Single Type Research Articles

Multiview Clustering With Propagating Information Bottleneck.

In many practical applications, massive data are observed from multiple sources, each of which contains multiple cohesive views, called hierarchical multiview (HMV) data, such as image-text objects with different types of visual and textual features. Naturally, the inclusion of source and view relationships offers a comprehensive view of the input HMV data and achieves an informative and correct clustering result. However, most existing multiview clustering (MVC) methods can only process single-source data with multiple views or multisource data with single type of feature, failing to consider all the views across multiple sources. Observing the rich closely related multivariate (i.e., source and view) information and the potential dynamic information flow interacting among them, in this article, a general hierarchical information propagation model is first built to address the above challenging problem. It describes the process from optimal feature subspace learning (OFSL) of each source to final clustering structure learning (CSL). Then, a novel self-guided method named propagating information bottleneck (PIB) is proposed to realize the model. It works in a circulating propagation fashion, so that the resulting clustering structure obtained from the last iteration can "self-guide" the OFSL of each source, and the learned subspaces are in turn used to conduct the subsequent CSL. We theoretically analyze the relationship between the cluster structures learned in the CSL phase and the preservation of relevant information propagated from the OFSL phase. Finally, a two-step alternating optimization method is carefully designed for optimization. Experimental results on various datasets show the superiority of the proposed PIB method over several state-of-the-art methods.

A Hybrid CMOS-Memristor Spiking Neural Network Supporting Multiple Learning Rules.

Artificial intelligence (AI) is changing the way computing is performed to cope with real-world, ill-defined tasks for which traditional algorithms fail. AI requires significant memory access, thus running into the von Neumann bottleneck when implemented in standard computing platforms. In this respect, low-latency energy-efficient in-memory computing can be achieved by exploiting emerging memristive devices, given their ability to emulate synaptic plasticity, which provides a path to design large-scale brain-inspired spiking neural networks (SNNs). Several plasticity rules have been described in the brain and their coexistence in the same network largely expands the computational capabilities of a given circuit. In this work, starting from the electrical characterization and modeling of the memristor device, we propose a neuro-synaptic architecture that co-integrates in a unique platform with a single type of synaptic device to implement two distinct learning rules, namely, the spike-timing-dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM). This architecture, by exploiting the aforementioned learning rules, successfully addressed two different tasks of unsupervised learning.

Multi-Behavior Graph Neural Networks for Recommender System.

Recommender systems have been demonstrated to be effective to meet user's personalized interests for many online services (e.g., E-commerce and online advertising platforms). Recent years have witnessed the emerging success of many deep-learning-based recommendation models for augmenting collaborative filtering (CF) architectures with various neural network architectures, such as multilayer perceptron and autoencoder. However, the majority of them model the user-item relationship with single type of interaction, while overlooking the diversity of user behaviors on interacting with items, which can be click, add-to-cart, tag-as-favorite, and purchase. Such various types of interaction behaviors have great potential in providing rich information for understanding the user preferences. In this article, we pay special attention on user-item relationships with the exploration of multityped user behaviors. Technically, we contribute a new multi-behavior graph neural network (MBRec), which specially accounts for diverse interaction patterns and the underlying cross-type behavior interdependencies. In the MBRec framework, we develop a graph-structured learning framework to perform expressive modeling of high-order connectivity in behavior-aware user-item interaction graph. After that, a mutual relationship encoder is proposed to adaptively uncover complex relational structures and make aggregations across layer-specific behavior representations. Through comprehensive evaluation on real-world datasets, the advantages of our MBRec method have been validated under different experimental settings. Further analysis verifies the positive effects of incorporating the multi-behavioral context into the recommendation paradigm. In addition, the conducted case studies offer insights into the interpretability of user multi-behavior representations. We release our model implementation at https://github.com/akaxlh/MBRec.

MIS: A Multi-Identifier Management and Resolution System in the Metaverse

The metaverse gradually evolves into a virtual world containing a series of interconnected sub-metaverses. Diverse digital resources, including identities, contents, services, and supporting data, are key components of the sub-metaverse. Therefore, a Domain Name System (DNS)-like system is necessary for efficient management and resolution. However, the legacy DNS was designed with security vulnerabilities and trust risks due to centralized issues. Blockchain is used to mitigate these concerns due to its decentralized features. Additionally, it supports identity management as a default feature, making it a natural fit for the metaverse. While there are several DNS alternatives based on the blockchain, they either manage only a single type of identifiers or isolate identities from other sorts of identifiers, making it difficult for sub-metaverses to coexist and connect with each other. This article proposes a M ulti- I dentifier management and resolution S ystem (MIS) in the metaverse, supporting the registration, resolution, and inter-translation functions. The basic MIS is portrayed as a four-tier architecture on a consortium blockchain due to its manageability, enhanced security, and efficiency properties. On-chain data is lightweight and compressed to save on storage while accelerating reading and writing operations. The resource data is encrypted based on the attributes of the sub-metaverse in the storage tier for privacy protection and access control. For users with decentralization priorities, a modification named EMIS is built on top of Ethereum. Finally, MIS is implemented on two testbeds and is available online as the open-source system. The first testbed consists of 4 physical servers located in the UK and Malaysia while the second is made up of 200 virtual machines (VMs) spread over 26 countries across all 5 continents on Google Cloud. Experiments indicate that MIS provides efficient reading and writing performance than the legacy DNS and other public blockchain-based workarounds including EMIS and Ethereum Name Service (ENS).

Controlling the Collision Type and Frequency of Single Pt Nanoparticles at Chemically Modified Gold Electrodes.

Studying the electrochemical response of single nanoparticles at an electrode surface gives insight into the dynamic and stochastic processes that occur at the electrode interface. Herein, we investigated single platinum nanoparticle collision dynamics and type (elastic vs inelastic) at gold electrode surfaces modified with self-assembled monolayers (SAMs) of varying terminal chemistries. Collision events are measured via the faradaic current from catalytic reactions at the Pt surface. By changing the terminal, solution-facing group of a thiolate monolayer, we observed the effect of hydrophobicity at the solution-electrode interface on single-particle collisions by employing either a hydrophobic -CH3 terminal group (1-hexanethiol), a hydrophilic -OH terminal group (6-mercaptohexanol), or an equimolar mixture of the two. Changes in the terminal group lead to alterations in collision-induced current magnitude, collisional frequency, and the distinct shape of the collision event current transient. The effects of the terminal group of the SAM were probed by measuring quantitative differences in the events monitored through both the hydrogen evolution reaction (HER) and hydrazine oxidation. In both cases, a platinum nanoparticle (PtNP) favors adsorption to bare and hydrophilic surfaces but demonstrates elastic collision behavior when it collides with a hydrophobic surface. In the case of a mixed monolayer, distinct characteristics of hydrophobic and hydrophilic surfaces are observed. We report how single nanoparticle collisions can reveal nanoscale surface heterogeneity and can be used to manipulate the nature of single-particle interactions on an electrode surface by functionalized self-assembled monolayers.

(187) Efficiency and Safety of Combining Long-Acting and Short-Acting PDE5 Inhibitors

Abstract Introduction Erectile dysfunction is a condition that affects many men worldwide, significantly impacting their quality of life. PDE5 inhibitors (PDE5i) are commonly prescribed medications for treating this condition, but some patients do not adequately respond to these treatments Objective This study aims to evaluate the efficacy and safety of a combination of long-acting and short-acting PDE5 inhibitors in non-responding patients Methods The study was conducted on 40 men with erectile dysfunction refractory to PDE5 inhibitors. Participants were evaluated using questionnaires, clinical examinations, and paraclinical tests. All patients received a combination treatment of daily 5 mg tadalafil and 100 mg sildenafil on-demand, one hour before sexual intercourse. Patients were followed up for 12 weeks, and the results were assessed using questionnaires and recording of side effects. Results The average age of the participants was 47.4 years. The main etiologies of erectile dysfunction were vascular, psychogenic, age-related, or neurological. After 6 weeks of treatment, a statistically significant improvement was observed in erectile function scores (SHIM) and erectile rigidity scores (EHS). This improvement was maintained until the 12th week, although not significantly. Side effects were generally minor and well-tolerated. Conclusions The combination of long-acting and short-acting PDE5 inhibitors proved to be effective and safe in non-responding patients to PDE5 inhibitors. This therapeutic approach offers a promising alternative before resorting to other more invasive options, such as prostaglandins or penile prosthesis. The combined use of these two types of inhibitors improves erectile function while minimizing undesirable side effects. Further studies could explore the addition of vacuum devices, shockwave therapy, or platelet-rich plasma injections to optimize outcomes. Overall, this study provides valuable insights for urologists in the treatment of non-responding patients to a single type of PDE5 inhibitor, opening up new therapeutic perspectives. Disclosure No.

When contract clauses conflict: The Salvador light rail vehicle concession

Infrastructure concession contracts may include clauses that allow for capacity expansion, term extensions, or even risk management strategies such as cap and floor schemes. These clauses provide project managers with the flexibility required to adapt their operational strategies as new information is revealed in time and limit investor risk. However, when contracts include combinations of these clauses, determining their overall impact on the objectives of the contract and the project is non-trivial and require the combined analysis of these effects. While the literature on the valuation of infrastructure projects tends to focus on a single type of flexibility at a time, we analyze the combined interaction between these clauses and their impact on the overall value of the project under the real options approach. Using the Salvador Light Rail Vehicle (LRV) concession project as a numerical example, our results suggest that these clause interactions can have non-intuitive results and negatively affect the stated objectives of both parties in the contract. We also show how policy makers can use this information to improve the value of the project to society and reduce the risk to the government.

Male and female red-cheeked cordon bleus sing similar yet individualistic songs.

Birdsong is an excellent system for studying complex vocal signaling in both males and females. Historically, most research in captivity has focused only on male song. This has left a gap in our understanding of the environmental, neuroendocrine, and mechanistic control of female song. Here, we report the overall acoustic features, repertoire, and stereotypy of both male and female Red-Cheeked Cordon Bleus (Uraeginthus bengalus) (RCCBs) songs in the lab. We found few sex differences in the acoustic structure, song repertoire, and song stereotypy of RCCBs. Both sexes had similar song entropy, peak frequency, and duration. Additionally, individuals of both sexes sang only a single song type each and had similar levels of song and syllable stereotypy. However, we did find that female RCCBs had higher song bandwidth but lower syllable repertoires. Finally, and most strikingly, we found highly individualistic songs in RCCBs. Each individual produced a stereotyped and unique song with no birds sharing song types and very few syllable types being shared between birds of either sex. We propose that RCCBs represent a promising species for future investigations of the acoustic sex differences in song in a lab environment, and also for understanding the evolutionary driving forces behind individualistic songs.

Mutual interaction of neurons and astrocytes derived from iPSCs with APP V717L mutation developed the astrocytic phenotypes of Alzheimer’s disease

BackgroundThe development of induced pluripotent stem cells (iPSCs) technology has enabled human cellular disease modeling for inaccessible cell types, such as neural cells in the brain. However, many of the iPSC-derived disease models established to date typically involve only a single cell type. These monoculture models are inadequate for accurately simulating the brain environment, where multiple cell types interact. The limited cell type diversity in monoculture models hinders the accurate recapitulation of disease phenotypes resulting from interactions between different cell types. Therefore, our goal was to create cell models that include multiple interacting cell types to better recapitulate disease phenotypes.MethodsTo establish a co-culture model of neurons and astrocytes, we individually induced neurons and astrocytes from the same iPSCs using our novel differentiation methods, and then co-cultured them. We evaluated the effects of co-culture on neurons and astrocytes using immunocytochemistry, immuno-electron microscopy, and Ca2+ imaging. We also developed a co-culture model using iPSCs from a patient with familial Alzheimer's disease (AD) patient (APPV717L mutation) to investigate whether this model would manifest disease phenotypes not seen in the monoculture models.ResultsThe co-culture of the neurons and astrocytes increased the branching of astrocyte processes, the number of GFAP-positive cells, neuronal activities, the number of synapses, and the density of presynaptic vesicles. In addition, immuno-electron microscopy confirmed the formation of a tripartite synaptic structure in the co-culture model, and inhibition of glutamate transporters increased neuronal activity. Compared to the co-culture model of the control iPSCs, the co-culture model of familial AD developed astrogliosis-like phenotype, which was not observed in the monoculture model of astrocytes.ConclusionsCo-culture of iPSC-derived neurons and astrocytes enhanced the morphological changes mimicking the in vivo condition of both cell types. The formation of the functional tripartite synaptic structures in the co-culture model suggested the mutual interaction between the cells. Furthermore, the co-culture model with the APPV717L mutation expressed in neurons exhibited an astrocytic phenotype reminiscent of AD brain pathology. These results suggest that our co-culture model is a valuable tool for disease modeling of neurodegenerative diseases.

A French classification to describe medical deserts: a multi-professional approach based on the first contact with the healthcare system

BackgroundIncreasing inequalities in accessibility to primary care has generated medical deserts. Identifying them is key to target the geographic areas where action is needed. An extensive definition of primary care has been promoted by the World Health Organization: a first level of contact with the health system, which involves the co-presence of different categories of health professionals alongside the general practitioner for the diagnosis and treatment of patients. Previous analyses have focused mainly on a single type of provider while this study proposes an integrated approach including various ones to define medical deserts in primary care.MethodOur empirical approach focuses on the first point of contact with the health system: general practitioners, proximity primary care providers (nurses, physiotherapists, pharmacies, laboratories, and radiologists), and emergency services. A multiple analysis approach was performed, to classify French municipalities using the information on the evolution and needs of health care accessibility, combining a principal component analysis and a hierarchical ascending classification.ResultsTwo clusters of medical deserts were identified with low accessibility to all healthcare professionals, socio-economic disadvantages, and a decrease in care supply. In other clusters, accessibility difficulties only concern a part of the health supply considered, which raises concern for the efficiency of primary care for optimal healthcare pathways. Even for clusters with better accessibility, issues were identified, such as a decrease and high needs of health care supply, revealing potential future difficulties.ConclusionThis work proposes a multi-professional and multi-dimensional approach to medical deserts based mainly on an extensive definition of primary care that shows the relevance of the co-presence of various healthcare professionals. The classification also makes it possible to identify areas with future problems of accessibility and its potential consequences. This framework could be easily applied to other countries according to their available data and their health systems’ specificities.

Ready-to-Eat Food Environments and Risk of Incident Heart Failure: A Prospective Cohort Study.

Food environments have been linked to cardiovascular diseases; however, few studies have assessed the relationship between food environments and the risk of heart failure (HF). We aimed to evaluate the association between ready-to-eat food environments and incident HF at an individual level in a large prospective cohort. Exposure to ready-to-eat food environments, comprising pubs or bars, restaurants or cafeterias, and fast-food outlets, were individually measured as both proximity and density metrics. We also developed a composite ready-to-eat food environment density score by summing the densities of 3 types of food environments. Cox proportional analyses were applied to assess the associations of each single type and the composite food environments with HF risk. Closer proximity to and greater density of ready-to-eat food environments, particularly for pubs and bars and fast-food outlets (P<0.05 for both proximity and density metric) were associated with an elevated risk of incident HF. Compared with those with no exposure to composite ready-to-eat food environments, participants in the highest density score category had a 16% (8%-25%; P<0.0001) higher risk of HF. In addition, we found significant interactions of food environments with education, urbanicity, and density of physical activity facilities on HF risk (all Pinteraction<0.05); the ready-to-eat food environments-associated risk of HF was stronger among participants who were poorly educated, living in urban areas, and without physical activity facilities. Exposure to ready-to-eat food environments is associated with a higher risk of incident HF, suggesting the potential importance of minimizing unfavorable food environments in the prevention of HF.

Classification of asbestos and their nonasbestiform analogues using FTIR and multivariate data analysis

This study presents a possible application of Fourier transform infrared (FTIR) spectrometry and multivariate data analysis, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) for classifying asbestos and their nonasbestiform analogues. The objectives of the study are: 1) to classify six regulated asbestos types and 2) to classify between asbestos types and their nonasbestiform analogues. The respirable fraction of six regulated asbestos types and their nonasbestiform analogues were prepared in potassium bromide pellets and collected on polyvinyl chloride membrane filters for FTIR measurement. Both PCA and PLS-DA classified asbestos types and their nonasbestiform analogues on the score plots showed a very distinct clustering of samples between the serpentine (chrysotile) and amphibole groups. The PLS-DA model provided ∼95% correct prediction with a single asbestos type in the sample, although it did not provide all correct predictions for all the challenge samples due to their inherent complexity and the limited sample number. Further studies are necessary for a better prediction level in real samples and standardization of sampling and analysis procedures.

Complementarity of offshore energy resources on the Spanish coasts: Wind, wave, and photovoltaic energy

The complementarity of the solar, wind, and wave energy resource in hybrid offshore platforms has the potential to increase productivity and reduce the variability in the energy output that a single type of energy source can generate. In this study, ERA5 reanalysis is used to calculate wind, solar and wave energy resources in Spanish potential locations for offshore platforms. The results indicate that wind energy presents the largest energy resource for all Spanish offshore regions, followed by photovoltaic energy. However, taking in count the “drought periods” (periods in which no energy is obtained from any of the analysed technologies), wave energy presents an opportunity to provide a continuous flow of energy, especially in the northwestern Iberian Peninsula and the Canary Islands. The evaluation of the complementarity of the three energy sources shows that the use of hybrid platforms would not only increase energy production but also reduce variability. In terms of energy production, wind energy along with solar energy are the largest energy generators. But in terms of minimizing variability, wave energy along with solar photovoltaic energy are the most important. Thus, this study is paving the way to introduce multiple energy converters on hybrid platforms as a pathway toward more powerful and sustainable energy production.

Influence of Particle Size on Compressed Earth Blocks Properties and Strategies for Enhanced Performance

In the context of sustainable building development, Compressed Earth Blocks (CEBs) have garnered increasing attention in recent years owing to their minimal environmental and economic impact. However, owing to the inherent diversity of raw soil and the production process’s reliance on expertise, the properties of these blocks are subjected to multifaceted influences. Among these, the significance of soil particle size variation often remains overlooked, leaving its impact ambiguous. This study endeavours to address this gap in existing research by delving into this aspect. Two distinct batches of CEBs were produced by adjusting the grain size curve of a single type of sieved soil with different maximum mesh openings: 2 mm for R1 CEBs and 12.5 mm for R2 CEBs. Experimental results reveal significant differences in thermophysical characteristics: on average, R1 blocks show superior thermal performance, boasting a 23% reduction in thermal conductivity compared to R2 blocks, and are lighter, with an 8% decrease in dry bulk density. Although no significant changes in mechanical parameters were observed, finer-structured R1 blocks showed a 25% greater tendency to absorb water due to changes in their porous structure. This study sheds light on the sensitivity of thermal parameters to changes in soil particle size and shows that blocks with finer particles exhibit poorer heat conduction and heat diffusion. Besides providing new insights into the literature, this research also provides a strategic approach to optimise the thermophysical properties of CEBs. By understanding the influence of particle size, researchers and practitioners can now develop strategies to enhance these properties and improve the overall performance of CEBs.

Optimal allocation of resources among general and species-specific tools for plant pest biosecurity surveillance.

This paper proposes a surveillance model for plant pests that can optimally allocate resources among survey tools with varying properties. While some survey tools are highly specific for the detection of a single pest species, others are more generalized. There is considerable variation in the cost and sensitivity of these tools, but there are no guidelines or frameworks for identifying which tools are most cost-effective when used in surveillance programs that target the detection of newly invaded populations. To address this gap, we applied our model to design a trapping surveillance program in New Zealand for bark- and wood-boring insects, some of the most serious forest pests worldwide. Our findings show that exclusively utilizing generalized traps (GTs) proves to be highly cost-effective across a wide range of scenarios, particularly when they are capable of capturing all pest species. Implementing surveillance programs that only employ specialized traps (ST) is cost-effective only when these traps can detect highly damaging pests. However, even in such cases, they significantly lag in cost-effectiveness compared to GT-only programs due to their restricted coverage. When both GTs and STs are used in an integrated surveillance program, the total expected cost (TEC) generally diminishes when compared to programs relying on a single type of trap. However, this relative reduction in TEC is only marginally larger than that achieved with GT-only programs, as long as highly damaging species can be detected by GTs. The proportion of STs among the optimal required traps fluctuates based on several factors, including the relative pricing of GTs and STs, pest arrival rates, potential damage, and, more prominently, the coverage capacity of GTs. Our analysis suggests that deploying GTs extensively across landscapes appears to be more cost-effective in areas with either very high or very low levels of relative risk density, potential damage, and arrival rate. Finally, STs are less likely to be required when the pests that are detected by those tools have a higher likelihood of successful eradication because delaying detection becomes less costly for these species.

Refleksi Orientasi Pendidikan Islam Berbasis Kecerdasan Profetik

The orientation of Islamic education generally refers to two directions of interest, namely serving Allah SWT and understanding humans as caliphs on earth with various kinds of duties and responsibilities. The focus of this research is prophetic intelligence in growing the potential of Islamic life through the orientation of Islamic education at the Puncak Darussalam Islamic Boarding School and maintaining a good balance of relationships between individual intelligence (Intelligence Quotient), emotional intelligence (Emotional Quotient) and spiritual intelligence (Spiritual Quotient) which is prophetic. The figure of the Prophet Muhammad SAW as uswah hasanah or the best role model for mankind. This research uses a descriptive qualitative approach with a single case study type. The important findings of this research are: (a) the growth of the potential for students' Islamic life and an environment based on prophetic intelligence (b) the maintenance of a good, balanced relationship between humans and their God as caliph on earth. These findings indicate the importance of Islamic boarding schools responding positively in managing students and their environment in accordance with the orientation of Islamic education and the Islamic boarding school's vision and mission

FedHIP: Federated learning for privacy-preserving human intention prediction in human-robot collaborative assembly tasks

Human-robot collaboration is a promising solution to relieve construction workers from repetitive and physically demanding tasks, thus improving construction safety and productivity. Many studies have developed various deep learning models for human intention prediction, which will form the basis for proactive and adaptive robot planning and control to enable intelligent human-robot collaboration. However, there remain two challenges. First, most research only focuses on a single type of human intention, without a holistic understanding of multi-level intention, including both high-level intended actions and objects of interest, and low-level body movements. Second, conventional deep learning approaches train a centralized model with aggregated datasets, which requires the sharing of sensitive information (e.g., personal images and behavior data), posing broad privacy concerns in practical implementation. This study proposes a vision-based multi-task federated learning (FL) framework, FedHIP, for multi-level human intention prediction in human-robot collaborative assembly tasks. Specifically, taking body movements and assembly components as inputs, a long short-term memory based multi-task learning model was developed to simultaneously predict multi-level human intention in assembly tasks. FL was employed to train the model in a distributed and privacy-preserving way on local clients without the need of transmitting sensitive data. The results show that the proposal FedHIP without and with pre-train can achieve an accuracy of 80.1% and 85.7% in action prediction, 97.6% and 97.8% in object prediction, and an average displacement error of 12.7 and 11.7 pixels in motion prediction, respectively. Models trained from FedHIP were also compared with those obtained from traditional centralized training and local training. It was found that FL leads to compatible accuracy with centralized training and much higher accuracy than local training while preserving data privacy.

C2A-DC: A context-aware adaptive data cube framework for environmental monitoring and climate change crisis management

A context-aware adaptive data cube (C2A-DC) framework based on Earth Observation (EO) data for environmental monitoring to mitigate Climate Change (CC) effects is proposed. It has the property of combining DC formation, calculation of Remote Sensing (RS) operations, deep and machine learning algorithms for classification and data harmonization by updating the layers of the DC using information obtained from the previous operations, all applied to EO data originating from a satellite selected by a stakeholder. Moreover, the proposed framework is context-aware adaptive in the sense that it allows for environmental monitoring tasks according to a stakeholder’s needs, which is in contrast to existing works being constrained to a single type of environmental monitoring. Furthermore, with the proposed framework end-users do not have to make any additional processing action to handle the data as they are given to them as a set of analysis-ready data, harmonically aligned under a unique DC. To showcase the C2A-DC framework’s effectiveness and applicability in environmental monitoring, crisis management, and post-disaster assessment, it is applied to two real-world cases. The first one corresponds to cloud and shadow detection in a flood event, with several Convolutional Neural Network (CNN) architectures being trained on the C2S-MS floods dataset for classification, while k-means clustering and RS product calculations are used for monitoring scenery changes. The second one corresponds to deforestation monitoring from fires, evaluated using RS products.

Force-triggered hypso- and bathochromic bidirectional fluorescence switching beyond 120 nm and its anticounterfeiting applications.

Achieving high-contrast tricolor emissive regulation of a single-component molecule using a single type of external stimulus is highly desirable but challenging. In the present study, we report a symmetric acceptor-donor-acceptor (A-D-A)-type aggregation-induced emission-active luminogen, which displays a sequential high-contrast fluorescence switching just by anisotropic mechanical grinding. Specifically, upon light grinding, an orange-yellow-to-blue hypsochromic mechanofluorochromic response with a distinct color contrast (change in the maximum emission wavelength, Δλem,max = 122 nm) is noticed, and the slightly ground solid exhibits a blue-to-red high-contrast (Δλem,max = 185 nm) bathochromic mechanofluorochromic conversion upon vigorous grinding. Thus, using a single luminogen developed here, we can realize wide-range (Δλem,max > 100 nm) hypso- and bathochromic fluorescence mechanochromisms simultaneously. The tricolored mechanofluorochromic phenomenon is attributed to two different morphological transitions involving crystalline-to-crystalline and crystalline-to-amorphous states. Furthermore, three information anticounterfeiting systems are developed using the luminogen presented here.

Cell-type-specific cis-regulatory divergence in gene expression and chromatin accessibility revealed by human-chimpanzee hybrid cells.

Although gene expression divergence has long been postulated to be the primary driver of human evolution, identifying the genes and genetic variants underlying uniquely human traits has proven to be quite challenging. Theory suggests that cell-type-specific cis-regulatory variants may fuel evolutionary adaptation due to the specificity of their effects. These variants can precisely tune the expression of a single gene in a single cell-type, avoiding the potentially deleterious consequences of trans-acting changes and non-cell type-specific changes that can impact many genes and cell types, respectively. It has recently become possible to quantify human-specific cis-acting regulatory divergence by measuring allele-specific expression in human-chimpanzee hybrid cells-the product of fusing induced pluripotent stem (iPS) cells of each species in vitro. However, these cis-regulatory changes have only been explored in a limited number of cell types. Here, we quantify human-chimpanzee cis-regulatory divergence in gene expression and chromatin accessibility across six cell types, enabling the identification of highly cell-type-specific cis-regulatory changes. We find that cell-type-specific genes and regulatory elements evolve faster than those shared across cell types, suggesting an important role for genes with cell-type-specific expression in human evolution. Furthermore, we identify several instances of lineage-specific natural selection that may have played key roles in specific cell types, such as coordinated changes in the cis-regulation of dozens of genes involved in neuronal firing in motor neurons. Finally, using novel metrics and a machine learning model, we identify genetic variants that likely alter chromatin accessibility and transcription factor binding, leading to neuron-specific changes in the expression of the neurodevelopmentally important genes FABP7 and GAD1. Overall, our results demonstrate that integrative analysis of cis-regulatory divergence in chromatin accessibility and gene expression across cell types is a promising approach to identify the specific genes and genetic variants that make us human.