Publications

Assessing the Impact of Aircraft Arrival on Ambient Ultrafine Particle Number Concentrations in Near-Airport Communities in Boston, Massachusetts
Chloe S. Chung
Chloe S. Kim
Kevin James Lane
K. Lane
Flannery Black-Ingersoll
Eric D. Kolaczyk
Claire Schollaert
Sijia Li
Matthew C. Simon
Jonathan I. Levy
Jerrold H. Levy
2023-02-28
Environmental Research (published)
doi.org
Automated Detection of Ileocecal Valve, Appendiceal Orifice, and Polyp During Colonoscopy Using a Deep Learning Model
M. Taghiakbari
S. Hamidi Ghalehjegh
E. Jehanno
T. Berthier
L. di Jorio
A. N. Barkun
E. Deslandres
S. Bouchard
S. Sidani
Y. Bengio
D. von Renteln
Identification and photo-documentation of the ileocecal valve (ICV) and appendiceal orifice (AO) confirm completeness of colonoscopy examina… (see more)tions. We hypothesized that an artificial intelligence (AI)-empowered solution could help us automatically differentiate anatomical landmarks such as AO and ICV from polyps and normal colon mucosa. We aimed to develop and test a deep convolutional neural network (DCNN) model that can automatically identify ICV and AO, and differentiate these landmarks from normal mucosa and colorectal polyps. We prospectively collected annotated full-length colonoscopy videos of 318 patients undergoing outpatient colonoscopies. We created three non-overlapping training, validation, and test datasets with 25,444 unaltered frames extracted from the colonoscopy videos showing four landmarks/image classes (AO, ICV, normal mucosa, and polyps). For each landmark, we extracted an average of 30 frames for each time of its appearance. All the extracted frames were reviewed and annotated by a team of three clinicians. Using a quality assessment tool, the clinicians examined a total of 86,754 frames (7982 AO, 8374 ICV, 32,971 polyps, and 37,427 normal mucosa) and verified whether or not the frame contained one unique landmark. For this research, all frames were extracted from the white-light colonoscopies, and all narrow-band imaging frames were excluded. A DCNN classification model was developed, validated, and tested in separate datasets of images. The primary outcome was the proportion of patients in whom the AI model could identify both ICV and AO, and differentiate them from polyps and normal mucosa, with an accuracy of detecting both AO and ICV above a threshold of 40% (representing a value in which reliable identification of the landmarks can be assumed without increasing false-positive alerts). We trained a DCNN AI model on 21,503 unaltered frames extracted from the recorded colonoscopy videos of 272 patients, and validated and tested the model on 1,924 (25 patients) and 2,017 (21 patients) unaltered frames, respectively. We applied a transfer learning technique to fine-tune the model parameters to the endoscopic images using a cross-entropy loss function and back-propagation algorithm. After training and validation, the DCNN model could identify both AO and ICV in 18 out of 21 patients (85.71%), if accuracies were above the threshold of 40%. The accuracy of the model for differentiating AO from normal mucosa, and ICV from normal mucosa were 86.37% (95% CI 84.06% to 88.45%), and 86.44% (95% CI 84.06% to 88.59%), respectively. Furthermore, the accuracy of the model for differentiating polyps from normal mucosa was 88.57% (95% CI 86.60% to 90.33%). The model can reliably distinguish these anatomical landmarks from normal mucosa and colorectal polyps. It can be implemented into automated colonoscopy report generation, photo-documentation, and quality auditing solutions to improve colonoscopy reporting quality. Other MEDTEQ M. Taghiakbari: None Declared, S. Hamidi Ghalehjegh Employee of: Imagia Canexia Health Inc. , E. Jehanno Employee of: Imagia Canexia Health Inc. , T. Berthier Employee of: Imagia Canexia Health Inc. , L. di Jorio Employee of: Imagia Canexia Health Inc. , A. N. Barkun Grant / Research support from: co-awardee in funded research projects with Imagia Canexia Health Inc., Consultant of: Medtronic Inc. and A.I. VALI Inc, E. Deslandres: None Declared, S. Bouchard: None Declared, S. Sidani: None Declared, Y. Bengio: None Declared, D. von Renteln Grant / Research support from: ERBE, Ventage, Pendopharm, and Pentax, Consultant of: Boston Scientific and Pendopharm
2023-02-28
Journal of the Canadian Association of Gastroenterology (published)
doi.org
A Convex Reformulation and an Outer Approximation for a Large Class of Binary Quadratic Programs
Borzou Rostami
Fausto Errico
Andrea Lodi
2023-02-28
Operations Research (published)
doi.org
Design and Implementation of Smooth Renewable Power in Cloud Data Centers
Xinxin Liu
Yu Hua
Xue Liu
Ling Yang
Yuanyuan Sun
The renewable power has been widely used in modern cloud data centers, which also produce large electricity bills and the negative impacts o… (see more)n environments. However, frequent fluctuation and intermittency of renewable power often cause the challenges in terms of the stability of both electricity grid and data centers, as well as decreasing the utilization of renewable power. Existing schemes fail to alleviate the renewable power fluctuation, which is caused by the essential properties of renewable power. In order to address this problem, we propose an efficient and easy-to-use smooth renewable power-aware scheme, called Smoother, which consists of Flexible Smoothing (FS) and Active Delay (AD). First, in order to smooth the fluctuation of renewable power, FS carries out the optimized charge/discharge operation via computing the minimum variance of the renewable power that is supplied to data centers per interval. Second, AD improves the utilization of renewable power via actively adjusting the execution time of deferrable workloads. Extensive experimental results via examining the traces of real-world data centers demonstrate that Smoother significantly reduces the negative impact of renewable power fluctuations on data centers and improves the utilization of renewable power by 250.88 percent on average. We have released the source codes for public use.
2023-02-28
IEEE Transactions on Cloud Computing (published)
doi.org
Dynamic Shimming in the Cervical Spinal Cord for Multi-Echo Gradient-Echo Imaging at 3 T
E. Alonso-Ortiz
D. Papp
A. D’Astous
J. Cohen-Adad
Obtaining high quality images of the spinal cord with MRI is difficult, partly due to the fact that the spinal cord is surrounded by a numbe… (see more)r of structures that have differing magnetic susceptibility. This causes inhomogeneities in the magnetic field, which in turn lead to image artifacts. In order to address this issue, linear compensation gradients can be employed. The latter can be generated using an MRI scanner's first order gradient coils and adjusted on a per-slice basis, in order to correct for through-plane ("z") magnetic field gradients. This approach is referred to as z-shimming. The aim of this study is two-fold. The first aim was to replicate aspects of a previous study wherein z-shimming was found to improve image quality in T2*-weighted echo-planar imaging. Our second aim was to improve upon the z-shimming approach by including in-plane compensation gradients and adjusting the compensation gradients during the image acquisition process so that they take into account respiration-induced magnetic field variations. We refer to this novel approach as realtime dynamic shimming. Measurements performed in a group of 12 healthy volunteers at 3 T show improved signal homogeneity along the spinal cord when using z-shimming. Signal homogeneity may be further improved by including realtime compensation for respiration-induced field gradients and by also doing this for gradients along the in-plane axes.
2023-02-28
Neuroimage. Reports (published)
doi.org
arxiv.org
Effects of incoming particle energy and cluster size on the G-value of hydrated electrons.
Alaina Bui
H. Bekerat
Lilian Childress
Jack C Sankey
Jan Seuntjens
S. Enger
2023-02-28
Physica Medica (published)
doi.org
An Extended State Space Model for Aggregation of Large-Scale EVs Considering Fast Charging
Sina Kiani
Keyhan Sheshyekani
Hanane Dagdougui
This article presents an extended state space model for aggregation of large-scale electric vehicles (EVs) for frequency regulation and peak… (see more) load shaving in power systems. The proposed model systematically deals with the fast charging of EVs as an effective solution for immediate charging requirements. Furthermore, the proposed extended state space model increases the flexibility of the EV aggregator (EVA) by enabling the EVs to participate in ancillary services with both regular and fast charging/discharging rates. This will help the EVA to provide a prompt and efficient response to severe generation-consumption imbalances. A probabilistic control approach is developed which reduces the communication burden of the EVA. Furthermore, the uncertainties related to EV users' behavior are modeled in real-time. The simulations are conducted for a typical power system including a large population of EVs, a conventional generator (CG), and a wind generation system. It is shown that the proposed aggregation model can accurately describe the aggregated behavior of a large population of EVs enabling them to efficiently participate in frequency regulation and peak load shaving services. Finally, the performance of EVA is evaluated for different driving behaviors and state of charge (SOC) levels of the EV population.
2023-02-28
IEEE Transactions on Transportation Electrification (published)
doi.org
Memory-Efficient FPGA Implementation of Stochastic Simulated Annealing
Duckgyu Shin
Naoya Onizawa
Warren J. Gross
Takahiro Hanyu
Simulated annealing (SA) is a well-known algorithm for solving combinatorial optimization problems. However, the computation time of SA incr… (see more)eases rapidly, as the size of the problem grows. Recently, a stochastic simulated annealing (SSA) algorithm that converges faster than conventional SA has been reported. In this paper, we present a hardware-aware SSA (HA-SSA) algorithm for memory-efficient FPGA implementations. HA-SSA can reduce the memory usage of storing intermediate results while maintaining the computing speed of SSA. For evaluation purposes, the proposed algorithm is compared with the conventional SSA and SA approaches on maximum cut combinatorial optimization problems. HA-SSA achieves a convergence speed that is up to 114-times faster than that of the conventional SA algorithm depending on the maximum cut problem selected from the G-set which is a dataset of the maximum cut problems. HA-SSA is implemented on a field-programmable gate array (FPGA) (Xilinx Kintex-7), and it achieves up to 6-times the memory efficiency of conventional SSA while maintaining high solution quality for optimization problems.
2023-02-28
IEEE Journal on Emerging and Selected Topics in Circuits and Systems (published)
doi.org
arxiv.org
Organizing principles of astrocytic nanoarchitecture in the mouse cerebral cortex
Christopher K. Salmon
Tabish A. Syed
J. Benjamin Kacerovsky
Nensi Alivodej
Alexandra L. Schober
Tyler F.W. Sloan
Michael T. Pratte
Michael P. Rosen
Miranda Green
Adario Chirgwin-Dasgupta
Shaurya Mehta
Affan Jilani
Yanan Wang
Hojatollah Vali
Craig A. Mandato
Kaleem Siddiqi
Keith K. Murai
Using high-resolution serial electron microscopy datasets and computer vision, this study provides a systematic analysis of astrocytic nanoa… (see more)rchitecture from multiple samples of layer 2/3 of adult mouse neocortex, and presents quantitative evidence that astrocytes organize their morphology into purposeful, classifiable assemblies with unique structural and subcellular organelle adaptations related to their physiological functions.
2023-02-28
Current Biology (published)
doi.org
Predicting histopathology markers of endometrial carcinoma with a quantitative image analysis approach based on spherical harmonics in multiparametric MRI
Thierry L. Lefebvre
Ozan Ciga
Sahir Rai Bhatnagar
Yoshiko Ueno
Sameh Saif
Eric Winter-Reinhold
Anthony Dohan
Philippe Soyer
Reza Forghani
Kaleem Siddiqi
Jan Seuntjens
Caroline Reinhold
Peter Savadjiev
2023-02-28
Diagnostic and Interventional Imaging (published)
doi.org
A Probabilistic Framework for Mutation Testing in Deep Neural Networks
Florian Tambon
Foutse Khomh
Giuliano Antoniol
2023-02-28
Information and Software Technology (published)
doi.org
arxiv.org
Processing of social and monetary rewards in autism spectrum disorders
Sarah Baumeister
Carolin Moessnang
Nico Bast
Sarah Hohmann
Pascal Aggensteiner
Anna Kaiser
Julian Tillmann
David Goyard
Tony Charman
Sara Ambrosino
Simon Baron-Cohen
Christian Beckmann
Sven Bölte
Thomas Bourgeron
Annika Rausch
Daisy Crawley
Flavio Dell’Acqua
Guillaume Dumas
Sarah Durston
Christine Ecker … (see 21 more)
Dorothea L. Floris
Vincent Frouin
Hannah Hayward
Rosemary Holt
Mark Johnson
Emily J. H. Jones
Meng-Chuan Lai
Michael V. Lombardo
Luke Mason
Beth Oakley
Marianne Oldehinkel
Antonio Persico
Antonia San José Cáceres
Thomas Wolfers
Eva Loth
Declan Murphy
Jan K. Buitelaar
Heike Tost
Andreas Meyer-Lindenberg
Tobias Banaschewski
Daniel Brandeis
Background Reward processing has been proposed to underpin the atypical social feature of autism spectrum disorder (ASD). However, previous … (see more)neuroimaging studies have yielded inconsistent results regarding the specificity of atypicalities for social reward processing in ASD. Aims Utilising a large sample, we aimed to assess reward processing in response to reward type (social, monetary) and reward phase (anticipation, delivery) in ASD. Method Functional magnetic resonance imaging during social and monetary reward anticipation and delivery was performed in 212 individuals with ASD (7.6–30.6 years of age) and 181 typically developing participants (7.6–30.8 years of age). Results Across social and monetary reward anticipation, whole-brain analyses showed hypoactivation of the right ventral striatum in participants with ASD compared with typically developing participants. Further, region of interest analysis across both reward types yielded ASD-related hypoactivation in both the left and right ventral striatum. Across delivery of social and monetary reward, hyperactivation of the ventral striatum in individuals with ASD did not survive correction for multiple comparisons. Dimensional analyses of autism and attention-deficit hyperactivity disorder (ADHD) scores were not significant. In categorical analyses, post hoc comparisons showed that ASD effects were most pronounced in participants with ASD without co-occurring ADHD. Conclusions Our results do not support current theories linking atypical social interaction in ASD to specific alterations in social reward processing. Instead, they point towards a generalised hypoactivity of ventral striatum in ASD during anticipation of both social and monetary rewards. We suggest this indicates attenuated reward seeking in ASD independent of social content and that elevated ADHD symptoms may attenuate altered reward seeking in ASD.
2023-02-28
The British Journal of Psychiatry (published)
doi.org