption contrast, high resolution, and high penetration depth. This combination of factors provides a distinct advantage over alternative imaging modalities in oncology, cardiology, dermatology, or endocrinology among other medical fields. Optoacoustic technology affords the medical practitioner the possibility of visualizing light-absorbing biomolecules like water, lipids, oxy/deoxy hemoglobin, or melanin in deep tissue with relatively cheap and small form factor instruments, without the need of contrast agents or ionizing radiation. Moreover, exogenous fluorescent contrast agents like indocyanine green or methylene blue can also be observed. Driven by its potential for new applications in the clinic, the field has experienced intense changes in the last 20 years. Continuous technological advancements in instrumentation, image formation methods, or acquisition geometries, have led to the development of numerous clinical devices also motivating the creation of several startup companies that aim to translate the technology from the lab to the clinic. Thus, the question arises: how far are we from clinical adoption? In this article, we aim to shed light on such question by reviewing the clinical studies that have been performed in the last 10 years. Our intention is to go beyond the classical review format by providing a brief general overview of the steps that new imaging methods undergo on their journey from the lab to clinical adoption. Then we examine the progress that optoacoustic technology has made on such a journey.

The gene translation system possesses inherent complexity in distinguishing codons with degeneracy through repertoires of aminoacyl-tRNAs in the ribosome. Implementation of inosine nucleoside at the anticodon wobble-position in certain tRNAs, especially in eukaryotes, might be arisen to establish complex decoding systems. This report introduces the novel concept of “decoding tables”, which serve as concise summaries of codon-anticodon pairings provided in codon table-like format. Utilizing tRNA genome database information and a procedure based on compiled tRNA studies, the tRNA repertoire and the use of wobble pairings can be depicted for any organisms. The decoding table sheds light on the use of wobble pairings across diverse organisms.

Low-resource languages often face the problem of insufficient data, which leads to poor quality in machine translation. One approach to address this issue is data augmentation. Data augmentation involves creating new data by transforming existing data through methods such as flipping, cropping, rotating, and adding noise. Traditionally, pseudo-parallel corpora are generated by randomly replacing words in low-resource language machine translation. However, this method can introduce ambiguity as the same word may have different meanings in different contexts. This study proposes a new approach for low-resource language machine translation, which involves generating pseudo-parallel corpora by replacing phrases. The performance of this approach is compared with other data augmentation methods, and it is observed that combining it with other data augmentation methods further improves performance. To enhance the robustness of the model, R-Drop regularization is also used. R-Drop is an effective method for improving the quality of machine translation. The proposed method was tested on Chinese-Kazakh (Latin script) translation tasks, resulting in performance improvements of 4.99 and 7.7 for Chinese-to-Kazakh and Kazakh-to-Chinese translations, respectively.

Hypoxia — reduction in oxygen availability—plays key roles in both physiological and pathological processes. Given the importance of oxygen for cell and organism viability, mechanisms to sense and respond to hypoxia are in place. A variety of enzymes utilise molecular oxygen, but of particular importance to oxygen sensing are the 2-oxoglutarate (2-OG) dependent dioxygenases (2-OGDs). Of these, Prolyl-hydroxylases have long been recognised to control the levels and function of Hypoxia Inducible Factor (HIF), a master transcriptional regulator in hypoxia, via their hydroxylase activity. However, recent studies are revealing that dioxygenases are involved in almost all aspects of gene regulation, including chromatin organisation, transcription and translation. We highlight the relevance of HIF and 2-OG dioxyenases in the control of gene expression in response to hypoxia and their relevance to human cancers.

We tested the hypothesis that concatemers of ancestral tRNAs gave rise to the 16S ribosomal RNA. We built an ancestral sequence of proto-tRNAs that showed a significant identity of 51.69% and a percentage of structural identity of 0.941 with the 16S ribosomal molecule. We also propose a hypothesis for the emergence of translation.

The vertebrate anterior-posterior (A-P = craniocaudal) axis is evidently made by a timing mechanism. Evidence has accumulated that tentatively identifies the A-P timer as being or involving Hox temporal collinearity. Here, I focus on the two current competing models based on this premise. Common features and points of dissent are examined and a common model is distilled from what remains. This is an attempt to make sense of the literature.

The tremendous diversity and complexity of proteins invariably results in protein misfolding, to which cells have evolved numerous mechanisms of mitigating. Degrading misfolded proteins is perhaps the most intuitive strategy, but also critical to managing proteostasis are the elaborate mechanisms of translational control. Attenuated rates of translation ameliorate protein misfolding by downregulating the flux of new protein and conserving ATP. Loss of translational control, particularly in neurons, constitutes a major proteostatic dysfunction capable of causing or exacerbating neurodegeneration, while interventions aimed at downregulating protein synthesis are generally neuroprotective. In this review, I examine the critical neuronal signaling networks employed to control translation with an emphasis on current research. This includes the Unfolded Protein Response (UPR), the mitochondrial UPR (mtUPR), mTORC1 signaling, and stress granule formation.

Chikungunya is mosquito born disease that is spread across continents. This research provides valuable insights into the potential functional significance of the hydrophobic tail and cysteine-rich regions in E2 proteins. These findings lay the groundwork for future investigations into the specific roles of the C-terminal domain and post-translational modifications, which could ultimately lead to a better understanding of viral mechanisms and potential therapeutic targets..

Recent advancements in neural machine translation (NMT) offer promising potential for generating cross-language clinical natural language processing (NLP) resources. There is a pressing need to be able to foster the development of clinical NLP tools that extract key clinical entities in a comparable way for a multitude of medical application scenarios, hindered by lack of multilingual annotated data. This study explores the efficacy of using NMT and annotation projection techniques with expert in the loop validation to develop named entity recognition (NER) systems for an under resourced target language (Catalan) by leveraging Spanish clinical corpora annotated by domain experts. We employed a state-of-the-art NMT system to translate three clinical case corpora. The translated annotations were then projected onto the target language texts and subsequently validated and corrected by clinical domain experts. The efficacy of the resulting NER systems was evaluated against manually annotated test sets in the target language. Our findings indicate that this approach not only facilitates the generation of high-quality training data for the target language (Catalan) but also demonstrates the potential to extend this methodology to other languages, thereby enhancing multilingual clinical NLP resource development. The generated corpora and components are publicly accessible, providing potentially a valuable resource for further research and application in multilingual clinical settings: https://zenodo.org/doi/10.5281/zenodo.13133124.

Regulation at the level of translation plays an essential role in modulating gene expression. Given the importance of knowledge about translationally active mRNAs as an indicator of translation efficiency, it is crucial for researchers to have at their disposal organised sources of functional information on translation - plant translatomic maps similar to transcription maps. The development of a general experimental and computational methodology for constructing translatome maps is essential to address this challenge. In this manuscript, we discuss what researchers can do to design plant translatome maps, namely, we review: (i) experimental methods to obtain translationally active mRNA fractions, considering their advantages and limitations; (ii) additional methodological approaches to obtain complete pools of translationally active mRNAs; (iii) sequencing methods to qualitatively and quantitatively assess different mRNA fractions; and (iv) multivariate analyses of sequencing results by bioinformatics methods, including metrics for assessing mRNA translational activity.

The Pandemic in 2019 forced a digital adaptation with direct consequences on all educational stakeholders. On behalf of teachers and trainers, while many regarded these changes with some scepticism, others embraced the opportunity to integrate technology into their teaching methods and as learning resources. As translation trainers, it is essential to follow and understand the translation market. Thus, the exponential changes that digital technology has brought to this sector over the years impose constant shifts in teaching and learning methods and resources. In fact, translators require vast competencies, amongst which is the flexibility to adapt. In translation training Project-Based Learning (PBL) has been established as an essential teaching and learning method, as it has proven to foster the development of competencies as it simulates the translator's work environment. Thus, the need to adapt new strategies reinforced PBL and its benefits. PBL, however, similar to a freelance translator, places the student in the centre of the learning process, where self-regulation becomes essential, as it is necessary to analyse the market/situation and be flexible enough to adapt to the context accordingly. As of 2018-2019, technical translation courses at ISCAP have implemented PBL as their main teaching and learning method. At the same time, a study on student self-regulation began. The purpose was to understand student perception on their own self-regulation competence and its development or lack thereof after one year of PBL. Results indicate that PBL is seen as a useful simulation of the translation labour market and that it does enhance many essential competences, amongst which is student self-regulation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that caused the COVID-19 pandemic still able to infect the population in many countries. The Omicron strain is the most mutated variant of SARS-CoV-2. The high transmissibility of the strain and the ability to evade immunity require a priority study of its properties in order to quickly create effective means of preventing it. The present work is devoted to the study of in silico interaction of piRNAs with the genome of the SARS-CoV-2 (gRNA) in order to identify endogenous piRNAs and propose synthetic piRNAs with high antiviral activity for drug development. The studies were carried out using proven bioinformatic methods of interaction of the entire SARS-CoV-2 genome with more than eight million piRNAs. Binding sites (BSs) of piRNAs in the 5'UTR were located with overlapping nucleotide sequences called clusters of BSs. Several clusters of BSs were found in the nsp3, nsp7, RNA-dependent RNA polymerase, endoRNAse, S surface glycoprotein, ORF7a and nucleocapsid. 16 synthetic piRNAs have been proposed that interact with gRNA with free binding energy from -170 kJ/mol to -175 kJ/mol, which can be used to create drugs that suppress the reproduction of SARS-CoV-2.

In the present study, we investigate the post-editese phenomenon, i.e., the unique features that set machine translated post-edited texts apart from human-translated texts. We use two literary texts, namely, the English children's novel by Lewis Carroll Alice’s Adventures in Wonderland (AW) and Paula Hawkins' popular book The Girl on the Train (TGOTT) translated from English into Brazilian-Portuguese to investigate whether the post-editese features can be found on the surface of the post-edited (PE) texts. In addition, we examine how the features found in the PE texts differ from the features encountered in the human-translated (HT) and machine translation (MT) versions of the same source text. Results revealed evidence for post-editese for TGOTT only with PE versions being more similar to the MT output than to the HT texts.

The notion that there is a one to one mapping from genotype to phenotype was overturned a long time ago. Along with genotype and environment, ‘non-genetic changes’ orchestrated by altered RNA and protein molecules also guide the development of phenotype. The idea that there is a route through which changes in phenotype can lead to changes in genotype impinges on several phenomena of molecular, developmental, evolutionary and applied interest. Phenotypic changes that do not alter the underlying DNA sequence have been studied across model systems (eg: DNA and histone modifications, RNA editing, prion formation) and are known to play an important role in short term adaptation. However, because of their transient nature and unstable inheritance, the role of such changes in long term evolution has remained controversial. I classify and review three ways in which non-genetic changes can influence genotype and impact cellular fitness across generations, with an emphasis on the enticing idea that they may act as stepping stones for genetic adaptation. I focus on work from microbial systems and attempt to highlight recent experiments and models that bear on this idea. Overall, I review evidence which suggests that non-genetic changes can impact phenotype via their influence on the genotype, and thus play a role in evolutionary change.

A dogma is normally considered as a principle or a belief accepted as an indisputable truth by some individuals and/or groups. Theoretically there can be no dogmas in science, but it has been demonstrated that scientific thought operates by conceptual changes. A dogma therefore can be understood as a concept present at the heart of some contemporary research programmes that need to be altered to overcome paradigms. Here we argue that two ideas relating to emergence of the biological system research need to be re-evaluated. First, is the idea that research programmes about the emergence of the biological system are the same as those of the origin of cells. Cells are strikingly important biological entities, hard core concepts for the entire field of biology. The emergence of the biological system happened much earlier than the origin of cells and thus the First Universal Common Ancestor (FUCA) should be viewed as a great-grandfather to the Last Universal Cellular Ancestor (LUCA); i.e. the latter is the first cellular life form. Second, RNA-world theories are the focus of mainstream research programmes for the origin of life stricto sensu. In the RNA-world view, self-replication of nucleic acids is seen as one of the most relevant events in the pre-biotic world. Without denying the relevance of self-replication, we argue here that the most germane event which occurred in the pre-biotic world was the crosstalk between nucleic acids and peptides. When these two macromolecules started to interact, the singularity that aggregated the complexity required to produce life emerged. Thus, comprehension of the early origins of the translation machinery and the assembly of the genetic code is key. Therefore, the relevance of cell theory and self-replication should be re-evaluated as well as the concept of life itself.

Connexins are tetraspan transmembrane proteins that form gap junctions and facilitate direct intercellular communication, a critical feature for the development, function and homeostasis of tissues and organs. In addition, a growing number of gap junction-independent functions are being ascribed to these proteins. The connexin gene family is under extensive regulation at the transcriptional and post-transcriptional level, and undergoes numerous modifications at the protein level, including phosphorylation, which ultimately affects their trafficking, stability and function. Here, we summarize these key regulatory events, with emphasis on how these affect their multi-functionality in health and disease.

Musyakalah is one of the Arabic linguistic styles included under the category of majaz. This style is commonly used in Al-Quran. The Indonesian translation of Al-Quran is a case where many of the figures of speech are translated literally, thereby causing serious semantic problems. Thus, the research problem of this is formulated with the following questions: 1) How many musyakalah ayahs are there in Al-Quran?; 2) How are the musyakalah ayahs translated, literally (harfiyya) or interpretatively (tafsiriyya)?; 3) How many ayahs are translated literally and how many are translated interpretatively?; and 4) Which translated musyakalah ayahs have the potential to raise semantic and theological problems? The corpus in this research consists of all musyakalah ayahs in Al-Quran and their translation to Indonesian published by the Department of Religious Affairs of Indonesia. The research adopted a descriptive-semantic method. The findings of this research are: 1) There are only eleven ayahs in Al-Quran using musyakalah style, namely: Alhasyr ayah 19, Ali Imran ayah 54, Annaml ayah 50, Alanfal ayah 30, Asysyura ayah 40, Albaqarah ayah 15, Almaidah 116, Aljatsiah ayah 34, Attaubah ayah 79, Annisa ayah 142, and Albaqarah 194; 2) The musyakalah ayahs translated literally are: Aljatsiah 34, Almaidah 116, Asysura 40, Annaml 50, and Alhasyr 19, whereas the musyakalah ayahs translated interpretatively are Albaqarah 194, Annisa 142, Attaubah 79, Albaqarah 15, Alanfal 30, and Ali Imran 54; 3) Of the eleven musyakalah ayahs, only Alhasyr ayah 19 that is translated correctly and does not have the potential of creating misinterpretation. Meanwhile, the interpretation of the other four ayahs can potentially cause misinterpretation or are against the basic principles of Islam. The six remaining ayahs are translated interpretatively and thus do not have the potential to be misinterpreted; 4) The findings suggest that musyakalah ayahs are more appropriately translated interpretatively. Therefore, the following is recommended: 1) Considering the different characteristics of Arabic and Indonesian languages, studies on Al-Quran translation into Indonesian should continuously be carried out; 2) In order to avoid misinterpretation, it is better that the translation of musyakalah ayahs uses tafsiriyya (interpretative) approach; 3) The harfiyya (literal) and tafsiriyya (interpretative) approaches should be developed for other styles beside musyakalah..

Information is the currency of life, but the origin of prebiotic information remains a mystery. We propose transitional pathways from the cosmic building blocks of life to the complex prebiotic organic chemistry that led to the origin of information systems. The prebiotic information system, specifically the genetic code, is segregated, linear, and digital and probably appeared during biogenesis four billion years ago. In the peptide/RNA world, lipid membranes randomly encapsulated amino acids, RNA, and protein molecules, drawn from the prebiotic soup, to initiate a molecular symbiosis inside the protocells. This endosymbiosis led to the hierarchical emergence of several requisite components of the translation machine: tRNAs, aaRS, mRNAs, and ribosomes. When assembled in the right order, the translation machine created biosynthetic polypeptides, a process that transferred information from mRNAs to proteins. This was the beginning of the prebiotic information age. The molecular attraction between tRNA and amino acids led to different stages of the translation machines and the genetic code. tRNA is an ancient molecule that designed and built mRNA for storing the information of its cognate amino acid. Each mRNA strand became the storage device for the genetic information that encoded the amino acid sequences in triplet nucleotides. As information appeared in the digital languages of the codon within mRNA, and the genetic code for protein synthesis evolved, the prebiotic chemistry then became more organized and directional. The origin of the genetic code is enigmatic; herein we propose an evolutionary explanation: the demand for a wide range of specific enzymes in the peptide/RNA world was the main selective pressure for the origin of information-directed protein synthesis. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and the adaptive theory. These three theories are compatible with our coevolution model of the translation machines and the genetic code. We suggest biosynthetic pathways as the origin of the specific translation machines which provided the framework for the origin of the genetic code. During translation, the genetic code developed in three stages coincident with the refinement of the translation machines: GNC code developed by the pre-tRNA/pre-aaRS /pre-mRNA machine, SNS code by the tRNA/aaRS/mRNA machine, and finally the universal genetic code by the tRNA/aaRS/mRNA/ribosome machine. Our hypothesis provides the logical and incremental steps for the origin of the programmed protein synthesis. In order to understand the prebiotic information system better, we converted letter codons into numerical codons in the Universal Genetic Code Table. We have developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa. This conversion has granted us insight into how the translation might have worked in the peptide/RNA world. There is great potential in the application of numerical codons to bioinformatics such as barcoding, DNA mining, or DNA fingerprinting. We constructed the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. Using AnyLogic software we were able to simulate and visualize the entire evolution of the translation machines and the genetic code. The results indicate that the emergence of the information age from the peptide/RNA world was a watershed event in the origin of life about four billion years ago.

The Late Heavy Bombardment Period (4.1 to 3.8 billion years ago) of heightened impact cratering activity on young Earth is likely the driving force for the origin of life. During the Eoarchean, asteroids such as carbonaceous chondrites delivered the building blocks of life and water to early Earth. Asteroid collisions created innumerable hydrothermal crater lakes in the Eoarchean crust which inadvertently became the perfect cradle for prebiotic chemistry. These hydrothermal crater lakes were filled with cosmic water and the building blocks of life. forming a thick prebiotic soup. The unique combination of exogenous delivery of extraterrestrial building blocks of life, and the endogenous biosynthesis in hydrothermal impact crater lakes very likely gave rise to life. A new symbiotic model for the origin of life within the hydrothermal crater lakes is here proposed. In this scenario, life arose around four billion years ago through five hierarchical stages of increasing molecular complexity: cosmic, geologic, chemical, information, and biological. During the prebiotic synthesis, membranes first appeared in the hydrothermal crater lakes, followed by the simultaneous origin of RNA and protein molecules, creating the RNA/protein world. These proteins were noncoded protein enzymes that facilitated chemical reactions. RNA molecules formed in the hydrothermal crater basin by polymerization of the nucleotides on the montmorillonite mineral substrate. Similarly, the initial synthesis of abiotic protein enzymes was mediated by the condensation of amino acids on pyrite surfaces. The regular wet-dry cycles within the crater lakes assisted further concentration, condensation, and polymerization of the RNAs and proteins. Lipid membranes randomly encapsulated amino acids, RNA, and protein molecules from the prebiotic soup to initiate a molecular symbiosis inside the protocells, this led to the hierarchical emergence of several cell components. As the role of protein enzymes became essential for catalytic process in the RNA/protein world, Darwinian selection from noncoded to coded protein synthesis led to translation systems and the genetic code, heralding the information stage. In this stage, the biochemical pathways suggest the successive emergence of translation machineries such as tRNAs, aaRS, mRNAs, and of ribosomes for protein synthesis. The molecular attraction between tRNA and amino acid led to the emergence of translation machinery and the genetic code. tRNA is an ancient molecule that created mRNA for the purpose of storing amino acid information like a digital strip. Each mRNA strand became the storage device for genetic information that encoded the amino acid sequences in triplet nucleotides. As information became available in the digital languages of the codon within mRNA, biosynthesis became less random and more organized and directional. The original translation machinery was simpler before the emergence of the ribosome than that of today. We review three main concepts on the origin and evolution of the genetic code: the stereochemical theory, the coevolution theory, and adaptive theory. We believe that these three theories are not mutually exclusive, but are compatible with our coevolution model of translations machines and the genetic code. We suggest biosynthetic pathways as the origin of the translation machine that provided the framework for the origin of the genetic code. During translation, the genetic code developed in three stages coincident with the refinement of the translation machinery: GNC code with four codons and four amino acids during interactions of pre-tRNA/pre-aaRS /pre-mRNA, SNS code consisting of 16 codons and 10 amino acids appeared during the tRNA/aaRS/mRNA interaction, and finally the universal genetic code evolved with the emergence of the tRNA/aaRS/mRNA/ribosome machine. The universal code consists of 64 codons and 20 amino acids, with a redundancy that minimizes errors in translation. To address the question of the origin of the biological information system in the RNA/protein world, we converted letter codons into numerical codons in the Universal Genetic Code Table. We developed a software called CATI (Codon-Amino Acid-Translator-Imitator) to translate randomly chosen numerical codons into corresponding amino acids and vice versa, gaining insight into how translation might have worked in the RNA/protein world. We simulated the likely biochemical pathways for the origin of translation and the genetic code using the Model-View-Controller (MVC) software framework, and the translation machinery step-by-step. We used AnyLogic software to simulate and visualize the evolution of the translation machines and the genetic code. We conclude that the emergence of the information age from the RNA/protein world was a watershed event in the origin of life about four billion years ago.

Brain plasticity is induced by learning during wakefulness and is consolidated during sleep. But the molecular mechanisms involved are poorly understood and their relation to experience-dependent changes in brain activity remains to be clarified. Localized mRNA translation is im-portant for the structural changes at synapses supporting brain plasticity consolidation. Sleep has been shown activate the translation mTOR pathway, via phosphorylation of 4E-BPs, during brain plasticity, but whether this activation is specific to synapses is not known. We investigated this question using acute exposure of rats to an enriched environment (EE). We measured brain activity with EEGs and 4E-BPs phosphorylation at cortical and cerebellar synapses with Western Blot. Sleep significantly increased the conversion of 4E-BPs to its hyperphosphorylated form at synapses, especially after EE exposure. EE exposure increased oscillations in the alpha band dur-ing active exploration and in the theta to beta (4-30Hz) range, as well as spindle density, during NREM sleep. Theta activity during exploration and NREM spindle frequency predicted changes in 4E-BPs hyperphosphorylation at synapses. Our results provide a link between EEG and mo-lecular markers of plasticity across wake and sleep.

Huntington’s Disease (HD) is a devastating neurodegenerative disorder caused by a CAG trinucleotide repeat expansion in the HTT gene, for which no disease modifying therapies are currently available. Much of the recent research has focused on developing therapies to directly lower HTT expression, and while promising, these therapies have presented several challenges regarding administration and efficacy. Another promising therapeutic approach is the modulation of HTT post-translational modifications (PTMs) that are dysregulated in disease and have shown to play a key role in HTT toxicity. Among all PTMs, modulation of HTT phosphorylation has been proposed as an attractive therapeutic option due to the possibility of orally administering specific kinase effectors. One of the kinases described to participate in HTT phosphorylation is Protein Kinase CK2. CK2 has recently emerged as a target for the treatment of several neurological and psychiatric disorders, although its role in HD remains controversial. While pharmacological studies in vitro inhibiting CK2 resulted in reduced HTT phosphorylation and increased toxicity, genetic approaches in mouse models of HD have provided beneficial effects. In this review we discuss potential therapeutic approaches related to the manipulation of HTT-PTMs with special emphasis on the role of CK2 as a therapeutic target in HD.

It was thought until the 1990s that the eukaryotic translation machinery was unable to translate a circular RNA. However internal ribosome entry sites (IRESs) and m⁶A-induced ribosome engagement sites (MIRESs) were discovered, promoting 5’end-independent translation initiation. Today a new family of so-called “non-coding” circular RNAs (circRNAs) has emerged, revealing the pivotal role of 5’end-independent translation. CircRNAs have a strong impact on translational control via their sponge function, and form a new mRNA family as they are translated into proteins with pathophysiological roles. While there is no more doubt about translation of covalently closed circRNA, the linearity of canonical mRNA is only theoretical: it has been shown for more than thirty years that polysomes exhibit a circular form and mRNA functional circularization has been demonstrated in the 1990s by the interaction of initiation factor eIF4G with poly(A) binding protein. More recently, additional mechanisms of 3’-5’ interaction have been reported, including m⁶A modification. Functional circularization enhances translation via ribosome recycling and acceleration of the translation initiation rate. This update of covalently and non-covalently circular mRNA translation landscape shows that RNA circular shape might be the rule for translation with an important impact on disease development and biotechnological applications.

The vertebrate anterior-posterior (A-P) body axis arises due to time space translation (TST). BMP dependent Hox temporal collinearity in early embryonic mesoderm generates the initial vertebrate axial pattern because the Hox codes associated with sequential times are frozen sequentially by BMP inhibiting signals from the embryonic organiser or node. There are three reasons why it is now opportune to review TST. 1/ It has become clear that this mechanism is highly relevant for current and emergent directions in medicine. Making a particular tailored stem cell or culturing a specific organoid in vitro both depend on it. 2/ This unexpected and perhaps unlikely sounding mechanism has recently been thoroughly validated. 8 recent primary publications from 6 major groups confirm that TST is the mechanism for primary axial patterning in the 4 best investigated vertebrate embryos. 3/ Its mechanism is now becoming clear. Previous publications propose it involves Hox regulation of cell movement during gastrulation or sequential stabilisation of Hox codes by anti BMP as above. Neither of these processes works alone but together they amount to a very convincing mechanism.

Contrary to optical images, Synthetic Aperture Radar (SAR) images are in different electromagnetic spectrum where the human visual system is not accustomed to. Thus, with more and more SAR applications, the demand for enhanced high-quality SAR images has increased considerably. However, high-quality SAR images entail high costs due to the limitations of current SAR devices and their image processing resources. To improve the quality of SAR images and to reduce the costs of their generation, we propose a Dialectical Generative Adversarial Network (Dialectical GAN) to generate high-quality SAR images. This method is based on the analysis of hierarchical SAR information and the “dialectical” structure of GAN frameworks. As a demonstration, a typical example will be shown where a low-resolution SAR image (e.g., a Sentinel-1 image) with large ground coverage is translated into a high-resolution SAR image (e.g., a TerraSAR-X image). Three traditional algorithms are compared, and a new algorithm is proposed based on a network framework by combining conditional WGAN-GP (Wasserstein Generative Adversarial Network - Gradient Penalty) loss functions and Spatial Gram matrices under the rule of dialectics. Experimental results show that the SAR image translation works very well when we compare the results of our proposed method with the selected traditional methods.

This study presents a French translation and validation of the Durand Adaptive Psychopathic Traits Questionnaire (DAPTQ), an instrument for assessing adaptive traits known to correlate with the psychopathic personality. Bilingual (French and English) individuals from France and Canada (N = 141, 52% in France, M_age = 29.73, SD = 9.09) completed both versions of the DAPTQ (French and English), alongside measurements of perceived stress, trait anxiety, authentic leadership and creativity. Correlation between the DAPTQ total and subscales across versions showed strong associations (r = .84 to .96). The DAPTQ – French version also demonstrated good internal consistency (α = .87), convergent validity, and concurrent validity. These findings support the cross-cultural equivalence of the DAPTQ and therefore its effectiveness as a valid assessment method of adaptive traits.

During the response of plants to adverse stresses, aquaporin (AQP) plays a prominent role in membrane water transport based on received upstream signals. In addition, they have various physical parts for dealing with environmental stresses. Due to the importance of the AQP gene family, studies have been conducted investigating the function and regulatory system of these genes. However, many of their molecular aspects are still unknown. This study aims to carry out a genomic-wide investigation of the AQP gene family in durum wheat using bioinformatics tools and to investigate the expression patterns of some members in response to salt stress. Our results showed that there are 80 TtAQP genes in durum wheat, which are classified into four main groups based on phylogenetic analysis. Many duplications were observed between the members of the TtAQP gene family, and high diversity in response to post-translational modifications was observed between TtAQP family members. The expression pattern of TtAQP genes disclosed that these genes are primarily upregulated in response to salt stress. Besides, qPCR data revealed that TtAQPs are more induced in delayed responses to salinity stress. Overall, our findings illustrate that TtAQP members are diverse in terms of their structure, regulatory systems and expression levels.

For disciplines like biological science, security, and the medical field, link prediction is a popular research area. To demonstrate the link prediction many methods have been proposed. Some of them that have been demonstrated through this review paper are TransE, Complex, DistMult, and DensE models. Each model defines link prediction with different perceptions. We argue that the practical performance potential of these methods, having similar parameter values, using the fine-tuning technique to evaluate their reliability and reproducibility of results. We describe those methods and experiments; provide theoretical proofs and experimental examples, demonstrating how current link prediction methods work in such settings. We use the standard evaluation metrics for testing the model's ability.

Bacteria participate in a wide diversity of symbiotic associations with eukaryotic hosts that require precise interactions for bacterial recognition and persistence. Most commonly, host-associated bacteria interfere with host gene expression to modulate the immune response to the infection. However, many of these bacteria also interfere with host cellular differentiation pathways to create a hospitable niche, resulting in the formation of novel cell types, tissues, and organs. In both of these situations, bacterial symbionts must interact with eukaryotic regulatory pathways. Here, we detail what is known about how bacterial symbionts, from pathogens to mutualists, control host cellular differentiation across the central dogma, from epigenetic chromatin modifications, to transcription and mRNA processing, to translation and protein modifications. We identify four main trends from this survey. First, mechanisms for controlling host gene expression appear to evolve from symbionts co-opting cross-talk between host signalling pathways. Second, symbiont regulatory capacity is constrained by the processes that drive reductive genome evolution in host-associated bacteria. Third, the regulatory mechanisms symbionts exhibit correlate with the cost/benefit nature of the association. And, fourth symbiont mechanisms for interacting with host genetic regulatory elements are not bound by native bacterial capabilities. Using this knowledge, we explore how the ubiquitous intracellular Wolbachia symbiont of arthropods and nematodes may modulate host cellular differentiation to manipulate host reproduction. Our survey of the literature on how infection alters gene expression in Wolbachia and its hosts revealed that, despite their intermediate-sized genomes, different strains appear capable of a wide diversity of regulatory manipulations. Given this and Wolbachia’s diversity of phenotypes and eukaryotic-like proteins, we expect that many symbiont-induced host differentiation mechanisms will be discovered in this system.

This article is a tribute to Lewis Wolpert on the occasion of the recent 50th anniversary of the publication of his article ‘Positional Information and the Spatial Pattern of Differentiation’. This tribute relates to another of his ideas: his early ‘Progress Zone’ timing model for limb development. Recent evidence is reviewed that a mechanism sharing features with this model patterns the main body axis in early vertebrate development. This tribute celebrates the golden era of Developmental Biology.

Mammalian genomes encode tens of thousands of long-noncoding RNAs (lncRNAs), which are capable of interactions with DNA, RNA and protein molecules, thereby enabling a variety of transcriptional and post-transcriptional regulatory activities. Strikingly, about 40% of lncRNAs are expressed specifically in the brain in precisely regulated temporal and spatial expression patterns. In stark contrast to the highly conserved repertoire of protein-coding genes, thousands of new lncRNAs have appeared during primate nervous system evolution with hundreds of human-specific lncRNAs. Their evolvable nature and the myriad of potential functions make lncRNAs ideal candidates for drivers of human brain evolution. The human brain displays the largest relative volume of any animal species and the most remarkable cognitive abilities. In addition to brain size, structural reorganization and adaptive changes represent crucial hallmarks of human brain evolution. LncRNAs are increasingly reported to be involved in neurodevelopmental processes including proliferation, neurite outgrowth and synaptogenesis, as well as in neuroplasticity, suggested to underlie human brain evolution. Hence, evolutionary human brain adaptations are proposed to be essentially driven by lncRNAs, which will be discussed in this review.

The existence of a common ancestor to all living organisms in Earth is a necessary corollary of Darwin idea of common ancestry. The Last Universal Common Ancestor (LUCA) has been normally considered as the ancestor of cellular organisms that originated the three domains of life: Bacteria, Archaea and Eukarya. Recent studies about the nature of LUCA indicate that this first organism should present hundreds of genes and a complex metabolism. Trying to bring another of Darwin ideas into the origins of life discussion, we went back into the prebiotic chemistry trying to understand how LUCA could be originated under gradualist assumptions. Along this line of reasoning, it became clear to us that the definition of another ancestral should be of particular relevance to the understanding about the emergence of biological systems. Together with the view of biology as a language for chemical translation, on which proteins are encoded into nucleic acids polymers, we glimpse a point in the deep past on which this Translation mechanism could have taken place. Thus, we propose the emergence of this process shared by all biological systems as a point of interest and propose the existence of this pre-cellular entity named FUCA, as the First Universal Common Ancestor. FUCA was born in the very instant on which RNA-world replicators started to be capable to catalyze the bonding of amino acids into oligopeptides. FUCA has been considered mature when the translation system apparatus has been assembled together with the establishment of a primeval, possibly error-prone genetic code. This is FUCA, the earliest ancestor of LUCA’s lineage.

The evolution of release factors catalyzing the hydrolysis of the final peptidyl-tRNA bond and the release of the polypeptide from the ribosome has been a longstanding paradox. While the components of the translation apparatus are generally well-conserved across extant life, structurally-unrelated release factor peptidyl hydrolases (RF-PHs) emerged in the stems of the bacterial and archaeo-eukaryotic lineages. We analyze the diversification of RF-PH domains within the broader evolutionary framework of the translation apparatus. Thus, we reconstruct the possible state of translation termination in the Last Universal Common Ancestor with possible tRNA-like terminators. Further, evolutionary trajectories of the several auxiliary release factors in ribosome quality control (RQC) and rescue pathways point to multiple independent solutions to this problem and frequent transfers between superkingdoms including the recently-characterized ArfT, which is more widely-distributed across life than previously appreciated. The eukaryotic RQC system was pieced together from components with disparate provenance, which include the long-sought Vms1/ANKZF1 RF-PH of bacterial origin. We also uncover an under-appreciated evolutionary driver of innovation in rescue pathways: effectors deployed in biological conflicts that target the ribosome. At least three rescue pathways (centered on the prfH/RFH, baeRF-1, and C12orf65 RF-PH domains), were likely innovated in response to such conflicts.

Localized protein translation occurs through trafficking of mRNAs and protein translation machineries to different compartments of the cell, leading to rapid on-site synthesis of proteins in response to signaling cues. The spatiotemporally precise nature of the local translation process necessitates continual developments of technologies reviewed herein to visualize and map biomolecular components and the translation process with better spatial and temporal resolution, and with fewer artifacts. We also discuss approaches to control local translation, which can serve as a design paradigm for subcellular genetic devices for eukaryotic synthetic biology.

Genetic factors influence medication response (pharmacogenetics), affecting the pharmacody-namics and pharmacokinetics of many medicaments used in clinical care. The ability of medica-tions to cross the blood-brain barrier (BBB) represents a critical putative factor in the effectiveness and tolerability of various central nervous system (CNS) medications at a pharmacokinetic (dosing) level. Pharmacogenetics has the potential to personalise medicine to a greater extent than has been possible, with the potential to help reduce heuristic delays to effective tolerable pharmacotherapy. Here, we critically examine and summarise the evidence, particularly for ABCB1 polymorphisms associated with various CNS medications. These transporters appear to have a role in BBB pharmacogenetics. We identify some of the most promising variants for clinical translation while spotlighting the complexities of the involved systems and limitations of the current empirical literature.

As large language models (LLMs) continue to advance, evaluating their comprehensive capabilities becomes significant for their application in various fields. This research study comprehensively evaluates the language, vision, speech, and multimodal capabilities of GPT-4o. The study employs standardized exam questions, reasoning tasks, and translation assessments to assess the model's language capability. Additionally, GPT-4o's vision and speech capabilities are tested through image classification and object recognition tasks, as well as accent classification. The multimodal evaluation assesses the model's performance in integrating visual and linguistic data. Our findings reveal that GPT-4o demonstrates high accuracy and efficiency across multiple domains in language and reasoning capabilities, excelling in tasks that require few-shot learning. GPT-4o also provides notable improvements in multimodal tasks compared to its predecessors. However, the model shows variability and faces limitations in handling complex and ambiguous inputs, particularly in audio and vision capabilities. This paper highlights the need for more comprehensive benchmarks and robust evaluation frameworks, encompassing qualitative assessments involving human judgment as well as error analysis. Future work should focus on expanding datasets, investigating prompt-based assessment, and enhancing few-shot learning techniques to test the model's practical applicability and performance in real-world scenarios.

Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages. hor[RU,RA]{\large Richard Anderson} \address[RU]{\large Rutgers University, USA} \address[RA]{\large rick.anderson@rutgers.edu} \author[RU]{\large Carmela Scala} \author[RU]{\large Jim Samuel} \author[UC]{\large Vivek Kumar} \address[UC]{\large University of Cagliari, Italy} \author[RU]{\large Parth Jain}

The intricacies of translational strategies make this stage of the implementation of genetic information one of the most difficult to understand and, at the same time, perhaps the most interesting. It also becomes clear that all this variety of strategies is not just a consequence of a long evolutionary history but is of paramount importance for fine-tuning gene expression and metabolic modulation. This is especially true for those organisms that demonstrate predominantly biochemical and physiological reactions with a lack of behavioural ones. Plants are just such a group of organisms. Overcoming unfavourable environmental conditions occupies a key place in plant physiology. This is especially true with the changing conditions of global warming and the irretrievable loss or depletion of natural ecosystems. Conceptually, the response of plants to abiotic stress is a set of complex and intricate strategies. This is dictated both by a variety of abiotic factors that provoke stressful conditions and by a variety of molecular genetic mechanisms that make it possible to fine-tune the metabolic pathways through which the plant organism overcomes non-standard and non-optimal conditions. In the presented review, we tried to focus on the state-of-the-art in the field of translational regulation in plants under abiotic stress conditions. Various regulatory elements and patterns are considered in relative chronological order. We also considered it necessary to pay attention to key high-performance methods for studying the dynamics of genetic information at the stage of translation.

The conceptual and theoretical backbones of symmetry/asymmetry detections or similarity/dissimilarity, and identity/unidentity study are automorphism or isomorphism respectively. However, the development of equations and methods for symmetry/asymmetry detections, similarity/dissimilarity, and identity/unidentity measures deviates from these backbones. In this article, an equation was proposed for symmetry/asymmetry detections, similarity/dissimilarity, and identity/unidentity measures, and proved that its isoreflective pairs-points are functionally bijective and inverse. The proposal, called Kabirian-based optinalysis, is based on the conceptual and theoretical frameworks of automorphism and isomorphism. The Kabirian-based optinalysis is also proven and characterized as invariant (robust) under translation (i.e., scaling and location shift), and rotation or reflection. Computing codes were written in python language for Kabirian-based optinalysis to serve as working codes for application and verification.

Myostatin inhibition therapy has held much promise for the treatment of muscle wasting disorders. This is particularly true for the fatal myopathy, Duchenne Muscular Dystrophy (DMD). Following on from promising pre-clinical data in dystrophin-deficient mice and dogs, several clinical trials were initiated in DMD patients using different modality myostatin inhibition therapies. All failed to show modification of disease course as dictated by the primary and secondary outcomes measures selected: the myostatin inhibition story thus far, is a failed clinical story. These trials have recently been extensively reviewed and reasons why pre-clinical data collected in animal models has failed to translate into clinical benefit to patients has been purported. However, the biological mechanisms underlying translational failure need to be examined to ensure future myostatin inhibitor development endeavors do not meet with the same fate. Here, we explore the biology which could explain the failed translation of myostatin inhibitors in the treatment of DMD.

The impact of metals bioaccumulation on marine organisms is under investigation. This study was designed to determine the association of oxidative stress in mussels Mytilus galloprovincialis induced by seawater enriched with trace metals with protein synthesis. Mussels were exposed to 40 μg/L Cu, 30 μg/L Hg, or 100 μg/L Cd for 5 and 15 days, and the pollution effect was evaluated by measuring established oxidative biomarkers. The results showed damage on the protein synthesis machine integrity and specifically, on translation factors and ribosomal proteins expression and modifications. Exposure of mussels to all metals caused oxidative damage that was milder in the cases of Cu and Hg, and more pronounced for Cd. However, after prolonged exposure of mussels to Cd (15 days), the effects receded. These changes that perturb protein biosynthesis can serve as a great tool for elucidating the mechanisms of toxicity and could be integrated in biomonitoring programs.

A novel method for finding linear mappings among word embeddings for several languages, taking as pivot a shared, universal embedding space, is proposed in this paper. Previous approaches learn translation matrices between two specific languages, but this method learn translation matrices between a given language and a shared, universal space. The system was first trained on bilingual, and later on multilingual corpora as well. In the first case two different training data were applied; Dinu’s English-Italian benchmark data, and English-Italian translation pairs extracted from the PanLex database. In the second case only the PanLex database was used. The system performs on English-Italian languages with the best setting significantly better than the baseline system of Mikolov et al. [1], and it provides a comparable performance with the more sophisticated systems of Faruqui and Dyer [2] and Dinu et al. [3]. Exploiting the richness of the PanLex database, the proposed method makes it possible to learn linear mappings among an arbitrary number of languages.

The [PSI⁺] nonsense-suppressor determinant of Saccharomyces cerevisiae is related to formation of heritable amyloids of the Sup35 (eRF3) translation termination factor. [PSI⁺] amyloids have variants in amyloid structure and in the strength of suppressor phenotype. Appearance of [PSI⁺], its propagation and manifestation depend primarily on chaperones. Besides chaperones, the Upf1/2/3, Siw14 and Arg82 proteins restrict [PSI⁺] formation, while Sla2 can prevent the [PSI⁺] toxicity. Here, we identify two more non-chaperone proteins involved in [PSI⁺] detoxification. We show that simultaneous lack of the Pub1 and Upf1 proteins causes lethality of [PSI⁺] cells with a strong, but not with weak suppressor phenotype. This lethality results from excessive depletion of the Sup45 (eRF1) termination factor due to its sequestration into Sup35 polymers. We also show that Pub1 acts to restrict excessive Sup35 prion polymerization, while Upf1 interferes with Sup45 binding to Sup35 polymers. These data allow considering the Pub1 and Upf1 proteins as a novel [PSI⁺] detoxification system.

Nearly 50 years ago, Francis Crick propounded the frozen accident scenario for the evolution of the genetic code along with the hypothesis that the early translation system consisted primarily of RNA. Under the frozen accident perspective, the code is universal among modern life forms because any change in codon assignment would be highly deleterious. The frozen accident can be considered the default theory of code evolution because it does not imply any specific interactions between amino acids and the cognate codons or anticodons, or any particular properties of the code. The subsequent 49 years of code studies have elucidated notable features of the standard code, such as high robustness to errors, but failed to develop a compelling explanation for codon assignments. In particular, stereochemical affinity between amino acids and the cognate codons or anticodons does not seem to account for the origin and evolution of the code. Here I expand Crick’s hypothesis on RNA-only translation system by presenting evidence that this early translation already attained high fidelity that allowed protein evolution. I outline an experimentally testable scenario for the evolution of the code that combines a distinct version of the stereochemical hypothesis, in which amino acids are recognized via unique sites in the tertiary structure of proto-tRNAs, rather than by anticodons, expansion of the code via proto-tRNA duplication and the frozen accident.

Alignment of picornavirus proteinase/polymerase sequences reveals this family evolved into five ‘supergroups’. Interestingly, the nature of the 2A region of the picornavirus polyprotein is highly correlated with this phylogeny. Viruses within supergroup 4, the Paavivirinae, have complex 2A regions with many viruses encoding multiple 2A^NPGP sequences. In vitro transcription/translation analyses of a synthetic polyprotein comprising green fluorescent protein (GFP) linked to b-glucuronidase (GUS) via individual 2A^NPGPs showed two main phenotypes: highly active 2A^NPGP sequences - similar to Foot-and-Mouth Disease Virus 2A^NPGP and, surprisingly, a novel phenotype of some 2A^NPGP sequences which apparently terminate translation at the C-terminus of 2A^NPGP - without detectable re-initiation of downstream sequences (GUS). Probing databases with the short sequences between 2A^NPGPs did not reveal any potential ‘accessory’ functions. The novel, highly active, 2A-like sequences we identified substantially expands the toolbox for biomedical / biotechnological co-expression applications.

According to a recent study by OpenAI, Open Research, and the University of Pennsylvania, large language models (LLMs) based on artificial intelligence (AI), such as Generative Pretrained Transformers (GPTs), may have potential implications on the job market, specifically regarding job occupations that demand writing or programming skills. The research points out Interpreters and Translators as one of the main job occupations with greater exposure to AI in the US job market (76.5%). This article, following a mixed-methods survey-based research, provides insights into the Portuguese Language Service Providers (LSPs) awareness and knowledge about AI, specifically regarding neural machine translation (NMT) and large language models (LLM), its actual use and usefulness, as well as their potential influence on work performance and labour market. The results show that most professionals are unable to identify if AI and/or automation technologies support the tools that are most used in the profession. The usefulness of AI is essentially low to moderate and the professionals who are less familiar with it and less knowledgeable also demonstrate lack of trust in it. Two thirds of the sample estimate negative or very negative effects of AI in the profession, expressing as main concerns the devaluation and replacement of experts, the reduction of income, and the reconfiguration of the career of translator to mere post-editors.

Viruses feature an evolutionary shaped minimal genome that is obligately dependent on the cellular transcription and translation machinery for propagation. To suppress host cell immune responses and ensure efficient replication, viruses employ numerous tactics to favor viral gene expression and protein synthesis. This necessitates a carefully balanced network of virus- and host-encoded components, of which the RNA-based regulatory mechanisms have emerged as particularly interesting albeit insufficiently studied, especially in unicellular organisms. Here, recent advances that further our understanding of RNA-based translation regulation, mainly through post-transcriptional chemical modification of ribonucleosides, codon usage, and (virus-encoded) transfer RNAs, will be discussed in the context of viral infection.

Globalization has had an impact on the education system in the UAE, where increased use of bilingual curriculum (Arabic-English) is held in high regard. Nevertheless, literacy among Emirati children and teenagers remains low. This study uses a 15-item, open-ended questionnaire completed by Emirati parents and an 8-item, open-ended questionnaire completed by Emirati primary school children from 8 to 11 years of age, and compares the translation of The Little prince into Modern Standard Arabic (MSA) and into the Emirati dialect. The results suggest that not only the Emirati dialect, but any Arabic dialect (in its respective Arab country) could be used in primary schools to motivate children to read in Arabic and bridge the gap between their spoken language (dialect) and formal written Arabic. Not only must an Arab child learn how to read, but also, they have to understand a very formal language system that they are not used to speaking at home, i.e., Modern Standard Arabic (MSA). Having books in their own dialects may make children more interested in reading, as they can understand them more easily.

Viruses are the most prevalent infectious agents, populating almost every ecosystem on earth. Most viruses carry only a handful of genes supporting their replication and the production of capsids. It came as a great surprise in 2003 when the first giant virus was discovered and found to have a >1Mbp genome encoding almost a thousand proteins. Following this first discovery, dozens of giant virus strains across several viral families have been reported. Here, we provide an updated quantitative and qualitative view on giant viruses and elaborate on their shared and variable features. We review the complexity of giant virus proteomes, which include functions traditionally associated only with cellular organisms. These unprecedented functions include components of the translation machinery, DNA maintenance, and metabolic enzymes. We discuss the possible underlying evolutionary processes and mechanisms that might have shaped the diversity of giant viruses and their genomes, highlighting their remarkable capacity to hijack genes and genomic sequences from their hosts and environments. This leads us to examine prominent theories regarding the origin of giant viruses. Finally, we present the emerging ecological view of giant viruses, found across widespread habitats and ecological systems, with respect to the environment and human health.

Glioblastoma (GBM) presents a significant public health challenge as the deadliest and most common malignant brain tumor in adults. Despite standard-of-care treatment, which includes surgery, radiation, and chemotherapy, mortality rates are high, underscoring the critical need for advancing GBM therapy. Over the past two decades, numerous clinical trials have been performed, yet only a small fraction demonstrated benefit, raising concerns about the predictability of current preclinical models. Traditionally, preclinical studies utilize treatment-naïve tumors, failing to model the clinical scenario where patients undergo standard-of-care treatment prior to recurrence. Recurrent GBM generally exhibits distinct molecular alterations influenced by treatment selection pressures. In this review, we discuss the impact of treatment - surgery, radiation, and chemotherapy - on GBM. We also provide a summary of treatments used in preclinical models, advocating for their integration to enhance the translation of novel strategies to improve therapeutic outcomes in GBM.

The genetic engineering of plants often relies on the use of antibiotic or herbicide resistance genes for initial selection of primary transgenic events. Nevertheless, the commercial release of genetically modified crops containing any marker gene encounters several challenges stemming from the lack of consumer acceptance. Development of strategies enabled the generation of marker-free transgenic plants is a possible alternative to deal with public concerns over the safety of biotech crops. Leveraging the high in vitro regeneration and transformation capabilities of potato, this study examined the factors associated with the development of transgenic plants without presence of selective substances in medium. To compare the antibiotic-assisted with the antibiotic-free transformation, nodal explants from five highly regenerative potato cultivars were inoculated with the Agrobacterium strain AGL0 carrying the pBIN-mGFP5-ER (35SGFP/nosNPTII) plasmid. The efficiency of transient expression and the formation of chimeric and completely transgenic shoots were assessed through vital GFP observations. Under non selective conditions, the regeneration efficiency of stable non-chimeric transgenic shoots varied from 0.9 (‘Chicago’) to 2.7% (#12-36-42). In order to streamline transformation protocol, a specific timeline for collecting transgenic plantlets was established for each analyzed cultivar. This timeline strategy was implemented to generate transgenic potato plants, which demonstrated the suppression of homologous SteIF4Es family genes as a result of the successful introduction of the marker-free hpRNAi construct.

Stress is one of the important factors affecting the growth and development of plants. In recent years, a large body of data on transcriptional changes in response to stress in plants has been obtained. Despite the fact that the important role of the regulation of translation in response to stress has been proven in plants, knowledge in this area remains extremely scarce. In this review, we focused on mRNA translation in the cytoplasm of higher plants. Most of the current research on the effects of translation has been done in yeast and mammalian systems. Translation factors and mechanisms are mostly conserved among eukaryotes; however, some differences are known to exist among plants. A comprehensive understanding of the complex apparatus of translation and its regulation in plants is required. In this work, we have tried to update modern ideas about the regulation of translation under conditions of abiotic stress.

Cardiovascular diseases remain the leading cause of mortality worldwide. Although new therapies are actively being developed and used for cardiovascular pathologies, these attempts have not significantly decreased mortality rates. Regenerative medicine has made enormous progress and set promising approaches over the past half-century. However, since autologous (donor-derived) vascular grafts are lacking, an alternative prosthesis must be constructed for cardiovascular disease patients. In vascular tissue manufacturing and regenerative medicine, scientists seek to improve this significant clinical challenge using bio-fabrication techniques combining additive manufacturing, biomaterials science, and advanced cellular biology. In the last few decades, many improvements and changes in various approaches have helped develop bioengineered concepts that reflect native blood vessels’ structure and function. However, numerous challenges must be overcome to clinically translate the next generation of tissue-engineered vascular transplants. This review provides update on the cell sources, scaffold essential for cardiovascular tissue engineering, and tissue engineering approaches as prospective options for curative therapy for blood vessel disease.

The increase in pandemics caused by RNA viruses of zoonotic origin highlights the urgent need for broad-spectrum antivirals against novel and re-emerging RNA viruses. Broad-spectrum antivirals could be deployed as first-line interventions during an outbreak while virus-specific drugs and vaccines are developed and rolled out. Viruses depend on the host’s protein synthesis machinery for replication. Several natural compounds that target the cellular DEAD-box RNA helicase eIF4A, a key component of the eukaryotic translation initiation complex eIF4F, have emerged as potential broad-spectrum antivirals. Rocaglates, a group of flavaglines of plant origin that clamp mRNAs with highly structured 5’UTRs onto the surface of eIF4A through specific stacking interactions, exhibit the largest selectivity and potential therapeutic indices among all known eIF4A inhibitors. Their unique mechanism of action limits the inhibitory effect of rocaglates to the translation of eIF4A-dependent viral mRNAs and a minor fraction of host mRNAs exhibiting stable RNA secondary structures and/or polypurine sequence stretches in their 5´UTRs, resulting in minimal potential toxic side effects. Maintaining a favorable safety profile while inducing efficient inhibition of a broad-spectrum of RNA viruses makes rocaglates into primary candidates for further development as pan-antiviral therapeutics.

Neural network regularized learning has garnered significant attention in recent years. We give a systematic investigation on the performance of regularized regression associated zonal translation networks. We propose the concept of Marcinkiewicz-Zygmund inequality Setting (MZIS) for the scattered nodes collected from the unit sphere. We show that, under the MZIS, the corresponding convolutional zonal translation network has reproducing property. Based on these facts,we propose a kind of kernel regularized regression learning framework and provide upper bound estimate for the learning rate with the kernel approach. We also give proof for the density of the zonal translation network with spherical Fourier analysis.We provide the approximation error with a K-functional.

The importance of frailty in older people is getting constant recognition as an important aspect both in terms of public health, as well in personal level for the appropriate management of an older person health condition. This is reflected by the continuously increasing number of research studies carried out in several settings across different countries. Sometimes this is very solid, but in other cases there is a considerable gap in terms of accurate and well grounded documentation of frailty status. This is the case in Greece, where we are missing clinically validated tools to approach frailty. We are missing frailty screening tools, such as for instance PRISMA 7, the gold standard tool of Fried criteria is somehow problematic since the question referring to physical activity originates from a questionnaire that has not been translated and validated, while Clinical Frailty Scale has been validated for the translation but not for the capacity to detect frailty. Aim of this study is to validate these tools for their accuracy to detect frailty by using a measurable index of frailty, previously proposed for use in clinical studies: the Short Physical Performance Battery (SPPB). 74 male and female participants have been evaluated for their frailty status using different tools. We observed that PRISMA 7 translation detects frailty only when one question is removed at a cut-off of ≥ 2 and indicated a sensitivity of 88.1%, and specificity of 99.9% with a good correlation with SPPB measurements (r = 0.858; p < 0.001). When CFS was validated using SPPB it demonstrated a very good correlation (r = -0.838; p < 0.001 respectively) as it was the case for the modified Fried Criteria (r = -0.725; p < 0.001). All items demonstrated a good correlation between them. We can here propose that we can accurately assess frailty status in the community setting by using a modified version of Fried criteria, Clinical Frailty Scale translation in Greek and we can screen for frailty by using the Greek translation of PRISMA 7 only after removing the item 6 of the questionnaire.

Nollywood, based on the idea of Bollywood from India, is a series of outstanding movies that originate from Nigeria. Unfortunately, while the movies are in English, they are hard to understand for many native speakers due to the dialect of English that is spoken. In this article, we accomplish two goals: (1) create a phonetic sub-title model that is able to translate Nigerian English speech to American English and (2) use the most advanced toxicity detectors to discover how toxic the speech is. Our aim is to highlight the text in these videos which is often times ignored for lack of dialectal understanding due the fact that many people in Nigeria speak a native language like Hausa at home.

Feedback from the earthquake zone in Kahramanmaraş, Turkey has established that the emerging field of Crisis Translation and Interpreting (CTInt) requires a free smartphone application, to enable Crisis Translators and Interpreters (CTIs) to access multilingual terminology resources on-/offline when assisting Search and Rescue (SAR) teams responding to man-made and/or natural disasters.The aim is to design not only a multilingual compilation of Crisis Terminology (CTerm), downloadable to smartphones enabling access when there is a failure in the internet connection, but also a unified search tool which will browse and redirect the user to existing termbanks when the internet is accessible. Nevertheless, terminological records available in the domain of CTerm are inadequate and dispersed, with different interfaces and language combinations, requiring enhancement and harmonisation. Furthermore, automated translation and interpreting software/systems supported by the multilingual compilation of the crisis terminology portal (CTermPort) ideally need to be developed or, alternatively, existing ones integrated into the crisis termbank (CTermBank) collection for those who would lack the support of CTIs.The design of the CTermPort will significantly impact civil societies by providing a lifesaving tool for CTIs who need a reliable terminological reference point and automated translation options, and those unable to be assisted. In this respect, the project also promises strong prospects of sustainability past the project duration, as the tool to be developed will serve international SAR teams working closely with CInts during response times, but also medical staff in field hospitals collaborating with CTIs in rehabilitation and recovery phases, and beyond.

A Twi-English parallel corpus is certainly an important resource for Machine Translation of Twi (ISO 639-3), a Low-Resource African Language (LRAL) which is mainly spoken in Ghana and Ivory Coast. Currently large-scale multi-domain Twi-English parallel corpus is still unavailable partly due to the difficulties and the arduous efforts required in its design. In this paper, we present TWIENG: a large-scale multi-domain Twi-English parallel corpus. We crawled the sentences from the web using web crawlers, translated, aligned, tokenized and compiled to create the corpus. We crawled English sentences from Ghanaian indigenous electronic news portals, Ghanaian Parliamentary Hansards, Twi Bible and crowdsourcing via google forms. The sentences were translated by professional translators and linguists, they were then aligned, tokenized and compiled. The corpus was curated using the sketch engine, a corpus manager and analysis software developed by Lexical Computing Limited. The corpus was manually evaluated by Twi professional linguists. The Corpus has 5,419 parallel sentences.

Traditional urban design often overlooks the synchronisation of human and ecological connectivities, typically favouring corridors for ecological continuity. Our study challenges this convention by introducing a computational design approach, Meta-Connectivity, leveraging the deep generative models performing cross-domain translation to integrate human-wildlife landscape connectivity in urban morphology amidst the planetary urbanisation. Utilising chained Pix2Pix models, our research illustrates a novel meta-connectivity design reasoning framework, combining landscape connectivity modelling with conditional reasoning based on deep generative models. This framework enables the adjustment of both human and wildlife landscape connectivities based on their correlative patterns in one single design process, guiding the re-materialisation of urban landscapes without the need for explicit prior ecological or urban data. Our empirical study in East London demonstrated the framework's efficacy in suggesting wildlife connectivity adjustments based on human connectivity metrics. The results demonstrate the feasibility of creating an innovative urban form in which the land cover guided by the connectivity gradients replaces the corridors based on simple geometries. This research thus presents a methodology shift in urban design, proposing a symbiotic approach to integrating disparate yet interrelated landscape connectivities within urban contexts.

Detector-based spectral CT offers the possibility of obtaining spectral information from which discrete acquisitions at different energy levels can be derived, yielding so-called virtual monoenergetic images (VMI). In this study, we aimed to develop a jointly optimized deep learning framework based on dual-energy CT pulmonary angiography (DE-CTPA) data to generate synthetic monoenergetic images (SMI) for improving automatic pulmonary embolism (PE) detection in single-energy CTPA scans. For this purpose, we used two data sets: our institutional DE-CTPA data set D1 comprising polyenergetic arterial series and the corresponding VMI at low-energy levels (40 keV) with 7,892 image pairs, and a 10% subset of the 2020 RSNA Pulmonary Embolism Detection Challenge data set D2, which consisted of 161,253 polyenergetic images with dichotomous slice-wise annotations (PE/no PE). We trained a fully convolutional encoder-decoder on D1 to generate SMI from single-energy CTPA scans of D2, which were then fed into a ResNet50 network for training of the downstream PE classification task. The quantitative results on the reconstruction ability of our framework revealed high-quality visual SMI predictions with reconstruction results of 0.984 ± 0.002 (structural similarity) and 41.706 ± 0.547 dB (peak-signal-to-noise ratio). PE classification resulted in an AUC of 0.84 for our model, which achieved improved performance compared to other naive approaches with AUCs up to 0.81. Our study stresses the role of using joint optimization strategies for deep learning algorithms to improve automatic PE detection. The proposed pipeline may prove to be beneficial for computer-aided detection systems and could help rescue CTPA studies with suboptimal opacification of the pulmonary arteries from single-energy CT scanners.

Leishmania species are parasitic protozoans which cause leishmaniasis affecting millions of people worldwide. Sterols are important components of plasma and organellar membranes and serve as precursors for the synthesis of signaling molecules. In contrast to animals, Leishmania do not produce cholesterol but instead synthesize ergostane-based sterols. C14-demethylase is a key enzyme involved in the biosynthesis of sterols in Leishmania parasites and an important drug target. Its inactivation leads to multiple defects including increased plasma membrane fluidity, mitochondrion dysfunction, hypersensitivity to stress and reduced virulence. In this study, we revealed a novel role for sterol synthesis in the maintenance of RNA stability and translation. Sterol alteration in C14-demethylase knockout mutant leads to increased RNA degradation, reduced translation and impaired heat shock response. Thus, sterol biosynthesis plays an unexpected role in global gene regulation in Leishmania parasites.

The recent advancements of the Semantic Web and Linked Data have changed the working of the traditional web. There is significant adoption of the Resource Description Framework (RDF) format for saving of web-based data. This massive adoption has paved the way for the development of various centralized and distributed RDF processing engines. These engines employ various mechanisms to implement critical components of the query processing engines such as data storage, indexing, language support, and query execution. All these components govern how queries are executed and can have a substantial effect on the query runtime. For example, the storage of RDF data in various ways significantly affects the data storage space required and the query runtime performance. The type of indexing approach used in RDF engines is critical for fast data lookup. The type of the underlying querying language (e.g., SPARQL or SQL) used for query execution is a crucial optimization component of the RDF storage solutions. Finally, query execution involving different join orders significantly affects the query response time. This paper provides a comprehensive review of centralized and distributed RDF engines in terms of storage, indexing, language support, and query execution.

Radiotherapy commonly utilizes CBCT for patient positioning and treatment monitoring. CBCT is deemed to be secure for patients, making it suitable for the delivery of fractional doses. However, limitations such as a narrow field of view, beam hardening, scattered radiation artifacts, and variability in pixel intensity hinder the direct use of raw CBCT for dose recalculation during treatment. To address this issue, reliable correction techniques are necessary to remove artifacts and remap pixel intensity into HU values. This study proposes a deep-learning framework for calibrating CBCT images acquired with narrow FOV systems and demonstrates its potential use in proton treatment planning updates. Cycle-consistent GAN processes raw CBCT to reduce scatter and remap HU. Monte Carlo simulation is used to generate CBCT scans, enabling the possibility to focus solely on the algorithm’s ability to reduce artifacts and cupping effects without considering intra-patient longitudinal variability and producing a fair comparison between planning CT and calibrated CBCT dosimetry. To showcase the viability of the approach using real-world data, experiments were also conducted using real CBCT. Tests were performed on a publicly available dataset of 40 patients who received ablative radiation therapy for pancreatic cancer. The simulated CBCT calibration led to a difference in proton dosimetry of less than 2%, compared to the planning CT. The potential toxicity effect on the organs at risk decreased from about 50% (uncalibrated) up the 2% (calibrated). The gamma pass rate at 3%/2mm produced an improvement of about 37% in replicating the prescribed dose before and after calibration (53.78% vs 90.26%). Real data also confirmed this with slightly inferior performances for the same criteria (65.36% vs 87.20%). These results may confirm that generative artificial intelligence brings the use of narrow FOV CBCT scans incrementally closer to clinical translation in proton therapy planning updates.

The popularity of electronic cigarettes in the United States and around the world has led to a startling rise in youth nicotine use. The Juul e-cigarette was introduced in the U.S. market in 2015 and had captured approximately 13% of the U.S. market by 2017. Unlike many other contemporary electronic cigarette companies, the founders behind the Juul® e-cigarette approached their product launch like a traditional high-tech start-up company, not like a tobacco company. This article presents a case study of Juul’s corporate and product development history in the context of US regulatory actions. The objective of this article is to demonstrate the value of government-curated archives as leading indicators which can (a) provide insight into emergent technologies and (b) inform emergent regulatory science research questions. A variety of sources were used to gather data about the Juul e-cigarette and the corporations that surround it. Sources included government agencies, published academic literature, non-profit organizations, corporate and retail websites, and the popular press. Data was disambiguated, authenticated, and categorized prior to being placed on a timeline of events. A timeline of four significant milestones, nineteen corporate filings and events, twelve US regulatory actions, sixty four patent applications, eighty seven trademark applications, twenty three design patents and thirty two utility patents related to Juul Labs and its associates is presented spanning the years 2004 thought 2020. This work demonstrates the probative value of findings from patent, trademark, and SEC filing literature in establishing a premise for emergent regulatory science research questions which may not yet be supported by traditional archival research literature. The methods presented here can be used to identify key aspects of emerging technologies before products actually enter the market, this shifting policy formulation and problem identification from a paradigm of being reactive in favor of becoming proactive. Such a proactive approach may permit anticipatory regulatory science research and ultimately shorten the elapsed time between market technology innovation and regulatory response.

We propose a method for creating parallel Bessel-like vortical optical needles with an arbitrary axial intensity distribution via superposition of different cone-angle Bessel vortices. We analyze the interplay between the separation of individual optical vortical needles and their respective lengths and introduce a super-Gaussian function as their axial profile. We also analyze the physical limitations to observe well-separated optic needles, as they are influenced by mutual interference of the individual beams. To verify our theoretical and numerical results, we generate controllable spatial arrays of individual Bessel beams with various numbers and spatial separations by altering the spectrum of the incoming laser beam via the spatial light modulator. We demonstrate experimentally how to implement such beams using a diffractive mask. The presented method facilitates the creation of diverse spatial intensity distributions in three dimensions, potentially finding applications in specific microfabrication tasks or other contexts. These beams may have benefits in laser material processing applications such as nanochannel machining, glass via production, modification of glass refractive indices, and glass dicing.

This paper presents the current lexical, morphological, syntactic and rule-based machine translation work for Erzya and Moksha that can and should be used in the development of a roadmap for Mordvin linguistic research. We seek to illustrate and outline initial problem types to be encountered in the construction of an Apertium-based shallow-transfer machine translation system for the Mordvin language forms. We indicate reference points within Mordvin Studies and other parts of Uralic studies, as a point of departure for outlining a linguistic studies with a means for measuring its own progress and developing a roadmap for further studies.

This paper provides the solution to understanding the hitherto unknown writing system used for the manuscript listed as MS 408 at the Beinecke Library, Yale University. The writing system uses symbols, punctuation, grammar and language that are each unique. The manuscript is not encrypted, in the sense that its author made an effort to conceal the contents of the manuscript, as has been presumed by some scholars. Instead, it is code only in the sense that the modern reader needs to be versed in the calligraphic and linguistic rules to be able to translate and read the texts. Furthermore, in discovering its writing system, it became apparent that the manuscript is of invaluable importance to the study of the evolution of the Romance languages and the scheme of Italic letters and associated punctuation marks now commonplace in those and other modern languages. In short; it is revealed to be the only known document both written in Vulgar Latin, or proto-Romance, and using proto-Italic symbols. The original title for the manuscript, given by its female author, is: What one needs to be sure to acquire for the evils set in one’s fate. It is a book offering homeopathic advice and instruction to women of court on matters of the heart, of sexual congress, of reproduction, of motherhood and of the physical and emotional complications that can arise along the way through life. The manuscript has now been dated to the year 1444 and the location of its creation has been pinpointed to the court of Castello Aragonese, on the island of Ischia: as expounded in the companion paper Linguistically Dating and Locating Manuscript MS408: http://ling.auf.net/lingbuzz/003808.

As with all new fields of discovery, work on the biological role of G-quadruplexes (GQ) has produced a number of results that at first glance are quite baffling, sometimes because they do not fit well together, but mostly because they are different from commonly held expectations. Like other classes of flipons, those that form G-quadruplexes have a repeat sequence motif that enables the fold. The canonical DNA motif (G3N1-7)3G3, where N is any nucleotide and G is guanine, a feature that is under active selection in avian and mammalian genomes. The involvement of G-flipons in genome maintenance traces back to the invertebrate Caenorhabditis elegans and to ancient DNA repair pathways. A role for GQ in transcription is supported by the observation that yeast Rap1 protein binds both B-DNA, in a sequence-specific manner, and GQ, in a structure-specific manner, through the same helix. Other sequence-specific transcription factors (TF) also engage both conformations to actuate cellular transactions. Noncoding RNAs can also modulate GQ formation in a sequence-specific manner and engage the same cellular machinery as localized by TF, linking the ancient RNA world with the modern protein world. The coevolution of noncoding RNAs and sequence-specific proteins is supported by studies of early embryonic development, where the transient formation of G-quadruplexes coordinates the epigenetic specification of cell fate.

Abstract: Founders of quantum theory have pointed out the irreducibility of living systems to quantum mechanics. Quantum mechanics is never violated they rightfully argue but works under a set of active macroscopic constraints that selectively order the behavior of the micro components and these constraints are not reducible to the reversible, causally closed formalism of quantum mechanics. The whole of biological evolution is characterized by the production of increasingly more highly-ordered levels of such macroscopic constraints and biological evolutionary theory has no account for this either. It is assumed outside its theoretical first principles. In fact this generative macroscopic ordering, the production of increasingly more highly differentiated levels of order—more things, more kinds of things, cannot be explained within biological theory because it is generic to the universe itself. Irreducible to quantum mechanics this begs the question of fundamentality in physics. Fundamental laws in a universe defined by active macroscopic ordering, level-building, and differentiation must be able to causally cross levels, and more importantly explain why there is any level-building at all to begin with. The laws of thermodynamics are fundamental laws in this sense, and 4th Law path selection explains the generative universal ordering and differentiation (“complexification”).

Since its initial characterization in 2016, the interferon stimulated gene Shiftless (SHFL) has proven to be a critical piece of the innate immune response to viral infection. SHFL expression stringently restricts the replication of multiple DNA, RNA, and retroviruses with an extraordinary diversity of mechanisms that differ from one virus to the next. These inhibitory strategies include the negative regulation of viral RNA stability, translation, and even the manipulation of RNA granule formation during viral infection. Even more surprisingly, SHFL is the first human protein found to directly inhibit the activity of the -1 programmed ribosomal frameshift, a translation recoding strategy utilized across nearly all domains of life and a several human viruses. Recent literature has shown that SHFL expression also significantly impacts viral pathogenesis in mouse models, highlighting its in-vivo efficacy. To help reconcile the many mechanisms by which SHFL restricts viral replication, we provide here a comprehensive review of this complex ISG, its influence over viral RNA fate, and the implications of its functions on the virus-host arms race for control of the cell.

Objective: The Acute Cystitis Symptom Score (ACSS) was developed and validated as a self-reporting questionnaire for diagnosing and monitoring acute, uncomplicated cystitis (AC) in female patients. The study aims at the translation of the ACSS into Greek from original Russian as a source and American English as a new master version and at its linguistic validation. Material and Methods: Three independent professional native Greek translators, two of them experts in Russian and one in English, translated the ACSS from Russian and American English into Greek. The second group of three translators translated each of the three versions back into the original language to detect or correct any important discrepancies. These three Greek versions were then used for linguistic validation. Results: The English to Greek translation reflected more the spoken language, the two Russian to Greek translations more the written, formal language. A total of 60 randomly selected females and 30 healthcare professionals was asked about their preferences and to comment on each of the three translations. Considering all comments the scientific committee (SC) developed the Greek consensus version. For cognitive assessment additional 30 healthcare professionals and 30 females were asked to comment on the intelligibility of all items. Considering their comments the linguistically validated Greek study version was established by the SC. Conclusion: The linguistically validated Greek version of the ACSS can now be used for the clinical validation study.

CT scans are currently the most common imaging modality used for suspected stroke patients due to their short acquisition time and wide availability. However, MRI offers superior tissue contrast and image quality. In this study, eight deep learning models are developed, trained, and tested using a dataset of 181 CT/MR pairs from stroke patients. The resultant synthetic MRIs generated by these models are compared through a variety of qualitative and quantitative methods. The synthetic MRIs generated by a 3D UNet model consistently demonstrated superior performance across all methods of evaluation. Overall, the generation of synthetic MRIs from CT scans using the methods described in this paper produces realistic MRIs that can guide the registration of CT scans to MRI atlases. The synthetic MRIs enable the segmentation of white matter, gray matter, and cerebrospinal fluid using algorithms designed for MRIs, exhibiting a high degree of similarity to true MRIs.