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The deployment of microneedles in biological fluid sampling and drug delivery is an emerging field in biotechnology, which contributes greatly to minimally-invasive methods in medicine. Prior studies on microneedles proposed designs based on the optimization of physical parameters through trial-and-error method. While these methods showed adequate results, it is possible to enhance the performance of a microneedle using a large dataset of parameters and their respective performance using advanced data analysis methods. Machine Learning (ML) offers the ability to mimic human learning behavior to expedite decision-making processes in biotechnology. In this study, the finite element analysis and ML models are combined to determine the optimal physical parameters for a microneedle design to maximize the amount of collected biological fluid. The fluid behavior in a microneedle patch is modeled using COMSOL Multiphysics (R), and the model is simulated with a set of initial physical and geometrical parameters in MATLAB (R) using LiveLink (TM). The mathematical model is used as the input to MATLAB's Bayesian Optimization function (bayesopt) and optimized for the maximum volumetric flow rate with pre-defined number of iterations. Within the parameter bounds, maximum volumetric flow rate is determined to be 21.16 mL min-1, which is 60% higher with respect to a system, where geometrical parameters are chosen randomly on average. This study introduces an online method for designing microneedles, where user can define the upper and lower bounds of the parameters to obtain an optimal design. The deployment of microneedles in biological fluid sampling and drug delivery is an emerging field in biotechnology, which contributes greatly to minimally-invasive methods in medicine., Scientific and Technological Research Council of Turkey (TÜBİTAK); TÜBİTAK 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Marie Sk?odowska-Curie Individual Fellowship; Alexander von Humboldt Research Fellowship for Experienced Researchers, and Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership award for financial support of this research. This work was partially supported by Science Academy's Young Scientist Awards Program (BAGEP), Outstanding Young Scientists Awards (GEBİP), and Bilim Kahramanlari Dernegi The Young Scientist Award.

The science of microrobots is accelerating towards the creation of new functionalities for biomedical applications such as targeted delivery of agents, surgical procedures, tracking and imaging, and sensing. Using magnetic properties to control the motion of microrobots for these applications is emerging. Here, 3D printing methods are introduced for the fabrication of microrobots and their future perspectives are discussed to elucidate the path for enabling their clinical translation., Scientific and Technological Research Council of Turkey (TÜBİTAK); 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Horizon 2020; Marie Skłodowska-Curie Individual Fellowship; Alexander von Humboldt Research Fellowship for Experienced Researchers, and Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership Award (120N019) for financial support of this research. This work was partially supported by Science Academy’s Young Scientist Awards Program (BAGEP), Outstanding Young Scientists Awards (GEBİP), and Bilim Kahramanlari Dernegi The Young Scientist Award. This study was conducted using the service and infrastructure of Koç University Translational Medicine Research Center (KUTTAM).

Fifteen percent of couples of reproductive age suffer frominfertility globally and the burden of infertility disproportionately impacts residents of developing countries. Assisted reproductive technologies (ARTs), including in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI), have been successful in overcoming various reasons for infertility including borderline and severe male factor infertility which consists of 20%-30% of all infertile cases. Approximately half of male infertility cases stem from suboptimal sperm parameters. Therefore, healthy/normal sperm enrichment and sorting remains crucial in advancing reproductive medicine. Microfluidic technologies have emerged as promising tools to develop in- home rapid fertility tests and point-of-care (POC) diagnostic tools. Here, we review advancements in fabrication methods for paper-based-microfluidic devices and their emerging fertility testing applications assessing sperm concentration, sperm motility, sperm DNA analysis, and other sperm functionalities, and provide a glimpse into future directions for paper-based fertility microfluidic systems., Scientific and Technological Research Council of Turkey (TÜBİTAK); 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship; Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership Award; Alexander von Humboldt Research Fellowship for Experienced Researchers; Science Academy Young Scientist Awards Program (BAGEP); Outstanding Young Scientists Awards (GEBİP); Bilim Kahramanlari Dernegi Young Scientist Award

Humans are able to perceive, understand and reason about causal events. Developing models with similar physical and causal understanding capabilities is a long-standing goal of artificial intelligence. As a step towards this direction, we introduce CRAFT1, a new video question answering dataset that requires causal reasoning about physical forces and object interactions. It contains 58K video and question pairs that are generated from 10K videos from 20 different virtual environments, containing various objects in motion that interact with each other and the scene. Two question categories in CRAFT include previously studied descriptive and counterfactual questions. Additionally, inspired by the Force Dynamics Theory in cognitive linguistics, we introduce a new causal question category that involves understanding the causal interactions between objects through notions like cause, enable, and prevent. Our results show that even though the questions in CRAFT are easy for humans, the tested baseline models, including existing state-of-the-art methods, do not yet deal with the challenges posed in our benchmark., CRAFT was supported in part by GEBIP 2018 Award of the Turkish Academy of Sciences to E. Erdem and T. Goksun, BAGEP 2021 Award of the Science Academy to A. Erdem, and AI Fellowship to Ilker Kesen provided by the KUIS AI Center.

Microrobots have attracted the attention of scientists owing to their unique features to accomplish tasks in hard-to-reach sites in the human body. Microrobots can be precisely actuated and maneuvered individually or in a swarm for cargo delivery, sampling, surgery, and imaging applications. In addition, microrobots have found applications in the environmental sector (e.g., water treatment). Besides, recent advancements of three-dimensional (3D) printers have enabled the high-resolution fabrication of microrobots with a faster design-production turnaround time for users with limited micromanufacturing skills. Here, the latest end applications of 3D printed microrobots are reviewed (ranging from environmental to biomedical applications) along with a brief discussion over the feasible actuation methods (e.g., on- and off-board), and practical 3D printing technologies for microrobot fabrication. In addition, as a future perspective, we discussed the potential advantages of integration of microrobots with smart materials, and conceivable benefits of implementation of artificial intelligence (AI), as well as physical intelligence (PI). Moreover, in order to facilitate bench-to-bedside translation of microrobots, current challenges impeding clinical translation of microrobots are elaborated, including entry obstacles (e.g., immune system attacks) and cumbersome standard test procedures to ensure biocompatibility., Scientific and Technological Research Council of Turkey (TÜBİTAK); 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship; Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership Award; Alexander von Humboldt Research Fellowship for Experienced Researchers; Science Academy Young Scientist Awards Program (BAGEP); Outstanding Young Scientists Awards (GEBİP); Bilim Kahramanlari Dernegi Young Scientist Award

Having sufficient resources for language X lifts it from the under-resourced languages class, but not necessarily from the under-researched class. In this paper, we address the problem of the absence of organized benchmarks in the Turkish language. We demonstrate that languages such as Turkish are left behind the state-of-the-art in NLP applications. As a solution, we present MUKAYESE, a set of NLP benchmarks for the Turkish language that contains several NLP tasks. We work on one or more datasets for each benchmark and present two or more baselines. Moreover, we present four new bench-marking datasets in Turkish for language modeling, sentence segmentation, and spell checking., KUIS AI Center Fellowship

Although speech driven facial animation has been studied extensively in the literature, works focusing on the affective content of the speech are limited. This is mostly due to the scarcity of affective audio-visual data. In this article, we improve the affective facial animation using domain adaptation by partially reducing the data scarcity. We first define a domain adaptation to map affective and neutral speech representations to a common latent space in which cross-domain bias is smaller. Then the domain adaptation is used to augment affective representations for each emotion category, including angry, disgust, fear, happy, sad, surprise, and neutral, so that we can better train emotion-dependent deep audio-to-visual (A2V) mapping models. Based on the emotion-dependent deep A2V models, the proposed affective facial synthesis system is realized in two stages: first, speech emotion recognition extracts soft emotion category likelihoods for the utterances; then a soft fusion of the emotion-dependent A2V mapping outputs form the affective facial synthesis. Experimental evaluations are performed on the SAVEE audio-visual dataset. The proposed models are assessed with objective and subjective evaluations. The proposed affective A2V system achieves significant MSE loss improvements in comparison to the recent literature. Furthermore, the resulting facial animations of the proposed system are preferred over the baseline animations in the subjective evaluations., Scientific and Technological Council of Turkey (TÜBİTAK)

Marine-based biomolecules are emerging metabolites that have gained attention for developing novel biomaterials, drugs, and pharmaceutical in vitro platforms. Here, we developed a 3D engineered neural co-culture model via a 3D prototyped sliding frame-platform for multi-step UV lithography and investigated the neurovascular potential of citreohybridonol in neuroblastoma treatment. Citreohybridonol was isolated from a sponge-derived fungus Penicillium atrovenetum. The model was characterized by Fourier-transform infrared spectroscopy and scanning electron microscopy analysis. Human umbilical cord vein endothelial cells (HUVECs) and neuroblastoma (SH-SY5Y) cell lines were encapsulated in gelatin methacrylate (GelMA) with and without citreohybridonol. The effect of citreohybridonol on the proliferation capacity of cells was assessed via cell viability and immunostaining assays. GelMA and 3D culture characterization indicated that the cells were successfully encapsulated as axenic and mixed with/without citreohybridonol. The cytotoxic test confirmed that the 3D microenvironment was non-toxic for cultural experiments, and it showed the inhibitory effects of citreohybridonol on SH-SY5Y cells and induced the proliferation of HUVECs. Finally, immunohistochemical staining demonstrated that citreohybridonol suppressed SH-SY5Y cells and induced vascularization of HUVECs in mixed 3D cell culture., Scientific and Technological Research Council of Turkey (TÜBİTAK); 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship; Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership Award; Alexander von Humboldt Research Fellowship for Experienced Researchers; Science Academy Young Scientist Awards Program (BAGEP); Outstanding Young Scientists Awards (GEBİP); Bilim Kahramanlari Dernegi Young Scientist Award

Organ-on-a-chip (OOC) platforms recapitulate human in vivo-like conditions more realistically compared to many animal models and conventional two-dimensional cell cultures. OOC setups benefit from continuous perfusion of cell cultures through microfluidic channels, which promotes cell viability and activities. Moreover, microfluidic chips allow the integration of biosensors for real-time monitoring and analysis of cell interactions and responses to administered drugs. Three-dimensional (3D) bioprinting enables the fabrication of multicell OOC platforms with sophisticated 3D structures that more closely mimic human tissues. 3D-bioprinted OOC platforms are promising tools for understanding the functions of organs, disruptive influences of diseases on organ functionality, and screening the efficacy as well as toxicity of drugs on organs. Here, common 3D bioprinting techniques, advantages, and limitations of each method are reviewed. Additionally, recent advances, applications, and potentials of 3D-bioprinted OOC platforms for emulating various human organs are presented. Last, current challenges and future perspectives of OOC platforms are discussed., Scientific and Technological Research Council of Turkey (TÜBİTAK); International Fellowship for Outstanding Researchers Award; Alexander von Humboldt Research Fellowship for Experienced Researchers; European Union(EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship; Royal Academy Newton-Katip Celebi Transforming Systems Through Partnership

Polytopic matrix factorization (PMF) is a recently introduced matrix decomposition method in which the data vectors are modeled as linear transformations of samples from a polytope. The successful recovery of the original factors in the generative PMF model is conditioned on the”identifiability” of the chosen polytope. In this article, we investigate the problem of determining the identifiability of a polytope. The identifiability condition requires the polytope to be permutation- and/or-sign-only invariant. We show how this problem can be efficiently solved by using a graph automorphism algorithm. In particular, we show that checking only the generating set of the linear automorphism group of a polytope, which corresponds to the automorphism group of an edge-colored complete graph, is sufficient. This property prevents checking all the elements of the permutation group, which requires factorial algorithm complexity. We demonstrate the feasibility of the proposed approach through some numerical experiments., This work is partially supported by an AI Fellowship provided by the KUIS AI Lab.

A wide variety of children's products such as mobile apps, toys, and assistant systems now have integrated smart features. Designing such AI-powered products with children, the users, is essential. Using high-fidelity prototypes can be a means to reveal children's needs and behaviors with AI-powered systems. Yet, a prototype that can show unpredictable features similar to the final AI-powered product can be expensive. A more manageable and inexpensive solution is using web-based AI prototyping tools such as Teachable Machine. In this work, we developed a Teachable Machine-powered game-development environment to inform our tangible programming environment's design decisions. Using this kind of an AI-powered high-fidelity prototype in the research process allowed us to observe children in a very similar setting to our final AI-powered product and extract design considerations. This paper reports our experience of prototyping AI-powered solutions with children and shares our design considerations for children's self-made tangible representations., Scientific and Technological Research Council of Turkey (TÜBİTAK); Koç Üniversitesi İş Bankası Yapay Zeka Uygulama ve Araştırma Merkezi (KUIS AI)/ Koç University İş Bank Artificial Intelligence Center (KUIS AI)

Deep learning (DL) is a subfield of machine learning (ML), which has recently demon-strated its potency to significantly improve the quantification and classification workflows in bio-medical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases., Scientific and Technological Research Council of Turkey (TÜBİTAK); TÜBİTAK 2232 International Fellowship for Outstandig Researchers Award; European Union (EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship; Alexander von Humboldt Research Fellowship for Experienced Researchers; Royal Academy Newton-Katip Çelebi Transforming Systems Through Partnership

Advances in microfabrication and biomaterials have enabled the development of microfluidic chips for studying tissue and organ models. While these platforms have been developed primarily for modeling human diseases, they are also used to uncover cellular and molecular mechanisms through in vitro studies, especially in the neurovascular system, where physiological mechanisms and three-dimensional (3D) architecture are difficult to reconstruct via conventional assays. An extracellular matrix (ECM) model with a stable structure possessing the ability to mimic the natural extracellular environment of the cell efficiently is useful for tissue engineering applications. Conventionally used techniques for this purpose, for example, Matrigels, have drawbacks of owning complex fabrication procedures, in some cases not efficient enough in terms of functionality and expenses. Here, we proposed a fabrication protocol for a GelMA hydrogel, which has shown structural stability and the ability to imitate the natural environment of the cell accurately, inside a microfluidic chip utilizing co-culturing of two human cell lines. The chemical composition of the synthesized GelMA was identified by Fourier transform infrared spectrophotometry (FTIR), its surface morphology was observed by field emission electron microscopy (FESEM), and the structural properties were analyzed by atomic force microscopy (AFM). The swelling behavior of the hydrogel in the microfluidic chip was imaged, and its porosity was examined for 72 h by tracking cell localization using immunofluorescence. GelMA exhibited the desired biomechanical properties, and the viability of cells in both platforms was more than 80% for seven days. Furthermore, GelMA was a viable platform for 3D cell culture studies and was structurally stable over long periods, even when prepared by photopolymerization in a microfluidic platform. This work demonstrated a viable strategy to conduct co-culturing experiments as well as modeling invas, Scientific and Technological Research Council of Turkey (TÜBİTAK) 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship; Royal Academy Newton-Katip Celebi Transforming Systems through Partnership Award; Alexander von Humboldt Research Fellowship for Experienced Researchers; Science Academy’s Young Scientist Awards Program (BAGEP); Outstanding Young Scientists Awards (GEBİP); Bilim Kahramanlari Dernegi The Young Scientist Award

Microneedles (MNs) are an emerging technology for user-friendly and minimally invasive injection, offering less pain and lower tissue damage in comparison to conventional needles. With their ability to extract body fluids, MNs are among the convenient candidates for developing biosensing setups, where target molecules/biomarkers are detected by the biosensor using the sample collected with the MNs. Herein, we discuss the 3D printing of microneedle arrays (MNAs) toward enabling point-of-care (POC) biosensing applications., Scientific and Technological Research Council of Turkey (TÜBİTAK) 2232 International Fellowship for Outstanding Researchers Award; European Union (EU); Horizon 2020; Marie Sklodowska-Curie Individual Fellowship; Royal Academy Newton-Katip Celebi Transforming Systems through Partnership Award; Alexander von Humboldt Research Fellowship for Experienced Researchers; Science Academy’s Young Scientist Awards Program (BAGEP); Outstanding Young Scientists Awards (GEBİP); Bilim Kahramanlari Dernegi The Young Scientist Award

Programming has become a core subject in primary and middle school curricula. Yet, conventional solutions for in-class programming activities require each student to have expensive equipment, which creates an opportunity gap for low-income students. Paper programming can provide an affordable, engaging, and collaborative in-class programming experience by allowing groups of students to use inexpensive materials and share smartphones. However, current paper-programming examples are limited in terms of language expressivity and generalizability. Addressing these limitations, we developed a paper-programming flow and its variants in different abstraction levels and input/output styles. The programming environments consist of pre-defined tangible programming cards and a mobile application that runs computer vision models to recognize them. This paper describes our educational and technical development process, presents a qualitative analysis of the early user study results and shares our design considerations to help develop wide-reaching paper programming environments., Scientific and Technological Research Council of Turkey (TÜBİTAK); KUIS AI Center

Motion is an important cue for video prediction and often utilized by separating video content into static and dynamic components. Most of the previous work utilizing motion is deterministic but there are stochastic methods that can model the inherent uncertainty of the future. Existing stochastic models either do not reason about motion explicitly or make limiting assumptions about the static part. In this paper, we reason about appearance and motion in the video stochastically by predicting the future based on the motion history. Explicit reasoning about motion without history already reaches the performance of current stochastic models. The motion history further improves the results by allowing to predict consistent dynamics several frames into the future. Our model performs comparably to the state-of-the-art models on the generic video prediction datasets, however, significantly outperforms them on two challenging real-world autonomous driving datasets with complex motion and dynamic background., KUIS AI Center fellowship; Scientific and Technological Research Council of Turkey (TÜBİTAK); 2232 International Fellowship for Outstanding Researchers Programme; Turkish Academy of Sciences GEBIP 2018; Turkish Academy of Sciences BAGEP 2021.