Control of Polar/Antipolar Layered Organic Semiconductors by the Odd-Even Effect of Alkyl Chain

Some rodlike organic molecules exhibit exceptionally high layered crystallinity when composed of a link between π-conjugated backbone (head) and alkyl chain (tail). These molecules are aligned side-by-side unidirectionally to form self-organized polar monomolecular layers, providing promising 2D materials and devices. However, their interlayer stacking arrangements have never been tunable, preventing the unidirectional arrangements of molecules in whole crystals. Here, it is demonstrated that polar/antipolar interlayer stacking can be systematically controlled by the alkyl carbon number n, when the molecules are designed to involve effectively weakened head-to-head affinity. They exhibit remarkable odd-even effect in the interlayer stacking: alternating head-to-head and tail-to-tail (antipolar) arrangement in odd-n crystals, and uniform head-to-tail (polar) arrangement in even-n crystals. The films show excellent field-effect transistor characteristics presenting unique polar/antipolar dependence and considerably improved subthreshold swing in the polar films. Additionally, the polar films present enhanced second-order nonlinear optical response along normal to the film plane. These findings are key for creating polarity-controlled optoelectronic materials and devices.

Deep Learning Enables Rapid Identification of a New Quasicrystal from Multiphase Powder Diffraction Patterns

Since the discovery of the quasicrystal, approximately 100 stable quasicrystals are identified. To date, the existence of quasicrystals is verified using transmission electron microscopy; however, this technique requires significantly more elaboration than rapid and automatic powder X-ray diffraction. Therefore, to facilitate the search for novel quasicrystals, developing a rapid technique for phase-identification from powder diffraction patterns is desirable. This paper reports the identification of a new Al-Si-Ru quasicrystal using deep learning technologies from multiphase powder patterns, from which it is difficult to discriminate the presence of quasicrystalline phases even for well-trained human experts. Deep neural networks trained with artificially generated multiphase powder patterns determine the presence of quasicrystals with an accuracy >92% from actual powder patterns. Specifically, 440 powder patterns are screened using the trained classifier, from which the Al-Si-Ru quasicrystal is identified. This study demonstrates an excellent potential of deep learning to identify an unknown phase of a targeted structure from powder patterns even when existing in a multiphase sample.

Eribulin is an immune potentiator in breast cancer that upregulates human leukocyte antigen class I expression via the induction of NOD-like receptor family CARD domain-containing 5

Eribulin inhibits microtubule polymerization and improves the overall survival of patients with recurrent metastatic breast cancer. A subgroup analysis revealed a low neutrophil to lymphocyte ratio (NLR) (<3) to be a prognostic factor of eribulin treatment. We thus hypothesized that eribulin might be related to the immune response for breast cancer cells and we analyzed the effects of eribulin on the immune system. Immunohistochemical staining revealed that human leukocyte antigen (HLA) class I expression was increased in clinical samples after eribulin treatment. In vitro assays revealed that eribulin treatment increased HLA class I expression in breast cancer line cells. RNA-sequencing demonstrated that eribulin treatment increased the expression of the NOD-like family CARD domain-containing 5 (NLRC5), a master regulator of HLA class I expression. Eribulin treatment increased the NY-ESO-1-specific T-cell receptor (TCR) transduced T (TCR-T) cell response for New York oesophageal squamous cell carcinoma 1 (NY-ESO-1) overexpressed breast cancer cells. The eribulin and TCR-T combined therapy model revealed that eribulin and immunotherapy using TCR-T cells has a synergistic effect. In summary, eribulin increases the expression of HLA class 1 via HLA class 1 transactivatior NLRC5 and eribulin combination with immunotherapy can be effective for the treatment of breast cancer.

Zonula occludens-1 distribution and barrier functions are affected by epithelial proliferation and turnover rates

Zonula occludens-1 (ZO-1) is a scaffolding protein of tight junctions, which seal adjacent epithelial cells, that is also expressed in adherens junctions. The distribution pattern of ZO-1 differs among stratified squamous epithelia, including that between skin and oral buccal mucosa. However, the causes for this difference, and the mechanisms underlying ZO-1 spatial regulation, have yet to be elucidated. In this study, we showed that epithelial turnover and proliferation are associated with ZO-1 distribution in squamous epithelia. We tried to verify the regulation of ZO-1 by comparing normal skin and psoriasis, known as inflammatory skin disease with rapid turnover. We as well compared buccal mucosa and oral lichen planus, known as an inflammatory oral disease with a longer turnover interval. The imiquimod (IMQ) mouse model, often used as a psoriasis model, can promote cell proliferation. On the contrary, we peritoneally injected mice mitomycin C, which reduces cell proliferation. We examined whether IMQ and mitomycin C cause changes in the distribution and appearance of ZO-1. Human samples and mouse pharmacological models revealed that slower epithelial turnover/proliferation led to the confinement of ZO-1 to the uppermost part of squamous epithelia. In contrast, ZO-1 was widely distributed under conditions of faster cell turnover/proliferation. Cell culture experiments and mathematical modelling corroborated these ZO-1 distribution patterns. These findings demonstrate that ZO-1 distribution is affected by epithelial cell dynamics.

Arginine cluster introduction on framework region in anti-lysozyme antibody improved association rate constant by changing conformational diversity of CDR loops

Antibodies are used for many therapeutic and biotechnological purposes. Because the affinity of an antibody to the antigen is critical for clinical efficacy of pharmaceuticals, many affinity maturation strategies have been developed. Although we previously reported an affinity maturation strategy in which the association rate of the antibody toward its antigen is improved by introducing a cluster of arginine residues into the framework region of the antibody, the detailed molecular mechanism responsible for this improvement has been unknown. In this study, we introduced five arginine residues into an anti-hen egg white lysozyme antibody (HyHEL10) Fab fragment to create the R5-mutant and comprehensively characterized the interaction between antibody and antigen using thermodynamic analysis, X-ray crystallography, and molecular dynamics (MD) simulations. Our results indicate that introduction of charged residues strongly enhanced the association rate, as previously reported, and the antibody-antigen complex structure was almost the same for the R5-mutant and wild-type Fabs. The MD simulations indicate that the mutation increased conformational diversity in complementarity-determining region loops and thereby enhanced the association rate. These observations provide the molecular basis of affinity maturation by R5 mutation.

Image-based robot navigation with task achievability

Image-based robot action planning is becoming an active area of research owing to recent advances in deep learning. To evaluate and execute robot actions, recently proposed approaches require the estimation of the optimal cost-minimizing path, such as the shortest distance or time, between two states. To estimate the cost, parametric models consisting of deep neural networks are widely used. However, such parametric models require large amounts of correctly labeled data to accurately estimate the cost. In real robotic tasks, collecting such data is not always feasible, and the robot itself may require collecting it. In this study, we empirically show that when a model is trained with data autonomously collected by a robot, the estimation of such parametric models could be inaccurate to perform a task. Specifically, the higher the maximum predicted distance, the more inaccurate the estimation, and the robot fails navigating in the environment. To overcome this issue, we propose an alternative metric, "task achievability" (TA), which is defined as the probability that a robot will reach a goal state within a specified number of timesteps. Compared to the training of optimal cost estimator, TA can use both optimal and non-optimal trajectories in the training dataset to train, which leads to a stable estimation. We demonstrate the effectiveness of TA through robot navigation experiments in an environment resembling a real living room. We show that TA-based navigation succeeds in navigating a robot to different target positions, even when conventional cost estimator-based navigation fails.

Imaging the uptake of deuterated methionine in Drosophila with stimulated Raman scattering

Introduction: Visualizing small individual biomolecules at subcellular resolution in live cells and tissues can provide valuable insights into metabolic activity in heterogeneous cells, but is challenging.

Methods: Here, we used stimulated Raman scattering (SRS) microscopy to image deuterated methionine (d-Met) incorporated into Drosophila tissues in vivo.

Results: Our results demonstrate that SRS can detect a range of previously uncharacterized cell-to-cell differences in d-Met distribution within a tissue at the subcellular level.

Discussion: These results demonstrate the potential of SRS microscopy for metabolic imaging of less abundant but important amino acids such as methionine in tissue.

Systematic analysis of cell morphodynamics in C. elegans early embryogenesis

The invariant cell lineage of Caenorhabditis elegans allows unambiguous assignment of the identity for each cell, which offers a unique opportunity to study developmental dynamics such as the timing of cell division, dynamics of gene expression, and cell fate decisions at single-cell resolution. However, little is known about cell morphodynamics, including the extent to which they are variable between individuals, mainly due to the lack of sufficient amount and quality of quantified data. In this study, we systematically quantified the cell morphodynamics in 52 C. elegans embryos from the two-cell stage to mid-gastrulation at the high spatiotemporal resolution, 0.5 μm thickness of optical sections, and 30-second intervals of recordings. Our data allowed systematic analyses of the morphological features. We analyzed sphericity dynamics and found a significant increase at the end of metaphase in every cell, indicating the universality of the mitotic cell rounding. Concomitant with the rounding, the volume also increased in most but not all cells, suggesting less universality of the mitotic swelling. Combining all features showed that cell morphodynamics was unique for each cell type. The cells before the onset of gastrulation could be distinguished from all the other cell types. Quantification of reproducibility in cell-cell contact revealed that variability in division timings and cell arrangements produced variability in contacts between the embryos. However, the area of such contacts occupied less than 5% of the total area, suggesting the high reproducibility of spatial occupancies and adjacency relationships of the cells. By comparing the morphodynamics of identical cells between the embryos, we observed diversity in the variability between cells and found it was determined by multiple factors, including cell lineage, cell generation, and cell-cell contact. We compared the variabilities of cell morphodynamics and cell-cell contacts with those in ascidian Phallusia mammillata embryos. The variabilities were larger in C. elegans, despite smaller differences in embryo size and number of cells at each developmental stage.

Roles of circular RNAs in the pathogenesis and treatment of pancreatic cancer

Circular RNAs are single-stranded RNAs with a covalently closed structure formed by the process of back-splicing. Aberrant expression of circular RNAs contributes to the pathogenesis of a wide range of cancers. Pancreatic cancer is one of the most lethal cancers due to diagnostic difficulties and limited therapeutic options. Circular RNAs are emerging as novel diagnostic biomarkers and therapeutic targets for pancreatic cancer. Moreover, recent advances in the therapeutic application of engineered circular RNAs have provided a promising approach to overcoming pancreatic cancer. This review discusses the roles of circular RNAs in the pathogenesis of pancreatic cancer and in potential treatment applications and their usefulness as diagnostic biomarkers.

Multiple resonance type thermally activated delayed fluorescence by dibenzo [1,4] azaborine derivatives

We studied the photophysical and electroluminescent (EL) characteristics of a series of azaborine derivatives having a pair of boron and nitrogen aimed at the multi-resonance (MR) effect. The computational study with the STEOM-DLPNO-CCSD method clarified that the combination of a BN ring-fusion and a terminal carbazole enhanced the MR effect and spin-orbit coupling matrix element (SOCME), simultaneously. Also, we clarified that the second triplet excited state (T₂) plays an important role in efficient MR-based thermally activated delayed fluorescence (TADF). Furthermore, we obtained a blue–violet OLED with an external EL quantum efficiency (EQE) of 9.1%, implying the presence of a pronounced nonradiative decay path from the lowest triplet excited state (T₁).

Psychological distance and user engagement in online exhibitions: Visualization of moiré patterns based on electroencephalography signals

The COVID-19 pandemic has significantly affected the exhibition of artworks in museums and galleries. Many have displayed their collection online. In this context, experiencing an online exhibition is essential for visitors to appreciate and understand the artwork. Compared with offline exhibitions, visitors to online exhibitions are often unable to communicate their experiences with other visitors. Therefore, in this study, by facilitating communication via Zoom call, we established a system that allows two people to visit the museum together through the Google Arts and Culture (GA&C) website. To reduce the psychological distance between online visitors and help increase user engagement, we designed and developed a media device based on moiré pattern visualization of electroencephalography (EEG) signals. The participants were divided into two groups to visit the online museum, communicating remotely through Zoom. The objective of this study was to test whether a real-time EEG signal visualization device could help close the psychological distance between participants and whether it could increase user engagement with the online exhibition. Participants were randomly assigned to either the normal online exhibition experience (NOEE) group or EEG signal visualization device (ESVD) group. Participants in the NOEE group experienced four online exhibitions (Task1, Task2, Task3, and Task4) together (two participants per test unit). The conditions for participants in the ESVD group remained the same, apart from adding a media device to enable them to visualize EEG signals. A total of 40 university students participated in this study. Independent samples t-tests revealed that participants in the ESVD group perceived a significantly closer psychological distance between themselves and the participants on the opposite side than those in the NOEE group (t = −2.699; p = 0.008 < 0.05). A one-way ANOVA revealed that participants experienced Task3 with significantly closer psychological distance assessments than Task1 (p = 0.002 < 0.05), Task2 (p = 0.000 < 0.05), and Task4 (p = 0.001 < 0.05). Repeated ANOVAs revealed that participants in the ESVD group had higher overall user engagement than those in the NOEE group, with marginal significance (p = 0.056 < 0.1). Thus, this study shows that EEG visualization media devices can reduce the psychological distance between two participants when experiencing an online exhibition. Moreover, it can increase user engagement to some extent.

Comparative Study of Repertoire Classification Methods Reveals Data Efficiency of k-mer Feature Extraction

The repertoire of T cell receptors encodes various types of immunological information. Machine learning is indispensable for decoding such information from repertoire datasets measured by next-generation sequencing (NGS). In particular, the classification of repertoires is the most basic task, which is relevant for a variety of scientific and clinical problems. Supported by the recent appearance of large datasets, efficient but data-expensive methods have been proposed. However, it is unclear whether they can work efficiently when the available sample size is severely restricted as in practical situations. In this study, we demonstrate that their performances can be impaired substantially below critical sample sizes. To complement this drawback, we propose MotifBoost, which exploits the information of short k-mer motifs of TCRs. MotifBoost can perform the classification as efficiently as a deep learning method on large datasets while providing more stable and reliable results on small datasets. We tested MotifBoost on the four small datasets which consist of various conditions such as Cytomegalovirus (CMV), HIV, α-chain, β-chain and it consistently preserved the stability. We also clarify that the robustness of MotifBoost can be attributed to the efficiency of k-mer motifs as representation features of repertoires. Finally, by comparing the predictions of these methods, we show that the whole sequence identity and sequence motifs encode partially different information and that a combination of such complementary information is necessary for further development of repertoire analysis.

Machine Learning Approaches to TCR Repertoire Analysis

Sparked by the development of genome sequencing technology, the quantity and quality of data handled in immunological research have been changing dramatically. Various data and database platforms are now driving the rapid progress of machine learning for immunological data analysis. Of various topics in immunology, T cell receptor repertoire analysis is one of the most important targets of machine learning for assessing the state and abnormalities of immune systems. In this paper, we review recent repertoire analysis methods based on machine learning and deep learning and discuss their prospects.

Psychiatric symptoms influence reward-seeking and loss-avoidance decision-making through common and distinct computational processes

AIM

Psychiatric symptoms are often accompanied by impairments in decision-making to attain rewards and avoid losses. However, due to the complex nature of mental disorders (e.g., high comorbidity), symptoms that are specifically associated with deficits in decision-making remain unidentified. Furthermore, the influence of psychiatric symptoms on computations underpinning reward-seeking and loss-avoidance decision-making remains elusive. Here, we aim to address these issues by leveraging a large-scale online experiment and computational modeling.

METHODS

In the online experiment, we recruited 1900 non-diagnostic participants from the general population. They performed either a reward-seeking or loss-avoidance decision-making task, and subsequently completed questionnaires about psychiatric symptoms.

RESULTS

We found that one trans-diagnostic dimension of psychiatric symptoms related to compulsive behavior and intrusive thought (CIT) was negatively correlated with overall decision-making performance in both the reward-seeking and loss-avoidance tasks. A deeper analysis further revealed that, in both tasks, the CIT psychiatric dimension was associated with lower preference for the options that recently led to better outcomes (i.e. reward or no-loss). On the other hand, in the reward-seeking task only, the CIT dimension was associated with lower preference for recently unchosen options.

CONCLUSION

These findings suggest that psychiatric symptoms influence the two types of decision-making, reward-seeking and loss-avoidance, through both common and distinct computational processes.

Age-related changes in the ease of dynamical transitions in human brain activity

Executive functions, a set of cognitive processes that enable flexible behavioral control, are known to decay with aging. Because such complex mental functions are considered to rely on the dynamic coordination of functionally different neural systems, the age-related decline in executive functions should be underpinned by alteration of large-scale neural dynamics. However, the effects of age on brain dynamics have not been firmly formulated. Here, we investigate such age-related changes in brain dynamics by applying "energy landscape analysis" to publicly available functional magnetic resonance imaging data from healthy younger and older human adults. We quantified the ease of dynamical transitions between different major patterns of brain activity, and estimated it for the default mode network (DMN) and the cingulo-opercular network (CON) separately. We found that the two age groups shared qualitatively the same trajectories of brain dynamics in both the DMN and CON. However, in both of networks, the ease of transitions was significantly smaller in the older than the younger group. Moreover, the ease of transitions was associated with the performance in executive function tasks in a doubly dissociated manner: for the younger adults, the ability of executive functions was mainly correlated with the ease of transitions in the CON, whereas that for the older adults was specifically associated with the ease of transitions in the DMN. These results provide direct biological evidence for age-related changes in macroscopic brain dynamics and suggest that such neural dynamics play key roles when individuals carry out cognitively demanding tasks.