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1. Teleportation With Null Space Gradient Projection for Optimization Acceleration

Author

Wu, Zihao, Dong, Juncheng, Aloui, Ahmed, and Tarokh, Vahid

Subjects
Computer Science - Machine Learning
Abstract

Optimization techniques have become increasingly critical due to the ever-growing model complexity and data scale. In particular, teleportation has emerged as a promising approach, which accelerates convergence of gradient descent-based methods by navigating within the loss invariant level set to identify parameters with advantageous geometric properties. Existing teleportation algorithms have primarily demonstrated their effectiveness in optimizing Multi-Layer Perceptrons (MLPs), but their extension to more advanced architectures, such as Convolutional Neural Networks (CNNs) and Transformers, remains challenging. Moreover, they often impose significant computational demands, limiting their applicability to complex architectures. To this end, we introduce an algorithm that projects the gradient of the teleportation objective function onto the input null space, effectively preserving the teleportation within the loss invariant level set and reducing computational cost. Our approach is readily generalizable from MLPs to CNNs, transformers, and potentially other advanced architectures. We validate the effectiveness of our algorithm across various benchmark datasets and optimizers, demonstrating its broad applicability.

Published
2025

2. Score-Based Metropolis-Hastings Algorithms

Author

Aloui, Ahmed, Hasan, Ali, Dong, Juncheng, Wu, Zihao, and Tarokh, Vahid

Subjects
Computer Science - Machine Learning, Statistics - Computation
Abstract

In this paper, we introduce a new approach for integrating score-based models with the Metropolis-Hastings algorithm. While traditional score-based diffusion models excel in accurately learning the score function from data points, they lack an energy function, making the Metropolis-Hastings adjustment step inaccessible. Consequently, the unadjusted Langevin algorithm is often used for sampling using estimated score functions. The lack of an energy function then prevents the application of the Metropolis-adjusted Langevin algorithm and other Metropolis-Hastings methods, limiting the wealth of other algorithms developed that use acceptance functions. We address this limitation by introducing a new loss function based on the \emph{detailed balance condition}, allowing the estimation of the Metropolis-Hastings acceptance probabilities given a learned score function. We demonstrate the effectiveness of the proposed method for various scenarios, including sampling from heavy-tail distributions.

Published
2024

3. GemmAr: Enhancing LLMs Through Arabic Instruction-Tuning

Author

Chouikhi, Hasna, Aloui, Manel, Hammou, Cyrine Ben, Chaabane, Ghaith, Kchaou, Haithem, and Dhaouadi, Chehir

Subjects
Computer Science - Computation and Language, Computer Science - Artificial Intelligence
Abstract

Large language models (LLMs) have greatly impacted the natural language processing (NLP) field, particularly for the English language. These models have demonstrated capabilities in understanding and generating human-like text. The success of language models largely depends on the availability of high-quality instruction datasets, which consist of detailed task descriptions and corresponding responses that are essential for training the models to address a variety of prompts accurately. However, the availability and quality of these resources vary by language. While models perform well in English, they often need help with languages like Arabic, due to the lack of datasets for fine-tuning Arabic-specific tasks. To address this issue, we introduce InstAr-500k, a new Arabic instruction dataset created by generating and collecting content that covers several domains and instruction types. We assess this dataset by fine-tuning an open-source Gemma-7B model on several downstream tasks to improve its functionality. Based on multiple evaluations, our fine-tuned model achieves excellent performance on several Arabic NLP benchmarks. These outcomes emphasize the effectiveness of our dataset in elevating the capabilities of language models for Arabic. Our instruction dataset bridges the performance gap between English and Arabic language models by providing resources that amplify Arabic NLP development. Building on this foundation, we developed a model, GemmAr-7B-V1, specifically tuned to excel at a wide range of Arabic NLP tasks.

Published
2024

6. TunSLR-25: A New Static Tunisian Sign Language Recognition System

Author

Aloui, Sarra, Wang, Yu, Fan-fan, Zhou, Goos, Gerhard, Series Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Magnenat-Thalmann, Nadia, editor, Kim, Jinman, editor, Sheng, Bin, editor, Deng, Zhigang, editor, Thalmann, Daniel, editor, and Li, Ping, editor

Published
2025
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7. 101 Billion Arabic Words Dataset

Author

Aloui, Manel, Chouikhi, Hasna, Chaabane, Ghaith, Kchaou, Haithem, and Dhaouadi, Chehir

Subjects
Computer Science - Computation and Language
Abstract

In recent years, Large Language Models have revolutionized the field of natural language processing, showcasing an impressive rise predominantly in English-centric domains. These advancements have set a global benchmark, inspiring significant efforts toward developing Arabic LLMs capable of understanding and generating the Arabic language with remarkable accuracy. Despite these advancements, a critical challenge persists: the potential bias in Arabic LLMs, primarily attributed to their reliance on datasets comprising English data that has been translated into Arabic. This reliance not only compromises the authenticity of the generated content but also reflects a broader issue -the scarcity of original quality Arabic linguistic data. This study aims to address the data scarcity in the Arab world and to encourage the development of Arabic Language Models that are true to both the linguistic and nuances of the region. We undertook a large-scale data mining project, extracting a substantial volume of text from the Common Crawl WET files, specifically targeting Arabic content. The extracted data underwent a rigorous cleaning and deduplication process, using innovative techniques to ensure the integrity and uniqueness of the dataset. The result is the 101 Billion Arabic Words Dataset, the largest Arabic dataset available to date, which can significantly contribute to the development of authentic Arabic LLMs. This study not only highlights the potential for creating linguistically and culturally accurate Arabic LLMs but also sets a precedent for future research in enhancing the authenticity of Arabic language models.

Published
2024

14. Intraperitoneal rupture of a degenerated uterine fibroid during pregnancy: A case report and literature review

Author

Eya Azouz, MD, Haithem Aloui, MD, Hatem Frikha, MD, Hammami Rami, MD, Sana Minjli, MD, Ahlem Omri, MD, Mouna Baklouti, MD, Hassine Saber Abouda, MD, Mohamed Badis Channoufi, PhD, and Abir Karoui, PhD

Subjects
Leiomyomas, Hemoperitoneum, Degeneration, Pregnancy, Magnetic resonance imaging, Uterine fibroids, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

This case report describes a rare instance of intraperitoneal rupture in a redegenerated uterine fibroid during pregnancy. A 38-year-old woman at 38 weeks gestation, with a history of a single pregnancy, presented with mild pelvic pain persisting through late pregnancy. An emergency caesarean section was performed due to acute fetal distress during labor. Intraoperatively, a ruptured fundal subserosal fibroid with hemorrhagic necrosis was observed. Postoperative MRI confirmed the presence of a FIGO (International Federation of Gynecology and Obstetrics) type 6 fibroid, with evidence of red degeneration and a 4 cm defect. The patient underwent conservative management with favorable outcomes.

Published
2025
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15. Fœtal sacrococcygeal teratoma type I: A case report

Author

Haithem Aloui, MD, Eya Azouz, MD, Hatem Frikha, MD, Hammami Rami, MD, Douha Aoun, MD, Neila Ben Aba, MD, Ayeb Saad, MD, Yasmine Houas, MD, and Riadh jouini, MD

Subjects
Teratoma, Sacrococcygeal, Foetus, Medical physics. Medical radiology. Nuclear medicine, R895-920
Abstract

Sacrococcygeal teratoma (SCT) is a rare congenital tumor typically diagnosed in neonates, with management challenges arising from the size of the tumor and associated delivery complications. In this case, a 32-year-old gravida 5 para 5 woman with a history of three prior cesarean sections was diagnosed with a giant type I SCT at 30 weeks of gestation through prenatal ultrasound, confirmed by fetal MRI. At 34 weeks, an emergency cesarean section was performed due to acute fetal distress, resulting in a newborn with transient respiratory distress. Postnatal imaging classified the tumor as type I SCT, and it was successfully excised on the third day of life without complications. The patient recovered well and was discharged on day fifteen. This case highlights the importance of early prenatal diagnosis, surgical intervention, and multidisciplinary care in managing large SCTs.

Published
2025
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17. Random Linear Projections Loss for Hyperplane-Based Optimization in Neural Networks

Author

Venkatasubramanian, Shyam, Aloui, Ahmed, and Tarokh, Vahid

Subjects
Computer Science - Machine Learning
Abstract

Advancing loss function design is pivotal for optimizing neural network training and performance. This work introduces Random Linear Projections (RLP) loss, a novel approach that enhances training efficiency by leveraging geometric relationships within the data. Distinct from traditional loss functions that target minimizing pointwise errors, RLP loss operates by minimizing the distance between sets of hyperplanes connecting fixed-size subsets of feature-prediction pairs and feature-label pairs. Our empirical evaluations, conducted across benchmark datasets and synthetic examples, demonstrate that neural networks trained with RLP loss outperform those trained with traditional loss functions, achieving improved performance with fewer data samples, and exhibiting greater robustness to additive noise. We provide theoretical analysis supporting our empirical findings.

Published
2023

18. Counterfactual Data Augmentation with Contrastive Learning

Author

Aloui, Ahmed, Dong, Juncheng, Le, Cat P., and Tarokh, Vahid

Subjects
Computer Science - Machine Learning, Statistics - Methodology, Statistics - Machine Learning
Abstract

Statistical disparity between distinct treatment groups is one of the most significant challenges for estimating Conditional Average Treatment Effects (CATE). To address this, we introduce a model-agnostic data augmentation method that imputes the counterfactual outcomes for a selected subset of individuals. Specifically, we utilize contrastive learning to learn a representation space and a similarity measure such that in the learned representation space close individuals identified by the learned similarity measure have similar potential outcomes. This property ensures reliable imputation of counterfactual outcomes for the individuals with close neighbors from the alternative treatment group. By augmenting the original dataset with these reliable imputations, we can effectively reduce the discrepancy between different treatment groups, while inducing minimal imputation error. The augmented dataset is subsequently employed to train CATE estimation models. Theoretical analysis and experimental studies on synthetic and semi-synthetic benchmarks demonstrate that our method achieves significant improvements in both performance and robustness to overfitting across state-of-the-art models.

Published
2023

24. Asymptotic behavior and life-span estimates for the damped inhomogeneous nonlinear Schr\'odinger equation

Author

Aloui, Lassaad, Jbari, Sirine, and Tayachi, Slim

Subjects
Mathematics - Analysis of PDEs
Abstract

We are interested in the behavior of solutions to the damped inhomogeneous nonlinear Schr\"odinger equation $ i\partial_tu+\Delta u+\mu|x|^{-b}|u|^{\alpha}u+iau=0$, $\mu \in\mathbb{C} $, $b>0$, $a \in \mathbb{C}$ such that $\Re \textit{e}(a) \geq 0$, $\alpha>0$. We establish lower and upper bound estimates of the life-span. In particular for $a\geq 0$, we obtain explicit values $a_*,\; a^*$ such that if $aa^*,$ global existence holds. Also, we prove scattering results with precise decay rates for large damping. Some of the results are new even for $b=0.$

Published
2023

25. Methylene Blue degradation using plasma: A comparative study between the microwave plasma jet over water surface and the nanosecond pulsed discharge in gas bubbles in water

Author

Nadir Aloui and Ibtissem Belgacem

Subjects
nanosecond pulsed discharge, microwave plasma jet discharge, methylene blue, electrical conductivity, energy efficiency, Technology
Abstract

In this paper investigates the Methylene Blue degradation using two common reactors: the nanosecond pulsed discharge in oxygen bubbles and the Argon Microwave plasma jet above the treated solution. The aim of this study is to compare the evolution of the reactors' performance when the electrical conductivity increases. In deionized water, the Methylene Blue degradation and the energy efficiency in the nanosecond pulsed discharge configuration were significantly higher than the Argon microwave plasma jet. However, when the solution electrical conductivity is increased, the energy efficiency of the nanosecond pulsed discharge drops significantly by ~82%, whereas the performance of the microwave plasma jet reactors decreases by ~37% only for a greater variation of the electrical conductivity of the solution. Under these conditions, the efficiency of both reactors becomes comparable, suggesting the necessity to consider various parameters to compare the reactors' efficiency.

Published
2024

26. Comparative analysis of two double-plate fixation techniques for intercondylar fractures of the distal humerus

Author

Sami Bahroun, Hamdi Grami, Mohamed Samih Kacem, Ala Aloui, Zied Jlalia, and Mohamed Samir Daghfous

Subjects
Distal humerus fracture, Orthogonal plating method, Parallel plating method, Internal fixation, Medicine, Science
Abstract

Abstract This study was aimed to compare the clinical and radiographic outcomes of patients with intercondylar fractures of the humerus treated with orthogonal and parallel plating methods via precontoured plates. This was a retrospective comparative study conducted on 50 adult patients with intercondylar humerus fractures that were surgically treated over an eleven-year period. The patients were divided into two groups: Group A underwent internal fixation via parallel plating, whereas Group B received orthogonal plating. Clinical outcomes were evaluated via the Mayo Elbow Performance Score (MEPS), and the radiographic assessments included time to consolidation, pseudoarthrosis, malunion and hardware removal. Both groups presented similar demographic and preoperative characteristics. The functional outcomes assessed by the MEPS were not significantly different between the groups. Radiographically, comparable healing times and rates of complications, including pseudoarthrosis, malunion and hardware removal, were observed. The study findings suggest that both orthogonal and parallel plating methods yield comparable clinical and radiographic outcomes in the treatment of intercondylar humerus fractures. These results underscore the effectiveness of both techniques and emphasize the importance of further research to elucidate the optimal plating method for specific fracture patterns.

Published
2024
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27. Long-term viscoelastic behavior and evolution of the Schapery model for mirror epoxy

Author

Dardouri Mohsen, Fellah Ali, Gmir Fethi, and Aloui Abdessattar

Subjects
viscoelasticity, creep, relaxation, mirror epoxy, schapery model, Mechanical engineering and machinery, TJ1-1570
Abstract

Mirror epoxy, used in its pure form with a resin-to-hardener ratio of 100:50, is emerging as an innovative material widely used in modern flooring. Its appeal lies in its smooth, shiny surface, offering a unique and contemporary aesthetic. However, understanding its long-term viscoelastic behavior is essential to ensure the durability and performance of floor coverings under various conditions of use. This study examines the evolution of the Schapery model for mirror epoxy, focusing on its long-term viscoelastic behavior. Creep tests at constant loads and ambient temperature are carried out in order to numerically determine the static nonlinearity factors g and g 0 formulated in the Schapery model. To validate this model, other relaxation tests at constant deformations are carried out under the same conditions, which allowed us to determine the nonlinearity factors h and h 0 formulated in this model using the same method. A remarkable consistency between the variations in the experimental and numerical values of the model programmed on MATLAB allows us to conclude that the Schapery model describes the real behavior of the mirror epoxy in a satisfactory manner.

Published
2024
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31. Treatment Effects in Extreme Regimes

Author

Aloui, Ahmed, Hasan, Ali, Ng, Yuting, Pajic, Miroslav, and Tarokh, Vahid

Subjects
Statistics - Methodology, Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Understanding treatment effects in extreme regimes is important for characterizing risks associated with different interventions. This is hindered by the unavailability of counterfactual outcomes and the rarity and difficulty of collecting extreme data in practice. To address this issue, we propose a new framework based on extreme value theory for estimating treatment effects in extreme regimes. We quantify these effects using variations in tail decay rates of potential outcomes in the presence and absence of treatments. We establish algorithms for calculating these quantities and develop related theoretical results. We demonstrate the efficacy of our approach on various standard synthetic and semi-synthetic datasets.

Published
2023

32. Causal Mediation Analysis with Multi-dimensional and Indirectly Observed Mediators

Author

Jiang, Ziyang, Liu, Yiling, Klein, Michael H., Aloui, Ahmed, Ren, Yiman, Li, Keyu, Tarokh, Vahid, and Carlson, David

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Causal mediation analysis (CMA) is a powerful method to dissect the total effect of a treatment into direct and mediated effects within the potential outcome framework. This is important in many scientific applications to identify the underlying mechanisms of a treatment effect. However, in many scientific applications the mediator is unobserved, but there may exist related measurements. For example, we may want to identify how changes in brain activity or structure mediate an antidepressant's effect on behavior, but we may only have access to electrophysiological or imaging brain measurements. To date, most CMA methods assume that the mediator is one-dimensional and observable, which oversimplifies such real-world scenarios. To overcome this limitation, we introduce a CMA framework that can handle complex and indirectly observed mediators based on the identifiable variational autoencoder (iVAE) architecture. We prove that the true joint distribution over observed and latent variables is identifiable with the proposed method. Additionally, our framework captures a disentangled representation of the indirectly observed mediator and yields accurate estimation of the direct and mediated effects in synthetic and semi-synthetic experiments, providing evidence of its potential utility in real-world applications., Comment: 16 pages, 4 figures, 5 tables

Published
2023

33. Mode-Aware Continual Learning for Conditional Generative Adversarial Networks

Author

Le, Cat P., Dong, Juncheng, Aloui, Ahmed, and Tarokh, Vahid

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

The main challenge in continual learning for generative models is to effectively learn new target modes with limited samples while preserving previously learned ones. To this end, we introduce a new continual learning approach for conditional generative adversarial networks by leveraging a mode-affinity score specifically designed for generative modeling. First, the generator produces samples of existing modes for subsequent replay. The discriminator is then used to compute the mode similarity measure, which identifies a set of closest existing modes to the target. Subsequently, a label for the target mode is generated and given as a weighted average of the labels within this set. We extend the continual learning model by training it on the target data with the newly-generated label, while performing memory replay to mitigate the risk of catastrophic forgetting. Experimental results on benchmark datasets demonstrate the gains of our continual learning approach over the state-of-the-art methods, even when using fewer training samples.

Published
2023

34. Epidemiological investigation of depressive disorders in healthcare workers throughout 12 years in the governorate of Sousse, Tunisia

Author

Marwa Bouhoula, Amen Moussa, Asma Chouchane, Amal Ghenim, Rania Elghezal, Asma Gaddour, Narjes Belhadj, Sonia Kammoun, Asma Aloui, Imène Kacem, Maher Maoua, Houda Kalboussi, Olfa Elmaalel, Souheil Chatti, and Aicha Brahem

Subjects
COVID-19, Depressive disorder, Healthcare workers, Mental health, Occupational hazard, Public aspects of medicine, RA1-1270
Abstract

Background: Depressive disorders are a major global health challenge and are projected to become a leading cause of disability by 2030. Aims: Examine the epidemiological characteristics of depressive disorders among healthcare workers and identify key associated risk factors. Methods: A retrospective descriptive study was conducted at the Regional Health Directorate of Sousse, focusing on healthcare workers on long-term sick leave (LTSL) due to depressive disorders from 2010 to 2021. Data were collected from medical and administrative records and a questionnaire covering socio-professional and medical information. Diagnoses were based on DSM-5 criteria. Statistical analysis was conducted using SPSS version 26. Results: Among 650 healthcare workers studied, 12.8 % were diagnosed with depressive disorders. Of these, 13 % reported work-related conflicts, 3.5 % had exposure to severe infectious diseases before COVID-19, and 17 % had direct contact with COVID-19 patients. Additionally, 71.4 % of participants with depressive disorders presented with anxiety, and 50 % had moderate depression. Significant associations (p

Published
2025
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35. Enhancing Medical Education: Building a Comprehensive E-Learning Platform with CodeIgniter 4

Author

Meftah Zouai, Ahmed ALOUI, Houcine BELOUAAR, Ilyes Naidji, and Okba KAZAR

Subjects
Technology, Technology (General), T1-995, Science (General), Q1-390
Abstract

The emergence of the COVID-19 pandemic has presented unprecedented difficulties for medical education, forcing institutions worldwide to adjust quickly to ensure that learning continues despite the implementation of restrictive measures and social distancing procedures. This article explores creating and implementing a cutting-edge e-learning platform designed exclusively for medical education. Utilising the CodeIgniter 4 PHP framework for backend development and Bootstrap for frontend design, the platform provides a wide range of interactive quizzes, including Multiple Choice Questions (QCM), Single Choice Questions (QCU), and clinical cases (Cas Clinique’s). The platform's adaptable design enables medical students to easily access and engage in remote learning across different platforms, allowing them to continue their education without interruption. The main characteristics consist of instruments for analysing performance, allowing students to track their progress and personalise their study sessions, thus improving the effectiveness and adaptability of medical education. This article highlights the significant impact of e-learning in addressing the educational challenges caused by the COVID-19 pandemic and provides insights into the future of medical education. It achieves this by thoroughly examining the platform's architecture, features, and pedagogical implications.

Published
2025
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36. Assessing groundwater quality and suitability in Qatar: Strategic insights for sustainable water management and environmental protection

Author

Sarra Aloui, Adel Zghibi, Annamaria Mazzoni, Ahmad S. Abushaikha, and Adel Elomri

Subjects
Groundwater hydrogeochemistry, Groundwater salinization, Water quality index (WQI), Geographic information system (GIS), Arid environment, Qatar, Environmental sciences, GE1-350
Abstract

Groundwater resources in arid regions are crucial for water and food security and the maintenance of ecosystem services but face significant challenges from overexploitation and contamination, exacerbated by climate change. In Qatar, understanding the spatial variability of groundwater quality and its suitability for use remains limited. This study examines groundwater quality across the peninsula and evaluates its suitability for drinking and irrigation employing comprehensive Geographic Information System (GIS) techniques and water quality assessments. Twelve physicochemical parameters were analyzed to evaluate groundwater quality and determine the hydrogeochemical facies. Geospatial analysis was employed to visualize and quantify spatial correlations between groundwater salinity and geology, rainfall, distance to sea, land use, and abstraction and recharge activities. Suitability for use was assessed using the drinking water quality index (DWQI) and a novel integrated irrigation water quality index (IWQI), constructed upon seven standard indices. Results revealed predominantly high salinity in coastal and southern parts, suggesting extensive saltwater intrusion. Spatial correlations showed combined environmental and anthropogenic influences on groundwater salinity. Recharge efforts positively influenced groundwater quality, highlighting their effectiveness. The DWQI showed excellent to good drinking suitability over 1053.3 km2 (9.1%) of the study area. The IWQI indicated high to very high irrigation suitability over 3611 km2 (31.1%), concentrated in the north-central region. These findings highlight the critical importance of groundwater quality indicators for effective monitoring and underscore the need for integrated groundwater management strategies. This study offers practical guidance for local water resource managers, policymakers, and communities while also contributing to global climate adaptation strategies.

Published
2025
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37. New surgical technique for managing placenta accreta spectrum and pilot study of the 'CMNT PAS' studyAJOG Global Reports at a Glance

Author

Hassine Saber Abouda, MD, Haithem Aloui, MD, Eya Azouz, MD, Sofiene Ben Marzouk, MD, Hatem Frikha, MD, Rami Hammami, MD, Sana Minjli, MD, Rachid Hentati, MD, Mehdi Khila, MD, Badis Mohamed Chanoufi, MD, Abir Karoui, MD, and Maghrebi Hayen, MD

Subjects
conservative surgery, hysterectomy, massive hemorrhage, placenta accreta, Gynecology and obstetrics, RG1-991
Abstract

Introduction: The gold standard for treating the placenta accreta spectrum (PAS) is a cesarean hysterectomy, which harms fertility. Another conservative surgical approach allows the uterus to be preserved: one-step conservative surgery. We will compare these two approaches through the “CMNT PAS” study. Before this main study, we conducted a pilot study to determine the required sample size. Study Design: This pilot study, conducted over 31 months, included patients who underwent surgery for suspected PAS based on imaging findings. Participants were divided into the conservative surgery group (CSG: 6 patients) and the Caesarean Hysterectomy Group (control group [CG]: 6 patients). For the CSG, our team adapted the approach described in previous research by Palacios-Jaraquemada. Results: The primary objective of our study is to ascertain the appropriate sample size for our main investigation on the conservative surgical management of PAS. Concerning the primary outcome, the estimated amount of blood loss was lower in CSG compared to CG, although this difference was not statistically significant (1298.04±556 mL vs 891.051±348 mL, P=.159). The mean decrease in hemoglobin (Δ Hb) was 2.8±1.3251 g/dL in the CG group compared to 1.933±1.0614 g/dL in the CSG group (P=.240). The mean number of transfused red blood cell units was 3±3.2249 in the CG group and 1.5±1.64317 in the CSG group (P=.334). Conclusion: The estimated blood loss between the two groups is not statistically significant. The required sample size is 22 patients.

Published
2025
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38. Water homeostasis gene expression in the kidney of broilers divergently selected for water conversion ratio

Author

Kentu Lassiter, Loujain Aloui, Elizabeth S. Greene, Marciela Maqaeda, Travis Tabler, Sami Dridi, Robert F. Wideman, Jr., Sara Orlowski, and Walter G. Bottje

Subjects
Broilers, Genetic selection, Water conversion ratio, Kidney, Gene expression, Heat stress, Animal culture, SF1-1100
Abstract

Divergent selection of broilers for water conversion ratio has established and high-(HWE) and low- water efficient (LWE) broiler lines. Two 2 × 2 factorial experiments were conducted to assess the gene expression profile of systems involved in renal water homeostasis. In Exp. 1, male and female HWE and LWE broilers were individually phenotyped between 4 and 6 wks of age to determine growth performance and water conversion ratio (g water intake/g body weight gain). Kidney samples were obtained from 5 males and 5 females from each line. In Exp. 2, HWE and modern random bred (MRB) broilers were placed in 12 controlled-environmental chambers (2 floor pens/chamber, 6 chambers/line, 11 birds per pen, 132 birds/line) on day of hatch. The broilers were brooded at thermoneutral temperatures from 0 to 4 wks. From 4 to 7 wks, broilers were maintained at thermoneutral (TN, 25 °C) or exposed to cyclic heat stress (HS, 35 °C, 8h/day) conditions. Body weight, feed intake, and water intake were recorded. Kidney samples were collected, flash frozen in liquid nitrogen, and kept at -80 °C for gene expression analysis. Data were analyzed by Two-way ANOVA and means compared by Tukey's HSD multiple comparison test. Molecular analyses from Exp. 1 showed that the renal expression of arginine vasopressin (AVP), angiotensinogen (AGT), angiotensin II receptor type 1 and 2 (AT1/2), sodium-potassium ATPase subunit B1 (ATP1B1), and aquaporin 3 (AQP3) were upregulated in HWE compared to the LWE line. In contrast, mRNA expression of mesotocin receptor (MTR), AT1/2, AQP1/2, and occludin were significantly higher in females than in males. In Exp. 2, target genes were regulated in environment and/or line-dependent manner. The renal expression of heat shock proteins 70 and 90, AVP receptor 2 (AVPR2), AGT, renin, AT1/2, and AQP1was significantly upregulated in HS compared to TN birds, however AVPR2 expression was significantly higher in HWE compared to MRB birds. Together, the up-regulation of AVP, the renin-angiotensin system (RAS), and AQP in HWE, female, or under HS conditions suggests a better renal water reabsorption to support water use efficiency.

Published
2025
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39. Domain Adaptation via Rebalanced Sub-domain Alignment

Author

Liu, Yiling, Dong, Juncheng, Jiang, Ziyang, Aloui, Ahmed, Li, Keyu, Klein, Hunter, Tarokh, Vahid, and Carlson, David

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Unsupervised domain adaptation (UDA) is a technique used to transfer knowledge from a labeled source domain to a different but related unlabeled target domain. While many UDA methods have shown success in the past, they often assume that the source and target domains must have identical class label distributions, which can limit their effectiveness in real-world scenarios. To address this limitation, we propose a novel generalization bound that reweights source classification error by aligning source and target sub-domains. We prove that our proposed generalization bound is at least as strong as existing bounds under realistic assumptions, and we empirically show that it is much stronger on real-world data. We then propose an algorithm to minimize this novel generalization bound. We demonstrate by numerical experiments that this approach improves performance in shifted class distribution scenarios compared to state-of-the-art methods., Comment: 20 pages, 6 figures, 4 tables

Published
2023

43. Transfer Learning for Individual Treatment Effect Estimation

Author

Aloui, Ahmed, Dong, Juncheng, Le, Cat P., and Tarokh, Vahid

Subjects
Computer Science - Machine Learning, Statistics - Methodology, Statistics - Machine Learning
Abstract

This work considers the problem of transferring causal knowledge between tasks for Individual Treatment Effect (ITE) estimation. To this end, we theoretically assess the feasibility of transferring ITE knowledge and present a practical framework for efficient transfer. A lower bound is introduced on the ITE error of the target task to demonstrate that ITE knowledge transfer is challenging due to the absence of counterfactual information. Nevertheless, we establish generalization upper bounds on the counterfactual loss and ITE error of the target task, demonstrating the feasibility of ITE knowledge transfer. Subsequently, we introduce a framework with a new Causal Inference Task Affinity (CITA) measure for ITE knowledge transfer. Specifically, we use CITA to find the closest source task to the target task and utilize it for ITE knowledge transfer. Empirical studies are provided, demonstrating the efficacy of the proposed method. We observe that ITE knowledge transfer can significantly (up to 95%) reduce the amount of data required for ITE estimation.

Published
2022

45. Rare variants in ANO1, encoding a calcium-activated chloride channel, predispose to moyamoya disease.

Author

Pinard, Amélie, Ye, Wenlei, Fraser, Stuart M, Rosenfeld, Jill A, Pichurin, Pavel, Hickey, Scott E, Guo, Dongchuan, Cecchi, Alana C, Boerio, Maura L, Guey, Stéphanie, Aloui, Chaker, Lee, Kwanghyuk, Kraemer, Markus, Alyemni, Saleh Omar, Bamshad, Michael J, Nickerson, Deborah A, Tournier-Lasserve, Elisabeth, Haider, Shozeb, Jin, Sheng Chih, Smith, Edward R, Kahle, Kristopher T, Jan, Lily Yeh, He, Mu, and Milewicz, Dianna M

Subjects
Genetics, Clinical Research, Neurosciences, 2.1 Biological and endogenous factors, Aetiology, stroke genetics, smooth muscle cells, genetic heterogeneity, pathogenesis, genotype-phenotype, University of Washington Center for Mendelian Genomics, Medical and Health Sciences, Psychology and Cognitive Sciences, Neurology & Neurosurgery
Abstract

Moyamoya disease, a cerebrovascular disease leading to strokes in children and young adults, is characterized by progressive occlusion of the distal internal carotid arteries and the formation of collateral vessels. Altered genes play a prominent role in the etiology of moyamoya disease, but a causative gene is not identified in the majority of cases. Exome sequencing data from 151 individuals from 84 unsolved families were analyzed to identify further genes for moyamoya disease, then candidate genes assessed in additional cases (150 probands). Two families had the same rare variant in ANO1, which encodes a calcium-activated chloride channel, anoctamin-1. Haplotype analyses found the families were related, and ANO1 p.Met658Val segregated with moyamoya disease in the family with an LOD score of 3.3. Six additional ANO1 rare variants were identified in moyamoya disease families. The ANO1 rare variants were assessed using patch-clamp recordings, and the majority of variants, including ANO1 p.Met658Val, displayed increased sensitivity to intracellular Ca2+. Patients harboring these gain-of-function ANO1 variants had classic features of MMD, but also had aneurysm, stenosis, and/or occlusion in the posterior circulation. Our studies support that ANO1 gain-of-function pathogenic variants predispose to moyamoya disease and are associated with unique involvement of the posterior circulation.

Published
2023

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