Recent advancements in implantable neural links based on organic synaptic transistors.

The progress of brain synaptic devices has witnessed an era of rapid and explosive growth. Because of their integrated storage, excellent plasticity and parallel computing, and system information processing abilities, various field effect transistors have been used to replicate the synapses of a human brain. Organic semiconductors are characterized by simplicity of processing, mechanical flexibility, low cost, biocompatibility, and flexibility, making them the most promising materials for implanted brain synaptic bioelectronics. Despite being used in numerous intelligent integrated circuits and implantable neural linkages with multiple terminals, organic synaptic transistors still face many obstacles that must be overcome to advance their development. A comprehensive review would be an excellent tool in this respect. Therefore, the latest advancements in implantable neural links based on organic synaptic transistors are outlined. First, the distinction between conventional and synaptic transistors are highlighted. Next, the existing implanted organic synaptic transistors and their applicability to the brain as a neural link are summarized. Finally, the potential research directions are discussed.

Multitask Learning Strategy with Pseudo-Labeling: Face Recognition, Facial Landmark Detection, and Head Pose Estimation.

Most facial analysis methods perform well in standardized testing but not in real-world testing. The main reason is that training models cannot easily learn various human features and background noise, especially for facial landmark detection and head pose estimation tasks with limited and noisy training datasets. To alleviate the gap between standardized and real-world testing, we propose a pseudo-labeling technique using a face recognition dataset consisting of various people and background noise. The use of our pseudo-labeled training dataset can help to overcome the lack of diversity among the people in the dataset. Our integrated framework is constructed using complementary multitask learning methods to extract robust features for each task. Furthermore, introducing pseudo-labeling and multitask learning improves the face recognition performance by enabling the learning of pose-invariant features. Our method achieves state-of-the-art (SOTA) or near-SOTA performance on the AFLW2000-3D and BIWI datasets for facial landmark detection and head pose estimation, with competitive face verification performance on the IJB-C test dataset for face recognition. We demonstrate this through a novel testing methodology that categorizes cases as soft, medium, and hard based on the pose values of IJB-C. The proposed method achieves stable performance even when the dataset lacks diverse face identifications.

Identification of B cell subsets based on antigen receptor sequences using deep learning.

B cell receptors (BCRs) denote antigen specificity, while corresponding cell subsets indicate B cell functionality. Since each B cell uniquely encodes this combination, physical isolation and subsequent processing of individual B cells become indispensable to identify both attributes. However, this approach accompanies high costs and inevitable information loss, hindering high-throughput investigation of B cell populations. Here, we present BCR-SORT, a deep learning model that predicts cell subsets from their corresponding BCR sequences by leveraging B cell activation and maturation signatures encoded within BCR sequences. Subsequently, BCR-SORT is demonstrated to improve reconstruction of BCR phylogenetic trees, and reproduce results consistent with those verified using physical isolation-based methods or prior knowledge. Notably, when applied to BCR sequences from COVID-19 vaccine recipients, it revealed inter-individual heterogeneity of evolutionary trajectories towards Omicron-binding memory B cells. Overall, BCR-SORT offers great potential to improve our understanding of B cell responses.

Stand age-driven tree size variation and stand type regulate aboveground biomass in alpine-subalpine forests, South Korea.

Alpine and subalpine forests in mountains worldwide are ecologically significant because of their unique biodiversity and increased vulnerability to climate change. This study was conducted to explore the possibilities and ways to preserve the ecological diversity of alpine-subalpine forests and their function as important carbon sinks. In this study, data from 664 plots (400 m2) were collected in the alpine-subalpine zones above 1000 m elevation in South Korea, we divided 664 plots into four stand types: conifer, conifer-dominant mixed, broadleaved-dominant mixed, and broadleaved stands. Abiotic drivers and forest successional stage-related factor including topographic, climatic drivers and stand age class were used. Biotic drivers including taxonomic, phylogenetic, functional, stand structural diversity, and community-weighted mean of functional traits were used to find independent variables controlling aboveground biomass (AGB) for each stand type. We employed multi-model averaging approach as well as piecewise structural equation modeling (pSEM) for the identification of the most influential variables affecting AGB in each stand type of alpine-subalpine forests and to quantify their interrelationships and strengths. The main results showed that tree size variation (i.e., DBH STD) induced by stand age had direct effects on AGB, with varying degrees of significance (ß) ranging from 0.146 to 0.241 across all stand types in alpine-subalpine forests. Following these results, as forest succession progresses, tree species adapted to the specific environmental conditions, such as topography and climate, become dominant by creating their own niche, which increases AGB in each stand type. Additionally, climatic and topographic conditions played an important role in controlling biotic drivers depending on the stand type. In this study, we suggest that AGB should be managed and conserved depending on forest stand types according to forest succession. Furthermore, increasing size variation among tree individuals through proper forest treatments is important for increasing AGB in alpine-subalpine forests.

Progress in organic photovoltaics for indoor application.

Organic photovoltaics (OPVs) have recently emerged as feasible alternatives for indoor light harvesting because of their variable optical absorption, high absorption coefficients, and low leakage currents under low lighting circumstances. Extensive research has been performed over the last decade in the quest for highly efficient, ecologically stable, and economically feasible indoor organic photovoltaics (IOPVs). This research covers a wide range of topics, including the development of new donor-acceptor materials, interlayers (such as electron and hole transport layers), energy loss reduction, open-circuit voltage enhancement via material and device engineering, and device architecture optimization. The maximum power conversion efficiency (PCE) of IOPVs has already topped 35% as a consequence of these collaborative efforts. However, further research is needed to improve numerous elements, such as manufacturing costs and device longevity. IOPVs must preserve at least 80% of their initial PCE for more than a decade in order to compete with traditional batteries used in internet of things devices. A thorough examination of this issue is urgently required. We intend to present an overview of recent developments in the evolution of IOPVs.

SiO-induced thermal instability and interplay between graphite and SiO in graphite/SiO composite anode.

Silicon monoxide (SiO), which exhibits better cyclability compared to silicon while delivering higher capacity than that of graphite, is an adequate material for the development of lithium-ion batteries (LIBs) having higher energy densities. However, incorporating silicon-based materials including SiO into stable graphite anode inevitably degrades not only cycle life but also calendar life of LIBs, while little is known about their aging mechanisms. Here, SiO-induced thermal instability of the graphite/SiO composite anode is investigated. We reveal that under thermal exposure, SiO accelerates the loss of lithium inventory and concomitantly facilitates the lithium de-intercalation from graphite. This self-discharge phenomenon, which is weakly observed in the graphite anode without SiO, is the result of preferential parasitic reaction on the SiO interface and spontaneous electron and lithium-ion migration to equilibrate the electron energy imbalance between graphite and SiO. Understanding this underlying electron-level interplay between graphite and SiO in the composite anode will contribute toward improving shelf life of SiO-containing LIBs in actual operating conditions.

Washable Fabric Triboelectric Nanogenerators for Potential Application in Face Masks.

In order to counteract the COVID-19 pandemic by wearing face masks, we examine washable fabric-based triboelectric nanogenerators (FTENGs). We applied the flash-spun nonwoven fabric (FS fabric) into the FTENGs, comparing the melt-blown nonwoven fabric (MB fabric) based FTENGs, which is conventionally studied in the field of energy harvesting. For reusability, all our proposed FTENGs are systematically investigated by controlling the washing conditions. After washing, the degradation ratio of the obtained output voltage is found to be only 12.5% for FS FTENGs, compared to the ratio of about 50% for the typical MB FTENGs. A rather small degradation ratio for FS fabric cases has resulted from less changed fabric structure after washing due to more dense fabric nature. Additionally, in order to improve the electrical characteristics of FS FTENGs. Note that the output voltage of FTENGs exhibits as much as 600 V.

Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning.

Methods of computational pathology applied to the analysis of whole-slide images (WSIs) do not typically consider histopathological features from the tumour microenvironment. Here, we show that a graph deep neural network that considers such contextual features in gigapixel-sized WSIs in a semi-supervised manner can provide interpretable prognostic biomarkers. We designed a neural-network model that leverages attention techniques to learn features of the heterogeneous tumour microenvironment from memory-efficient representations of aggregates of highly correlated image patches. We trained the model with WSIs of kidney, breast, lung and uterine cancers and validated it by predicting the prognosis of 3,950 patients with these four different types of cancer. We also show that the model provides interpretable contextual features of clear cell renal cell carcinoma that allowed for the risk-based retrospective stratification of 1,333 patients. Deep graph neural networks that derive contextual histopathological features from WSIs may aid diagnostic and prognostic tasks.

Nanoscale Visualization of the Electron Conduction Channel in the SiO/Graphite Composite Anode.

Conductive atomic force microscopy (C-AFM) is widely used to determine the electronic conductivity of a sample surface with nanoscale spatial resolution. However, the origin of possible artifacts has not been widely researched, hindering the accurate and reliable interpretation of C-AFM imaging results. Herein, artifact-free C-AFM is used to observe the electron conduction channels in Si-based composite anodes. The origin of a typical C-AFM artifact induced by surface morphology is investigated using a relevant statistical method that enables visualization of the contribution of artifacts in each C-AFM image. The artifact is suppressed by polishing the sample surface using a cooling cross-section polisher, which is confirmed by Pearson correlation analysis. The artifact-free C-AFM image was used to compare the current signals (before and after cycling) from two different composite anodes comprising single-walled carbon nanotubes (SWCNTs) and carbon black as conductive additives. The relationship between the electrical degradation and morphological evolution of the active materials depending on the conductive additive is discussed to explain the improved electrical and electrochemical properties of the electrode containing SWCNTs.

Spatial epitranscriptomics reveals A-to-I editome specific to cancer stem cell microniches.

Epitranscriptomic features, such as single-base RNA editing, are sources of transcript diversity in cancer, but little is understood in terms of their spatial context in the tumour microenvironment. Here, we introduce spatial-histopathological examination-linked epitranscriptomics converged to transcriptomics with sequencing (Select-seq), which isolates regions of interest from immunofluorescence-stained tissue and obtains transcriptomic and epitranscriptomic data. With Select-seq, we analyse the cancer stem cell-like microniches in relation to the tumour microenvironment of triple-negative breast cancer patients. We identify alternative splice variants, perform complementarity-determining region analysis of infiltrating T cells and B cells, and assess adenosine-to-inosine base editing in tumour tissue sections. Especially, in triple-negative breast cancer microniches, adenosine-to-inosine editome specific to different microniche groups is identified.

Core-shell structured Li-Fe electrode for high energy and stable thermal battery.

The thermal battery, a key source for powering defensive power systems, employs Li alloy-based anodes. However, the alloying increases the reduction potential of Li which lowers the overall working voltage and energy output. To overcome these issues, Li alloy must be replaced with pure Li. Utilizing pure Li requires a structure that can hold liquefied Li because the working temperature for the thermal battery exceeds the melting point of Li. The liquefied Li can leak out of the anode, causing short-circuit. A Li-Fe electrode (LiFE) in which Fe powder holds liquefied Li has been developed. In LiFE, higher Li content can lead to higher energy output but increases the risk of Li leakage. Thus, Li content in the LiFE has been limited. Here, we demonstrate a novel core-shell electrode structure to achieve a higher energy output. The proposed core-shell LiFE incorporates a high Li content core and a low Li content shell; high energy comes from the core and the shell prevents the Li from leakage. The fabricated core-shell structured electrode demonstrates the high energy of 9074 W s, an increase by 1.66 times compared to the low Li content LiFE with the conventionally used Li content (5509 W s).

Deep-Learning-Based Stress Recognition with Spatial-Temporal Facial Information.

In recent times, as interest in stress control has increased, many studies on stress recognition have been conducted. Several studies have been based on physiological signals, but the disadvantage of this strategy is that it requires physiological-signal-acquisition devices. Another strategy employs facial-image-based stress-recognition methods, which do not require devices, but predominantly use handcrafted features. However, such features have low discriminating power. We propose a deep-learning-based stress-recognition method using facial images to address these challenges. Given that deep-learning methods require extensive data, we constructed a large-capacity image database for stress recognition. Furthermore, we used temporal attention, which assigns a high weight to frames that are highly related to stress, as well as spatial attention, which assigns a high weight to regions that are highly related to stress. By adding a network that inputs the facial landmark information closely related to stress, we supplemented the network that receives only facial images as the input. Experimental results on our newly constructed database indicated that the proposed method outperforms contemporary deep-learning-based recognition methods.

Advances in Tumor Sampling and Sequencing in Breast Cancer and their Application in Precision Diagnostics and Therapeutics.

Intra- and Inter-tumoral heterogeneity is one of the main hurdles in diagnosing and treating breast cancer. Selecting, sampling, and sequencing the samples appropriately provide unique opportunities in realizing precision medicine. This chapter reviews some of the past landmarks, state-of-the-art technologies, and future directions of translational research in terms of tumor sampling technologies and sequencing in breast cancer. In the state-of-the-art technologies section, the technologies are categorized in terms of scientific, precision diagnostic, and precision therapeutic tools. Finally, limitations and future directions regarding various translational research for clinical applications using these technologies will be discussed.

Changes in Intestinal Microbiota Due to the Expanded Polystyrene Diet of Mealworms (Tenebrio molitor).

Expanded polystyrene (EPS), which is difficult to decompose, is usually buried or incinerated, causing the natural environment to be contaminated with microplastics and environmental hormones. Digestion of EPS by mealworms has been identified as a possible biological solution to the problem of pollution, but the complete degradation mechanism of EPS is not yet known. Intestinal microorganisms play a significant role in the degradation of EPS by mealworms, and relatively few other EPS degradation microorganisms are currently known. This study observed significant differences in the intestinal microbiota of mealworms according to the dietary results of metagenomics analysis and biodiversity indices. We have proposed two new candidates of EPS-degrading bacteria, Cronobacter sakazakii and Lactococcus garvieae, which increased significantly in the EPS feeding group population. The population change and the new two bacteria will help us understand the biological mechanism of EPS degradation and develop practical EPS degradation methods.

Promoting the Reversible Oxygen Redox Reaction of Li-Excess Layered Cathode Materials with Surface Vanadium Cation Doping.

Li-excess layered cathode (LLC) materials have a high theoretical specific capacity of 250 mAh g-1 induced by transition metal (cationic) and oxygen (anionic) redox activity. Especially, the oxygen redox reaction related to the activation of the Li2MnO3 domain plays the crucial role of providing a high specific capacity. However, it also induces an irreversible oxygen release and accelerates the layered-to-spinel phase transformation and capacity fading. Here, it is shown that surface doping of vanadium (V5+) cations into LLC material suppresses both the irreversible oxygen release and undesirable phase transformation, resulting in the improvement of capacity retention. The V-doped LLC shows a high discharge capacity of 244.3 ± 0.8 mAh g-1 with 92% retention after 100 cycles, whereas LLC delivers 233.6 ± 1.1 mAh g-1 with 74% retention. Furthermore, the average discharge voltage of V-doped LLC drops by only 0.33 V after 100 cycles, while LLC exhibits 0.43 V of average discharge voltage drop. Experimental and theoretical investigations indicate that doped V-doping increase the transition metal-oxygen (TM-O) covalency and affect the oxidation state of peroxo-like (O2) n - species during the delithiation process. The role of V-doping to make the oxygen redox reversible in LLC materials for high-energy density Li-ion batteries is illustrated here.

Carbon Nano Tube-Polymer Hybrid Nanocomposite Electrodes for Porous Polydimethylsiloxane Sponge-Based Flexible Triboelectric Nanogenerators.

Flexible triboelectric nanogenerators (TENGs) have attracted much attention because of its environmentally friendly, practical, and cost-producing advantages. In flexible TENGs, it is important to study the flexible electrodes in order to fabricate the fully flexible devices. Here, we compared electrical characteristics of the sponge porous polydimethylsiloxane (PDMS)-based flexible TENGs with two types of flexible electrodes, copper and carbon nanotube (CNT)-PDMS electrodes. The output voltage and maximum power density of sponge PDMS-based flexible TENGs with copper and CNTPDMS electrodes were compared. The voltage and power density of sponge PDMS-based flexible TENGs with CNT-PDMS electrodes were improved compare to those with copper electrodes. The output voltage and the maximum power density of sponge PDMS-based flexible TENGs with copper and CNT-PDMS electrodes increased 4 times and 7 times, respectively. It is attributed to higher electrical conductivity and stably flow electricity of CNT than those of copper.

Selective Lithium Adsorption of Silicon Oxide Coated Lithium Aluminum Layered Double Hydroxide Nanocrystals and Their Regeneration.

Silicon oxide-coated lithium aluminum layered double hydroxide (Lix Al2 -LDH@SiO2 ) nanocrystals (NCs) are investigated to selectively separate lithium cations in aqueous lithium resources. We directly synthesized Lix Al2 -LDH NC arrays by oxidation of aluminum foil substrate under a urea and lithium solution. Various lithium salts, including Cl- , CO3 2- , NO3 - , and SO4 2- , were applied in aqueous solution to confirm the anion effect on the captured and released lithium quantity of the Lix Al2 -LDH NCs. In a 5% solution of sulfate ions mix with lithium chloride, the Lix Al2 -LDH NCs separated a larger quantity of lithium than in other anion conditions. To enhance regeneration stability and lithium selectivity, thin layers of SiO2 were coated onto the Lix Al2 -LDH nanostructure arrays for inhibition of nanostructure destruction after desorption of lithium cations in hot water. The Lix Al2 -LDH@SiO2 nanostructures showed enhanced properties for lithium adsorption, including increase of stable regeneration cycles from three to five cycles, and they showed high lithium selectivity in the Mg2+ , Na+ , and K+ cation mixed aqueous resource. Our nanostructured LDH lithium adsorbents would provide a facile and efficient application for cost-efficient and large-scale lithium production.

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration.

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

Fabrication of piezoelectric poly(L-lactic acid)/BaTiO3 fibre by the melt-spinning process.

Poly(L-lactic acid) (PLLA) based piezoelectric polymers are gradually becoming the substitute for the conventional piezoelectric ceramic and polymeric materials due to their low cost and biodegradable, non-toxic, piezoelectric and non-pyroelectric nature. To improve the piezoelectric properties of melt-spun poly(L-lactic acid) (PLLA)/BaTiO3, we optimized the post-processing conditions to increase the proportion of the ß crystalline phase. The α → ß phase transition behaviour was determined by two-dimensional wide-angle x-ray diffraction and differential scanning calorimetry. The piezoelectric properties of PLLA/BaTiO3 fibres were characterised in their yarn and textile form through a tapping method. From these results, we confirmed that the crystalline phase transition of PLLA/BaTiO3 fibres was significantly enhanced under the optimised post-processing conditions at a draw ratio of 3 and temperature of 120 °C during the melt-spinning process. The results indicated that PLLA/BaTiO3 fibres could be a one of the material for organic-based piezoelectric sensors for application in textile-based wearable piezoelectric devices.

Challenge for Diagnostic Assessment of Deep Learning Algorithm for Metastases Classification in Sentinel Lymph Nodes on Frozen Tissue Section Digital Slides in Women with Breast Cancer.

PURPOSE: Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of SLNs by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin-stained frozen tissue sections of SLNs in breast cancer patients. MATERIALS AND METHODS: A total of 297 digital slides were obtained from frozen SLN sections, which include post-neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). RESULTS: The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. CONCLUSION: In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative SLN biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.