Open Model Research Articles

MODL-11. LOW-GRADE GLIOMA 3D PATIENT-DERIVED ORGANOID PLATFORM AS TRANSFORMATIVE TOOL FOR TUMOR BIOLOGY AND TRANSLATIONAL RESEARCH

Abstract Unlike many high-grade tumors, low-grade glioma (LGG) has faced challenges in advancing scientific and clinical breakthroughs, affected by the limited capacity to develop representative preclinical tumor models. Major barriers to establishing traditional LGG cell culture systems have been cellular senescence and the need to introduce additional genetic modifications to obtain cultured growth. Such LGG models, while extremely valuable, require extensive periods for development and result in clonally selected lines which may not present the complexity of LGG tumor biology. To mitigate such challenges in LGG model development and address the need for three-dimensional tissue cultures, we have successfully generated organoids from fresh tissue specimens obtained from surgical resection. We have demonstrated success of this method on various pediatric brain tumor histologies and have expanded these approaches successfully to LGG. Through such efforts, we have developed 14 LGG tumors’ organoid models as well as other low-grade tumor’s models such as craniopharyngioma, schwannoma, meningioma, optic glioma, and oligodendroglioma. The tissue was processed immediately post-extraction and cultured in media solution on rotating platform. Organoid growth was observed within 1-3 weeks of initiation and tested for growth for up to 3 months. Phenotypic analysis revealed organoid cell composition representing clinical histology. The immune component was preserved in organoids for up to 40 days. MAPK pathway activity, a hallmark marker for KIAA-BRAF fusion positive pilocytic astrocytoma was preserved for up to 4 weeks. Our successful deployment of three-dimensional patient-tissue derived organoids provides for an efficient, simple workflow for the generation of unique and transformative preclinical models for the LGG field. Supported by the Children’s Brain Tumor network’s open science model, 3D organoids will provide investigations with broader resources for LGG tumor biology preclinical testing and support translational studies on behalf of the development of novel clinical trials.

A Strategy for Preparing Quantum Squeezed States Using Reinforcement Learning

AbstractA scheme leveraging reinforcement learning to engineer control fields for generating non‐classical states is proposed. It is exemplified by the application to prepare spin‐squeezed states for an open collective spin model where a linear control field is designed to govern the dynamics. The reinforcement learning agent determines the temporal sequence of control pulses, commencing from a coherent spin state in an environment characterized by dissipation and dephasing. Compared to the constant control scenario, this approach provides various control sequences maintaining collective spin squeezing and entanglement. It is observed that denser application of the control pulses enhances the performance of the outcomes. However, there is a minor enhancement in the performance by adding control actions. The proposed strategy demonstrates increased effectiveness for larger systems. Thermal excitations of the reservoir are detrimental to the control outcomes. Feasible experiments are suggested to implement this control proposal based on the comparison with the others. The extensions to continuous control problems and another quantum system are discussed. The replaceability of the reinforcement learning module is also emphasized. This research paves the way for its application in manipulating other quantum systems.

Open cranium model for the study of cerebrovascular dynamics in intracranial hypertension

BackgroundSignificant research has been devoted to developing noninvasive approaches to neuromonitoring. Clinical validation of such approaches is often limited, with minimal data available in the clinically relevant elevated ICP range. New MethodTo allow ultrasound-guided placement of an intraventricular catheter and to perform simultaneous long-duration ICP and ultrasound recordings of cerebral blood flow, we developed a large unilateral craniectomy in a swine model. We also used a microprocessor-controlled actuator for intraventricular saline infusion to reliably and reversibly manipulate ICP according to pre-determined profiles. ResultsThe model was reproducible, resulting in over 80 hours of high-fidelity, multi-parameter physiological waveform recordings in twelve animals, with ICP ranging from 2 to 78 mmHg. ICP elevations were reversible and reproducible according to two predetermined profiles: a stepwise elevation up to an ICP of 30–35 mmHg and return to normotension, and a clinically significant plateau wave. Finally, ICP was elevated to extreme levels of greater than 60 mmHg, simulating extreme clinical emergency. Comparison with existing methodsExisting methods for ICP monitoring in large animals typically relied on burr-hole approaches for catheter placement. Accurate catheter placement can be difficult in pigs, given the thickness of their skull. Additionally, ultrasound is significantly attenuated by the skull. The open cranium model overcomes these limitations. ConclusionsThe hemicraniectomy model allowed for verified placement of the intraventricular catheter, and reversible and reliable ICP manipulation over a wide range. The large dural window additionally allowed for long-duration recording of cerebral blood flow velocity from the middle cerebral artery.

Research on Carbon Emission Peaks in Large Energy Production Region in China —Based on the Open STIRPAT Model

<p>The interdependence between different regions within a country has an important impact on the prediction of regional carbon emission peaks. In this paper, taking Shanxi Province, a large energy producing region in China, as an example, we select the driving factors to construct an open STIRPAT model from both Shanxi and national levels, and use scenario analysis to predict the carbon emissions of Shanxi Province from 2021 to 2040. The study shows that： (1) In the Open STIRPAT model, the year and amount of peak carbon emissions in Shanxi province are mainly determined by the national scenarios. If the baseline scenario is chosen for the whole country, then no matter which scenario is chosen for Shanxi, it will not be able to realize the carbon peak in 2030. Under the scenario combination of low carbon-low carbon, Shanxi will realize the carbon peak in 2027. Under the scenario combination of low carbon-energy saving and energy saving-low carbon, Shanxi will realize the carbon peak in 2028. (2) The peak carbon emissions in Shanxi are mainly affected by the national scenario setting and Shanxi's emission reduction strategy. Controlling per capita GDP and energy consumption of 10,000 yuan GDP is an effective emission reduction strategy. (3) Compared with the open STIRPAT model, the closed STIRPAT model delays the year of peak carbon in Shanxi and overestimates carbon emissions by 20%-44%. Therefore, Shanxi should pay attention to the development of energy saving at the national level in formulating its carbon emission strategy, take it as an important constraint for Shanxi's peak carbon policy, and formulate a flexible carbon emission peaking program.</p>

Self-discharge of redox electrolyte enhanced supercapacitors based on nanosheet-like CoS2

Transition metal sulfides used as electrode materials are becoming increasingly attractive to researchers because of their excellent conductivity and capacitance in supercapacitors with alkaline electrolytes (KOH). However, the mechanism of self-discharge at an open circuit is still unclear. Herein, KOH electrolyte was replaced with redox electrolyte KOH + K3Fe(CN)6 (PFC) to explore the self-discharge mechanism of nanosheet-like CoS2/CC. It was found that the CoS2/CC electrode in the redox electrolyte has high specific capacitance (2325.5 mF cm−2) at the current density of 1 mA cm−2. Afterwards, the self-discharge mechanism and influential factors of redox electrolyte enhanced supercapacitors were investigated using open circuit potential tests and mathematical models. It is worth noting that the device voltage assembled with CoS2/CC electrode decreased from 1.6 V to 0.8 V after the 2680 s in the redox electrolyte, which was faster than the self-discharge time (6435 s) in the KOH electrolyte. The main reason for the rapid self-discharge phenomenon is the limited surface area of the CoS2/CC electrode which has a weak adsorption capacity for redox active groups, allowing charged redox active substances to be transported freely in the electrolyte. At the same time, it objectively shows that the energy stored in the device is constantly dissipating.

Introduction to the Special Issue: Leveraging Global Value Chains for innovation and industrialization in Africa

The global nature of innovation has led to the emergence of Global Innovation Networks (GINs) and Global Value Chains (GVCs), facilitating knowledge exchange and economic activities across borders. GVCs offer platforms for accessing external knowledge and fostering innovation. They have evolved through three eras of innovation models: the Linear and Closed Model (LCM), the Interactive and Closed Model (ICM), and the Interactive and Open Model (IOM). The IOM emphasizes open innovation principles, intersecting with GINs and GVCs. Lessons from East Asian and Latin American countries highlight strategic practices for GVC integration, focusing on export-oriented industrialization, education, technology policies, infrastructure, and SME support. In Africa, GVC engagement spans agriculture, manufacturing, and services sectors, driving economic growth, job creation, and technological advancements. To advance GVC participation in Africa, a research agenda, policy directions, and action plans are proposed. The research agenda emphasizes GVC governance, value addition, sustainability, innovation, and regional integration. Policy directions focus on infrastructure development, trade facilitation, SME support, human capital development, and promoting sustainable and inclusive growth. African businesses are encouraged to conduct value chain mapping, form strategic partnerships, embrace technology, ensure quality assurance, invest in capacity building, and diversify markets to leverage the potential of GVCs for economic transformation and global competitiveness.

Modern Eğitim Örgütlerinin İhtiyaç Analizi Değerlendirmelerinde Açık Sistem Modelinin İncelenmesi

Studies related to needs analysis in modern educational organizations are important for the development of a reliable, holistic model to determine which competence area needs training in order to train and train school administrators as a professional professional group, using business and human resource management fields, in addition to the school/education lens, so that school administrators can acquire and update professional competencies that will help them perform their duties professionally. For this reason, considering the professional training needs of school administrators from a different disciplinary lens, such as education and development in the context of business management, can make different contributions to the professional development of school administrators. It is expected to be an effective and practical solution that will also make a significant contribution to the efficiency of today's school organizations, especially with the open system model application in education management. As a result of the application of this model, the determination of undergraduate and/or postgraduate education, training, appointment, pre-service and in-service education curricula according to the professional competencies of all educational institution managers/leaders and teachers based on the professional competencies of school administrators and teachers within the Ministry of National Education and universities, effective holistic implementation of measurement and evaluation processes as a result of these trainings can be ensured. Since this model has been developed according to the educational needs analysis literature, it can be used as a practical model for all educational processes related to all kinds of professions. It can also be used for the same purpose for the training and education of teachers from all branches who will be employed or are currently employed within the Ministry of National Education. Key Words: Modern Educational Organizations, Needs Analysis, Open-System Model

Characterisation of negative social impact risks within pre-deployment carbon dioxide utilisation projects

As the youngest of the three sub-fields within sustainability assessment, development of social impact assessment lags behind life cycle and techno-economic assessments. This is manifested in comparatively lacking methodological maturity. The calculation of impact indicator results, achieved through the use of characterisation models, is a prime example of the field’s immaturity. This research initially identifies current methodologies observed within social impact assessment. This reveals that impact pathway characterisation models, analogous to those seen in life cycle assessment, have primarily been neglected; instead, research has been focused on less reproducible and more subjective reference scale approaches. Redressing this balance, a set of seven UNEP and SETAC indicators are evaluated, developing novel impact pathway-based characterisation models. Focussing on the effects of stimulating and de-stimulating factors on indicators, identifying hotspots where negative social impacts are likely to arise. The presented methodologies are tailored to applications involving the assessment of potential carbon dioxide utilisation (CDU) value chains. This focus results in open access characterisation models that are indiscriminate of technology readiness level, requiring no primary process data. Data from the World Bank and its partner organisations are utilised, generating complete results across all indicators for 129 countries. Development of these quantifiable characterisation models delivers significant value in the standardisation of assessment procedure and facilitates inter-assessment comparability, a benefit to stakeholders ranging from practitioners to commissioners.

Heterogeneous effects of carbon tax, subsidy and foreign carbon emissions on low- and high-carbon firms

Climate change engendered by carbon emissions has become a major threat to human life and the world economy. Many of early studies corroborate that carbon tax and subsidy are effective policy tools to alleviate climate change. Most of them, however, use closed-economy models with a single firm to analyze the effects of these two policies. This paper explores the effects of carbon tax, subsidy and foreign carbon emissions on economic activities in an open macroeconomic model that features low- and high-carbon firms. The main findings suggest that carbon tax, subsidy and foreign carbon emissions all deliver heterogeneous outcomes by largely affecting high-carbon firms. The shortage of national income due to weak economic activities leads the economy to issue more international bonds. The takeaway from all the analyses is that government should introduce various carbon taxes that are geared towards target firms with different carbon emissions and adopt optimal means to manage national debt.

Screening oncology articles in a qualitative literature review using large language models: A comparison of GPT4 versus fine-tuned open source models using expert-annotated data.

e23196 Background: Clinical Outcome Assessment (COA) conceptual gap analyses for oncology are complex and time consuming. Artificial Intelligence may efficiently reduce time to completion of such analyses. We aimed to assess two AI models’ performance for literature screening to identify relevant qualitative oncology research. We also compared accuracy and run-time for both AI models. Methods: We manually curated a dataset of title/abstract screening (n = 1,700 study references) across 17 landscape reviews. Among these, 11 landscape reviews (n = 951 study references) spanning over were in oncology, including 8 solid cancers (breast, lung, urothelial, colorectal, esophageal, head and neck, pancreatic, and stomach) and 3 non-solid cancers (lymphoma, acute myeloid leukemia, and multiple myeloma). Each citation was annotated for eligibility (Y/N) by population, study design (qualitative), and reporting of concepts (how patients feel or function). We then compared the accuracy of two AI models at predicting the screening decisions of expert researchers. The two AI models were Generative Pre-trained Transformers 4 (GPT4, OpenAI) prompts and a fine-tuned SciFive biomedical large language model (LLM). We used 70% of the data for training and 30% for test. Accuracy estimates were obtained only for the models’ ability to label eligibility within the 11 oncology datasets. Results: Both LLMs performed well for assessing relevance by oncology population, with F1-scores for the GPT4 and SciFive models being 0.92 and 0.83 respectively (precision was 0.92 and 0.93 respectively). For concept reporting the fine-tuned SciFive model outperformed GPT4 with an F1-score and precision 0.88 and 0.92 versus 0.81 and 0.79. The same was true but less pronounced for eligibility by study design, with an F1-score and precision 0.81 and 0.90 versus 0.86 and 0.76. For overall eligibility, the customized SciFive model outperformed the GPT4 model with an F1-score and precision of 0.84 and 0.92 versus 0.85 and 0.82. Lastly, it took the GPT4 prompts between 10-30 minutes to screen 100 abstracts. By contrast, the customized SciFive model took 1-2 minutes on a computer with a Quadro RTX 8000 GPU. Conclusions: In conclusion, both AI models are promising. The fine-tuned SciFive model appears slightly more accurate and performs substantially faster than the GPT4 model.

Cosmological measurements from the CMB and BAO are insensitive to the tail probability in the assumed likelihood

When fitting cosmological models to data, a Bayesian framework is commonly used, requiring assumptions on the form of the likelihood and model prior. In light of current tensions between different data, it is interesting to investigate the robustness of cosmological measurements to statistical assumptions about the likelihood distribution from which the data was drawn. We consider the impact of changes to the likelihood caused by uncertainties due to the finite number of mock catalogs used to estimate the covariance matrix, leading to the replacement of the standard Gaussian likelihood with a multivariate t-distribution. These changes to the likelihood have a negligible impact on recent cosmic microwave background (CMB) lensing and baryon acoustic oscillation (BAO) measurements, for which covariance matrices were measured from mock catalogs. We then extend our analysis to perform a sensitivity test on the Gaussian likelihoods typically adopted, considering how increasing the size of the tails of the likelihood (again using a t-distribution) affects cosmological inferences. For an open ΛCDM model constrained by BAO alone, we find that increasing the weight in the tails shifts and broadens the resulting posterior on the parameters, with a ∼0.2–0.4σ effect on ΩΛ and Ωk. In contrast, the CMB temperature and polarization constraints in ΛCDM showed less than 0.03σ changes in the parameters, except for {τ, ln(1010 A s), σ 8, S 8, σ 8Ω0.25 m, z re, 109 A s e -2τ } which shifted by around 0.1–0.2σ. If we use solely ℓ < 30 data, the amplitude A s e -2τ varies in the posterior mean by 0.7σ and the error bars increase by 6%. We conclude, at least for current-generation CMB and BAO measurements, that uncertainties in the shape and tails of the likelihood do not contribute to current tensions.

Extending CityGML with public input – social ADE

PurposeThis paper aims to enrich the 3D urban models with data contributed by citizens to support data-driven decision-making in urban infrastructure projects. We introduced a new application domain extension to CityGML (social – input ADE) to enable citizens to store, classify and exchange comments generated by citizens regarding infrastructure elements. The main goal of social – input ADE is to add citizens’ feedback as semantic objects to the CityGML model.Design/methodology/approachFirstly, we identified the key functionalities of the suggested ADE and how to integrate them with existing 3D urban models. Next, we developed a high-level conceptual design outlining the main components and interactions within the social-input ADE. Then we proposed a package diagram for the social – input ADE to illustrate the organization of model elements and their dependencies. We also provide a detailed discussion of the functionality of different modules in the social-input ADE.FindingsAs a result of this research, it has seen that informative streams of information are generated via mining the stored data. The proposed ADE links the information of the built environment to the knowledge of end-users and enables an endless number of socially driven innovative solutions.Originality/valueThis work aims to provide a digital platform for aggregating, organizing and filtering the distributed end-users’ inputs and integrating them within the city’s digital twins to enhance city models. To create a data standard for integrating attributes of city physical elements and end-users’ social information and inputs in the same digital ecosystem, the open data model CityGML has been used.

A Journey Through the First to Eighteenth Issue of JKAHS

The Journal of Karnali Academy of Health Sciences (JKAHS) is an official publication of Karnali Academy of Health Sciences (KAHS), a government organization founded on October 20, 2011, by an act of the Parliament of Nepal [1]. KAHS was established with the objective of enhancing the quality of healthcare services, medical education, and research in remote and underserved areas of Nepal. The establishment of JKAHS in 2018 is a reflection of commitment of KAHS to these goals, providing a dedicated platform for the dissemination of scientific knowledge and clinical experiences in the health sciences [2].JKAHS is a peer-reviewed, international medical journal that operates under a diamond open access model, that means all its contents are freely accessible to readers without any subscription or paywall barriers [2]. This model not only broadens the journal’s audience, including researchers, students, policymakers, and the general public, but also aligns with the mission of KAHS to disseminate knowledge widely and equitably [1]. Importantly, JKAHS does not charge any fees for the submission, processing, or publication of manuscripts, thus removing financial barriers for authors and promoting inclusivity in scientific publishing. Additionally, to ensure the long-term preservation and accessibility of its content in the internet, JKAHS archives all its articles in LOCKSS (Lots of Copies Keep Stuff Safe) and CLOCKSS (Controlled Lots of Copies Keep Stuff Safe) [2]. These archiving systems safeguard the integrity and usability of the published work, ensuring that it remains available to future generations.It is triannual journal publishing three issues per year, and is available both online and in print with pISSN 2616-0064 and eISSN 2676-1327 [2]. This regular publication schedule ensures a steady flow of new research and insights, contributing to the continuous advancement of medical knowledge.Achievements and milestonesOne of our significant achievements has been in indexing of JKAHS in two national databases namely Nepal JournalOnline (NepJol) in 2019 [3] and NepalMedLine (NepMed) in 2020 [4]. The NepJol is hosted by Central Library (TUCL) and it is hosted by Ubiquity Press [3] whereas NepMed is hosted my Nepal Health Research Council (NHRC) [4]. A part from these national databases, we have been indexed in DOAJ (Directory of Open Access Journal) [5] and Hinari [6] in 2023. In the earlier days after indexing in NepJol, individual articles were assigned an unique DOI (digital object identifier) from NepJol (10.3126/jkahs). Later on we have taken our own DOI (10.61814/jkahs) directly from CrossRef under the sponsroship under GEM (Global Equitable Membership (GEM) Program from CrossRef [7]. This recognition is a testament to the quality and impact of our published research. Being indexed has enhanced the visibility and accessibility of our articles, attracted a broader readership and fostered greater academic collaboration.

Rigorous assessment of a unique tiger recovery in Southeast Asia based on photographic capture-recapture modeling of population dynamics

Tigers and other large predatory carnivores have suffered population extirpations and range contractions. This is particularly true for tiger populations in southeastern Asia, which harbours one-third of their remaining habitats. In stark contrast, a sustained recovery of a wild tiger population has occurred between 2007 and 2023, in three reserves of Thailand: Huai Kha Khaeng (HKK), Thung Yai East (TYE) and Thung Yai West (TYW), which together cover 6470 km2 (36%) of the larger Western Forest Complex (WEFCOM). We quantitatively monitored this recovery employing closed and open model analyses of data from photographic capture-recapture sampling. The resulting estimates of tiger population dynamic parameters showed: mean (±SE) tiger abundance annually varied from 36 (1.0) to 79 (1.53) in HKK, 2 (0.26) to 20 (4.45) in TYE and 3 (0.26) to 44 (2.11) in TYW, driven by mean annual survival rates of 0.79 (0.02) in HKK, 0.72 (0.05) in TYE, and 0.69 (0.05) in TYW. The annual numbers of recruits fluctuated from 0 (1.69) to 33 (1.93) tigers in HKK, 0 (0.47) to 13 (0.57) in TYE and 0 (1.13) to 36 (2.28) in TYW. Overall, the mean tiger population densities/100 km2 ranged between 1.3 (0.19) and 2.9 (0.29) in HKK, 0.2 (0.08) and 1.8 (0.34) in TYE, and 0.2 (0.07) and 3.1 (0.56) in TYW. Generally, the tiger population trended upward, with reserves protected over longer periods leading the tiger recovery. Our results are further backed by ancillary records on births of 67 cubs, 47 tiger dispersal events, as well as the recovery corresponding with incremental spatio-temporal coverage by the patrols. Cumulatively, our results provide evidence that effective law enforcement should be a critical component for achieving tiger population recoveries in Asia. Alternative conservation strategies that ignore this component do not appear to be evidence-based. Our results also demonstrate the utility of the independent collaborative monitoring framework adopted by the Thailand Government.

A numerical study of the R744 primary cooling system for ATLAS and CMS LHC detectors

A R744 (CO2) refrigeration system has been designed to cool down the Large Hadron Collider (LHC) silicon detectors ATLAS and CMS, located at CERN, Switzerland. The silicon detectors are subjected to high radiation levels. The system is composed of a pri- mary CO2 trans-critical booster vapor compression loop operated with piston compressors, and an oil-free liquid pumped loop on the evaporation side, to preserve the detectors. To ensure the system's reliability, the cooling facility is designed to operate under a parallel operation mode of several modular 70 kW plant units providing evaporation temperature as low as -53 °C. This layout, is also useful in case of components failure and maintenance. A numerical model is developed using a dynamic simulation software Dymola that is based on the open source Modelica modelling language. The simulation results are proven on a first demonstration plant (System A) experimentally to explore the systems control logic and to validate the reliability of the system before it is built on the detectors side. In this paper the models development is explained and the results of the experimental validation of the numerical model are shown.

Static and dynamic conductivity of amorphous nanogranular composites (CoTaNb)x(MgO)1-x

The paper presents the results of study of amorphous nanogranular composite films CoTaNbxMgO1−x, (21.73≤ x ≤73.27 at%) with a thickness of 311–1040 nm on a lavsan substrate with a thickness of about 20 μm. The results of experimental studies of microstructure, static conductivity (at direct current), dynamic conductivity (50 kHz – 15 MHz), determined from measurements of the total impedance and phase angle, and dynamic microwave conductivity (8–12 GHz), calculated from measurements microwave reflectance, were presented. The spectra of dynamic conductivity as a function of frequency, the dependence of static conductivity and the ratio of dynamic to static conductivity on the content of the metal phase and on the ratio of the sizes of granules to the spaces between granules were obtained. Using the mechanisms of closed and open circuit models of intragranular currents and an algorithm for calculating structural and conductive characteristics from measured values of microwave reflectance, the conductivity of granules was calculated and its contribution to the dynamic microwave conductivity of composites was assessed.

Commodity cycles and financial instability in emerging economies

Commodity-exporting economies display procyclicality with the price of commodity exports. However, the evidence for the relative importance of commodity price shocks for aggregate fluctuations remains inconclusive. Using Russian data from 2001 to 2018 we estimate a small open economy New Keynesian model with a banking system and leveraged domestic firms who default on their unsecured domestic debt. We show that allowing default rates to vary endogenously over the business cycle amplifies the estimated contribution of commodity price shocks. Endogenous default introduces time-varying wedges that amplify the response of commodity price shocks through demand and income effects rather than the relative price effects that are found in the country risk-premium, balance sheet, and financial accelerator channels. We find that the contribution of commodity prices to explaining fluctuations in GDP rises from 2.5 to 33.6% while for deposits and non-performing loans, it increases from 5.3% and 1.6% to 71.3% and 60.4%, respectively.

Development, comparison, and internal validation of prediction models to determine the visual prognosis of patients with open globe injuries using machine learning approaches

IntroductionOpen globe injuries (OGI) represent a main preventable reason for blindness and visual impairment, particularly in developing countries. The goal of this study is evaluating key variables affecting the prognosis of open globe injuries and validating internally and comparing different machine learning models to estimate final visual acuity.Materials and methodsWe reviewed three hundred patients with open globe injuries receiving treatment at Khatam-Al-Anbia Hospital in Iran from 2020 to 2022. Age, sex, type of trauma, initial VA grade, relative afferent pupillary defect (RAPD), zone of trauma, traumatic cataract, traumatic optic neuropathy (TON), intraocular foreign body (IOFB), retinal detachment (RD), endophthalmitis, and ocular trauma score (OTS) grade were the input features. We calculated univariate and multivariate regression models to assess the association of different features with visual acuity (VA) outcomes. We predicted visual acuity using ten supervised machine learning algorithms including multinomial logistic regression (MLR), support vector machines (SVM), K-nearest neighbors (KNN), naïve bayes (NB), decision tree (DT), random forest (RF), bagging (BG), adaptive boosting (ADA), artificial neural networks (ANN), and extreme gradient boosting (XGB). Accuracy, positive predictive value (PPV), recall, F-score, brier score (BS), Matthew correlation coefficient (MCC), receiver operating characteristic (AUC-ROC), and calibration plot were used to assess how well machine learning algorithms performed in predicting the final VA.ResultsThe artificial neural network (ANN) model had the best accuracy to predict the final VA. The sensitivity, F1 score, PPV, accuracy, and MCC of the ANN model were 0.81, 0.85, 0.89, 0.93, and 0.81, respectively. In addition, the estimated AUC-ROC and AUR-PRC of the ANN model for OGI patients were 0.96 and 0.91, respectively. The brier score and calibration log-loss for the ANN model was 0.201 and 0.232, respectively.ConclusionAs classic and ensemble ML models were compared, results shows that the ANN model was the best. As a result, the framework that has been presented may be regarded as a good substitute for predicting the final VA in OGI patients. Excellent predictive accuracy was shown by the open globe injury model developed in this study, which should be helpful to provide clinical advice to patients and making clinical decisions concerning the management of open globe injuries.

Transposition and substitution-letter effects in a flanker task: Evidence from children and adults.

Several studies have shown that parafoveal processing is essential in reading development. In this study, we explore the effect of transposing and substituting inner and outer letters in a flanker lexical decision task administered to 78 children and 65 adults. The results show a significant interaction between the Group factor and the Flanker factor, suggesting differences in the effects of flankers for children and adults. In the case of adults, transposed and substituted letters generated benefit of the same magnitude in comparison with the unrelated condition, but of lesser magnitude than the Identity condition. In the case of children, the results show facilitation for the transposed conditions of the same magnitude as the Identity condition. However, the substitution conditions failed to generate any benefit in comparison with the unrelated condition. The results for the adults are in line with the predictions of the open bigram model, whereas the results for the children are explained through a developmental perspective of the dual-route architecture and open bigram framework.

Climate change effects on animal presence in the Massaciuccoli Lake basin

Big-data mining approaches based on Artificial Intelligence models can help forecast biodiversity changes before they happen. These approaches can predict macroscopic species distribution patterns and trends that can inform preventive measures to avoid the loss of ecosystem functions and services. They can, therefore, help study and mitigate climate change implications on biodiversity conservation in fragile ecosystems. Wetlands are particularly fragile ecosystems where climate change poses severe risks and has dramatically reduced their size over the past century, with profound consequences on biodiversity and ecosystem services. Through big-data mining approaches, we can predict future wetland biodiversity trends in the context of climate change. This paper proposes such predictive analysis for a specific wetland: The Massaciuccoli Lake basin in Tuscany, Italy. This basin is a critical tourist attraction due to its rich biodiversity, making it an area of interest for citizens, tourists, and scientists. However, the region's suitability for native and non-native species is at risk due to climate and land-use change. Using machine-learning models, we predict the potential effects of climate change on animal spatial distribution in the basin under different greenhouse gas emission scenarios. The results suggest that habitat suitability has generally improved from 1950 to today, presumably owing to the targeted conservation strategies adopted in the area, but climate change will severely reduce bird biodiversity by 2050 while favouring several insect species' proliferation and other species' habitat change, even under a medium-emission scenario. This will lead to significant changes in the basin's biodiversity. Our methodology is adaptable to other wetland basins, being fully based on open data and models. The spatially explicit modelling used in this research provides valuable information for policymakers and spatial planners, complementing traditional biodiversity trend analyses.