Molecular discovery has attracted significant attention in scientific fields for its ability to generate novel molecules with desirable properties. Although numerous methods have been developed to tackle this problem, most rely on an online setting that requires repeated online evaluation of candidate molecules using the oracle. However, in real-world molecular discovery, the oracle is often represented by wet-lab experiments, making this online setting impractical due to the significant time and resource demands. To fill this gap, we propose the Molecular Stitching (MolStitch) framework, which utilizes a fixed offline dataset to explore and optimize molecules without the need for repeated oracle evaluations. Specifically, MolStitch leverages existing molecules from the offline dataset to generate novel `stitched molecules' that combine their desirable properties. These stitched molecules are then used as training samples to fine-tune the generative model using preference optimization techniques. Experimental results on various offline multi-objective molecular optimization problems validate the effectiveness of MolStitch. The source code is available online.

Two-photon calcium imaging is a powerful technique for recording neuronal activities over extended periods. However, reliably capturing neuronal signals from the this data poses a significant challenge due to non-uniform neuropil distribution and densely packed neuronal populations. In this study, we leverage deep learning (DL) techniques to directly capture true neuronal signals from calcium imaging data, addressing these challenges effectively. Utilizing publicly available datasets, we demonstrate that our DL-based approach, which directly extracts neuronal signals from images, outperforms existing non-DL methods in accurately capturing neuronal signals. This highlights the significant potential of DL methods for unveiling neuronal activity hidden in calcium imaging data. Furthermore, we investigate the impact of calcium imaging data quality on the performance of DL models. While our approach demonstrates significant promise, ongoing improvements in the quality of calcium imaging data will further enhance DL techniques, leading to a deeper understanding of brain mechanisms.

Volumetric fluorescence microscopy is crucial for non-invasive three-dimension (3D) visualization of biological systems but faces challenges due to anisotropic blurring caused by the point spread function (PSF). Previous methods have struggled with adapting to the diverse PSFs and have not effectively addressed their overall impacts of PSF. We propose Isotropic Diffusion Posterior Sampling (IsotropicDPS), solving isotropic reconstruction as a blind inverse problem. Our method employs two specialized score-based diffusion models, each trained on high-resolution lateral images and a diverse set of blurring PSFs. This approach enables the joint estimation of both the clean axial images and the PSF through a conditional posterior sampling strategy with a parallel reverse diffusion process. Remarkably, IsotropicDPS achieves zero-shot reconstruction and PSF estimation without requiring axial images during training. We validated our method through experiments on synthetic and real data, demonstrating superior performance and adaptability to varying PSF scenarios compared to existing methods.

BACKGROUND: Deep neural networks (DNNs) have the potential to revolutionize our understanding and treatment of genetic diseases. An inherent limitation of deep neural networks, however, is their high demand for data during training. To overcome this challenge, other fields, such as computer vision, use various data augmentation techniques to artificially increase the available training data for DNNs. Unfortunately, most data augmentation techniques used in other domains do not transfer well to genomic data. RESULTS: Most genomic data possesses peculiar properties and data augmentations may significantly alter the intrinsic properties of the data. In this work, we propose a novel data augmentation technique for genomic data inspired by biology: point mutations. By employing point mutations as substitutes for codons, we demonstrate that our newly proposed data augmentation technique enhances the performance of DNNs across various genomic tasks that involve coding regions, such as translation initiation and splice site detection. CONCLUSION: Silent and missense mutations are found to positively influence effectiveness, while nonsense mutations and random mutations in non-coding regions generally lead to degradation. Overall, point mutation-based augmentations in genomic datasets present valuable opportunities for improving the accuracy and reliability of predictive models for DNA sequences.

There are many attentions for the fact-checking to prevent the hallucination, malfunction of circulating content on the web such as deception, counterfeit, fake news regardless of whether they are intended or not. For fact-checking, ConFcheKG (Context-based Fact-checking using Knowledge Graph) is proposed based on the Knowledge Graph (KG). In the content including multiple entities, the KG is adopted to check coherence between the entities to determine whether there are semantic conflicts among them. The coherence is based on the temporal, spatial, and logical arrangement of the contents based on the associated relationships among the entities using KG. To do this, it checks the existence of intersected KGs and conflicted KGs through mutual comparison of KGs step by step. According to the verifying process, if is the constructed KT with coherent entities of the content, then the reliability of the content’s coherence is high because it has a high probability of not being false, hallucination, fake-news, and so on. Otherwise, the probability of being false increases. To check the fact, ConFcheKG can be applied based on semantic coherence of the content, to reduce the hallucination, to determine the fake news, to detect deceptions, to prevent counterfeits, to generate the well-composed prompt for the generative AI, and so on.

MOTIVATION: The primary regulatory step for protein synthesis is translation initiation, which makes it one of the fundamental steps in the central dogma of molecular biology. In recent years, a number of approaches relying on deep neural networks (DNNs) have demonstrated superb results for predicting translation initiation sites. These state-of-the art results indicate that DNNs are indeed capable of learning complex features that are relevant to the process of translation. Unfortunately, most of those research efforts that employ DNNs only provide shallow insights into the decision-making processes of the trained models and lack highly sought-after novel biologically relevant observations. RESULTS: By improving upon the state-of-the-art DNNs and large-scale human genomic datasets in the area of translation initiation, we propose an innovative computational methodology to get neural networks to explain what was learned from data. Our methodology, which relies on in silico point mutations, reveals that DNNs trained for translation initiation site detection correctly identify well-established biological signals relevant to translation, including (i) the importance of the Kozak sequence, (ii) the damaging consequences of ATG mutations in the 5'-untranslated region, (iii) the detrimental effect of premature stop codons in the coding region, and (iv) the relative insignificance of cytosine mutations for translation. Furthermore, we delve deeper into the Beta-globin gene and investigate various mutations that lead to the Beta thalassemia disorder. Finally, we conclude our work by laying out a number of novel observations regarding mutations and translation initiation. AVAILABILITY AND IMPLEMENTATION: For data, models, and code, visit github.com/utkuozbulak/mutate-and-observe.

We are proposing a smart orchestrator as a platform to create and share values in ecosystem in combination with heterogeneous networks. As a smart orchestrator, generative Smart ORCHestrator for converged Tourism ecoSystem (SORCHeTS) supports opportunities to ecologically link multi-organism with symbiotic relationships for the seamlessly plugging. In this research, we are focusing on creation and adaptation of the medical tourism ecosystem on SORCHeTS. The medical tourism as a context-intensive ecosystem is constructed by symbiotic combination of common tourism and medical treatment networks. To do this, it is necessary to combine the keystones and flagships from medical treatment and common tourism organism to create and share productive values. To combine two ecosystems, SORCHeTS are comprised of Genotype and Phenotype modules. Genotype plays a role of creation values in combination with heterogeneous networks. Phenotype dynamically adapts the generative smart ecosystem depending on contextual conditions. Ontology is applied to support dynamic creation and adaptation of the smart ecosystem. To prove effectiveness of the proposed SORCHeTS, we proved interoperability, robustness, creativity, and productivity of newly combined medical tourism ecosystem.

In electronic catalogs, commodities like computers and electronic equipment are specified as standard models even though a variety of possible alternative specifications can exist as a combination of selected options; therefore, customized configurations are essential to support various customers with individual needs. So the problem here is the selection of a standard model and reconfiguration with this selected model. An issue is that requirements may be fulfilled by more than one standard model. To develop an algorithm that can find the near minimum price without causing unacceptable computation effort, we devised the Standard Model Selection and Modification (SMSM) Algorithm. To establish the SMSM Algorithm, we propose the Concurrent Local Propagation procedure complemented with pruning capability owing to the nature of standard models. The effective strategies for selection of seed variables and stopping rules are devised through comparative experiments.