Textbook outcome in patients with biliary duct injury during cholecystectomy.

BACKGROUND: Iatrogenic bile duct injury (BDI) during cholecystectomy is associated with a complex and heterogeneous management owing to the burden of morbidity until their definitive treatment. This study aimed to define the textbook outcomes (TOs) after BDI with the purpose to indicate the ideal treatment and to improve it management. METHODS: We collected data from patients with an BDI between 1990 and 2022 from 27 hospitals. TO was defined as a successful conservative treatment of the iatrogenic BDI or only minor complications after BDI or patients in whom the first repair resolves the iatrogenic BDI without complications or with minor complications. RESULTS: We included 808 patients and a total of 394 patients (46.9%) achieved TO. Overall complications in TO and non-TO groups were 11.9% and 86%, respectively (P < .001). Major complications and mortality in the non-TO group were 57.4% and 9.2%, respectively. The use of end-to-end bile duct anastomosis repair was higher in the non-TO group (23.1 vs 7.8, P < .001). Factors associated with achieving a TO were injury in a specialized center (adjusted odds ratio [aOR], 4.01; 95% CI, 2.68-5.99; P < .001), transfer for a first repair (aOR, 5.72; 95% CI, 3.51-9.34; P < .001), conservative management (aOR, 5.00; 95% CI, 1.63-15.36; P = .005), or surgical management (aOR, 2.45; 95% CI, 1.50-4.00; P < .001). CONCLUSION: TO largely depends on where the BDI is managed and the type of injury. It allows hepatobiliary centers to identify domains of improvement of perioperative management of patients with BDI.

Explainable artificial intelligence prediction-based model in laparoscopic liver surgery for segments 7 and 8: an international multicenter study.

BACKGROUND: Artificial intelligence (AI) is becoming more useful as a decision-making and outcomes predictor tool. We have developed AI models to predict surgical complexity and the postoperative course in laparoscopic liver surgery for segments 7 and 8. METHODS: We included patients with lesions located in segments 7 and 8 operated by minimally invasive liver surgery from an international multi-institutional database. We have employed AI models to predict surgical complexity and postoperative outcomes. Furthermore, we have applied SHapley Additive exPlanations (SHAP) to make the AI models interpretable. Finally, we analyzed the surgeries not converted to open versus those converted to open. RESULTS: Overall, 585 patients and 22 variables were included. Multi-layer Perceptron (MLP) showed the highest performance for predicting surgery complexity and Random Forest (RF) for predicting postoperative outcomes. SHAP detected that MLP and RF gave the highest relevance to the variables "resection type" and "largest tumor size" for predicting surgery complexity and postoperative outcomes. In addition, we explored between surgeries converted to open and non-converted, finding statistically significant differences in the variables "tumor location," "blood loss," "complications," and "operation time." CONCLUSION: We have observed how the application of SHAP allows us to understand the predictions of AI models in surgical complexity and the postoperative outcomes of laparoscopic liver surgery in segments 7 and 8.

Exploring Technology- and Sensor-Driven Trends in Education: A Natural-Language-Processing-Enhanced Bibliometrics Study.

Over the last decade, there has been a large amount of research on technology-enhanced learning (TEL), including the exploration of sensor-based technologies. This research area has seen significant contributions from various conferences, including the European Conference on Technology-Enhanced Learning (EC-TEL). In this research, we present a comprehensive analysis that aims to identify and understand the evolving topics in the TEL area and their implications in defining the future of education. To achieve this, we use a novel methodology that combines a text-analytics-driven topic analysis and a social network analysis following an open science approach. We collected a comprehensive corpus of 477 papers from the last decade of the EC-TEL conference (including full and short papers), parsed them automatically, and used the extracted text to find the main topics and collaborative networks across papers. Our analysis focused on the following three main objectives: (1) Discovering the main topics of the conference based on paper keywords and topic modeling using the full text of the manuscripts. (2) Discovering the evolution of said topics over the last ten years of the conference. (3) Discovering how papers and authors from the conference have interacted over the years from a network perspective. Specifically, we used Python and PdfToText library to parse and extract the text and author keywords from the corpus. Moreover, we employed Gensim library Latent Dirichlet Allocation (LDA) topic modeling to discover the primary topics from the last decade. Finally, Gephi and Networkx libraries were used to create co-authorship and citation networks. Our findings provide valuable insights into the latest trends and developments in educational technology, underlining the critical role of sensor-driven technologies in leading innovation and shaping the future of this area.

Annotated Flickr dataset for identification of professional photographers.

We collected and computed various data and statistics from a sample of Flickr users who uploaded photos to the platform in December 2021 and their photos, obtaining a final number of 27,516 users and 2,647,928 photos. Having the total number of photos uploaded and the number of photos uploaded in December by each user, we selected a representative sample of those whose activity was not overly concentrated in December and obtained data from those who specified their occupation. In addition to the data collected directly from Flickr, we enriched the dataset with new features resulting from the automated analysis of the photos and their comments. One of the most valuable features of this data collection is that each photo has three Image Quality Assessment scores representing aesthetic and technical aspects. For this, we used Convolutional Neural Networks trained with human-labeled data. Furthermore, we added labels to indicate whether the user is a professional photographer, so the data are specially prepared for supervised training.

How woody plants adjust above- and below-ground traits in response to sustained drought.

Future increases in drought severity and frequency are predicted to have substantial impacts on plant function and survival. However, there is considerable uncertainty concerning what drought adjustment is and whether plants can adjust to sustained drought. This review focuses on woody plants and synthesises the evidence for drought adjustment in a selection of key above-ground and below-ground plant traits. We assess whether evaluating the drought adjustment of single traits, or selections of traits that operate on the same plant functional axis (e.g. photosynthetic traits) is sufficient, or whether a multi-trait approach, integrating across multiple axes, is required. We conclude that studies on drought adjustments in woody plants might overestimate the capacity for adjustment to drier environments if spatial studies along gradients are used, without complementary experimental approaches. We provide evidence that drought adjustment is common in above-ground and below-ground traits; however, whether this is adaptive and sufficient to respond to future droughts remains uncertain for most species. To address this uncertainty, we must move towards studying trait integration within and across multiple axes of plant function (e.g. above-ground and below-ground) to gain a holistic view of drought adjustments at the whole-plant scale and how these influence plant survival.

Microgeographic variation in early fitness traits of Pinus sylvestris from contrasting soils.

PREMISE: The possibility of fine-scale intraspecific adaptive divergence under gene flow is established by theoretical models and has been confirmed empirically in tree populations distributed along steep altitudinal clines or across extreme edaphic discontinuities. However, the possibility of microgeographic adaptive divergence due to less severe but more frequent kinds of soil variation is unclear. METHODS: In this study, we looked for evidence of local adaptation to calcareous versus siliceous soil types in two nearby Mediterranean Pinus sylvestris populations connected via pollen flow. Using a greenhouse experiment, we tested for variation in early (up to three years of age) seedling performance among open-pollinated maternal families originating from each edaphic provenance when experimentally grown on both types of natural local substrate. RESULTS: Although seedlings were clearly affected by the edaphic environment, exhibiting lower and slower emergence as well as higher mortality on the calcareous than in the siliceous substrate, neither the performance on each substrate nor the plasticity among substrates varied significantly with seedling edaphic provenance. CONCLUSIONS: We found no evidence of local adaptation to a non-extreme edaphic discontinuity over a small spatial scale, at least during early stages of seedling establishment. Future studies on microgeographic soil-driven adaptation should consider long-term experiments to minimize maternal effects and allow a potentially delayed expression of edaphic adaptive divergence.

Genomic data and common garden experiments reveal climate-driven selection on ecophysiological traits in two Mediterranean oaks.

Improving our knowledge of how past climate-driven selection has acted on present-day trait population divergence is essential to understand local adaptation processes and improve our predictions of evolutionary trajectories in the face of altered selection pressures resulting from climate change. In this study, we investigated signals of selection on traits related to drought tolerance and growth rates in two Mediterranean oak species (Quercus faginea and Q. lusitanica) with contrasting distribution ranges and climatic niches. We genotyped 182 individuals from 24 natural populations of the two species using restriction-site-associated DNA sequencing and conducted a thorough functional characterization in 1602 seedlings from 21 populations cultivated in common garden experiments under contrasting watering treatments. Our genomic data revealed that both Q. faginea and Q. lusitanica have very weak population genetic structure, probably as a result of high rates of pollen-mediated gene flow among populations and large effective population sizes. In contrast, common garden experiments showed evidence of climate-driven divergent selection among populations on traits related to leaf morphology, physiology and growth in both species. Overall, our study suggests that climate is an important selective factor for Mediterranean oaks and that ecophysiological traits have evolved in drought-prone environments even in a context of very high rates of gene flow among populations.

From Sensor Data to Educational Insights.

Technology is gradually becoming an integral part of learning at all levels of educational [...].

Vascular injury during cholecystectomy: A multicenter critical analysis behind the drama.

BACKGROUND: The management of a vascular injury during cholecystectomy is still very complicated, especially in centers not specialized in complex hepatobiliary surgery. METHODS: This was a multi-institutional retrospective study in patients with vascular injuries during cholecystectomy from 18 centers in 4 countries. The aim of the study was to analyze the management of vascular injuries focusing on referral, time to perform the repair, and different treatments options outcomes. RESULTS: A total of 104 patients were included. Twenty-nine patients underwent vascular repair (27.9%), 13 (12.5%) liver resection, and 1 liver transplant as a first treatment. Eighty-four (80.4%) vascular and biliary injuries occurred in nonspecialized centers and 45 (53.6%) were immediately transferred. Intraoperative diagnosed injuries were rare in referred patients (18% vs 84%, P = .001). The patients managed at the hospital where the injury occurred had a higher number of reoperations (64% vs 20%, P ˂ .001). The need for vascular reconstruction was associated with higher mortality (P = .04). Two of the 4 patients transplanted died. CONCLUSION: Vascular lesions during cholecystectomy are a potentially life-threatening complication. Management of referral to specialized centers to perform multiple complex multidisciplinary procedures should be mandatory. Late vascular repair has not shown to be associated with worse results.

Machine Learning-Based Analysis in the Management of Iatrogenic Bile Duct Injury During Cholecystectomy: a Nationwide Multicenter Study.

BACKGROUND: Iatrogenic bile duct injury (IBDI) is a challenging surgical complication. IBDI management can be guided by artificial intelligence models. Our study identified the factors associated with successful initial repair of IBDI and predicted the success of definitive repair based on patient risk levels. METHODS: This is a retrospective multi-institution cohort of patients with IBDI after cholecystectomy conducted between 1990 and 2020. We implemented a decision tree analysis to determine the factors that contribute to successful initial repair and developed a risk-scoring model based on the Comprehensive Complication Index. RESULTS: We analyzed 748 patients across 22 hospitals. Our decision tree model was 82.8% accurate in predicting the success of the initial repair. Non-type E (p < 0.01), treatment in specialized centers (p < 0.01), and surgical repair (p < 0.001) were associated with better prognosis. The risk-scoring model was 82.3% (79.0-85.3%, 95% confidence interval [CI]) and 71.7% (63.8-78.7%, 95% CI) accurate in predicting success in the development and validation cohorts, respectively. Surgical repair, successful initial repair, and repair between 2 and 6 weeks were associated with better outcomes. DISCUSSION: Machine learning algorithms for IBDI are a novel tool may help to improve the decision-making process and guide management of these patients.

Multi-trait genetic variation in resource-use strategies and phenotypic plasticity correlates with local climate across the range of a Mediterranean oak (Quercus faginea).

Resource-use strategies are hypothesized to evolve along climatic gradients. However, our understanding of the environmental factors driving divergent evolution of resource-use strategies and the relationship between trait genetic variation and phenotypic plasticity is far from complete. Using the Mediterranean tree Quercus faginea as study system, we tested the hypothesis that a conservative resource-use strategy with increased drought tolerance and reduced phenotypic plasticity has evolved in areas with longer and more severe dry seasons. We conducted a glasshouse experiment in which we measured leaf morphological, physiological, growth and allocation traits in seedlings from 10 range-wide climatically contrasting populations, grown under two different watering treatments. Both univariate and multivariate analyses revealed a genetic gradient of resource-use strategies and phenotypic plasticity associated with provenance climate. In particular, populations from harsher (drier and colder) environments had more sclerophyllous leaves, lower growth rates, better physiological performance under dry conditions and reduced multi-trait phenotypic plasticity compared to populations from more mesic and milder environments. Our results suggest that contrasting precipitation and temperature regimes play an important role in the adaptive intraspecific evolution of multivariate phenotypes and their plasticity, resulting in coordinated morphology, physiology, growth and allometry according to alternative resource-use strategies.

Adaptive responses to temperature and precipitation variation at the early-life stages of Pinus sylvestris.

Early-stage fitness variation has been seldom evaluated at broad scales in forest tree species, despite the long tradition of studying climate-driven intraspecific genetic variation. In this study, we evaluated the role of climate in driving patterns of population differentiation at early-life stages in Pinus sylvestris and explored the fitness and growth consequences of seed transfer within the species range. We monitored seedling emergence, survival and growth over a 2-yr period in a multi-site common garden experiment which included 18 European populations and spanned 25° in latitude and 1700 m in elevation. Climate-fitness functions showed that populations exhibited higher seedling survival and growth at temperatures similar to their home environment, which is consistent with local adaptation. Northern populations experienced lower survival and growth at warmer sites, contrary to previous studies on later life stages. Seed mass was higher in populations from warmer areas and was positively associated with survival and growth at more southern sites. Finally, we did not detect a survival-growth trade-off; on the contrary, bigger seedlings exhibited higher survival probabilities under most climatic conditions. In conclusion, our results reveal that contrasting temperature regimes have played an important role in driving the divergent evolution of P. sylvestris populations at early-life stages.

Applying Learning Analytics to Detect Sequences of Actions and Common Errors in a Geometry Game.

Games have become one of the most popular activities across cultures and ages. There is ample evidence that supports the benefits of using games for learning and assessment. However, incorporating game activities as part of the curriculum in schools remains limited. Some of the barriers for broader adoption in classrooms is the lack of actionable assessment data, the fact that teachers often do not have a clear sense of how students are interacting with the game, and it is unclear if the gameplay is leading to productive learning. To address this gap, we seek to provide sequence and process mining metrics to teachers that are easily interpretable and actionable. More specifically, we build our work on top of Shadowspect, a three-dimensional geometry game that has been developed to measure geometry skills as well other cognitive and noncognitive skills. We use data from its implementation across schools in the U.S. to implement two sequence and process mining metrics in an interactive dashboard for teachers. The final objective is to facilitate that teachers can understand the sequence of actions and common errors of students using Shadowspect so they can better understand the process, make proper assessment, and conduct personalized interventions when appropriate.

Selection patterns on early-life phenotypic traits in Pinus sylvestris are associated with precipitation and temperature along a climatic gradient in Europe.

Understanding the dynamics of selection is key to predicting the response of tree species to new environmental conditions in the current context of climate change. However, selection patterns acting on early recruitment stages and their climatic drivers remain largely unknown in most tree species, despite being a critical period of their life cycle. We measured phenotypic selection on Pinus sylvestris seed mass, emergence time and early growth rate over 2 yr in four common garden experiments established along the latitudinal gradient of the species in Europe. Significant phenotypic plasticity and among-population genetic variation were found for all measured phenotypic traits. Heat and drought negatively affected fitness in the southern sites, but heavy rainfalls also decreased early survival in middle latitudes. Climate-driven directional selection was found for higher seed mass and earlier emergence time, while the form of selection on seedling growth rates differed among sites and populations. Evidence of adaptive and maladaptive phenotypic plasticity was found for emergence time and early growth rate, respectively. Seed mass, emergence time and early growth rate have an adaptive role in the early stages of P. sylvestris and climate strongly influences the patterns of selection on these fitness-related traits.

Bacteriemia por Lactococcus garvieae: primer caso en Colombia / Lactococcus garvieae bloodstream infections: first case in Colombia

Lactococcus garvieae es una causa inusual de bacteriemia. No existen guías diagnósticas y terapéuticas actuales disponibles para tratar las infecciones causadas por estos organismos. Con base en un informe de caso, proporcionamos una revisión de la literatura sobre bacteriemia causada por L. garvieae y destacamos los desafíos de diagnóstico y tratamiento de estas infecciones y las implicaciones para el manejo. Comunicamos el primer caso de bacteriemia por Lactococcus garvieae en Colombia.

Lactococcus garvieae is an unusual cause of bloodstreams infections. Currently, there are no diagnostic and therapeutic guides available to treat infections caused by these organisms. Based on a case report, we provide a literature review on bloodstreams caused by L. garvieae and highlighted the challenges for diagnose and treatment and direct implications for its management. We report the first case of bloodstream infections due to Lactococcus garvieae in Colombia.

Twitter social bots: The 2019 Spanish general election data.

The term social bots refer to software-controlled accounts that actively participate in the social platforms to influence public opinion toward desired directions. To this extent, this data descriptor presents a Twitter dataset collected from October 4th to November 11th, 2019, within the context of the Spanish general election. Starting from 46 hashtags, the collection contains almost eight hundred thousand users involved in political discussions, with a total of 5.8 million tweets. The proposed data descriptor is related to the research article available at [1]. Its main objectives are: i) to enable worldwide researchers to improve the data gathering, organization, and preprocessing phases; ii) to test machine-learning-powered proposals; and, finally, iii) to improve state-of-the-art solutions on social bots detection, analysis, and classification. Note that the data are anonymized to preserve the privacy of the users. Throughout our analysis, we enriched the collected data with meaningful features in addition to the ones provided by Twitter. In particular, the tweets collection presents the tweets' topic mentions and keywords (in the form of political bag-of-words), and the sentiment score. The users' collection includes one field indicating the likelihood of one account being a bot. Furthermore, for those accounts classified as bots, it also includes a score that indicates the affinity to a political party and the followers/followings list.

Genetic variation in early fitness traits across European populations of silver birch (Betula pendula).

Given that the ecological niche of tree species is typically narrower for earlier life stages, intraspecific genetic variation at early fitness traits may greatly influence the adaptive response of tree populations to changing environmental conditions. In this study, we evaluated genetic variation in early fitness traits among 12 populations of Betula pendula from a wide latitudinal range in Europe (41-55°N). We first conducted a chamber experiment to test for population differences in germination and the effect of pre-chilling treatment on seed dormancy release. We then established three common gardens spread across the species latitudinal range in order to evaluate levels of quantitative genetic variation and genotype-by-environment interaction at different early life traits. Our results showed significant variation in chamber germination rates among populations (0-60 %), with southern populations exhibiting lower germination. Pre-chilling treatments did not generally improve germination success. Population seedling emergence rates in the field were correlated with chamber germination rates, though being an order of magnitude lower, with an average ranging from 0 to 1.3 % across gardens. Highly significant variation was found in field emergence rates among populations, and between seed-crop years within populations, but not among families within populations. Populations differed in seedling height, diameter, slenderness and budburst date, with significant among-family variation. Population latitude was positively associated with chamber germination rate and with seedling emergence rate in one of the central field sites. Overall, genetic, environmental and demographic factors seem to influence the observed high levels of variation in early fitness traits among B. pendula populations. Our results suggest limited regeneration capacity for the study species under drier conditions, but further field trials with sufficient replication over environments and seed crops will improve our understanding of its vulnerability to climate change.

A Scalable Architecture for the Dynamic Deployment of Multimodal Learning Analytics Applications in Smart Classrooms.

The smart classrooms of the future will use different software, devices and wearables as an integral part of the learning process. These educational applications generate a large amount of data from different sources. The area of Multimodal Learning Analytics (MMLA) explores the affordances of processing these heterogeneous data to understand and improve both learning and the context where it occurs. However, a review of different MMLA studies highlighted that ad-hoc and rigid architectures cannot be scaled up to real contexts. In this work, we propose a novel MMLA architecture that builds on software-defined networks and network function virtualization principles. We exemplify how this architecture can solve some of the detected challenges to deploy, dismantle and reconfigure the MMLA applications in a scalable way. Additionally, through some experiments, we demonstrate the feasibility and performance of our architecture when different classroom devices are reconfigured with diverse learning tools. These findings and the proposed architecture can be useful for other researchers in the area of MMLA and educational technologies envisioning the future of smart classrooms. Future work should aim to deploy this architecture in real educational scenarios with MMLA applications.