Automatic Renal Segmentation in DCE-MRI using Convolutional Neural Networks

Kidney function evaluation using dynamic contrast-enhanced MRI (DCE-MRI) images could help in diagnosis and treatment of kidney diseases of children. Automatic segmentation of renal parenchyma is an important step in this process. In this paper, we propose a time and memory efficient fully automated segmentation method which achieves high segmentation accuracy with running time in the order of seconds in both normal kidneys and kidneys with hydronephrosis. The proposed method is based on a cascaded application of two 3D convolutional neural networks that employs spatial and temporal information at the same time in order to learn the tasks of localization and segmentation of kidneys, respectively. Segmentation performance is evaluated on both normal and abnormal kidneys with varying levels of hydronephrosis. We achieved a mean dice coefficient of 91.4 and 83.6 for normal and abnormal kidneys of pediatric patients, respectively

Context-Aware Recursive Bayesian Graph Traversal in BCIs

Noninvasive brain computer interfaces (BCI), and more specifically Electroencephalography (EEG) based systems for intent detection need to compensate for the low signal to noise ratio of EEG signals. In many applications, the temporal dependency information from consecutive decisions and contextual data can be used to provide a prior probability for the upcoming decision. In this study we proposed two probabilistic graphical models (PGMs), using context information and previously observed EEG evidences to estimate a probability distribution over the decision space in graph based decision-making mechanism. In this approach, user moves a pointer to the desired vertex in the graph in which each vertex represents an action. To select a vertex, a Select command, or a proposed probabilistic Selection criterion (PSC) can be used to automatically detect the user intended vertex. Performance of different PGMs and Selection criteria combinations are compared over a keyboard based on a graph layout. Based on the simulation results, probabilistic Selection criterion along with the probabilistic graphical model provides the highest performance boost for individuals with pour calibration performance and achieving the same performance for individuals with high calibration performance.Comment: This work has been submitted to EMBC 201

Optimization of Water-Energy-Food Nexus considering CO2 emissions from cropland: A case study in northwest Iran

Water-Energy-Food (WEF) Nexus and CO2 emissions for a farm in northwest Iran were analyzed to provide data support for decision-makers formulating national strategies in response to climate change. In the analysis, input–output energy in the production of seven crop species (alfalfa, barley, silage corn, potato, rapeseed, sugar beet, and wheat) was determined using six indicators, water, and energy consumption, mass productivity, and economic productivity. WEF Nexus index (WEFNI), calculated based on these indicators, showed the highest (best) value for silage corn and the lowest for potato. Nitrogen fertilizer and diesel fuel with an average of 36.8% and 30.6% of total input energy were the greatest contributors to energy demand. Because of the direct relationship between energy consumption and CO2 emissions, potato cropping, with the highest energy consumption, had the highest CO2 emissions with a value of 5166 kg CO2eq ha−1. A comparison of energy inputs and CO2 emissions revealed a direct relationship between input energy and global warming potential. A 1 MJ increase in input energy increased CO2 emissions by 0.047, 0.049, 0.047, 0.054, 0.046, 0.046, and 0.047 kg ha−1 for alfalfa, barley, silage corn, potato, rapeseed, sugar beet, and wheat, respectively. Optimization assessments to identify the optimal cultivation pattern, with emphasis on maximized WEFNI and minimized CO2 emissions, showed that barley, rapeseed, silage corn, and wheat performed best under the conditions studied.publishedVersio

Visual Outcomes of Adding Erythropoietin to Methylprednisolone for Treatment of Retrobulbar Optic Neuritis

Purpose: To compare the short-term visual function results and safety of erythropoietin as an add-on to the standard corticosteroid therapy in retrobulbar optic neuritis (RON). Methods: In this prospective pilot study, adult patients with isolated RON with less than 10 days of onset were enrolled. Patients were consecutively assigned to standard intravenous methylprednisolone treatment either in combination with intravenous erythropoietin (20,000 units/day for three days) (group-1) or alone (group-2). Primary outcome measure was best-corrected visual acuity (BCVA), which was assessed up to 120 days from the day the treatment was begun. Systemic evaluations were performed during and after treatment. Results: Sixty-two patients with RON (mean age = 26.6 ± 5.77 years; range = 18–40 years) were enrolled into the study (group-1, n = 35; group-2, n = 27). BCVA three months after the treatment was 0.19 ± 0.55 logMAR and 0.11 ± 0.32 logMAR in group-1 and group-2, respectively (95% CI: –0.61–0.16; P = 0.62). Change in BCVA after three months was 2.84 ± 3.49 logMAR in group-1 and 2.46 ± 1.40 logMAR in group-2 (95% CI: –0.93–1.91; P = 0.57). Pace of recovery was not significantly different between the groups. No complications were detected among patients. Conclusion: Intravenous erythropoietin as an add-on did not significantly improve the visual outcome in terms of visual acuity, visual field, and contrast sensitivity compared to traditional intravenous corticosteroid. This pilot study supports the safety profile of intravenous human recombinant erythropoietin, and it may help formulate future investigations with a larger sample size

Pseudo-Labeling Enhanced by Privileged Information and Its Application to In Situ Sequencing Images

Various strategies for label-scarce object detection have been explored by the computer vision research community. These strategies mainly rely on assumptions that are specific to natural images and not directly applicable to the biological and biomedical vision domains. For example, most semi-supervised learning strategies rely on a small set of labeled data as a confident source of ground truth. In many biological vision applications, however, the ground truth is unknown and indirect information might be available in the form of noisy estimations or orthogonal evidence. In this work, we frame a crucial problem in spatial transcriptomics - decoding barcodes from In-Situ-Sequencing (ISS) images - as a semi-supervised object detection (SSOD) problem. Our proposed framework incorporates additional available sources of information into a semi-supervised learning framework in the form of privileged information. The privileged information is incorporated into the teacher's pseudo-labeling in a teacher-student self-training iteration. Although the available privileged information could be data domain specific, we have introduced a general strategy of pseudo-labeling enhanced by privileged information (PLePI) and exemplified the concept using ISS images, as well on the COCO benchmark using extra evidence provided by CLIP.Comment: This paper has been accepted for publication at IJCAI 202

Risk assessment in complex data settings: algorithmic fairness and causal inference

We try to address some challenges in structured risk assessment tools in two application areas of recidivism risk in criminal justice and dropout risk in higher education domain. Using Machine Learning (ML) methods, we suggest a cost-benefit scenario to efficiently save time, expenses and staff in a data-driven assessment of violent recidivism risk. This leads to fewer evaluations in exchange for some small number of undetected changes. Importantly, we mitigate the model’s disparate impact in the rate of evaluation across some demographics. We obtain more accurate ML risk prediction models compared to the previous models and improve algorithmic fairness of the models across some sensitive groups in terms of error disparity and calibration. We determine the effect of a treatment on the outcome risk using statistical causal inference methods. We show that a reduction in university workload reduces dropout risk and conditional release can reduce general and violent recidivism risks.Intentamos abordar algunos desafíos en las herramientas estructuradas de evaluación de riesgos en dos áreas de aplicación de riesgo de reincidencia y riesgo de deserción universitaria. Usando métodos de ML, sugerimos un escenario de costo-beneficio para ahorrar tiempo, gastos y personal de manera eficiente en una evaluación basada en datos del riesgo de reincidencia violenta. Esto conduce a menos evaluaciones a cambio de una pequeña cantidad de cambios no detectados. Mitigamos impacto dispar del modelo en tasa de evaluación en algunos grupos demográficos. Obtenemos modelos de predicción de riesgo de ML más precisos en comparación con los modelos anteriores y mejoramos la equidad algorítmica de los modelos en algunos grupos sensibles en términos de disparidad de errores y calibración. Usando métodos estadísticos de inferencia causal, mostramos que una reducción en carga de trabajo universitaria reduce riesgo de deserción y liberación condicional puede reducir riesgos de reincidencia general y violenta

Evaluation Techniques of Creating Coherence in Poems of Kaiser Aminpour Relying on the Theory of Halliday’s Linguistics

AbstractOne of the linguistics theories that are utilized in present age in the analysis of literary texts is the theory of Hallidayâs linguistics which is called theory of systemic - functional linguistics. Hallidayâs theory in the second half of the twentieth century, in contrast to the "generative linguistics" approach, which is based on formal language like a mental and individual phenomenon.    Hallidayan called the semantic, literal, syntax and logical relationship of words as coherence of a text and believes that the coherence of the text, includes all the semantic relations by which, every piece of speech or writing can serve as text. Hence, cohesion, referred to relations that relates all elements of a sentence to the elements of other sentences. From Hallidayan's  point of view, elements of coherence of a text are divided into two parts: grammatical and lexical. Grammatical cohesion includes factors such as referrals, replacements, removals and relevancy, and lexical coherence includes repetition and collocation in literary language.   Kaiser is one of the contemporary famous poets that have composed many poets in different molds of poetry. Kaiser Manipur's poems benefit the integration due to the poet's fluency in Persian and his familiarity with the vocabulary and grammatical and lexical capacities of Persian literature. In this research, we have studied the cohesion in Kaiser Manipur's poems from two views of lexical and grammatical by using Holidaying theory of coherence as well as the techniques that the poet has applied to reach the cohesion in his poems.    The method of research in the present paper is descriptive-analytical and all the information and data has been collected from the sources of library and electronic. In this paper, we first briefly discussed the Holidaying coherence theory, and then, with a special approach to this theory, the most important techniques that Kaiser Manipur has benefited to create the coherence in his poems and poetry, analyzed and investigated.   The Research results indicate that the poems of Kaiser Manipur have a specific integration due to the poet's fluency in Persian and his familiarity with the vocabulary and grammatical and lexical capacities of Persian literature. Hence, techniques of creating coherence in two aspects of grammatical cohesion and lexical cohesion in Kaiser Manipur's poetry are considerable. Grammatical coherence in his poems is achieved through techniques such as references, omission, and relevance. And the techniques of lexica coherence in his poems are: using phonemes, repeating the words, pun, repeated sentence, the balance, the heart of the matter, proportion, contrast, sequence and so on.    Kaiser Aminpour has widely used the techniques of lexical and grammatical coherence in his poems, so that in most of his poems we can see several techniques used together in a way to reach the mentioned coherence. Hence, we can say he is a poet who knows his language as well as words and has the complete power of selection and combination of words and sentences. And his poems are very coherent and the sentences and phrases are intertwined and related to each other in his poetry.Keywords: Kaiser Aminpour, Hallidayan, functional linguistics, grammatical cohesion, lexical cohesion.References 1.      Abdollahi, Manije (2005). Vertical link and semantic cohesion in the ghazals of Hafez. Journal of Humanities and Social Sciences, University of Shiraz, (Vol. 22). No 3(44). P.p: 124- 134. 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Energy analysis in Water-Energy-Food-Carbon Nexus

This study evaluated the comprehensive Water-Energy-Food-Carbon Nexus (WEFC) by focusing on energy assessment in northwest Iran. The energy evaluation indices for different products were calculated by estimating the total input and output energies. Multi-objective optimization based on five individual objectives and WEFC Nexus policies was used to identify the optimal land-use allocation of wheat, barley, rapeseed, and sugar beet, silage corn, and potato while minimizing water and energy consumption and CO2 emissions, and maximizing food production and profit. The results indicate that the suggested framework provides a practical methodology for determining the optimal land-use allocation considering quantitative WEFC Nexus. To increase economic efficiency and reduce energy consumption, agricultural practices and policy recommendations should be adopted, including promoting renewable energy sources, implementing energy-saving technologies, improving fertilizer management, improving crop rotation practices, conservation tillage, and improving water management and adoption of sustainable farming practices. The results allow policymakers to optimize multiple resources and recommend the best resource allocation under recommendation policy, technology, and constraints to achieve sustainable development in agriculture

Survival predictors of preterm neonates: Hospital based study in Iran (2010-2011)

Background: Preterm birth (PTB) is responsible for 70% of neonatal mortalities. Various factors influence the risk of neonatal mortality in different populations.\ud Objective: Our objective was to evaluate neonatal survival rate of preterm infants, and to define its predictors in Iranian population.\ud Materials and Methods: This retrospective cohort study included all preterm (26-37 weeks) infants (n=1612) born alive in Shahid Akbar-abadi university hospital, during one year period (April 2010-2011). These infants were evaluated for fetal-neonatal, maternal, and pregnancy data. Survival analysis was performed and viability threshold and risk factors of neonatal mortality were evaluated.\ud Results: Total overall mortality rate was 9.1%. Survival rate were 11.11% for extremely low birth weights (LBW) and 45.12% for very early PTBs. The smallest surviving infant was a 750 gr female with gestational age (GA) of 30 weeks and the youngest infants was a 970 gram female with GA of 25weeks plus 2 days. History of previous dead neonate, need to cardio-pulmonary resuscitation (CPR), need to neonatal intensive care unit (NICU) admission, postnatal administration of surfactant, presence of anomalies, Apgar score <7, multiple pregnancy, non-cephalic presentation, early PTB, very early PTB, LBW, very low birth weight (VLBW) and extremely low birth weight (ELBW), were risk factors for mortality in preterm neonates