Working principles for training neural networks with highly incomplete dataset: vanilla (upper panel) vs GapNet (lower panel) (Image by Y.-W. Chang.)
Training of neural network with incomplete medical datasets
Yu-Wei Chang
Neural network training and validation rely on the availability of large high-quality datasets. However, in many cases, only incomplete datasets are available, particularly in health care applications, where each patient typically undergoes different clinical procedures or can drop out of a study. Here, we introduce GapNet, an alternative deep-learning training approach that can use highly incomplete datasets without overfitting or introducing artefacts. Using two highly incomplete real-world medical datasets, we show that GapNet improves the identification of patients with underlying Alzheimer’s disease pathology and of patients at risk of hospitalization due to Covid-19. Compared to commonly used imputation methods, this improvement suggests that GapNet can become a general tool to handle incomplete medical datasets.
Basudev Roy.Study of out-of-plane rotations in optical tweezers and applications in soft matter and biological systems
Basudev Roy
Indian Institute of Technology Madras, India Date: 24 May 2023 Time: 12:30 Place: Nexus
Abstract:
A rigid body can have 6 degrees of freedom, namely the three translational degrees of freedom and the three rotational degrees of freedom. Of these, the translational degrees have been well explored in optical tweezers community. However, only the in-plane rotational degree of freedom has been explored. We call this in-plane degree of rotational freedom, the yaw motion in the nomenclature of the airlines. The pitch and roll degrees are only beginning to be explored recently.
In this talk, I will show you 4 ways of generating pitch rotation using the optical tweezers. I will also show you one way of detection of pitch rotation at high resolution using birefringent particles. Further, I will discuss some applications of this pitch rotation in soft matter systems and biology. I will also show you a few other projects that we are working on inside the lab.
Bio:
Basudev Roy got his MSc from Indian Institute of Technology Kharagpur and MS from the University of Maryland, College Park. He got his PhD from Indian Institute of Science Education and Research, Kolkata in 2015. He was an Alexander von Humboldt fellow at the University of Tuebingen, Germany for his postdoctoral research from 2015 to 2017. He joined Indian Institute of Technology Madras, India since 2017 where he is now an Associate Professor.
Planktons imaged under a holographic microscope. (Illustration by J. Heuschele.)Deep learning in plankton ecology Harshith Bachimanchi
Presentation at Biology department, Lund University
12 May 2023, 13:00 CEST
In this mini-symposium organised by Dr. Erik Selander at Lund University, I have spoken about our recent work with marine microplankton, where we have combined holographic microscopy with deep learning to measure the ‘dry’ mass and 3D swimming dynamics of different species of planktons. The article related to this presentation can be found at the following here: Microplankton life histories revealed by holographic microscopy and deep learning.
The presentation was followed by discussions with Prof. Karin Rengefors group at Lund university on the topic of application of AI based methods for various kinds of studies in phytoplankton ecology and evolution.
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy.
Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions.
To overcome this issue, we have introduced a software, currently at version DeepTrack 2.1, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.1 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.
Coffee Ring exposition at science festival Gothenburg. (Photo by C. Beck Adiels.)Our recent work on “coffee rings” was presented at the Gothenburg Science Festival, which, with about 100 000 visitors each year, is one of the largest popular science events in Europe.
On Wednesday 19th April 2023, Marcel Rey, Laura Natali, Daniela Pérez Guerrero and Caroline Adiels set up a stand in Nordstan.
In this guided exhibition, visitors were able to observe the flow inside a drying droplet using optical microscopes. They learned how the suspended solid coffee particles flow from the inside towards the edge of the coffee droplet, where they accumulate and cause the characteristic coffee ring pattern after drying.
Nowadays, the coffee ring effect presents still a major challenge in ink-jet printing or coating technologies, where a uniform drying is required. We thus shared our recently developed strategies to overcome the coffee ring effect and obtain a uniform deposit of drying droplets.
And finally, visitors were also offered a freshly-brewed espresso to not only drink but also to experience the “coffee ring effect” hands on.
Spatial maps depicting the strongest connections from the medial parietal cortex to other cortical and subcortical areas in the PREVENT-AD cohort. (Reproduced from the publication.)Functional gradients of the medial parietal cortex in a healthy cohort with family history of sporadic Alzheimer’s disease
Dániel Veréb, Mite Mijalkov, Yu-Wei Chang, Anna Canal-Garcia, Emiliano Gomez-Ruis, Anne Maass, Sylvia Villeneuve, Giovanni Volpe Joana B. Pereira
Alzheimer’s Research & Therapy 15, 82 (2023)
doi: 10.1186/s13195-023-01228-3
Background
The medial parietal cortex is an early site of pathological protein deposition in Alzheimer’s disease (AD). Previous studies have identified different subregions within this area; however, these subregions are often heterogeneous and disregard individual differences or subtle pathological alterations in the underlying functional architecture. To address this limitation, here we measured the continuous connectivity gradients of the medial parietal cortex and assessed their relationship with cerebrospinal fluid (CSF) biomarkers, ApoE ε4 carriership and memory in asymptomatic individuals at risk to develop AD.
Methods
Two hundred sixty-three cognitively normal participants with a family history of sporadic AD who underwent resting-state and task-based functional MRI using encoding and retrieval tasks were included from the PREVENT-AD cohort. A novel method for characterizing spatially continuous patterns of functional connectivity was applied to estimate functional gradients in the medial parietal cortex during the resting-state and task-based conditions. This resulted in a set of nine parameters that described the appearance of the gradient across different spatial directions. We performed correlation analyses to assess whether these parameters were associated with CSF biomarkers of phosphorylated tau181 (p-tau), total tau (t-tau), and amyloid-ß1-42 (Aß). Then, we compared the spatial parameters between ApoE ε4 carriers and noncarriers, and evaluated the relationship between these parameters and memory.
Results
Alterations involving the superior part of the medial parietal cortex, which was connected to regions of the default mode network, were associated with higher p-tau, t-tau levels as well as lower Aß/p-tau levels during the resting-state condition (p < 0.01). Similar alterations were found in ApoE ε4 carriers compared to non-carriers (p < 0.003). In contrast, lower immediate memory scores were associated with changes in the middle part of the medial parietal cortex, which was connected to inferior temporal and posterior parietal regions, during the encoding task (p = 0.001). No results were found when using conventional connectivity measures.
Conclusions
Functional alterations in the medial parietal gradients are associated with CSF AD biomarkers, ApoE e4 carriership, and lower memory in an asymptomatic cohort with a family history of sporadic AD, suggesting that functional gradients are sensitive to subtle changes associated with early AD stages.
Actuation of active matter by different properties of light. (Image by M. Rey.)Light, Matter, Action: Shining light on active matter
Marcel Rey, Giovanni Volpe, Giorgio Volpe
ACS Photonics, 10, 1188–1201 (2023)
arXiv: 2301.13034
doi: 10.1021/acsphotonics.3c00140
Light carries energy and momentum. It can therefore alter the motion of objects from atomic to astronomical scales. Being widely available, readily controllable and broadly biocompatible, light is also an ideal tool to propel microscopic particles, drive them out of thermodynamic equilibrium and make them active. Thus, light-driven particles have become a recent focus of research in the field of soft active matter. In this perspective, we discuss recent advances in the control of soft active matter with light, which has mainly been achieved using light intensity. We also highlight some first attempts to utilize light’s additional degrees of freedom, such as its wavelength, polarization, and momentum. We then argue that fully exploiting light with all of its properties will play a critical role to increase the level of control over the actuation of active matter as well as the flow of light itself through it. This enabling step will advance the design of soft active matter systems, their functionalities and their transfer towards technological applications.
