Early Stage Research Articles

Want to try a registered report? Here are our lessons learned.

A Registered Report is a type of research journal article in which the introduction, methods, and analysis plan are proposed and peer-reviewed prior to the execution of the study. The goal is to limit publication bias based on study findings by conducting peer review on the merits of the study before the results are known. First introduced in 2012 (Chambers, 2013; Chambers & Tzavella, 2022), this format of journal article publication has become more commonplace. Here we provide an overview of the format as well as eight core lessons we learned while preparing Registered Reports. We integrate guidelines from the literature with our experience to provide insight into the process of preparing and publishing a Registered Report for those who have not yet tried it. Though Registered Reports require researchers to invest more effort at the earlier stages of idea generation, design, and analysis planning, they will benefit from the feedback of reviewers when it is most beneficial and leave behind the fear of rejection due to unanticipated study limitations or null results. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

CavitOmiX Drug Discovery: Engineering Antivirals with Enhanced Spectrum and Reduced Side Effects for Arboviral Diseases

Advancing climate change increases the risk of future infectious disease outbreaks, particularly of zoonotic diseases, by affecting the abundance and spread of viral vectors. Concerningly, there are currently no approved drugs for some relevant diseases, such as the arboviral diseases chikungunya, dengue or zika. The development of novel inhibitors takes 10–15 years to reach the market and faces critical challenges in preclinical and clinical trials, with approximately 30% of trials failing due to side effects. As an early response to emerging infectious diseases, CavitOmiX allows for a rapid computational screening of databases containing 3D point-clouds representing binding sites of approved drugs to identify candidates for off-label use. This process, known as drug repurposing, reduces the time and cost of regulatory approval. Here, we present potential approved drug candidates for off-label use, targeting the ADP-ribose binding site of Alphavirus chikungunya non-structural protein 3. Additionally, we demonstrate a novel in silico drug design approach, considering potential side effects at the earliest stages of drug development. We use a genetic algorithm to iteratively refine potential inhibitors for (i) reduced off-target activity and (ii) improved binding to different viral variants or across related viral species, to provide broad-spectrum and safe antivirals for the future.

Local spherical collapsing box in Athena++ : Numerical implementation and benchmark tests

We implement a local model for a spherical collapsing or expanding gas cloud in the Athena++ magnetohydrodynamic code. This local model consists of a Cartesian periodic box with time-dependent geometry. We present a series of benchmark test problems, including nonlinear solutions and linear perturbations of the local model, confirming the code's desired performance. During a spherical collapse, a horizontal shear flow is amplified, corresponding to angular momentum conservation of zonal flows in the global problem; wave speed and the amplitude of sound waves increase in the local frame, due to the reduction in the characteristic length scale of the box, which can lead to an anisotropic effective sound speed in the local box. Our code conserves both mass and momentum-to-machine precision. This numerical implementation of the local model has potential applications to the study of local physics and hydrodynamic instabilities during protostellar collapse, providing a powerful framework for better understanding the earliest stages of star and planet formation.

Fostering Computation Competence with Non-Counting Strategies and Conceptual Subitizing in Grade 1: An Intervention Study in Inclusive Classrooms

Computation competence (CC) with non-counting (NC) strategies is an important aim of Grade 1 mathematics education, but studies have shown that many students struggle to acquire these skills and continue to rely on counting strategies in later years. Therefore, even though counting is an important first strategy for young children, it is vital to support the development of CC with NC strategies from the earliest stages of mathematics education. This study examined the success of an intervention to promote the use of CC and NC strategies and conceptual subitizing (CS). The study was conducted with a sample of N = 1017 Grade 1 students, and investigated whether the achievement gain was affected by the intervention setting: cooperative learning, individual support for low achievers, a combination of the two settings, and a control group. Students in the combined setting had greater achievement gains in CC with NC strategy use and CS than the control group and outperformed those in the other intervention groups in CS. The combined setting also benefitted low achievers in mathematics for CS, but not for CC and strategy use. A combination of cooperative learning and individual support seems to be the best option for improving the CS and CC and NC strategy use of first graders, especially low achievers in mathematics.

Label-free multiphoton imaging reveals volumetric shifts across development in sensory-related brain regions of a miniature transparent vertebrate.

Animals integrate information from different sensory modalities as they mature and perform increasingly complex behaviors. This may parallel differential investment in specific brain regions depending on the demands of changing sensory inputs. To investigate developmental changes in the volume of canonical sensory integration brain regions, we used third harmonic generation imaging for morphometric analysis of forebrain and midbrain regions from 5 to 90 days post fertilization (dpf) in Danionella dracula , a transparent, miniature teleost fish whose brain is optically accessible throughout its lifespan. Relative to whole brain volume, increased volume or investment in telencephalon, a higher order sensory integration center, and torus longitudinalis (TL), a midbrain visuomotor integration center, is relatively consistent from 5 to 30 dpf, until it increases at 60 dpf, followed by another increase at 90 dpf, as animals reach adulthood. In contrast, investment in midbrain optic tectum (TeO), a retinal-recipient target, progressively decreases from 30-90 dpf, whereas investment is relatively consistent across all stages for the midbrain torus semicircularis (TS), a secondary auditory and mechanosensory lateral line center, and the olfactory bulb (OB), a direct target of the olfactory epithelium. In sum, increased investment in higher order integration centers (telencephalon, TL) occurs as juveniles reach adulthood and exhibit more complex cognitive tasks, whereas investment in modality-dominant regions occurs in earlier stages (TeO) or is relatively consistent across development (TS, OB). Complete optical access throughout Danionella 's lifespan provides a unique opportunity to investigate how changing brain structure over development correlates with changes in connectivity, microcircuitry, or behavior.

Symptom impact and health-related quality of life (HRQoL) assessment by cancer stage: a narrative literature review

BackgroundCancer stage at diagnosis is an important prognostic indicator for patient outcomes, with detection at later stages associated with increased mortality and morbidity. The impact of cancer stage on patient-reported outcomes is poorly understood. This research aimed to understand symptom burden and health related quality of life (HRQoL) impact by cancer stage for ten cancer types: 1) ovarian, 2) lung, 3) pancreatic, 4) esophageal, 5) stomach, 6) head and neck, 7) colorectal, 8) anal, 9) cervical, and 10) liver and bile duct.MethodsTen narrative literature reviews were performed to identify and collate published literature on patient burden at different stages of disease progression. Literature searches were conducted using an AI-assisted platform to identify relevant articles published in the last five (2017–2022) or ten years (2012–2022) where articles were limited. Conference abstracts were searched for the last two years (2020–2022). The geographic scope was limited to the United States, Canada, Europe, and global studies, and only journal articles written in English were included.ResultsA total of 26 studies with results stratified by cancer stage at diagnosis (and before treatment) were selected for the cancer types of lung, pancreatic, esophageal, stomach, head and neck, colorectal, anal, and cervical cancers. Two cancer types, ovarian cancer, and liver and bile duct cancer did not return any search results with outcomes stratified by disease stage. A general trend was observed for worse patient-reported outcomes in patients with cancer diagnosed at an advanced stage of disease compared with diagnosis at an earlier stage. Advanced disease stage was associated with greater symptom impact including general physical impairments such as pain, fatigue, and interference with functioning, as well as disease/region-specific symptom burden. Poorer HRQoL was also associated with advanced disease with commonly reported symptoms including anxiety and depression.ConclusionsOverall, the general trend for greater symptom burden and poorer HRQoL seen in late stage versus early-stage disease across the included cancer types supports the importance for early diagnosis and treatment to improve patient survival and decrease negative impacts on disease burden and HRQoL.

Refining the optimal CAF cluster marker for predicting TME-dependent survival expectancy and treatment benefits in NSCLC patients

The tumor microenvironment (TME) plays a pivotal role in the onset, progression, and treatment response of cancer. Among the various components of the TME, cancer-associated fibroblasts (CAFs) are key regulators of both immune and non-immune cellular functions. Leveraging single-cell RNA sequencing (scRNA) data, we have uncovered previously hidden and promising roles within this specific CAF subgroup, paving the way for its clinical application. However, several critical questions persist, primarily stemming from the heterogeneous nature of CAFs and the use of different fibroblast markers in various sample analyses, causing confusion and hindrance in their clinical implementation. In this groundbreaking study, we have systematically screened multiple databases to identify the most robust marker for distinguishing CAFs in lung cancer, with a particular focus on their potential use in early diagnosis, staging, and treatment response evaluation. Our investigation revealed that COL1A1, COL1A2, FAP, and PDGFRA are effective markers for characterizing CAF subgroups in most lung adenocarcinoma datasets. Through comprehensive analysis of treatment responses, we determined that COL1A1 stands out as the most effective indicator among all CAF markers. COL1A1 not only deciphers the TME signatures related to CAFs but also demonstrates a highly sensitive and specific correlation with treatment responses and multiple survival outcomes. For the first time, we have unveiled the distinct roles played by clusters of CAF markers in differentiating various TME groups. Our findings confirm the sensitive and unique contributions of CAFs to the responses of multiple lung cancer therapies. These insights significantly enhance our understanding of TME functions and drive the translational application of extensive scRNA sequence results. COL1A1 emerges as the most sensitive and specific marker for defining CAF subgroups in scRNA analysis. The CAF ratios represented by COL1A1 can potentially serve as a reliable predictor of treatment responses in clinical practice, thus providing valuable insights into the influential roles of TME components. This research marks a crucial step forward in revolutionizing our approach to cancer diagnosis and treatment.

Multi-Omics Classification of Intrahepatic Cholangiocarcinoma: A Systematic Review and Meta-Analysis.

Intrahepatic cholangiocarcinoma (ICC) is a heterogeneous disease characterized by a dismal prognosis. Various attempts have been made to classify ICC subtypes with varying prognoses, but a consensus has yet to be reached. This systematic review aims to gather relevant data on the multi-omics-based ICC classification. The PubMed, Embase, and Cochrane databases were searched for terms related to ICC and multi-omics analysis. Studies that identified multi-omics-derived ICC subtypes and investigated clinicopathological predictors of long-term outcomes were included. Nine studies, which included 910 patients, were considered eligible. Mean 3- and 5-year overall survival were 25.7% and 19.6%, respectively, for the multi-omics subtypes related to poor prognosis, while they were 70.2% and 63.3%, respectively, for the subtypes linked to a better prognosis. Several negative prognostic factors were identified, such as genes' expression profile promoting inflammation, mutations in the KRAS gene, advanced tumor stage, and elevated levels of oncological markers. The subtype with worse clinicopathological characteristics was associated with worse survival (Ref.: good prognosis subtype; pooled hazard ratio 2.06, 95%CI 1.67-2.53). Several attempts have been made to classify molecular ICC subtypes, but they have yielded heterogeneous results and need a clear clinical definition. More efforts are required to build a comprehensive classification system that includes both molecular and clinical characteristics before implementation in clinical practice to facilitate decision-making and select patients who may benefit the most from comprehensive molecular profiling in the disease's earlier stages.

Real-World Treatment Patterns and Timeliness of Clinical Care Pathway for Non-Small Cell Lung Cancer Patients in Austria: The PRATER Retrospective Study.

This was a retrospective study of the profile and initial treatments of adults diagnosed with early-stage (ES) non-small cell lung cancer (NSCLC) during January 2018-December 2021 at 16 leading hospital institutions in Austria, excluding patients enrolled in clinical trials. In total, 319 patients were enrolled at a planned ~1:1:1 ratio across StI:II:III. Most tested biomarkers were programmed death ligand 1 (PD-L1; 58% expressing), Kirsten rat sarcoma virus (KRAS; 22% positive), and epidermal growth factor receptor (EGFR; 18% positive). Of 115/98/106 StI/II/III patients, 82%/85%/36% underwent surgery, followed by systemic therapy in 9%/45%/47% of those [mostly chemotherapy (ChT)]. Unresected treated StIII patients received ChT + radiotherapy [43%; followed by immune checkpoint inhibitors (ICIs) in 39% of those], ICI ± ChT (35%), and ChT-alone/radiotherapy-alone (22%). Treatment was initiated a median (interquartile range) of 24 (7-39) days after histological confirmation, and 55 (38-81) days after first medical visit. Based on exploratory analyses of all patients newly diagnosed with any stage NSCLC during 2018-2021 at 14 of the sites (N = 7846), 22%/10%/25%/43% had StI/II/III/IV. The total number was not significantly different between pre-COVID-19 (2018-2019) and study-specific COVID-19 (2020-2021) periods, while StI proportion increased (21% vs. 23%; p = 0.012). Small differences were noted in treatments. In conclusion, treatments were aligned with guideline recommendations at a time which preceded the era of ICIs and targeted therapies in the (neo)adjuvant setting.

Memory representations are flexibly adapted to orthographic systems: A comparison of English and Hebrew

Across languages, speech unfolds in the same temporal order, constrained by the forward flow of time. But the way phonology is spatially mapped onto orthography is language-specific, ranging from left-to-right, right-to-left, and top-to-bottom, among others. While the direction of writing systems influences how known words are visually processed, it is unclear whether it influences learning and memory for novel orthographic regularities. The present study tested English and Hebrew speakers on an orthographic word-referent mapping task in their native orthographies (written left-to-right and right-to-left, respectively), where the onsets and offsets of words were equally informative cues to word identity. While all individuals learned orthographic word-referent mappings significantly above chance, the parts of the word that were most strongly represented varied. English monolinguals false alarmed most to competing foils that began with the same bigram as the target, representing word onsets most strongly. However, Hebrew bilinguals trained on their native orthography showed no difference between false alarm rates to onset and offset competitors, representing the beginning and ends of words equally strongly. Importantly, Hebrew bilinguals tested on English words displayed a more English-like false alarm pattern (although not a full switch) suggesting that memory biases adapt to the opposite directionality of encountered text while retaining traces of native language biases. These findings demonstrate that experience with different writing systems influences how individuals represent novel orthographic words, starting in the earliest stages of learning.

Traditional Chinese Medicine for Hashimoto's Thyroiditis: Focus on Selenium and Antioxidant Phytochemicals.

Hashimoto's thyroiditis (HT) is not only the most frequent autoimmune thyroid disease (AITD), but it also has a significant impact on patients' health-related quality of life (HRQoL), and it has been variably associated with differentiated thyroid carcinoma. Even though its pathogenesis is still incompletely understood, oxidative stress is believed to play an important role. Hypothyroidism related to later stages of HT can be treated with levothyroxine substitution therapy; various approaches such as selenium supplementation and iodine-restricted diets have been proposed as disease-modifying treatments for earlier stages, and even thyroidectomy has been suggested for refractory cases of painful HT. Nevertheless, many patients still report suboptimal HRQoL, highlighting an unmet medical need in this area. The concepts and approaches of traditional Chinese medicine (TCM) in treating HT are not broadly known in the West. Here, we provide an overview of TCM for HT, including combinations of TCM with selenium. We encompass evidence from clinical trials and other studies related to complex TCM prescriptions, single herbs used in TCM, and phytochemicals; wherever possible, we delineate the probable underlying molecular mechanisms. The findings show that the main active components of TCM for HT have commonly known or presumed antioxidant and anti-inflammatory activities, which may account for their potential utility in HT. Further exploring the practices of TCM for HT and combining them with evidence- and mechanism-based approaches according to Western standards may help to identify new strategies to alter the clinical course of the disease and/or to treat patients' symptoms better and improve their HRQoL.

Neural mechanisms for secondary suppression of emotional distractors: Evidence from concurrent electroencephalography-magnetoencephalography data.

Distractor suppression allows us to remain on-task in the presence of distractions by filtering task-irrelevant information from ongoing cognitive processing and responding. Electrophysiological studies have revealed that this key feature of selective attention is a dynamic process that involves at least two distinct stages of processing. Two important aspects of these processing stages remain unclear: Whether the processing of emotional distractors at an earlier stage is automatic, as reflected in the N2/early posterior negativity (EPN) component; and what functional-anatomical brain systems are recruited in each stage. The present study addresses these issues by measuring brain activity with concurrent electroencephalography-magnetoencephalography (MEG) recordings while participants performed a combined rapid serial visual presentation and motion tracking task. Event-related potentials (ERP) showed significant effects of attentional capture and attentional modulation during two time windows marked by the N2/EPN and P3b ERP components. Source reconstruction of concurrent MEG measurements revealed activation of the left visual association cortex and anterior cingulate cortex during the N2/EPN time window, activation of the insula during the early phase of the P3b and anterior cingulate cortex activity during the later phase of the P3b. The findings provide novel evidence establishing a connection between the increased N2 response to negative pictures and the activation of the cingulate gyrus, which facilitates the suppression of distractions during demanding cognitive tasks. In addition, distinct activation patterns were observed in the insula and anterior cingulate cortex during the P3b time window, indicating that attentional control mediated by the anterior cingulate cortex operates to suppress the processing of distracting emotional stimuli. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Unfinished Reconstructed Nature of the Last Universal Common Ancestor.

The ultimate consequence of Darwin's theory of common descent implies that all life on earth descends ultimately from a common ancestor. Biochemistry and molecular biology now provide sufficient evidence of shared ancestry of all extant life forms. However, the nature of the Last Universal Common Ancestor (LUCA) has been a topic of much debate over the years. This review offers a historical perspective on different attempts to infer LUCA's nature, exploring the debate surrounding its complexity. We further examine how different methodologies identify sets of ancient protein that exhibit only partial overlap. For example, different bioinformatic approaches have identified distinct protein subunits from the ATP synthetase identified as potentially inherited from LUCA. Additionally, we discuss how detailed molecular evolutionary analysis of reverse gyrase has modified previous inferences about an hyperthermophilic LUCA based mainly on automatic bioinformatic pipelines. We conclude by emphasizing the importance of developing a database dedicated to studying genes and proteins traceable back to LUCA and earlier stages of cellular evolution. Such a database would house the most ancient genes on earth.

Development of neuronal timescales in human cortical organoids and rat hippocampus dissociated cultures.

To support complex cognition, neuronal circuits must integrate information across multiple temporal scales, ranging from milliseconds to decades. Neuronal timescales describe the duration over which activity within a network persists, posing a putative explanatory mechanism for how information might be integrated over multiple temporal scales. Little is known about how timescales develop in human neural circuits or other model systems, limiting insight into how the functional dynamics necessary for cognition emerge. In our work, we show that neuronal timescales develop in a non-linear fashion in both human cortical organoids and dissociated rat hippocampus cultures. We use spectral parameterization of spiking activity to extract an estimate of neuronal timescale that is unbiased by co-evolving oscillations. Cortical organoid timescales begin to increase around month 6 post-differentiation. We complement these findings with an analysis of timescales in rodent hippocampal dissociated cultures over development and see that timescales decrease from in vitro days 13-23 before stabilizing. We speculate that cortical organoid development over the duration studied here reflects an earlier stage of a generalized developmental timeline in contrast to the rodent hippocampal cultures, potentially accounting for differences in timescale developmental trajectories. The fluctuation of timescales might be an important developmental feature that reflects the changing complexity and information capacity in developing neuronal circuits.

The possible benefit of artificial intelligence in an organized population-related screening program : Initial results and perspective

Mammography screening programs (MSP) have shown that breast cancer can be detected at an earlier stage enabling less invasive treatment and leading to abetter survival rate. The considerable numbers of interval breast cancer (IBC) and the additional examinations required, the majority of which turn out not to be cancer, are critically assessed. In recent years companies and universities have used machine learning (ML) to develop powerful algorithms that demonstrate astonishing abilities to read mammograms. Can such algorithms be used to improve the quality of MSP? The original screening mammographies of 251 cases with IBC were retrospectively analyzed using the software ProFound AI® (iCAD) and the results were compared (case score, risk score) with acontrol group. The relevant current literature was also studied. The distributions of the case scores and the risk scores were markedly shifted to higher risks compared to the control group, comparable to the results of other studies. Retrospective studies as well as our own data show that artificial intelligence (AI) could change our approach to MSP in the future in the direction of personalized screening and could enable asignificant reduction in the workload of radiologists, fewer additional examinations and areduced number of IBCs; however, the results of prospective studies are needed before implementation.

Machine Learning for Early Discrimination Between Lung Cancer and Benign Nodules Using Routine Clinical and Laboratory Data.

Lung cancer poses a global health threat necessitating early detection and precise staging for improved patient outcomes. This study focuses on developing and validating a machine learning-based risk model for early lung cancer screening and staging, using routine clinical data. Two medical center, observational, retrospective studies were conducted, involving 2312 lung cancer patients and 653 patients with benign nodules. Machine learning techniques, including differential analysis and feature selection, were employed to identify key factors for modeling. The study focused on variables such as nodule density, carcinoembryonic antigen (CEA), age, and lifestyle habits. The Logistic Regression model was utilized for early diagnoses, and the XGBoost model was utilized for staging based on selected features. For early diagnoses, the Logistic Regression model achieved an area under the curve (AUC) of 0.716 (95% confidence interval [CI] 0.607-0.826), with 0.703 sensitivity and 0.654 specificity. The XGBoost model excelled in distinguishing late-stage from early-stage lung cancer, exhibiting an AUC of 0.913 (95% CI 0.862-0.963), with 0.909 sensitivity and 0.814 specificity. These findings highlight the model's potential for enhancing diagnostic accuracy and staging in lung cancer. This study introduces a novel machine learning-based risk model for early lung cancer screening and staging, leveraging routine clinical information and laboratory data. The model shows promise in enhancing accuracy, mitigating overdiagnosis, and improving patient outcomes.

Meaning-making and transformative engagement – notes on Gunther Kress’s social semiotic and multimodal approach to learning

Abstract Against the background of a longstanding collaboration between Gunther Kress’s research group in London and my own research group in Stockholm, I reflect, in this paper, on the role of Kress’s ideas in our joint development of a social semiotic, multimodal, and design-oriented approach to learning, an approach which sees learning as performative, and as an activity in which learners create their own learning paths. I first discuss how, for Kress, this path has three elements, the affordances of the learning resources available to the learner, the learner’s ‘interest’ which turns aspects of these resources into ‘prompts’ for learning, and the learner’s active interpretation and transformation of these aspects, the results of which can then be recognized and valued as ‘signs of learning’. However, recognizing learning also needs to take account of the dimension of time, so as to make it possible to assess whether learners have gained knowledges and skills they did not have at an earlier stage. I then discuss the role of context in Kress’s thinking about learning. For Kress, context is another vital aspect of a social semiotic theory of learning. On the one hand, Kress focuses here on the specific, unique contexts in which individual learners create their own learning paths. On the other hand, he recognizes that signs will always carry social and political relations. Reflecting on the dynamic relation between individual learners and the way institutions regulate ways of learning, I discuss both the continued role of institutional learning contexts and their hidden curricula, and the way emerging technologies facilitate individual learning paths and interactive, participatory forms of learning.

Lateral Thinking: Pathway Specific Neurodegeneration of the Cortical Cholinergic System in Alzheimer's Disease.

Atrophy of the nucleus basalis of Meynert (NBM) is an early indicator of Alzheimer's disease (AD). However, reduced integrity of the NBM white matter tracts may be more relevant for cognitive impairment and progression to dementia than NBM volume. Research is needed to compare differences in NBM volume and integrity of the lateral and medial NBM tracts across early and later stages of AD progression. 187 participants were included in this study who were either healthy controls (HC; n=50) or had early mild cognitive impairment (EMCI; n=50), late MCI (LMCI; n=37), or AD (n=50). NBM volume was calculated using voxel-based morphometry and mean diffusivity (MD) of the lateral and medial NBM tracts were extracted using probabilistic tractography. Between group differences in NBM volume and tract MD were compared using linear mixed models controlling for age, sex, and either total intracranial volume or MD of a control mask, respectively. Associations between NBM volume and tract MD with executive function, memory, language, and visuospatial function were also analysed. NBM volume was smallest in AD followed by LMCI (p<0.0001), with no difference between EMCI and HC. AD had highest MD for both tracts compared to all other groups (p<0.001). Both MCI groups had higher lateral tract MD compared to HC (p<0.05). Medial tract MD was higher in LMCI (p=0.008), but not EMCI (p=0.09) compared to HC. Higher lateral tract MD was associated with executive function (p=0.001) and language (p=0.02). Integrity of the lateral NBM tract is most sensitive to the earliest stages of AD and should be considered an important therapeutic target for early detection and intervention.

Resilient cloud cluster with DevSecOps security model, automates a data analysis, vulnerability search and risk calculation

Automated, secure software development is an important digitalization task, solved with the DevSecOps approach. An important part of the DevSecOps approach is continuous risk assessment, which is necessary to identify and evaluate risk factors. Combining the development cycle with continuous risk assessment creates software development and operation synergies and minimizes vulnerabilities. The article presents the main methods of deploying web applications and ways to increase information security at all stages of product development. It also compares different types of infrastructures and cloud computing providers and analyzes modern tools used to automate processes. The cloud cluster was deployed using Terraform and the Jenkins pipeline, written in the Groovy programming language, which checks program code for vulnerabilities and allows you to fix violations at the earliest stages of developing secure web applications. The developed cluster implements the proposed algorithm for automated risk assessment based on the calculation (modeling) of cloud infrastructure threats and vulnerabilities, which operates in real-time, periodically collecting all information and adjusting the system according to the risk and applied controls. The algorithm for calculating risk and losses is based on statistical data and FAIR information risk assessment methodology. The risk value obtained using the proposed method is quantitative, which allows more efficient forecasting of information security costs in software development.

Planning sentences and sentence intonation in Estonian

The notion of advance planning of sentence intonation is grounded in the positive correlation between the sentence-initial intonation peaks and sentence duration. This study examined real-time sentence planning and intonation using visual world speech production. In two eye-tracking experiments, native Estonian speakers described transitive events involving multiple actors. Conceptual complexity of the resulting picture descriptions was manipulated through a pictorial design, while sentence length was controlled for by manipulating specific task characteristics. In Experiment I, conceptual complexity of the picture descriptions varied together with linguistic complexity, while linguistic complexity was held constant in Experiment II. As the conceptual complexity of utterances increased, the duration of naming gazes also increased, indicating less incremental conceptual planning. Notably, while utterance-initial intonation peaks did not correlate with the relative duration of naming gazes, they were influenced by utterance length. These findings highlight advance planning of intonation in Estonian. Furthermore, they suggest that intonation planning depends on linguistic information that is rapidly activated after establishing a comprehensive conceptual framework during earliest stages of preverbal planning.