Successful Application Research Articles

In the Era of pass/fail: Medical student perceptions on optimizing their surgical residency applications

BackgroundIt is unclear how changes to the 2023–2024 Electronic Residency Application Service (ERAS) application will impact future applicants. We investigate students’ perceptions about applying to residency during this transition period. MethodsAn anonymous electronic survey was sent to medical students through social media of a women's surgical society. Questions reflected aspects of the ERAS application. ResultsThere were 415 responses, with 68 ​% planning to apply into surgical specialties (SS) and 17 ​% into non-surgical specialties (NSS). Compared with the NSS group, SS students reported decreased confidence in successfully matching, less attainable applicant expectations, and increased personal sacrifices to pursue their residency of interest. Students interested in Plastic and Reconstructive Surgery (PRS) placed 3-times more importance on the number of publications than General Surgery students. Away rotations were rated significantly more important for Otolaryngology, Orthopaedic, and PRS students. ConclusionStudents applying into surgical residencies experience unique stressors. Values among SS students differed across surgical subspecialties in preparation for the match. Further investigation characterizing the relationship between these values and characteristics of successful resident applicants is warranted.

A Critical Review of Inductive Logic Programming Techniques for Explainable AI.

Despite recent advances in modern machine learning algorithms, the opaqueness of their underlying mechanisms continues to be an obstacle in adoption. To instill confidence and trust in artificial intelligence (AI) systems, explainable AI (XAI) has emerged as a response to improve modern machine learning algorithms' explainability. Inductive logic programming (ILP), a subfield of symbolic AI, plays a promising role in generating interpretable explanations because of its intuitive logic-driven framework. ILP effectively leverages abductive reasoning to generate explainable first-order clausal theories from examples and background knowledge. However, several challenges in developing methods inspired by ILP need to be addressed for their successful application in practice. For example, the existing ILP systems often have a vast solution space, and the induced solutions are very sensitive to noises and disturbances. This survey paper summarizes the recent advances in ILP and a discussion of statistical relational learning (SRL) and neural-symbolic algorithms, which offer synergistic views to ILP. Following a critical review of the recent advances, we delineate observed challenges and highlight potential avenues of further ILP-motivated research toward developing self-explanatory AI systems.

Competing for research funding: Key elements impacting the evaluation of grant proposal

As funding is one of the key pillars of research activity, identifying the factors that impact the evaluation results in research funding competitions remains challenging, due to the heterogeneity of funding instruments. In this context, our study aims to identify the elements that ensure the application's success, comparing two perspectives: one of the applicant and the other based on the evaluation grid. The empirical investigation focuses on a survey of 243 Romanian researchers. As analysis methods, we use a binary logistic regression model to correlate the success in funding competitions for research projects with a set of factors considered determinants. The results show that the researcher's past performance influences the proposal's future performance/success, with the quality of the project director's previous publications, and its international visibility being the key drivers of successful research project applications.

Can ChatGPT Fool the Match? Artificial Intelligence Personal Statements for Plastic Surgery Residency Applications: A Comparative Study

Introduction: Personal statements can be decisive in Canadian residency applications. With the rise in AI technology, ethical concerns regarding authenticity and originality become more pressing. This study explores the capability of ChatGPT in producing personal statements for plastic surgery residency that match the quality of statements written by successful applicants. Methods: ChatGPT was utilized to generate a cohort of personal statements for CaRMS (Canadian Residency Matching Service) to compare with previously successful Plastic Surgery applications. Each AI-generated and human-written statement was randomized and anonymized prior to assessment. Two retired members of the plastic surgery residency selection committee from the University of British Columbia, evaluated these on a 0 to 10 scale and provided a binary response judging whether each statement was AI or human written. Statistical analysis included Welch 2-sample t tests and Cohen's Kappa for agreement. Results: Twenty-two personal statements (11 AI-generated by ChatGPT and 11 human-written) were evaluated. The overall mean scores were 7.48 (SD 0.932) and 7.68 (SD 0.716), respectively, with no significant difference between AI and human groups ( P = .4129). The average accuracy in distinguishing between human and AI letters was 65.9%. The Cohen's Kappa value was 0.374. Conclusions: ChatGPT can generate personal statements for plastic surgery residency applications with quality indistinguishable from human-written counterparts, as evidenced by the lack of significant scoring difference and moderate accuracy in discrimination by experienced surgeons. These findings highlight the evolving role of AI and the need for updated evaluative criteria or guidelines in the residency application process.

EVALUACIÓN DE ESTRATEGIAS DE MANEJO ECOLÓGICO DE ANTRACNOSIS DE LA PAPAYA EN CAMPO Y POSCOSECHA

<p><strong>Background:</strong> Mexico is the third producer and the first exporter of papaya in the world. The crop has multiple phytosanitary limitations, among which anthracnose stands out, which can cause substantial production losses. The main management method is with chemical synthesis fungicides. <strong>Objective:</strong> To evaluate treatments with biorational characteristics to control anthracnose in papaya crops in pre- and post-harvest. <strong>Methodology:</strong> The causal agent of anthracnose was isolated and identified, the growth inhibitory effect of<em> </em><em>C. gloeosporioides</em><em> </em>was evaluated <em>in vitro</em> with extracts of <em>Datura stramonium</em>, <em>Argemone mexicana</em>, <em>Citrus sinensis</em> essential oil and <em>Eucalyptus globulus</em> essential oil; In addition, biorational treatments were applied pre- and post-harvest (botanical extracts, essential oils and <em>Trichoderma harzianum</em>); The incidence and severity were evaluated and the percentage of efficacy was calculated. A randomized complete block design was used and analysis of variances and comparisons of means were performed (Tukey, P ≤ 0.05). <strong>Results:</strong> The <em>E. globulus</em> oil extract reached the highest in vitro growth inhibition levels, followed by the <em>C. sinensis</em> essential oil and the <em>A.</em><em> mexicana</em> extract; In the field phase, <em>T. harzianum</em> reached 80.7% efficacy and 8% incidence, the essential oil of <em>E. globulus</em> and the essential oil of <em>C. sinensis</em> reached 73 and 66% efficacy, respectively. The effect of the treatments applied in pre-harvest demonstrated that successive applications of <em>T. harzianum</em> reduced severity by 81% and incidence on papaya fruits by 30%, the essential oil of <em>E. globulus</em> and that of <em>C. sinensis</em> achieved efficacy of 64 and 68%, in their order. Finally, in the storage phase, <em>T. harzianum</em> achieved a control efficiency of 89%, <em>C. sinensis</em> oil 84% and 85% for the essential oil of <em>E. globulus</em>, <em>A. mexicana</em> managed to inhibit 76.46%. <strong>Implications:</strong> Comparison of other biorational management treatments such as the use of essential elements (silicon, sulfur, etc.), nanotechnology, UV irradiation and resistance inducers is suggested. <strong>Conclusion:</strong> These results support that a successful and biorational control of anthracnose in papaya must be based on comprehensive management that considered the preharvest and storage phases.</p>

The Human Capital Effects of Access to Elite Jobs

We study the human capital effects of access to elite bureaucratic jobs in Taiwan, where performance on an examination determines entry into the civil service. Historically, quotas for successful applicants were set based on the 1948 populations of individuals’ native provinces in mainland China. This resulted in a higher probability of success on the exam—and, thus, greater access to elite positions—for descendants of certain migrant groups. These preferential quotas were replaced in 1962 with a uniform admissions policy. Using this variation, we find that the incentives created by preferential quotas increased human capital and improved long-run economic outcomes. (JEL D73, H83, J24, J45, M51)

MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3-D CT Lesions.

With the renaissance of deep learning, automatic diagnostic algorithms for computed tomography (CT) have achieved many successful applications. However, they heavily rely on lesion-level annotations, which are often scarce due to the high cost of collecting pathological labels. On the other hand, the annotated CT data, especially the 3-D spatial information, may be underutilized by approaches that model a 3-D lesion with its 2-D slices, although such approaches have been proven effective and computationally efficient. This study presents a multiview contrastive network (MVCNet), which enhances the representations of 2-D views contrastively against other views of different spatial orientations. Specifically, MVCNet views each 3-D lesion from different orientations to collect multiple 2-D views; it learns to minimize a contrastive loss so that the 2-D views of the same 3-D lesion are aggregated, whereas those of different lesions are separated. To alleviate the issue of false negative examples, the uninformative negative samples are filtered out, which results in more discriminative features for downstream tasks. By linear evaluation, MVCNet achieves state-of-the-art accuracies on the lung image database consortium and image database resource initiative (LIDC-IDRI) (88.62%), lung nodule database (LNDb) (76.69%), and TianChi (84.33%) datasets for unsupervised representation learning. When fine-tuned on 10% of the labeled data, the accuracies are comparable to the supervised learning models (89.46% versus 85.03%, 73.85% versus 73.44%, 83.56% versus 83.34% on the three datasets, respectively), indicating the superiority of MVCNet in learning representations with limited annotations. Our findings suggest that contrasting multiple 2-D views is an effective approach to capturing the original 3-D information, which notably improves the utilization of the scarce and valuable annotated CT data.

Overview of the support of geothermal energy investments in the programming period 2014-2020 in Hungary

Geothermal energy has great potential to reduce the greenhouse gas emissions of the energy sector, thus contributing to the realization of EU targets and, of course, those ones set for Hungary. The country has excellent geological and geographical conditions, well known due to the existing databases as well as the know-how to guarantee a high-level utilization of this renewable energy source. Still, only a small part of the estimated potential is utilized and the previous programming period, 2014-2020 did not change this situation significantly. On one hand, the poor planning of the Széchenyi 2020 Programme, on the other hand, the lack of risk management measures, discourage investors and provide a competitive advantage for other renewable energy systems over geothermal, especially for biomass. The present paper pro- vides a comprehensive overview of the EU co-financed support of geothermal energy in the period 2014-2020, identifying relevant calls and successful projects. A questionnaire survey among successful applicants is the other pillar of the article. The critical analysis and recom- mendations of the development policy may contribute to a more efficient support of geother- mal energy in the following programming period(s).

Attention-Like Multimodality Fusion With Data Augmentation for Diagnosis of Mental Disorders Using MRI.

The globally rising prevalence of mental disorders leads to shortfalls in timely diagnosis and therapy to reduce patients' suffering. Facing such an urgent public health problem, professional efforts based on symptom criteria are seriously overstretched. Recently, the successful applications of computer-aided diagnosis approaches have provided timely opportunities to relieve the tension in healthcare services. Particularly, multimodal representation learning gains increasing attention thanks to the high temporal and spatial resolution information extracted from neuroimaging fusion. In this work, we propose an efficient multimodality fusion framework to identify multiple mental disorders based on the combination of functional and structural magnetic resonance imaging. A multioutput conditional generative adversarial network (GAN) is developed to address the scarcity of multimodal data for augmentation. Based on the augmented training data, the multiheaded gating fusion model is proposed for classification by extracting the complementary features across different modalities. The experiments demonstrate that the proposed model can achieve robust accuracies of 75.1 ± 1.5 %, 72.9 ± 1.1 %, and 87.2 ± 1.5 % for autism spectrum disorder (ASD), attention deficit/hyperactivity disorder, and schizophrenia, respectively. In addition, the interpretability of our model is expected to enable the identification of remarkable neuropathology diagnostic biomarkers, leading to well-informed therapeutic decisions.

Exploring Brain Effective Connectivity Networks Through Spatiotemporal Graph Convolutional Models.

Learning brain effective connectivity networks (ECN) from functional magnetic resonance imaging (fMRI) data has gained much attention in recent years. With the successful applications of deep learning in numerous fields, several brain ECN learning methods based on deep learning have been reported in the literature. However, current methods ignore the deep temporal features of fMRI data and fail to fully employ the spatial topological relationship between brain regions. In this article, we propose a novel method for learning brain ECN based on spatiotemporal graph convolutional models (STGCM), named STGCMEC, in which we first adopt the temporal convolutional network to extract the deep temporal features of fMRI data and utilize the graph convolutional network to update the spatial features of each brain region by aggregating information from neighborhoods, which makes the features of brain regions more discriminative. Then, based on such features of brain regions, we design a joint loss function to guide STGCMEC to learn the brain ECN, which includes a task prediction loss and a graph regularization loss. The experimental results on a simulated dataset and a real Alzheimer's disease neuroimaging initiative (ADNI) dataset show that the proposed STGCMEC is able to better learn brain ECN compared with some state-of-the-art methods.

A Study of Talent Acquisition Lifecycle – A Review on Pyxidia Techlab

The research paper explores into the use of digital tools in talent acquisition, focusing on sourcing, screening, and shortlisting applicants. In today's digital age, recruitment has adopted a variety of digital tools, including video job descriptions (JDs) and resumes. These technologies make it possible to examine candidates efficiently, focusing on communication skills, personality, and cultural fit before inviting them to interviews. Keyword utilization speeds up applicant selection based on qualifications and experience. Following the initial screening, AI-driven questionnaires assess candidates' job-related skills and knowledge. This ensures that only the most competent candidates make it to HR and stakeholder interviews. These in-person or virtual interviews test interpersonal skills and problem-solving ability. Following completion, successful applicants go through onboarding, which includes team introductions and resource allocation for effective role performance. Talent acquisition has been altered by digital technologies like as video JDs, resumes, AI questions, and virtual interviews. This saves time and resources while also increasing productivity, employee satisfaction, and organizational success. Keywords— Talent Acquisition, Methodology, Stages, Challenges, E-Recruitment.

Application of artificial intelligence for the classification of the clinical outcome and therapy in patients with viral infections: The case of COVID-19.

With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients. The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage. An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tešanj General Hospital, Bosnia and Herzegovina. The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections. The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.

P24. Plastic Surgery Foundation (PSF) Funding Trends and Publication Metrics for PSF Grant Awardees: A Retrospective 10-year Analysis on Successful Applicants

P24. Plastic Surgery Foundation (PSF) Funding Trends and Publication Metrics for PSF Grant Awardees: A Retrospective 10-year Analysis on Successful Applicants

Lethal and sublethal effects of Citrus aurantium and Citrus sinensis essential oils and their major component limonene on Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae)

Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) is an insect pest with great socio-economic importance in different agricultural regions worldwide. This species is highly adaptive and polyphagous, feeding on wild and cultivated host plants. Successive pesticide applications, which negatively affect the environment, are mainly used for managing H. armigera. Studies have investigated alternative methods with low environmental and socioeconomic impacts, including using essential oils. Essential oils are secondary plant-derived metabolites with insecticidal activity in different insect pest populations. Thus, the objective of this study was to evaluate the insecticidal activity of essential oils of Citrus aurantium (Linnaeus) (Rutaceae) and Citrus sinensis (Linnaeus) (Rutaceae) as well as their major component limonene on the biology and reproduction of H. armigera. The essential oils were characterized using gas chromatography-mass spectrometry. Further, their toxicity and the effects of their lethal and sublethal concentrations in larvae and on biological and reproductive parameters in adults were evaluated. All treatments exhibited insecticidal activity and lethal and sub-lethal effects on the biology and reproduction of H. armigera. Therefore, these essential oils could be used to control H. armigera.

Long‐term successive biochar application increases plant lignin and microbial necromass accumulation but decreases their contributions to soil organic carbon in rice–wheat cropping system

AbstractBiochar application is widely recognized as an effective approach for increasing soil organic carbon (SOC) and mitigating climate change in agroecosystems. However, the effects of biochar application on net accumulations and relative contributions of different SOC sources remain unclear. Here, we explored the effects of biochar application on plant‐derived (PDC) and microbial necromass C (MNC) in a 10‐year experimental rice–wheat rotation field receiving four different intensities of biochar application (0, 2.25, 11.5, and 22.5 t ha−1 for each crop season), using phospholipid fatty acids (PLFAs), lignin phenols and amino sugars as biomarkers of microbial biomass, PDC and MNC, respectively. Our results showed that biochar application increased SOC content and stock by 32.6%–203% and 26.4%–145%, respectively. Higher biochar application (11.5 and 22.5 t ha−1) increased soil pH, total nitrogen (TN), total phosphorus (TP), SOC/TN, and root biomass. In addition, higher biochar application enhanced bacterial, fungal, and total microbial biomass. Plant lignin phenols and MNC contents significantly increased, whereas their contributions to SOC significantly decreased with the increase in biochar application rates due to the disproportionate increase in PDC and MNC, and SOC. Fungal necromass had a greater contribution to SOC than bacterial necromass. The fungal/bacterial necromass decreased from 2.56 to 2.26 with increasing biochar application rates, because of the higher abundances of bacteria than that of fungi as indicated by PLFAs under higher biochar application rates. Random forest analyses revealed that pH, TP, and SOC/TN were the main factors controlling plant lignin and MNC accumulation. Structural equation modeling revealed that biochar application increased lignin phenols by stimulating root biomass, whereas enhanced MNC accumulation was primarily from increased microbial biomass and lignin phenols. Overall, our findings suggest that biochar application increases the accumulation of the two SOC sources but decreases their contributions to SOC in paddy soils.

Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model

ABSTRACT Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins.

Bacterial resistance to antimicrobial photodynamic therapy: A critical update

Bacterial antibiotic resistance is one of the most significant challenges for public health. The increase in bacterial resistance, mainly due to microorganisms harmful to health, and the need to search for alternative treatments to contain infections that cannot be treated by conventional antibiotic therapy has been aroused. An alternative widely studied in recent decades is antimicrobial photodynamic therapy (aPDT), a treatment that can eliminate microorganisms through oxidative stress. Although this therapy has shown satisfactory results in infection control, it is still controversial in the scientific community whether bacteria manage to develop resistance after successive applications of aPDT. Thus, this work provides an overview of the articles that performed successive aPDT applications in models using bacteria published since 2010, focusing on sublethal dose cycles, highlighting the main PSs tested, and addressing the possible mechanisms for developing tolerance or resistance to aPDT, such as efflux pumps, biofilm formation, OxyR and SoxRS systems, catalase and superoxide dismutase enzymes and quorum sensing.

A comprehensive review of advances in hepatocyte microencapsulation: selecting materials and preserving cell viability.

Liver failure represents a critical medical condition with a traditionally grim prognosis, where treatment options have been notably limited. Historically, liver transplantation has stood as the sole definitive cure, yet the stark disparity between the limited availability of liver donations and the high demand for such organs has significantly hampered its feasibility. This discrepancy has necessitated the exploration of hepatocyte transplantation as a temporary, supportive intervention. In light of this, our review delves into the burgeoning field of hepatocyte transplantation, with a focus on the latest advancements in maintaining hepatocyte function, co-microencapsulation techniques, xenogeneic hepatocyte transplantation, and the selection of materials for microencapsulation. Our examination of hepatocyte microencapsulation research highlights that, to date, most studies have been conducted in vitro or using liver failure mouse models, with a notable paucity of experiments on larger mammals. The functionality of microencapsulated hepatocytes is primarily inferred through indirect measures such as urea and albumin production and the rate of ammonia clearance. Furthermore, research on the mechanisms underlying hepatocyte co-microencapsulation remains limited, and the practicality of xenogeneic hepatocyte transplantation requires further validation. The potential of hepatocyte microencapsulation extends beyond the current scope of application, suggesting a promising horizon for liver failure treatment modalities. Innovations in encapsulation materials and techniques aim to enhance cell viability and function, indicating a need for comprehensive studies that bridge the gap between small-scale laboratory success and clinical applicability. Moreover, the integration of bioengineering and regenerative medicine offers novel pathways to refine hepatocyte transplantation, potentially overcoming the challenges of immune rejection and ensuring the long-term functionality of transplanted cells. In conclusion, while hepatocyte microencapsulation and transplantation herald a new era in liver failure therapy, significant strides must be made to translate these experimental approaches into viable clinical solutions. Future research should aim to expand the experimental models to include larger mammals, thereby providing a clearer understanding of the clinical potential of these therapies. Additionally, a deeper exploration into the mechanisms of cell survival and function within microcapsules, alongside the development of innovative encapsulation materials, will be critical in advancing the field and offering new hope to patients with liver failure.

Boundary-Aware Abstractive Summarization with Entity-Augmented Attention for Enhancing Faithfulness

With the successful application of deep learning, document summarization systems can produce more readable results. However, abstractive summarization still suffers from unfaithful outputs and factual errors, especially in named entities. Current approaches tend to employ external knowledge to improve model performance while neglecting the boundary information and the semantics of the entities. In this article, we propose an entity-augmented method (EAM) to encourage the model to make full use of the entity boundary information and pay more attention to the critical entities. Experimental results on three Chinese and English summarization datasets show that our method outperforms several strong baselines and achieves state-of-the-art performance on the CLTS dataset. Our method can also improve the faithfulness of the summary and generalize well to different pre-trained language models. Moreover, we propose a method to evaluate the integrity of generated entities. Besides, we adapt the data augmentation method in the FactCC model according to the difference between Chinese and English in grammar and train a new evaluation model for factual consistency evaluation in Chinese summarization.

ELMP-Net: The successive application of a randomized local transform for texture classification

This work proposes a method for texture classification based on the successive application of a local transform presented here for the first time. Such transform comprises two steps: (1) We built a two-layer mapping relating each pixel with its neighborhood, with the weights in the first layer randomly assigned; (2) We use the parameters learned by such mapping to transform the original image. Finally, we extract local descriptors at different stages of the successive application of this transform to compose the texture descriptors. The performance of our method is verified in the classification of benchmark texture databases and compared with state-of-the-art approaches. We also present an application for plant species identification. The results confirm our expectation that a model that is not based on the classical learning-based approach can still be competitive in texture analysis.