bluuebunny/crossref_metadata_2025 huggingface dataset:
PILOT: Physics-Informed Learned Optimized Trajectories for Accelerated MRI
Authors: Weiss Tomer , Senouf Ortal , Vedula Sanketh , Michailovich Oleg , Zibulevsky Michael , Bronstein Alex
Published: May 2023

Magnetic Resonance Imaging (MRI) has long been considered to be among “the gold standards” of diagnostic medical imaging. The long acquisition times, however, render MRI prone to motion artifacts, let alone their adverse contribution to the relative high costs of MRI examination. Over the last few decades, multiple studies have focused on the development of both physical and post-processing methods for accelerated acquisition of MRI scans. These two approaches, however, have so far been addressed separately. On the other hand, recent works in optical computational imaging have demonstrated growing success of concurrent learning-based design of data acquisition and image reconstruction schemes. In this work, we propose a novel approach to the learning of optimal schemes for conjoint acquisition and reconstruction of MRI scans, with the optimization carried out simultaneously with respect to the time-efficiency of data acquisition and the quality of resulting reconstructions. To be of a practical value, the schemes are encoded in the form of general k-space trajectories, whose associated magnetic gradients are constrained to obey a set of predefined hardware requirements (as defined in terms of, e.g., peak currents and maximum slew rates of magnetic gradients). With this proviso in mind, we propose a novel algorithm for the end-to-end training of a combined acquisition-reconstruction pipeline using a deep neural network with differentiable forward- and back-propagation operators. We also demonstrate the effectiveness of the proposed solution in application to both image reconstruction and image segmentation, reporting substantial improvements in terms of acceleration factors as well as the quality of these end tasks.
View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT – An Investigation of Potential Improvement
Authors: Gottschalk Tristan M. , Maier Andreas , Kordon Florian , Kreher Björn W.
Published: May 2023

The positive outcome of a trauma intervention depends on an intraoperative evaluation of inserted metallic implants. Due to occurring metal artifacts, the quality of this evaluation heavily depends on the performance of so-called Metal Artifact Reduction methods (MAR). The majority of these MAR methods require prior segmentation of the inserted metal objects. Therefore, typically a rather simple thresholding-based segmentation method in the reconstructed 3D volume is applied, despite some major disadvantages. With this publication, the potential of shifting the segmentation task to a learning-based, view-consistent 2D projection-based method on the downstream MAR's outcome is investigated. For segmenting the present metal, a rather simple learning-based 2D projection-wise segmentation network that is trained using real data acquired during cadaver studies, is examined. To overcome the disadvantages that come along with a 2D projection-wise segmentation, a Consistency Filter is proposed. The influence of the shifted segmentation domain is investigated by comparing the results of the standard fsMAR with a modified fsMAR version using the new segmentation masks. With a quantitative and qualitative evaluation on real cadaver data, the investigated approach showed an increased MAR performance and a high insensitivity against metal artifacts. For cases with metal outside the reconstruction's FoV or cases with vanishing metal, a significant reduction in artifacts could be shown. Thus, increases of up to roughly 3 dB w.r.t. the mean PSNR metric over all slices and up to 9 dB for single slices were achieved. The shown results reveal a beneficial influence of the shift to a 2D-based segmentation method on real data for downstream use with a MAR method, like the fsMAR. The nature of the method further suggests the same beneficial behavior for all (also recent data-driven) MAR methods, that for now comprise a 3D-volume-based segmentation step for subsequent inpainting.
Locally orderless tensor networks for classifying two- and three-dimensional medical images
Authors: Selvan Raghavendra , Ørting Silas , Dam Erik B
Published: May 2023

Tensor networks are factorisations of high rank tensors into networks of lower rank tensors and have primarily been used to analyse quantum many-body problems. Tensor networks have seen a recent surge of interest in relation to supervised learning tasks with a focus on image classification. In this work, we improve upon the matrix product state (MPS) tensor networks that can operate on one-dimensional vectors to be useful for working with 2D and 3D medical images. We treat small image regions as orderless, squeeze their spatial information into feature dimensions and then perform MPS operations on these locally orderless regions. These local representations are then aggregated in a hierarchical manner to retain global structure. The proposed locally orderless tensor network (LoTeNet) is compared with relevant methods on three datasets. The architecture of LoTeNet is fixed in all experiments and we show it requires lesser computational resources to attain performance on par or superior to the compared methods.
Boosting Semi-supervised Image Segmentation with Global and Local Mutual Information Regularization
Authors: Peng Jizong , Pedersoli Marco , Desrosiers Christian
Published: May 2023

The scarcity of labeled data often impedes the application of deep learning to the segmentation of medical images. Semi-supervised learning seeks to overcome this limitation by leveraging unlabeled examples in the learning process. In this paper, we present a novel semi-supervised segmentation method that leverages mutual information (MI) on categorical distributions to achieve both global representation invariance and local smoothness. In this method, we maximize the MI for intermediate feature embeddings that are taken from both the encoder and decoder of a segmentation network. We first propose a global MI loss constraining the encoder to learn an image representation that is invariant to geometric transformations. Instead of resorting to computationally-expensive techniques for estimating the MI on continuous feature embeddings, we use projection heads to map them to a discrete cluster assignment where MI can be computed efficiently. Our method also includes a local MI loss to promote spatial consistency in the feature maps of the decoder and provide a smoother segmentation. Since mutual information does not require a strict ordering of clusters in two different assignments, we incorporate a final consistency regularization loss on the output which helps align the cluster labels throughout the network. We evaluate the method on four challenging publicly-available datasets for medical image segmentation. Experimental results show our method to outperform recently-proposed approaches for semi-supervised segmentation and provide an accuracy near to full supervision while training with very few annotated images.
ENHANCE (ENriching Health data by ANnotations of Crowd and Experts): A case study for skin lesion classification
Authors: Raumanns Ralf , Schouten Gerard , Joosten Max , Pluim Josien P. W. , Cheplygina Veronika
Published: May 2023

We present ENHANCE, an open dataset with multiple annotations to complement the existing ISIC and PH2 skin lesion classification datasets. This dataset contains annotations of visual ABC (asymmetry, border, colour) features from non-expert annotation sources: undergraduate students, crowd workers from Amazon MTurk and classic image processing algorithms. In this paper we first analyse the correlations between the annotations and the diagnostic label of the lesion, as well as study the agreement between different annotation sources. Overall we find weak correlations of non-expert annotations with the diagnostic label, and low agreement between different annotation sources. We then study multi-task learning (MTL) with the annotations as additional labels, and show that non-expert annotations can improve (ensembles of) state-of-the-art convolutional neural networks via MTL. We hope that our dataset can be used in further research into multiple annotations and/or MTL. All data and models are available on Github: https://github.com/raumannsr/ENHANCE.
Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis
Authors: Singh Nalini M. , Iglesias Juan Eugenio , Adalsteinsson Elfar , Dalca Adrian V. , Golland Polina
Published: May 2023

We propose neural network layers that explicitly combine frequency and image feature representations and show that they can be used as a versatile building block for reconstruction from frequency space data. Our work is motivated by the challenges arising in MRI acquisition where the signal is a corrupted Fourier transform of the desired image. The proposed joint learning schemes enable both correction of artifacts native to the frequency space and manipulation of image space representations to reconstruct coherent image structures at every layer of the network. This is in contrast to most current deep learning approaches for image reconstruction that treat frequency and image space features separately and often operate exclusively in one of the two spaces. We demonstrate the advantages of joint convolutional learning for a variety of tasks, including motion correction, denoising, reconstruction from undersampled acquisitions, and combined undersampling and motion correction on simulated and real world multicoil MRI data. The joint models produce consistently high quality output images across all tasks and datasets. When integrated into a state of the art unrolled optimization network with physics-inspired data consistency constraints for undersampled reconstruction, the proposed architectures significantly improve the optimization landscape, which yields an order of magnitude reduction of training time. This result suggests that joint representations are particularly well suited for MRI signals in deep learning networks. Our code and pretrained models are publicly available at https://github.com/nalinimsingh/interlacer.
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
Authors: Bhat Ishaan , Pluim Josien P.W. , Viergever Max A. , Kuijf Hugo J.
Published: May 2023

Deep learning techniques show success in detecting objects in medical images, but still suffer from false-positive predictions that may hinder accurate diagnosis. The estimated uncertainty of the neural network output has been used to flag incorrect predictions. We study the role played by features computed from neural network uncertainty estimates and shape-based features computed from binary predictions in reducing false positives in liver lesion detection by developing a classification-based post-processing step for different uncertainty estimation methods. We demonstrate an improvement in the lesion detection performance of the neural network (with respect to F1-score) for all uncertainty estimation methods on two datasets, comprising abdominal MR and CT images respectively. We show that features computed from neural network uncertainty estimates tend not to contribute much toward reducing false positives. Our results show that factors like class imbalance (true over false positive ratio) and shape-based features extracted from uncertainty maps play an important role in distinguishing false positive from true positive predictions.
Distributionally Robust Deep Learning using Hardness Weighted Sampling
Authors: Fidon Lucas , Aertsen Michael , Deprest Thomas , Emam Doaa , Guffens Frédéric , Mufti Nada , Van Elslander Esther , Schwartz Ernst , Ebner Michael , Prayer Daniela , Kasprian Gregor , David Anna L , Melbourne Andrew , Ourselin Sébastien , Deprest Jan , Langs Georg , Vercauteren Tom
Published: May 2023

Limiting failures of machine learning systems is of paramount importance for safety-critical applications. In order to improve the robustness of machine learning systems, Distributionally Robust Optimization (DRO) has been proposed as a generalization of Empirical Risk Minimization (ERM). However, its use in deep learning has been severely restricted due to the relative inefficiency of the optimizers available for DRO in comparison to the wide-spread variants of Stochastic Gradient Descent (SGD) optimizers for ERM.
We propose SGD with hardness weighted sampling, a principled and efficient optimization method for DRO in machine learning that is particularly suited in the context of deep learning. Similar to a hard example mining strategy in practice, the proposed algorithm is straightforward to implement and computationally as efficient as SGD-based optimizers used for deep learning, requiring minimal overhead computation. In contrast to typical ad hoc hard mining approaches, we prove the convergence of our DRO algorithm for over-parameterized deep learning networks with ReLU activation and finite number of layers and parameters.
Our experiments on fetal brain 3D MRI segmentation and brain tumor segmentation in MRI demonstrate the feasibility and the usefulness of our approach. Using our hardness weighted sampling for training a state-of-the-art deep learning pipeline leads to improved robustness to anatomical variabilities in automatic fetal brain 3D MRI segmentation using deep learning and to improved robustness to the image protocol variations in brain tumor segmentation.a decrease of 2% of the interquartile range of the Dice scores for the enhanced tumor and the tumor core regions.
Our code is available at https://github.com/LucasFidon/HardnessWeightedSampler
Multi-Atlas Segmentation and Spatial Alignment of the Human Embryo in First Trimester 3D Ultrasound
Authors: Bastiaansen Wietske A.P. , Rousian Melek , Steegers-Theunissen Régine P.M. , Niessen Wiro J. , Koning Anton H.J. , Klein Stefan
Published: May 2023

Segmentation and spatial alignment of ultrasound imaging data acquired in the in first trimester are crucial for monitoring early human embryonic growth and development throughout this crucial period of life. Current approaches are either manual or semi-automatic and are therefore very time-consuming and prone to errors. To automate these tasks, we propose a multi-atlas framework for automatic segmentation and spatial alignment of the embryo using deep learning with minimal supervision. Our framework learns to register the embryo to an atlas, which consists of the ultrasound images acquired at a range of gestational ages, segmented and spatially aligned to a predefined standard orientation. From this, we can derive the segmentation of the embryo and put the embryo in standard orientation. Ultrasound images acquired at 8+0 till 12+6 weeks gestational age were used and eight pregnancies were selected as atlas images. We evaluated different fusion strategies to incorporate multiple atlases: 1) training the framework using atlas images from a single subject, 2) training the framework with data of all available atlases and 3) ensembling of the frameworks trained per subject. To evaluate the performance, we calculated the Dice score over the test set. We found that training the framework using all available atlases outperformed ensembling and gave similar results compared to the best of all frameworks trained on a single subject. Furthermore, we found that selecting images from the four atlases closest in gestational age out of all available atlases, regardless of the individual quality, gave the best results with a median Dice score of $0.72$. We conclude that our framework can accurately segment and spatially align the embryo in first trimester 3D ultrasound images and is robust for the variation in quality that existed in the available atlases. Our code is publicly available at: https://gitlab.com/radiology/prenatal-image-analysis/multi-atlas-seg-reg
Computing Multiple Image Reconstructions with a Single Hypernetwork
Authors: Wang Alan Q. , Dalca Adrian V. , Sabuncu Mert R.
Published: May 2023

Deep learning based techniques achieve state-of-the-art results in a wide range of image reconstruction tasks like compressed sensing. These methods almost always have hyperparameters, such as the weight coefficients that balance the different terms in the optimized loss function. The typical approach is to train the model for a hyperparameter setting determined with some empirical or theoretical justification. Thus, at inference time, the model can only compute reconstructions corresponding to the pre-determined hyperparameter values. In this work, we present a hypernetwork-based approach, called HyperRecon, to train reconstruction models that are agnostic to hyperparameter settings. At inference time, HyperRecon can efficiently produce diverse reconstructions, which would each correspond to different hyperparameter values. In this framework, the user is empowered to select the most useful output(s) based on their own judgement. We demonstrate our method in compressed sensing, super-resolution and denoising tasks, using two large-scale and publicly-available MRI datasets. Our code is available at https://github.com/alanqrwang/hyperrecon.
Learning Representations with Contrastive Self-Supervised Learning for Histopathology Applications
Authors: Stacke Karin , Unger Jonas , Lundström Claes , Eilertsen Gabriel
Published: May 2023

Unsupervised learning has made substantial progress over the last few years, especially by means of contrastive self-supervised learning. The dominating dataset for benchmarking self-supervised learning has been ImageNet, for which recent methods are approaching the performance achieved by fully supervised training. The ImageNet dataset is however largely object-centric, and it is not clear yet what potential those methods have on widely different datasets and tasks that are not object-centric, such as in digital pathology. While self-supervised learning has started to be explored within this area with encouraging results, there is reason to look closer at how this setting differs from natural images and ImageNet. In this paper we make an in-depth analysis of contrastive learning for histopathology, pin-pointing how the contrastive objective will behave differently due to the characteristics of histopathology data. Using SimCLR and H&E stained images as a representative setting for contrastive self-supervised learning in histopathology, we bring forward a number of considerations, such as view generation for the contrastive objective and hyper-parameter tuning. In a large battery of experiments, we analyze how the downstream performance in tissue classification will be affected by these considerations. The results point to how contrastive learning can reduce the annotation effort within digital pathology, but that the specific dataset characteristics need to be considered. To take full advantage of the contrastive learning objective, different calibrations of view generation and hyper-parameters are required. Our results pave the way for realizing the full potential of self-supervised learning for histopathology applications. Code and trained models are available at https://github.com/k-stacke/ssl-pathology
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
Authors: Rony Jérôme , Belharbi Soufiane , Dolz Jose , Ben Ayed Ismail , McCaffrey Luke , Granger Eric
Published: May 2023

Using state-of-the-art deep learning (DL) models to diagnose cancer from histology data presents several challenges related to the nature and availability of labeled histology images, including image size, stain variations, and label ambiguity. In addition, cancer grading and the localization of regions of interest (ROIs) in such images normally rely on both image- and pixel-level labels, with the latter requiring a costly annotation process. Deep weakly-supervised object localization (WSOL) methods provide different strategies for low-cost training of DL models. Given only image-class annotations, these methods can be trained to simultaneously classify an image, and yield class activation maps (CAMs) for ROI localization. This paper provides a review of deep WSOL methods to identify and locate diseases in histology images, without the need for pixel-level annotations. We propose a taxonomy in which these methods are divided into bottom-up and top-down methods according to the information flow in models. Although the latter have seen only limited progress, recent bottom-up methods are currently driving a lot of progress with the use of deep WSOL methods. Early works focused on designing different spatial pooling functions. However, those methods quickly peaked in term of localization accuracy and revealed a major limitation, namely, – the under-activation of CAMs, which leads to high false negative localization. Subsequent works aimed to alleviate this shortcoming and recover the complete object from the background, using different techniques such as perturbation, self-attention, shallow features, pseudo-annotation, and task decoupling.
In the present paper, representative deep WSOL methods from our taxonomy are also evaluated and compared in terms of classification and localization accuracy using two challenging public histology datasets – one for colon cancer (GlaS), and a second, for breast cancer (CAMELYON16). Overall, the results indicate poor localization performance, particularly for generic methods that were initially designed to process natural images. Methods designed to address the challenges posed by histology data often use priors such as ROI size, or additional pixel-wise supervision estimated from a pre-trained classifier, allowing them to achieve better results. However, all the methods suffer from high false positive/negative localization. Classification performance is mainly affected by the model selection process, which uses either the classification or the localization metric. Finally, four key challenges are identified in the application of deep WSOL methods in histology, namely, – under-/over-activation of CAMs, sensitivity to thresholding, and model selection – and research avenues are provided to mitigate them. Our code is publicly available at https://github.com/jeromerony/survey_wsl_histology
Experiencias y dinámicas de vinculación científico-tecnológica en tiempos de pandemia. El caso de dos universidades de tamaño mediano de la Provincia de Buenos Aires, Argentina
Authors: Di Meglio María Fernanda
Published: May 2023

Las universidades argentinas tuvieron un rol proactivo durante la pandemia ya sea en el desarrollo de distintas acciones como partícipes de las políticas públicas que se definieron en diferentes ámbitos o mediante proyectos y acciones propias para resolver y/o mitigar los efectos de la pandemia. En este marco, la función de vinculación científico-tecnológica universitaria adquirió una notable presencia y significancia a la luz de los acontecimientos. En este sentido, el presente artículo pretende recoger las experiencias de vinculación científico-tecnológica en dos universidades de tamaño mediano de la Provincia de Buenos Aires y reflexionar sobre las dinámicas de vinculación desarrolladas frente a la pandemia. Dicho escenario, impactó en la forma en que las universidades movilizaron sus capacidades y recursos hacia adentro, y propició una dinámica de articulación más fluida con el entorno y sus actores, aspectos centrales para el desarrollo de una política de vinculación científico-tecnológica más efectiva y con impacto y/o incidencia inmediata en la sociedad.
Características y propósitos de los sistemas de aseguramiento de la calidad de Ecuador y Argentina: Un estudio de Educación Comparada
Authors: Moscoso Bernal Santiago , Castro López Doris Priscila
Published: May 2023

En la actualidad el aseguramiento de la calidad en las instituciones de educación superior se ha convertido en un eje estratégico del quehacer administrativo y académico de las universidades, no solo por el cumplimiento de requerimientos legales sino por satisfacer los requerimientos de la sociedad por la calidad en la planificación, ejecución y resultados del desarrollo de sus ejes sustantivos: docencia, investigación y vinculación con la sociedad (extensión). Los sistemas de evaluación, acreditación y aseguramiento de la calidad evolucionan constantemente debido al dinamismo propio del conocimiento, la modernización de los modelos de gestión, el incremento vertiginoso del número de aspirantes y estudiantes que acceden a la educación superior conduciendo a un punto de inflexión en la oferta educativa lo que ha provocado mayor requerimiento social por mejorar y asegurar la calidad de las universidades, y por ende de su oferta académica de grado y posgrado (González, 2005). El presente artículo pretende contrastar los sistemas de aseguramiento de la calidad entre dos países de América Latina: Argentina y Ecuador, fundamentado desde la metodología de la educación comparada, permitiendo evidenciar mediante un análisis las características, ventajas, desventajas y evolución de los procesos de acreditación y aseguramiento de la calidad entre Ecuador y Argentina; destacando además los objetivos, políticas y resultados de cada uno de ellos.
Nota Editorial
Authors: De Vincenzi Marcelo , Pons Claudia
Published: May 2023

Esta edición de la revista RAIA contiene cuatro artículos de actualidad en el área de las ciencias informáticas: El especialista en Tecnología Informática y Licenciado en Matemática, Jorge Kamlofsky, investigador del CAETI de la Universidad Abierta Interamericana, explica los diferentes tipos de servicios de Cluod Computing o Computación en la Nube denominados SaaS (Software as a Service), PaaS (Platform as a Service) e IaaS (Infrastructure as a Service) englobados bajo el concepto de “La Pila de Servicios en la Nube” y discute inconvenientes, posturas críticas y desafíos relacionadas con este concepto.
La gamificación como metodología activa del aprendizaje. Propuestas en la formación docente y para estudiantes de educación media
Authors: Bonetti Sandra Mabel , Lussiano Patricia Vanesa
Published: May 2023

El aislamiento social, preventivo y obligatorio determinado a raíz de la pandemia por COVID 19, configuró un escenario distinto para enseñar y aprender. La clase se trasladó a ambientes virtuales o híbridos de aprendizaje. Así, se establecieron implícitamente transformaciones en la forma de desarrollar las prácticas pedagógicas abordando modelos y propuestas basadas en e - learning y con el apoyo de las tecnologías digitales. Este artículo describe el diseño de dos propuestas de aprendizaje con la gamificación como metodología activa, una para el nivel medio y la otra para la formación docente, con el propósito de dar continuidad a estas prácticas innovadoras más allá del contexto de emergencia. Concluye que como metodología activa promueve la motivación de estudiantes y docentes y propicia la transformación de los modos de enseñar y aprender acordes al siglo XXI.
A vida de Marilyn Monroe a partir da teoria do amadurecimento pessoal de Winnicott
Authors: Machado Renata Lisboa , Abreu Kátia Hoffman de , Martini Ivanosca Inês
Published: May 2023

Este trabalho propõe pensar a vida de Marilyn Monroe a partir da teoria do amadurecimento pessoal proposta por D. W. Winnicott. Com base em uma pesquisa teórica de cunho bibliográfico, percorreu-se um eixo de leituras no grupo de estudos do Instituto de Terapias Integradas de Porto Alegre (ITIPOA). Escolheu-se conhecer a história da atriz, tendo como sustentação teórica e clínica as ideias que o psicanalista inglês teceu acerca do desenvolvimento inicial e do manejo no setting. Direcionamos nosso pensar para o ambiente e as primeiras relações, com o objetivo de alargar a compreensão sobre como se resgata o fio perdido de continuar a ser. Concluímos que, assim como Marilyn Monroe, lidamos na clínica contemporânea com pessoas muito fragilizadas e que necessitam de um olhar e de uma escuta que lhes propiciem descobrir que é possível viver criativamente. Acrescenta-se a isso a relevância de se desenvolver estudos como este, que contribuem para a transmissão da clínica e do pensamento de Winnicott.
O caso B e o conceito winnicottiano de objeto subjetivo
Authors: Muratori Ricardo
Published: May 2023

Ao estudar as etapas iniciais do desenvolvimento emocional do ser humano, Donald W. Winnicott deparou-se com diversos fenômenos próprios desse período, até então desconhecidos, ou não estudados, mas que ele constatou serem fundamentais no processo de amadurecimento pessoal. Ao longo dos anos, muitos conceitos foram sendo elaborados por Winnicott a fim de explicitar o material que foi sendo trazido à luz através das suas pesquisas. O conceito de objeto subjetivo é central no pensamento do autor exatamente porque, do ponto de vista teórico, ajuda a explicitar um aspecto fundamental do desenvolvimento emocional que é a experiência da realidade subjetiva, própria do estágio da primeira mamada teórica. Ao mesmo tempo esse conceito é essencial na compreensão de certos fenômenos que ocorrem na situação clínica. O que se pretende neste trabalho é fazer uma apresentação do conceito de objeto subjetivo, destacando alguns aspectos essenciais desse conceito e buscar no relato do caso B algumas situações ilustrativas desse conceito.
Culpa, ódio e hospital: um estudo de caso segundo a visão winnicottiana
Authors: Rossi André Augusto , Gradvohl Silvia Mayumi
Published: May 2023

O presente artigo apresenta um estudo de caso realizado por meio de um estágio supervisionado em psicologia hospitalar. Trata-se do favorecimento da elaboração de uma situação de culpa e ódio entre uma paciente e sua acompanhante, atendidas por um estagiário no contexto hospitalar, à luz da psicanálise winnicottiana. São mostrados as possibilidades e os limites da intervenção psicológica e ressaltadas as formas pelas quais a intervenção pode ser realizada, de modo a favorecer o amadurecimento pessoal sem ser invasiva e respeitando a singularidade dos sujeitos atendidos.
Adolescência e psicose
Authors: Dias Elsa Oliveira
Published: May 2023

A partir da perspectiva de Winnicott, este estudo visa mostrar que é possível, mas não fácil, discriminar se há doença num dado adolescente ou se ele apenas manifesta as características próprias do período que vive. A natureza das angústias que assolam o adolescente, especialmente aquele que teve um bom início, nos ajuda a entender as angústias psicóticas: em vias de abandonar a conhecida circunscrição do mundo familiar e lançar-se num mundo mais amplo, o adolescente é remetido às angústias primitivas. Ele não apenas ainda não se apropriou de seu corpo púbere, acossado pelos instintos e dotado agora de uma nova e assustadora potência, como também pouco sabe de si-mesmo, de sua identidade e dessa nova situação entre a infância recém-abandonada e o mundo adulto que o espera com expectativas. Nesse interregno mais ou menos longo, ele sofre de irrealidade e marasmos (doldrums) e luta para se sentir real. Além disso, se vê impelido por uma moralidade ferrenha, não em termos do que é socialmente estabelecido como bom e mau – exatamente o que ele despreza e contra o que se rebela –, mas em termos do que é sentido como real e do que é sentido como falso, o que o aproxima da esquizoidia.
O pai em Winnicott / The father in Winnicott
Authors: Claudia Claudia Dias
Published: May 2023

Tomando como base a psicanálise winnicottiana, esse artigo tem por objetivo: a) analisar a contribuição do pai para o processo de amadurecimento pessoal em seus diversos estágios, b) explicitar as possíveis falhas paternas e a maneira como elas se relacionam com a etiologia dos distúrbios emocionais, c) ilustrar essas falhas e suas consequências, desde os estágios iniciais até o estágio das relações triangulares. No início da vida, ainda que o bebê não tenha suficiente maturidade para estabelecer uma relação com o pai como pai, a presença paterna é fundamental para ajudar a mãe a ser “suficientemente boa” ou prejudicá-la nessa tarefa. No estágio do concernimento, a criança pode ser ajudada ou não pelo pai na aquisição da capacidade para tolerar o conflito entre o amor e o ódio. Por fim, no estágio das relações triangulares uma vez que a criança sadia já adquiriu maturidade suficiente para estabelecer, como pessoa inteira, relações diretas com o pai (sendo este também uma pessoa inteira), as contribuições ou as falhas paternas influenciam diretamente a resolução dos conflitos da criança relativos à administração dos seus impulsos instintuais do tipo genital em suas relações interpessoais. As falhas paternas foram avaliadas do ponto de vista teórico, seguindo os critérios maturacionais propostos por Winnicott para a compreensão da etiologia dos transtornos emocionais.
O pai e o monoteísmo em Winnicott / Winnicott on monotheism and the father
Authors: Loparic Zeljko
Published: May 2023

Depois apresentar, em linhas gerais, os fundamentos da derivação winnicottiana do monoteísmo – a situação do bebê no colo da mãe como situação-problema exemplar, a teoria do amadurecimento emocional e pessoal, o estágio do EU SOU –, o presente trabalho examina, em detalhes, esse estágio e o papel do pai nesse período do amadurecimento. Em seguida, aborda a concepção psicanalítica de Winnicott em relação ao surgimento do monoteísmo.
Considerações sobre o desenvolvimento excessivo da inteligência na criança e o papel do pai na dependência relativa/Considerations on excessive intelligence development in children and the role of the father in relative dependence
Authors: Ribeiro Maria José
Published: May 2023

Na linha do amadurecimento emocional humano, o desenvolvimento da mente faz parte do período da dependência relativa e possibilita que o bebê se diferencie da mãe.  Em muitos casos, em que o apoio do pai à mãe foi muito frágil ou ausente, a criança desenvolve uma exacerbação da inteligência na tentativa de suprir uma sustentação ambiental que caberia aos adultos: comumente a sobrecarga da mãe pela falha paterna dificulta sua identificação com o filho e a sustentação das regressões típicas desse período, assim como em todo o crescimento.  Este trabalho se refere ao estudo de crianças que desenvolvem um intelecto muito ativado no sentido de dar conta de uma desadaptação materna precoce no período da dependência relativa.
Reflexões sobre as funções do pai na inserção da criança na realidade/Reflections on the father functions for the insertion of the child in shared reality based on Winnicott
Authors: Martins André
Published: May 2023

O texto se propõe a uma reflexão sobre a função do pai hoje, a partir da teoria de Winnicott. A função paterna é importante antes de mais nada para lidar com o ambiente externo para a mãe, de modo que esta possa se dedicar a ser o ambiente do bebê. O pai pode participar dessa função-mãe, e eventualmente até mesmo assumi-la inteiramente, caso alguém faça a função-pai para ele. Além desse holding da mãe, a função-pai consiste em contribuir para a inserção da criança na cultura, reforçando o papel da mãe-objeto neste sentido; sendo, diferentemente da mãe, uma identificação externa à díade mãe-bebê; participando do ambiente que será afirmado a partir do concernimento; sobrevivendo à agressividade do filho; orientando o filho em meio à realidade partilhada; contribuindo para a aquisição e aceitação criativa da normatividade, e da formação de um falso-self poroso e não inibidor da espontaneidade do self.
A criança agressiva e o pai/The aggressive child and the father
Authors: Serralha Conceição
Published: May 2023

A teoria de Winnicott sobre o processo de amadurecimento humano permite identificar responsabilidades específicas no desempenho do papel paterno ao longo desse processo, de acordo com os diferentes períodos de dependência do indivíduo em relação ao ambiente. Sob esse ponto de vista, este texto apresenta uma análise da qualidade da presença paterna nos casos de crianças entre quatro e sete anos de idade, atendidas em um projeto de extensão universitária, que possa ser passível de ser relacionada à intensa agressividade apresentada por essas crianças. Nesta análise, falhas de responsabilidades do pai durante o desempenho do seu papel, anteriores à sua responsabilidade de intervir, colocando limites aos desejos sexuais da criança em relação ao genitor do sexo oposto, destacam-se, principalmente no tocante à criação de um ambiente estável e indestrutível no qual a criança possa crescer, e que se relacionam ao apoio à mãe e à sustentação da lei e da ordem inicialmente implantadas por esta na vida da criança.Palavras-chave: agressividade; papel paterno; responsabilidade.
O objeto transicional pode ser uma pessoa? Teoria e clínica/Can the transitional object be a person? Theory and clinic
Authors: Migliorini Walter José Martins
Published: May 2023

Resumo: o objetivo deste trabalho é responder à questão enunciada no título a partir dos textos de D. W. Winnicott. Para tanto, foram abordadas as origens do conceito de objeto transicional na obra do autor, o histórico de suas primeiras aparições, as referências diretas ao tema e a discussão de uma vinheta clínica. Winnicott raramente se referia a uma pessoa como objeto transicional de outra e, quando o fazia, situava essa experiência no campo da patologia, utilizando, para aludir a ela, termos como “objeto consolador”, “objeto-fetiche” e “objeto regressivo”. Em contrapartida, o conceito de objeto transicional está vinculado a uma experiência indicativa de saúde mental, de transição entre a fusão e a separação mãe-bebê e de uso pessoal e simbólico dos objetos do campo sensorial. Manter essa especificidade conceitual possibilita discriminar nuances da evolução do uso dos objetos materiais pela criança e orientar a anamnese, o diagnóstico e o processo terapêutico.Palavras-chave: objeto transicional; fenômenos transicionais; D. W. Winnicott; fetiche; psicanálise.Abstract: the objective of this work is to answer the question posed in the title, based on the texts of D. W. Winnicott. To this effect we addressed the origins of the concept of transitional object in the author’s work, the chronology of its first appearances, direct references to the topic and a discussion of a clinical case. Winnicott rarely referred to a person as being another person’s transitional object, and when he did so, he situated this experience in the field of pathology, and when referring to it, used such terms as “comforter object”, “fetish object” and “regressive object”. In contrast, the concept of transitional object is linked to an experience indicative of mental health, of transition between fusion and mother-baby separation, and of the personal and symbolic use of objects of the sensory field. Maintaining this conceptual specificity enables us to discriminate nuances in the evolution of the use of material objects by the child, and helps to guide anamnesis, diagnosis and the therapeutic process.Keywords: transitional object; transitional phenomena; D. W. Winnicott; fetish; psychoanalysis.
A adolescência com sintomas antissociais e o processo de ressocialização: Winnicott e o caso Peter/The adolescence with anti-social symptoms and the process of re-socialization: Winnicott and the patient Peter
Authors: NAFFAH NETO ALFREDO
Published: May 2023

Resumo: O presente artigo pretende discutir as diferenças entre o processo de socialização do adolescente saudável e do adolescente com sintomas antissociais e conclui que, no segundo caso, trata-se de um processo de ressocialização, já que o processo inicial de socialização foi quebrado pelo evento traumático produtor dos sintomas antissociais. Nesse caso, o adolescente necessita regredir a um estágio de dependência para poder ressocializar-se. Palavras-chaves: adolescência; sintomas antissociais; regressão a estágios de dependência. Abstract: This article intends to discuss the differences between the process of socialization assumed by healthy adolescents and that one characteristic of adolescents which present antisocial symptoms. It concludes that, in the second kind, the process is, really, of re-socialization, since the original process of socialization was broken by the traumatic event which produced the anti-social symptoms. In this case, the adolescent needs to regress to a dependence stage in order to re-socialize. Key-words: adolescence; anti-social symptoms; regression to dependence stages.
Pengaruh Profitabilitas, Likuiditas, Ukuran Perusahaan dan Kepemilikan Institusional Terhadap Financial Distress
Authors: Dita Puspitawati  , Rinny Meidiyustiani  , Indah Rahayu Lestari
Published: May 2023

: Financial distress is a condition where a company experiences a decline in financial condition which is usually temporary, but will develop worse if the condition is not quickly overcome and can cause the company to go bankrupt. The purpose of this study is to determine the effect of profitability, liquidity, company size and institutional ownership on financial distress. The population is property and real estate companies listed on the Indonesia Stock Exchange (IDX) in the financial statements for the 2016-2021 period. Samples were determined by purposive sampling techniques with certain criteria and obtained samples of 36 companies. This study used a multiple linear regression analysis method assisted by the SPSS Version 22 program. The results of this study show that profitability as measured by return on equity has a positive and significant effect on financial distress, liquidity as measured by curre ratio has a positive and significant effect on financial distress, while company size does not affect financial distress, institutional ownership positively affects financial distress
Analisis Penerapan Tanda Tangan Elektronik Pada Tata Naskah Dinas Elektronik (Attandie) Di Direktorat Jenderal Kependudukan Dan Pencatatan Sipil
Authors: Risya Amalia  , Arih Setyaningrum  , Evi Satispi
Published: May 2023

The existence of technology has become insignificant and affects all activities and human life in the world. As in today's era, almost everything is dependent on technology. Information technology in the e-government era can make it easy for people to obtain information and communicate interactively. Because with this technology, we can get information quickly. Information obtained through communication technology makes it easier for an organization to cooperate with each other in achieving the goals of the organization. Therefore the use of technology needs to be optimized and developed so as not to experience backwardness in obtaining information. This study aims to analyze the application of electronic signatures to the electronic official document system at the Jakarta Directorate General of Population and Civil Registration using qualitative research methods by collecting data through recording and observation. The theory used is Grindle's policy implementation theory. Based on the results of the research and discussion above, it can be concluded that the application of electronic signatures to the electronic official document system at the Directorate General of Population and Civil Registration in Jakarta in general has a positive impact and eases the work of its employees.
Analisis Faktor – Faktor Yang Mempengaruhi Keputusan Mahasiswa Memilih Melanjutkan Pendidikan Strata-1 Ekonomi Islam Universitas Diponegoro
Authors: Firdaus Helmi Adam  , Agus Hariyanto
Published: May 2023

This study aims to determine the effect of tuition fees, promotions, facilities, infrastructure, and also reputation on student decision-making in choosing the Islamic Economics study program, at the Faculty of Economics and Business (FEB) Diponegoro University, Semarang. This study uses a quantitative approach. The variables used in this study are tuition fees, promotions, facilities, and infrastructure, reputation, decision-making. The method used in this study was by taking questionnaire data which was distributed to active students of the Islamic Economics study program at Diponegoro University. The population used in this study is the number of active students in 2019, 2020, 2021, and 2022 which total 615 students. The samples taken were 97 students using the proportional random sampling technique. This study used multiple linear regression which was processed using the Statistical Package for Social Science (SPSS) version 26. The results showed that the infrastructure and reputation variables influenced student decision-making in choosing a study program, but the tuition and promotion variables did not affect student decisions in choosing the Islamic Economics study program, Faculty of Economics and Business (FEB) Diponegoro University Semarang.
Analisis Pengaruh Non Performing Loan (NPL) Dan Struktur Modal Terhadap Profitabillitas Yang Dimediasi Oleh Pertumbuhan Kredit (Studi pada BPR di Provinsi Jawa Tengah Periode Tahun 2019-2021)
Authors: Cahyo Cahyo  , Sri Harjanto  , Putu Sulastri
Published: May 2023

This study concerns profitability, aiming to analyze the effect of Non Performing Loans (NPL) and capital structure on profitability mediated by credit growth. The population of this study were all Rural Banks (BPR) in Central Java province, totaling 257 BPRs. This study used purposive sampling, with the criteria being a private BPR with its head office in Central Java province and reporting complete financial data for the 2019-2021 period. Methods of data analysis using multiple linear regression analysis and data processing using SPSS. The results of hypothesis testing show that Non-Performing Loans (NPL) have a significant negative effect on credit growth. Capital structure have a significant positive effect on credit growth. Credit growth have a positive but not significant effect on profitability. Non Performing Loans (NPL) have a significant negative effect on profitability. Capital structure have a significant positive effect on profitability. The results of the analysis of mediating variables using the Sobel test, found that credit growth cannot mediate the effect Non Performing Loans (NPL) against profitability.Credit growth cannot mediate the effectcapital structure on profitability.
Inclusion of Students with Intellectual Disability at Mainstream Classroom in Nepal
Authors:   Kathmandu University School of Education, Kathmandu, Nepal, Shrestha Salpa
Published: May 2023

The constitution of Nepal provides an inherent right to every child with special education need to attend community school nearby. But lack of necessary infrastructure and appropriate teacher trainings hinders the inclusive education process. In this context, the teachers’ attitude plays a major role in mainstreaming students with special education need in regular classroom. This study was carried out to measure the attitude of teachers towards the practice of inclusive education for one of the largest group of students with special education needs, the students with intellectual disability (SWID). The quantitative study investigated: a) the demography of teachers affecting the attitude and b) degree of strength of attitude affecting the intention of teachers to practice inclusive education in their classroom. The study design adopted the theory of planned behavior. Total of 197 general education teachers teaching in 33 different schools of Kathmandu participated in the study. The sub-scales of attitude significantly explained the variance in Intention with a strong effect. The perceived behavior control was a major behavioral predictor of intent of teachers to practice inclusiveness in the classroom. The age and level of experience of the teachers had inverse proportional relationship with the intention of teachers to teach SWID.
CONSULTA DE HIPERDIA PARA OBSERVAÇÃO DOS PADRÕES ALIMENTARES: UM RELATO DE CASO DE ALUNOS DO CURSO DE ENFERMAGEM DE UMA UNIVERSIDADE PÚBLICA ESTADUAL BAIANA
Authors: Coutinho Beatriz dos Santos
Published: May 2023

O presente trabalho tem por objetivo relatar as reflexões levantadas por estudantes em consultas de enfermagem, a partir da análise dos padrões alimentares relatados por paciente, e as relações de vulnerabilidades vivenciadas por esses indivíduos atendidos.