Managing Interfacial Defects and Charge-Carriers Dynamics by a Cesium-Doped SnO2 for Air Stable Perovskite Solar Cells.

A high-quality nanostructured tin oxide (SnO2) has garnered massive attention as an electron transport layer (ETL) for efficient perovskite solar cells (PSCs). SnO2 is considered the most effective alternative to titanium oxide (TiO2) as ETL because of its low-temperature processing and promising optical and electrical characteristics. However, some essential modifications are still required to further improve the intrinsic characteristics of SnO2, such as mismatch band alignments, charge extraction, transportation, conductivity, and interfacial recombination losses. Herein, an inorganic-based cesium (Cs) dopant is used to modify the SnO2 ETL and to investigate the impact of Cs-dopant in curing interfacial defects, charge-carrier dynamics, and improving the optoelectronic characteristics of PSCs. The incorporation of Cs contents efficiently improves the perovskite film quality by enhancing the transparency, crystallinity, grain size, and light absorption and reduces the defect states and trap densities, resulting in an improved power conversion efficiency (PCE) of ≈22.1% with Cs:SnO2 ETL, in-contrast to pristine SnO2-based PSCs (20.23%). Moreover, the Cs-modified SnO2-based PSCs exhibit remarkable environmental stability in a relatively higher relative humidity environment (>65%) and without encapsulation. Therefore, this work suggests that Cs-doped SnO2 is a highly favorable electron extraction material for preparing highly efficient and air-stable planar PSCs.

Unusual Raman Enhancement Effect of Ultrathin Copper Sulfide.

In surface-enhanced Raman spectroscopy (SERS), 2D materials are explored as substrates owing to their chemical stability and reproducibility. However, they exhibit lower enhancement factors (EFs) compared to noble metal-based SERS substrates. This study demonstrates the application of ultrathin covellite copper sulfide (CuS) as a cost-effective SERS substrate with a high EF value of 7.2 × 104 . The CuS substrate is readily synthesized by sulfurizing a Cu thin film at room temperature, exhibiting a Raman signal enhancement comparable to that of an Au noble metal substrate of similar thickness. Furthermore, computational simulations using the density functional theory are employed and time-resolved photoluminescence measurements are performed to investigate the enhancement mechanisms. The results indicate that polar covalent bonds (Cu─S) and strong interlayer interactions in the ultrathin CuS substrate increase the probability of charge transfer between the analyte molecules and the CuS surface, thereby producing enhanced SERS signals. The CuS SERS substrate demonstrates the selective detection of various dye molecules, including rhodamine 6G, methylene blue, and safranine O. Furthermore, the simplicity of CuS synthesis facilitates large-scale production of SERS substrates with high spatial uniformity, exhibiting a signal variation of less than 5% on a 4-inch wafer.

Synergistic Contribution of Oligo(ethylene glycol) and Fluorine Substitution of Conjugated Polymer Photocatalysts toward Solar Driven Sacrificial Hydrogen Evolution.

To separately explore the importance of hydrophilicity and backbone planarity of polymer photocatalyst, a series of benzothiadiazole-based donor-acceptor alternating copolymers incorporating alkoxy, linear oligo(ethylene glycol) (OEG) side chain, and backbone fluorine substituents is presented. The OEG side chains in the polymer backbone increase the surface energy of the polymer nanoparticles, thereby improving the interaction with water and facilitating electron transfer to water. Moreover, the OEG-attached copolymers exhibit enhanced intermolecular packing compared to polymers with alkoxy side chains, which is possibly attributed to the self-assembly properties of the side chains. Fluorine substituents on the polymer backbone produce highly ordered lamellar stacks with distinct π-π stacking features; subsequently, the long-lived polarons toward hydrogen evolution are observed by transient absorption spectroscopy. In addition, a new nanoparticle synthesis strategy using a methanol/water mixed solvent is first adopted, thereby avoiding the screening effect of surfactants between the nanoparticles and water. Finally, hydrogen evolution rate of 26 000 µmol g-1  h-1 is obtained for the copolymer incorporated with both OEG side chains and fluorine substituents under visible-light irradiation (λ > 420 nm). This study demonstrates how the glycol side chain strategy can be further optimized for polymer photocatalysts by controlling the backbone planarity.

Molecular basis of USP7 inhibition by selective small-molecule inhibitors.

Ubiquitination controls the stability of most cellular proteins, and its deregulation contributes to human diseases including cancer. Deubiquitinases remove ubiquitin from proteins, and their inhibition can induce the degradation of selected proteins, potentially including otherwise 'undruggable' targets. For example, the inhibition of ubiquitin-specific protease 7 (USP7) results in the degradation of the oncogenic E3 ligase MDM2, and leads to re-activation of the tumour suppressor p53 in various cancers. Here we report that two compounds, FT671 and FT827, inhibit USP7 with high affinity and specificity in vitro and within human cells. Co-crystal structures reveal that both compounds target a dynamic pocket near the catalytic centre of the auto-inhibited apo form of USP7, which differs from other USP deubiquitinases. Consistent with USP7 target engagement in cells, FT671 destabilizes USP7 substrates including MDM2, increases levels of p53, and results in the transcription of p53 target genes, induction of the tumour suppressor p21, and inhibition of tumour growth in mice.

Predicting mechanism of action of novel compounds using compound structure and transcriptomic signature coembedding.

MOTIVATION: Identifying mechanism of actions (MoA) of novel compounds is crucial in drug discovery. Careful understanding of MoA can avoid potential side effects of drug candidates. Efforts have been made to identify MoA using the transcriptomic signatures induced by compounds. However, these approaches fail to reveal MoAs in the absence of actual compound signatures. RESULTS: We present MoAble, which predicts MoAs without requiring compound signatures. We train a deep learning-based coembedding model to map compound signatures and compound structure into the same embedding space. The model generates low-dimensional compound signature representation from the compound structures. To predict MoAs, pathway enrichment analysis is performed based on the connectivity between embedding vectors of compounds and those of genetic perturbation. Results show that MoAble is comparable to the methods that use actual compound signatures. We demonstrate that MoAble can be used to reveal MoAs of novel compounds without measuring compound signatures with the same prediction accuracy as that with measuring them. AVAILABILITY AND IMPLEMENTATION: MoAble is available at https://github.com/dmis-lab/moable. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ChimerDB 4.0: an updated and expanded database of fusion genes.

Fusion genes represent an important class of biomarkers and therapeutic targets in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data (ChimerSeq) and text mining of publications (ChimerPub) with extensive manual annotations (ChimerKB). In this update, we present all three modules substantially enhanced by incorporating the recent flood of deep sequencing data and related publications. ChimerSeq now covers all 10 565 patients in the TCGA project, with compilation of computational results from two reliable programs of STAR-Fusion and FusionScan with several public resources. In sum, ChimerSeq includes 65 945 fusion candidates, 21 106 of which were predicted by multiple programs (ChimerSeq-Plus). ChimerPub has been upgraded by applying a deep learning method for text mining followed by extensive manual curation, which yielded 1257 fusion genes including 777 cases with experimental supports (ChimerPub-Plus). ChimerKB includes 1597 fusion genes with publication support, experimental evidences and breakpoint information. Importantly, we implemented several new features to aid estimation of functional significance, including the fusion structure viewer with domain information, gene expression plot of fusion positive versus negative patients and a STRING network viewer. The user interface also was greatly enhanced by applying responsive web design. ChimerDB 4.0 is available at http://www.kobic.re.kr/chimerdb/.

Improved survival analysis by learning shared genomic information from pan-cancer data.

MOTIVATION: Recent advances in deep learning have offered solutions to many biomedical tasks. However, there remains a challenge in applying deep learning to survival analysis using human cancer transcriptome data. As the number of genes, the input variables of survival model, is larger than the amount of available cancer patient samples, deep-learning models are prone to overfitting. To address the issue, we introduce a new deep-learning architecture called VAECox. VAECox uses transfer learning and fine tuning. RESULTS: We pre-trained a variational autoencoder on all RNA-seq data in 20 TCGA datasets and transferred the trained weights to our survival prediction model. Then we fine-tuned the transferred weights during training the survival model on each dataset. Results show that our model outperformed other previous models such as Cox Proportional Hazard with LASSO and ridge penalty and Cox-nnet on the 7 of 10 TCGA datasets in terms of C-index. The results signify that the transferred information obtained from entire cancer transcriptome data helped our survival prediction model reduce overfitting and show robust performance in unseen cancer patient samples. AVAILABILITY AND IMPLEMENTATION: Our implementation of VAECox is available at https://github.com/dmis-lab/VAECox. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

BioBERT: a pre-trained biomedical language representation model for biomedical text mining.

MOTIVATION: Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. RESULTS: We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. AVAILABILITY AND IMPLEMENTATION: We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.

ReSimNet: drug response similarity prediction using Siamese neural networks.

MOTIVATION: Traditional drug discovery approaches identify a target for a disease and find a compound that binds to the target. In this approach, structures of compounds are considered as the most important features because it is assumed that similar structures will bind to the same target. Therefore, structural analogs of the drugs that bind to the target are selected as drug candidates. However, even though compounds are not structural analogs, they may achieve the desired response. A new drug discovery method based on drug response, which can complement the structure-based methods, is needed. RESULTS: We implemented Siamese neural networks called ReSimNet that take as input two chemical compounds and predicts the CMap score of the two compounds, which we use to measure the transcriptional response similarity of the two compounds. ReSimNet learns the embedding vector of a chemical compound in a transcriptional response space. ReSimNet is trained to minimize the difference between the cosine similarity of the embedding vectors of the two compounds and the CMap score of the two compounds. ReSimNet can find pairs of compounds that are similar in response even though they may have dissimilar structures. In our quantitative evaluation, ReSimNet outperformed the baseline machine learning models. The ReSimNet ensemble model achieves a Pearson correlation of 0.518 and a precision@1% of 0.989. In addition, in the qualitative analysis, we tested ReSimNet on the ZINC15 database and showed that ReSimNet successfully identifies chemical compounds that are relevant to a prototype drug whose mechanism of action is known. AVAILABILITY AND IMPLEMENTATION: The source code and the pre-trained weights of ReSimNet are available at https://github.com/dmis-lab/ReSimNet. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

ChimerDB 3.0: an enhanced database for fusion genes from cancer transcriptome and literature data mining.

Fusion gene is an important class of therapeutic targets and prognostic markers in cancer. ChimerDB is a comprehensive database of fusion genes encompassing analysis of deep sequencing data and manual curations. In this update, the database coverage was enhanced considerably by adding two new modules of The Cancer Genome Atlas (TCGA) RNA-Seq analysis and PubMed abstract mining. ChimerDB 3.0 is composed of three modules of ChimerKB, ChimerPub and ChimerSeq. ChimerKB represents a knowledgebase including 1066 fusion genes with manual curation that were compiled from public resources of fusion genes with experimental evidences. ChimerPub includes 2767 fusion genes obtained from text mining of PubMed abstracts. ChimerSeq module is designed to archive the fusion candidates from deep sequencing data. Importantly, we have analyzed RNA-Seq data of the TCGA project covering 4569 patients in 23 cancer types using two reliable programs of FusionScan and TopHat-Fusion. The new user interface supports diverse search options and graphic representation of fusion gene structure. ChimerDB 3.0 is available at http://ercsb.ewha.ac.kr/fusiongene/.

Deep learning of mutation-gene-drug relations from the literature.

BACKGROUND: Molecular biomarkers that can predict drug efficacy in cancer patients are crucial components for the advancement of precision medicine. However, identifying these molecular biomarkers remains a laborious and challenging task. Next-generation sequencing of patients and preclinical models have increasingly led to the identification of novel gene-mutation-drug relations, and these results have been reported and published in the scientific literature. RESULTS: Here, we present two new computational methods that utilize all the PubMed articles as domain specific background knowledge to assist in the extraction and curation of gene-mutation-drug relations from the literature. The first method uses the Biomedical Entity Search Tool (BEST) scoring results as some of the features to train the machine learning classifiers. The second method uses not only the BEST scoring results, but also word vectors in a deep convolutional neural network model that are constructed from and trained on numerous documents such as PubMed abstracts and Google News articles. Using the features obtained from both the BEST search engine scores and word vectors, we extract mutation-gene and mutation-drug relations from the literature using machine learning classifiers such as random forest and deep convolutional neural networks. Our methods achieved better results compared with the state-of-the-art methods. We used our proposed features in a simple machine learning model, and obtained F1-scores of 0.96 and 0.82 for mutation-gene and mutation-drug relation classification, respectively. We also developed a deep learning classification model using convolutional neural networks, BEST scores, and the word embeddings that are pre-trained on PubMed or Google News data. Using deep learning, the classification accuracy improved, and F1-scores of 0.96 and 0.86 were obtained for the mutation-gene and mutation-drug relations, respectively. CONCLUSION: We believe that our computational methods described in this research could be used as an important tool in identifying molecular biomarkers that predict drug responses in cancer patients. We also built a database of these mutation-gene-drug relations that were extracted from all the PubMed abstracts. We believe that our database can prove to be a valuable resource for precision medicine researchers.

HiPub: translating PubMed and PMC texts to networks for knowledge discovery.

UNLABELLED: We introduce HiPub, a seamless Chrome browser plug-in that automatically recognizes, annotates and translates biomedical entities from texts into networks for knowledge discovery. Using a combination of two different named-entity recognition resources, HiPub can recognize genes, proteins, diseases, drugs, mutations and cell lines in texts, and achieve high precision and recall. HiPub extracts biomedical entity-relationships from texts to construct context-specific networks, and integrates existing network data from external databases for knowledge discovery. It allows users to add additional entities from related articles, as well as user-defined entities for discovering new and unexpected entity-relationships. HiPub provides functional enrichment analysis on the biomedical entity network, and link-outs to external resources to assist users in learning new entities and relations. AVAILABILITY AND IMPLEMENTATION: HiPub and detailed user guide are available at http://hipub.korea.ac.kr CONTACT: kangj@korea.ac.kr, aikchoon.tan@ucdenver.edu SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Large Language Models: A Guide for Radiologists.

Large language models (LLMs) have revolutionized the global landscape of technology beyond natural language processing. Owing to their extensive pre-training on vast datasets, contemporary LLMs can handle tasks ranging from general functionalities to domain-specific areas, such as radiology, without additional fine-tuning. General-purpose chatbots based on LLMs can optimize the efficiency of radiologists in terms of their professional work and research endeavors. Importantly, these LLMs are on a trajectory of rapid evolution, wherein challenges such as "hallucination," high training cost, and efficiency issues are addressed, along with the inclusion of multimodal inputs. In this review, we aim to offer conceptual knowledge and actionable guidance to radiologists interested in utilizing LLMs through a succinct overview of the topic and a summary of radiology-specific aspects, from the beginning to potential future directions.

Highly Macroporous Polyimide with Chemical Versatility Prepared from Poly(amic acid) Salt-Stabilized High Internal Phase Emulsion Template.

Macroporous polymers have gained significant attention due to their unique mass transport and size-selective properties. In this study, we focused on Polyimide (PI), a high-performance polymer, as an ideal candidate for macroporous structures. Despite various attempts to create macroporous PI (Macro PI) using emulsion templates, challenges remained, including limited chemical diversity and poor control over pore size and porosity. To address these issues, we systematically investigated the role of poly(amic acid) salt (PAAS) polymers as macrosurfactants and matrices. By designing 12 different PAAS polymers with diverse chemical structures, we achieved stable high internal phase emulsions (HIPEs) with >80 vol % internal volume. The resulting Macro PIs exhibited exceptional porosity (>99 vol %) after thermal imidization. We explored the structure-property relationships of these Macro PIs, emphasizing the importance of controlling pore size distribution. Furthermore, our study demonstrated the utility of these Macro PIs as separators in Li-metal batteries, providing stable charging-discharging cycles. Our findings not only enhance the understanding of emulsion-based macroporous polymers but also pave the way for their applications in advanced energy storage systems and beyond.

A retrospective analysis of conception per embryo transfer in dairy cattle in South Korea.

The bovine embryo production industry has seen significant growth over the past two decades, particularly in the production of in vitro produced embryos. This growth, driven by advancements in cryopreservation, in vitro culture mediums, ovum pick-up (OPU) procedures, ultrasonography devices, and embryo transfer (ET) has been notable. Particularly, ET is crucial for disseminating high genetic merit and amplifying foreign breeds by importing frozen embryos. This retrospective study aimed to assess factors affecting conception per embryo transfer (CPET) in Holstein-Friesian cattle in South Korea from October 2008 to July 2022. We evaluated type of embryo breed, type of embryo production (fresh and frozen; in vitro and in vivo production), recipient conditions including estrus type, corpus luteum quality, parity (nulliparous heifers, primiparous, and multiparous cows), and the daily mean temperature-humidity index (THI) as an index for heat stress. Type of embryo breed and estrus had no significant impact on CPET. However, we observed higher CPET in recipients with good quality corpus luteum, nulliparous heifers, and surrogates receiving fresh in vitro and frozen in vivo embryos. Importantly, CPET was not adversely affected by mild heat stress conditions (up to daily mean THI 76), indicating that using frozen in vivo embryos produced by multiple ovulation embryo transfer and fresh in vitro embryos by OPU-ET can help alleviate the subfertility issues in dairy cattle caused by global warming in Korea.

Discovery of (4-Pyrazolyl)-2-aminopyrimidines as Potent and Selective Inhibitors of Cyclin-Dependent Kinase 2.

CDK2 is a critical regulator of the cell cycle. For a variety of human cancers, the dysregulation of CDK2/cyclin E1 can lead to tumor growth and proliferation. Historically, early efforts to develop CDK2 inhibitors with clinical applications proved unsuccessful due to challenges in achieving selectivity over off-target CDK isoforms with associated toxicity. In this report, we describe the discovery of (4-pyrazolyl)-2-aminopyrimidines as a potent class of CDK2 inhibitors that display selectivity over CDKs 1, 4, 6, 7, and 9. SAR studies led to the identification of compound 17, a kinase selective and highly potent CDK2 inhibitor (IC50 = 0.29 nM). The evaluation of 17 in CCNE1-amplified mouse models shows the pharmacodynamic inhibition of CDK2, measured by reduced Rb phosphorylation, and antitumor activity.

Biomedical relation extraction with knowledge base-refined weak supervision.

Biomedical relation extraction (BioRE) is the task of automatically extracting and classifying relations between two biomedical entities in biomedical literature. Recent advances in BioRE research have largely been powered by supervised learning and large language models (LLMs). However, training of LLMs for BioRE with supervised learning requires human-annotated data, and the annotation process often accompanies challenging and expensive work. As a result, the quantity and coverage of annotated data are limiting factors for BioRE systems. In this paper, we present our system for the BioCreative VII challenge-DrugProt track, a BioRE system that leverages a language model structure and weak supervision. Our system is trained on weakly labelled data and then fine-tuned using human-labelled data. To create the weakly labelled dataset, we combined two approaches. First, we trained a model on the original dataset to predict labels on external literature, which will become a model-labelled dataset. Then, we refined the model-labelled dataset using an external knowledge base. Based on our experiment, our approach using refined weak supervision showed significant performance gain over the model trained using standard human-labelled datasets. Our final model showed outstanding performance at the BioCreative VII challenge, achieving 3rd place (this paper focuses on our participating system in the BioCreative VII challenge). Database URL: http://wonjin.info/biore-yoon-et-al-2022.

Green and Facile Synthesis of Hybrid Composites with Ultralow Dielectric Properties from Water-Soluble Polyimide and Dual-Porous Silica Nanoparticles.

Here, we proposed an eco-friendly synthetic method for synthesizing hybrid composites with ultralow dielectric properties at high frequencies up to 28 GHz for true 5G communication from aqueous aromatic polyimide (PI) polymers and dual-porous silica nanoparticles (DPS). The "one-step" water-based emulsion template method was used to synthesize the macroporous silica nanoparticles (MPS). A substantially negative ζ potential was produced along the surface of MPS by the poly(vinylpyrrolidone)-based chemical functionalization, enabling excellent aqueous dispersion stability. The water-soluble poly(amic acid) (PAA), as a precursor to PI, was also "one-step" polymerized in an aqueous solution. The MPS were dispersed in a water-soluble PAA matrix to create the hybrid composite films using an entirely water-based approach. The compatibility between the PAA matrix and MPS was elucidated by investigating relatively diverse end-terminated PAAs (with either amine or carboxyl group). It was also discovered that, during a thermally activated imidization reaction, the MPS are in situ converted into the DPS with macro- and microporous structures (with a surface area of 1522.4 m2/g). The thermal, dielectric, mechanical, and morphological characteristics of each composite film were examined, while the amount of DPS in the PI matrix varied from 1 to 20 wt %. With the addition of 5 wt % DPS as an optimum condition, it showed ultralow dielectric properties, with the Dk and Df being 1.615 and 0.003 at a frequency of 28 GHz, respectively, and compatible mechanical properties, with the tensile strength and elastic modulus being 78.2 MPa and 0.32 GPa, respectively. These results can comprehensively satisfy various physical properties required as a substrate material for 5G communication devices.

Evaluation of crowdsourced mortality prediction models as a framework for assessing artificial intelligence in medicine.

OBJECTIVE: Applications of machine learning in healthcare are of high interest and have the potential to improve patient care. Yet, the real-world accuracy of these models in clinical practice and on different patient subpopulations remains unclear. To address these important questions, we hosted a community challenge to evaluate methods that predict healthcare outcomes. We focused on the prediction of all-cause mortality as the community challenge question. MATERIALS AND METHODS: Using a Model-to-Data framework, 345 registered participants, coalescing into 25 independent teams, spread over 3 continents and 10 countries, generated 25 accurate models all trained on a dataset of over 1.1 million patients and evaluated on patients prospectively collected over a 1-year observation of a large health system. RESULTS: The top performing team achieved a final area under the receiver operator curve of 0.947 (95% CI, 0.942-0.951) and an area under the precision-recall curve of 0.487 (95% CI, 0.458-0.499) on a prospectively collected patient cohort. DISCUSSION: Post hoc analysis after the challenge revealed that models differ in accuracy on subpopulations, delineated by race or gender, even when they are trained on the same data. CONCLUSION: This is the largest community challenge focused on the evaluation of state-of-the-art machine learning methods in a healthcare system performed to date, revealing both opportunities and pitfalls of clinical AI.

Validating cancer drug targets.

A cancer drug target is only truly validated by demonstrating that a given therapeutic agent is clinically effective and acts through the target against which it was designed. Nevertheless, it is desirable to declare an early-stage drug target as 'validated' before investing in a full-scale drug discovery programme dedicated to it. Although the outcome of validation studies can guide cancer research programmes, strictly defined universal validation criteria have not been established.