Intrinsic Patterns Research Articles

Intrinsic immune evasion patterns predict temozolomide sensitivity and immunotherapy response in lower-grade gliomas

BackgroundAlthough intrinsic immune-evasion is important in cancer proliferation, metastasis and response to treatment, it is unclear whether intrinsic immune-evasion patterns of gliomas can aid in predicting clinical prognosis and determining treatment.MethodsA total of 182 immune-evasion genes intrinsic to cancer were subjected to consensus clustering to identify immune-evasion patterns in 1421 patients with lower-grade glioma (LGG). The levels of each cancer hallmark were determined by the Gene Set Variant Analysis (GSVA) method, and immune cell infiltrations were quantified using two algorithms, the single-sample Gene Set Enrichment Analysis (ssGSEA) and the Cell-type Identification By Estimating Relative Subsets Of RNA Transcripts (CIBERSORT) methods. IEVscore was determined by a method that combined univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression and principal component analysis (PCA).ResultsTranscriptional and genomic analysis showed that most immune evasion genes (IEVGs) were upregulated in LGGs, with aberrant expression driven by alterations in copy number variants (CNV). Based on the mRNA expression profiles of cancer-intrinsic IEVGs could be divided into three LGG subgroups with distinct prognosis, clinicopathological features and immune infiltrations. A combined scoring scheme designed to assess the immune-evasion levels of LGGs divided these 1421 patients into two subgroups that differed in IEVscores. LGG patients with low-IEVscore had a better prognosis, would be more likely to benefit from immune check-point inhibitors and would be more susceptible to temozolomide (TMZ) chemotherapy.ConclusionIntrinsic immune evasion in the tumor microenvironment (TME) has a crucial effect on glioma formation. Quantitatively assessing the IEV scores of individual LGG patients could enhance knowledge about the intra-glioma microenvironment and lead to the development of individualized therapeutic strategies for patients with LGG.

Multi-scale GAN with transformer for surface defect inspection of IC metal packages

Integrated circuit plays an important role in the information technology industry. Surface defect inspection of IC packages is an essential process in the IC packaging manufacturing. Here, an automatic optical inspection system is proposed based on semi-supervised deep learning for surface defect inspection of IC metal packages. Different from previous inspection methods, we propose an entirely multi-scale inspection framework to implement the evaluation of defects at multiple scales. To well capture the intrinsic patterns of qualified samples at multiple scales, an entirely multi-scale GAN with transformer is elaborately designed, which incorporates several novel modules. Specifically, multi-scale CNN encoder with a novel feature extraction scheme and a cross-scale feature fusion module is designed to sufficiently extract the features from the IC metal package image. Different from previous GAN models, a Swin Transformer decoder is designed to strengthen the modeling ability of the proposed GAN model. Also, several novel multi-scale inspection schemes, including multi-scale weight mask, multi-scale adaptive thresholding and multi-scale image patch-based defect evaluation, are proposed to suppress the reconstruction errors, highlight the potential defects and further evaluate them at multiple scales, respectively. Experimental results demonstrate the effectiveness and feasibility of the proposed multi-scale inspection framework, which achieves an excellent inspection performance of 0.70% error rate, 0.57% omission rate, 99.3% accuracy, 99.8% precision, 99.3% recall and 0.996 F-score with a reasonable speed of 70.9 FPS and is superior to the state-of-the-art semi-supervised deep learning inspection methods.

“In The Context Of Post Digital Aesthetics” Experiment Of An Interactive Circuit Sniffing

20. yüzyıl düşüncesinden hareketle, modernizm şemsiyesi altında yeni bir hayata başlayan insan, teknolojik gelişmelerin ışığında hatasız bir dünya yaratma telaşına kapılmış ve bir bakıma makinalara bağımlı yeni bir yüzyılın paydaşı olmuştur. Endüstri devriminin sonrasında hızla gelişimini sürdüren yeni iletişim ve ulaşım araçları ile insanoğlu eskiye göre daha da baskın bir biçimde gürültü ile yüzleşmiştir. Fütürizm sanat akımı ile sonik sanatların güdümüne girmeye başlayan gürültü; 20. Yüzyılın son evresinde post dijital estetik başlığı altında da ilk formlarını göstermiştir . Bir sonik pratik olarak internet aracılığı ile akademik bir alt yapı ve öğretimi olmadan kendi kendine öğrenme metodu ile gelişen glitch, microwave sinecore v.d; gibi isimler alarak hem analog hem de dijital dünyada dolaylı ya da dolaysız oluşan hataların ve kusurların bir sanatsal uğraşı olarak yansımasıdır Bu çalışmada bu düşünceden hareketle “post dijital estetik” kavramınının izinde çevremizi çepeçevre saran fark edemediğimiz donanımların, öz sonik örüntülerinden oluşan yeni bir sonik evreni interaktif olarak paylaşma çabası içerisinde olacaktır

Intrinsic brain activity patterns across large-scale networks predict reciprocity propensity.

Reciprocity is prevalent across human societies, but individuals are heterogeneous regarding their reciprocity propensity. Although a large body of task-based brain imaging measures has shed light on the neural underpinnings of reciprocity at group level, the neural basis underlying the individual differences in reciprocity propensity remains largely unclear. Here, we combined brain imaging and machine learning techniques to individually predict reciprocity propensity from resting-state brain activity measured by fractional amplitude of low-frequency fluctuation. The brain regions contributing to the prediction were then analyzed for functional connectivity and decoding analyses, allowing for a data-driven quantitative inference on psychophysiological functions. Our results indicated that patterns of resting-state brain activity across multiple brain systems were capable of predicting individual reciprocity propensity, with the contributing regions distributed across the salience (e.g., ventrolateral prefrontal cortex), fronto-parietal (e.g., dorsolateral prefrontal cortex), default mode (e.g., ventromedial prefrontal cortex), and sensorimotor (e.g., supplementary motor area) networks. Those contributing brain networks are implicated in emotion and cognitive control, mentalizing, and motor-based processes, respectively. Collectively, these findings provide novel evidence on the neural signatures underlying the individual differences in reciprocity, and lend support the assertion that reciprocity emerges from interactions among regions embodied in multiple large-scale brain networks.

The Revival of Structuralism: The Periodic Table of Mental Science

Physical science solved an age-old problem in the 19th century: what makes elements similar or dissimilar? Mendeleev generally is given credit for the discovery of the underlying structure of chemical elements, known as the periodic table. Like chemicals, qualia seem to share different relationships within a modality and between modalities. Wundt’s structuralism represents an early effort to build the structure of mind through data obtained by introspection. Unfortunately, as have many other subjects, structuralism has been victimized by behaviorism’s domination. And the cognitive revolution did not completely eliminate the unfavorable status of consciousness, thus hampering the revival of structuralism unlike many other topics in psychology. With the subject of consciousness having been just about fully sanctioned by science beginning in the early 1990s, the time has come to build the periodic table of mental science by uncovering the hidden patterns of qualia. This paper proposes four different scales of intrinsic patterns: the notion of bipolar dimension, the three complementaries, the principle of square of opposition, and the double-cone system. They are able to accommodate 15 different modalities in a systematic and unified way: chromatic, acoustic, tactile, olfactory, gustatory, exteroceptive motion, exteroceptive orientative, exteroceptive locus, proprioceptive motion, proprioceptive orientative, proprioceptive locus, magnitude, emotive, hedonistic, and predicative.

Multiscale Methods for Signal Selection in Single-Cell Data.

Analysis of single-cell transcriptomics often relies on clustering cells and then performing differential gene expression (DGE) to identify genes that vary between these clusters. These discrete analyses successfully determine cell types and markers; however, continuous variation within and between cell types may not be detected. We propose three topologically motivated mathematical methods for unsupervised feature selection that consider discrete and continuous transcriptional patterns on an equal footing across multiple scales simultaneously. Eigenscores () rank signals or genes based on their correspondence to low-frequency intrinsic patterning in the data using the spectral decomposition of the Laplacian graph. The multiscale Laplacian score (MLS) is an unsupervised method for locating relevant scales in data and selecting the genes that are coherently expressed at these respective scales. The persistent Rayleigh quotient (PRQ) takes data equipped with a filtration, allowing the separation of genes with different roles in a bifurcation process (e.g., pseudo-time). We demonstrate the utility of these techniques by applying them to published single-cell transcriptomics data sets. The methods validate previously identified genes and detect additional biologically meaningful genes with coherent expression patterns. By studying the interaction between gene signals and the geometry of the underlying space, the three methods give multidimensional rankings of the genes and visualisation of relationships between them.

Intrinsic brain abnormalities in female major depressive disorder patients with childhood trauma: A resting-state functional magnetic resonance imaging study.

ObjectiveChildhood trauma is a strong predictor of major depressive disorder (MDD). Women are more likely to develop MDD than men. However, the neural basis of female MDD patients with childhood trauma remains unclear. We aimed to identify the specific brain regions that are associated with female MDD patients with childhood trauma.MethodsWe recruited 16 female MDD patients with childhood trauma, 16 female MDD patients without childhood trauma, and 20 age- and education level-matched healthy controls. All participants underwent resting-state functional magnetic resonance imaging (MRI). Regional brain activity was evaluated as the amplitude of low-frequency fluctuation (ALFF). Furthermore, functional connectivity (FC) analyses were performed on areas with altered ALFF to explore alterations in FC patterns.ResultsThere was increased ALFF in the left middle frontal gyrus (MFG) and the right postcentral gyrus (PoCG) in MDD with childhood trauma compared with MDD without childhood trauma. The areas with significant ALFF discrepancies were selected as seeds for the FC analyses. There was increased FC between the left MFG and the bilateral putamen gyrus. Moreover, ALFF values were correlated with childhood trauma severity.ConclusionOur findings revealed abnormal intrinsic brain activity and FC patterns in female MDD patients with childhood trauma, which provides new possibilities for exploring the pathophysiology of this disorder in women.

Hexagonal Voronoi pattern detected in the microstructural design of the echinoid skeleton.

Repeated polygonal patterns are pervasive in natural forms and structures. These patterns provide inherent structural stability while optimizing strength-per-weight and minimizing construction costs. In echinoids (sea urchins), a visible regularity can be found in the endoskeleton, consisting of a lightweight and resistant micro-trabecular meshwork (stereom). This foam-like structure follows an intrinsic geometrical pattern that has never been investigated. This study aims to analyse and describe it by focusing on the boss of tubercles—spine attachment sites subject to strong mechanical stresses—in the common sea urchin Paracentrotus lividus. The boss microstructure was identified as a Voronoi construction characterized by 82% concordance to the computed Voronoi models, a prevalence of hexagonal polygons, and a regularly organized seed distribution. This pattern is interpreted as an evolutionary solution for the construction of the echinoid skeleton using a lightweight microstructural design that optimizes the trabecular arrangement, maximizes the structural strength and minimizes the metabolic costs of secreting calcitic stereom. Hence, this identification is particularly valuable to improve the understanding of the mechanical function of the stereom as well as to effectively model and reconstruct similar structures in view of future applications in biomimetic technologies and designs.

Spontaneous Thalamic Activity Modulates the Cortical Innervation of the Primary Visual Nucleus of the Thalamus

Sensory processing relies on the correct development of thalamocortical loops. Visual corticothalamic axons (CTAs) invade the dorsolateral geniculate nucleus (dLGN) of the thalamus in early postnatal mice according to a regulated program that includes activity-dependent mechanisms. Spontaneous retinal activity influences the thalamic incursion of CTAs, yet the perinatal thalamus also generates intrinsic patterns of spontaneous activity whose role in modulating afferent connectivity remains unknown. Here, we found that patterned spontaneous activity in the dLGN contributes to proper spatial and temporal innervation of CTAs. Disrupting patterned spontaneous activity in the dLGN delays corticogeniculate innervation under normal conditions and upon eye enucleation. The delayed innervation was evident throughout the first two postnatal weeks but resumes after eye-opening, suggesting that visual experience is necessary for the homeostatic recovery of corticogeniculate innervation.

Artificial intelligence - A boon for dentistry

Today artificial intelligence (AI) is becoming increasingly important in the healthcare industry. It can be useful in many areas where humans can be aided by new technologies. The concept of "artificial intelligence" (AI) refers to machines being capable of performing human tasks. Machine learning (ML) is an AI subdomain that "learns" intrinsic statistical patterns in data to ultimately make predictions on unseen data. Deep learning is a machine learning technique that employs multi-layer mathematical operations to learn and infer on complex data such as imagery. With the substantial growth in documented information and patient data, intelligent data computation software has become a requirement. Artificial intelligence has a wide range of applications in medicine and dentistry, from data processing and information retrieval to the use of neural networks for diagnosis and the incorporation of augmented reality and virtual reality into dental education. Artificial intelligence (AI) is being studied in dentistry for a variety of purposes, including the identification of normal and anomalous structures, disease diagnosis, and treatment outcome prediction. This review looks at some current and future applications of AI in dentistry. We are ushering in a modern age, and AI is undeniably the future of dental practise management.

Vegetation Formation in Staphylococcus Aureus Endocarditis Inversely Correlates With RNAIII and sarA Expression in Invasive Clonal Complex 5 Isolates.

Infective endocarditis (IE) is one of the most feared and lethal diseases caused by Staphylococcus aureus. Once established, the infection is fast-progressing and tissue destructive. S. aureus of the clonal complex 5 (CC5) commonly cause IE yet are severely understudied. IE results from bacterial colonization and formation of tissue biofilms (known as vegetations) on injured or inflamed cardiac endothelium. S. aureus IE is promoted by adhesins, coagulases, and superantigens, with the exotoxins and exoenzymes likely contributing to tissue destruction and dissemination. Expression of the large repertoire of virulence factors required for IE and sequelae is controlled by complex regulatory networks. We investigated the temporal expression of the global regulators agr (RNAIII), rot, sarS, sarA, sigB, and mgrA in 8 invasive CC5 isolates and established intrinsic expression patterns associated with IE outcomes. We show that vegetation formation, as tested in the rabbit model of IE, inversely correlates with RNAIII and sarA expression during growth in Todd-Hewitt broth (TH). Large vegetations with severe sequelae arise from strains with high-level expression of colonization factors but slower transition towards expression of the exotoxins. Overall, strains proficient in vegetation formation, a hallmark of IE, exhibit lower expression of RNAIII and sarA. Simultaneous high expression of RNAIII, sarA, sigB, and mgrA is the one phenotype assessed in this study that fails to promote IE. Thus, RNAIII and sarA expression that provides for rheostat control of colonization and virulence genes, rather than an on and off switch, promote both vegetation formation and lethal sepsis.

Predicting brain structural network using functional connectivity.

Uncovering the non-trivial brain structure-function relationship is fundamentally important for revealing organizational principles of human brain. However, it is challenging to infer a reliable relationship between individual brain structure and function, e.g., the relations between individual brain structural connectivity (SC) and functional connectivity (FC). Brain structure-function displays a distributed and heterogeneous pattern, that is, many functional relationships arise from non-overlapping sets of anatomical connections. This complex relation can be interwoven with widely existed individual structural and functional variations. Motivated by the advances of generative adversarial network (GAN) and graph convolutional network (GCN) in the deep learning field, in this work, we proposed a multi-GCN based GAN (MGCN-GAN) to infer individual SC based on corresponding FC by automatically learning the complex associations between individual brain structural and functional networks. The generator of MGCN-GAN is composed of multiple multi-layer GCNs which are designed to model complex indirect connections in brain network. The discriminator of MGCN-GAN is a single multi-layer GCN which aims to distinguish the predicted SC from real SC. To overcome the inherent unstable behavior of GAN, we designed a new structure-preserving (SP) loss function to guide the generator to learn the intrinsic SC patterns more effectively. Using Human Connectome Project (HCP) dataset and Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset as test beds, our MGCN-GAN model can generate reliable individual SC from FC. This result implies that there may exist a common regulation between specific brain structural and functional architectures across different individuals.

K2 Photometry on Oscillation Mode Variability: The New Pulsating Hot B Subdwarf Star EPIC 220422705

We present an analysis of oscillation mode variability in the hot B subdwarf star EPIC 220422705, a new pulsator discovered from ∼78 days of K2 photometry. The high-quality light curves provide a detection of 66 significant independent frequencies, from which we identified nine incomplete potential triplets and three quintuplets. Those g- and p-multiplets give rotation periods of ∼36 and 29 days in the core and at the surface, respectively, potentially suggesting a slightly differential rotation. We derived a period spacing of 268.5 s and 159.4 s for the sequence of dipole and quadrupole modes, respectively. We characterized the precise patterns of amplitude and frequency modulations (AM and FM) of 22 frequencies with high enough amplitude for our science. Many of them exhibit intrinsic and periodic patterns of AM and FM, with periods on a timescale of months as derived by the best fitting and Markov Chain Monte Carlo test. The nonlinear resonant mode interactions could be a natural interpretation for such AMs and FMs after other mechanisms are ruled out. Our results are the first step to building a bridge between mode variability from K2 photometry and the nonlinear perturbation theory of stellar oscillation.

Abnormal Voxel-Based Degree Centrality in Patients With Postpartum Depression: A Resting-State Functional Magnetic Resonance Imaging Study.

Postpartum depression (PPD) is a major public health concern with significant consequences for mothers, their children, and their families. However, less is known about its underlying neuropathological mechanisms. The voxel-based degree centrality (DC) analysis approach provides a new perspective for exploring the intrinsic dysconnectivity pattern of whole-brain functional networks of PPD. Twenty-nine patients with PPD and thirty healthy postpartum women were enrolled and received resting-state functional magnetic resonance imaging (fMRI) scans in the fourth week after delivery. DC image, clinical symptom correlation, and seed-based functional connectivity (FC) analyses were performed to reveal the abnormalities of the whole-brain functional network in PPD. Compared with healthy controls (HCs), patients with PPD exhibited significantly increased DC in the right hippocampus (HIP.R) and left inferior frontal orbital gyrus (ORBinf.L). The receiver operating characteristic (ROC) curve analysis showed that the area under the curve (AUC) of the above two brain regions is all over 0.7. In the seed-based FC analyses, the PPD showed significantly decreased FC between the HIP.R and right middle frontal gyrus (MFG.R), between the HIP.R and left median cingulate and paracingulate gyri (DCG.L), and between the ORBinf.L and the left fusiform (FFG.L) compared with HCs. The PPD showed significantly increased FC between the ORBinf.L and the right superior frontal gyrus, medial (SFGmed.R) compared with HCs. Mean FC between the HIP.R and DCG.L positively correlated with EDPS scores in the PPD group. This study provided evidence of aberrant DC and FC within brain regions in patients with PPD, which was associated with the default mode network (DMN) and limbic system (LIN). Identification of these above-altered brain areas may help physicians to better understand neural circuitry dysfunction in PPD.

Affective Enhancement of Episodic Memory Is Associated With Widespread Patterns of Intrinsic Functional Connectivity in the Brain Across the Adult Lifespan.

Subjectively arousing experiences tend to be better remembered than neutral ones. While numerous task-related neuroimaging studies have revealed the neural mechanisms associated with this phenomenon, it remains unclear how variability in the extent to which individuals show superior memory for subjectively arousing stimuli is associated with the intrinsic functional organization of their brains. Here, we addressed this issue using functional magnetic resonance imaging data collected at rest from a sample drawn from the Cambridge Centre for Ageing and Neuroscience cohort (N = 269, 18–86 years). Specifically, we performed multi-voxel pattern analysis of intrinsic functional connectivity, an unbiased, data-driven approach to examine whole-brain voxel-wise connectivity patterns. This technique allowed us to reveal the most important features from the high-dimensional, whole-brain connectivity structure without a priori hypotheses about the topography and direction of functional connectivity differences. Behaviorally, both item and associative memory accuracy were enhanced for trials with affectively arousing (positive or negative) stimuli than those with neutral ones. Whole-brain multi-voxel pattern analysis of functional connectivity revealed that the affective enhancement of memory was associated with intrinsic connectivity patterns of spatially distributed brain regions belonging to several functional networks in the cerebral cortex. Post hoc seed-based brain-behavior regression analysis and principal component analysis of the resulting correlation maps showed that these connectivity patterns were in turn primarily characterized by the involvement of heteromodal association and paralimbic (dorsal attention, salience, and default mode) networks of the cerebral cortex as well as select subcortical structures (striatum, thalamus, and cerebellum). Collectively, these findings suggest that the affective enhancement of episodic memory may be characterized as a whole-brain phenomenon, possibly supported by intrinsic functional interactions across several networks and structures in the brain.

RF-Gait: Gait-Based Person Identification with COTS RFID

Recently, person identification has been a prerequisite in many applications of the Internet of Things. As a new biometric identification technology, gait recognition has a wide application prospect with the advantages of long-distance recognition and difficulty to forge. However, the existing gait recognition methods have some problems, such as complex algorithm calculation, high user participation, and large equipment overhead. In this paper, we propose RF-Gait, a method that identifies a person through unobtrusive gait perception with COTS RFID. The key insight is that wireless signal fluctuation can be exploited to distinguish each person’s unique gait behavior. To this end, we first collect and preprocess the gait-induced data composed of multiple RFID tags. Furthermore, multivariate variational mode decomposition is utilized to extract the intrinsic features in the spatial multichannels cooperatively. By developing a support vector machine model, we identify a person via the intrinsic walking pattern. Finally, extensive experiments show that our method can identify a person with an average accuracy of 96.3% from a group of twenty persons in a complex indoor environment.

Altered Default Mode Network Functional Connectivity in Parkinson's Disease: A Resting-State Functional Magnetic Resonance Imaging Study.

PurposeWhether the intrinsic functional connectivity pattern of the default mode network (DMN) is involved in the progression of cognitive decline in Parkinson’s disease (PD) remains unclear. This study aimed to investigate the intrinsic functional connectivity (FC) pattern of the DMN anchored on the posterior cingulate cortex (PCC) in patients with PD by resting-state functional magnetic resonance imaging (fMRI).MethodsFifty patients with PD and 50 healthy controls (HCs) were included for resting-state fMRI scanning. A seed-based FC method was used to reveal FC patterns in the DMN with region of interest (ROI) in the PCC. Relationships between FC patterns and disease severity (UPDRS-III) were detected.ResultsCompared with the HCs, the patients with PD showed increased FC between the PCC and the right precuneus, left cuneus, and right angular gyrus. In the PD group, the increased FC values in the right precuneus were significantly and positively correlated with motor severity as assessed with UPDRS-III scores (rho = 0.337, p = 0.02).ConclusionOur result highlights that the patients with PD showed increased FC between the PCC and the right precuneus, left cuneus, and right angular gyrus in the DMN. The altered connectivity pattern in the DMN may play a crucial role in the neurophysiological mechanism of cognitive decline in patients with PD. These findings might provide new insights into neural mechanisms of cognitive decline in PD.

DIPG-05. How do potassium channels contribute to the growth and invasion of highly-aggressive brain cancers?

Glioblastoma (GBM) is a lethal form of high-grade glioma with a dismal median survival time of just 15 months. Characterised by its’ highly diffuse and intrinsic growth pattern, GBM invades the healthy brain at an alarming rate. While diffuse midline glioma (DMG) is a much rarer disease, it is even more lethal, with a 5-year survival rate of just 2%. Though GBM is largely diagnosed in adults, DMG primarily affects young children aged 5-9 years and accounts for 10% of all childhood CNS cancers. Thus, while rare, DMG is highly aggressive and currently has no effective treatments to extend survival times beyond the median of 9 months. There is a pressing need for the development of novel and improved targeted therapies for each of these devastating diseases. Ion channels have long been implicated in the progression of numerous cancer types, due to their integral roles in proliferation, cell cycle transition, apoptosis, migration, and cellular plasticity. Voltage-gated potassium channels (VGKCs), in particular, have strong links to the key processes of proliferation, migration and invasion in GBM tumours. Given that the majority of GBM-related deaths are attributed to secondary tumours and metastasis, targeting proteins that are integral to these processes could result in reduced recurrence. Preliminary evidence suggests a potential role for the VGKC subtypes Kv5.1, Kv7.2, and the Kv4 subfamily in GBM and DMG, due to observed upregulation of these genes in both patient-derived cell lines and tumour samples. These particular VGKC subtypes are highly novel with regards to these cancers, while their significant upregulation suggests they may be associated with tumour progression. Thus, we aim to further explore the relationship between ion channel function and tumourigenesis in GBM and DMG, with a specific focus on VGKC subtypes and their potential therapeutic value.

A confined-etching strategy for intrinsic anisotropic surface wetting patterning

Anisotropic functional patterned surfaces have shown significant applications in microfluidics, biomedicine and optoelectronics. However, surface patterning relies heavily on high-end apparatuses and expensive moulds/masks and photoresists. Decomposition behaviors of polymers have been widely studied in material science, but as-created chemical and physical structural changes have been rarely considered as an opportunity for wettability manipulation. Here, a facile mask-free confined-etching strategy is reported for intrinsic wettable surface patterning. With printing technology, the surface wetting state is regulated, enabling the chemical etching of setting locations and efficient fabrication of complex patterns. Notably, the created anisotropic patterns can be used for realizing water-responsive information storage and encryption as well as fabricating flexible electrodes. Featuring advantages of simple operation and economic friendliness, this patterning approach brings a bright prospect in developing functional materials with versatile applications.

Brain intrinsic connection patterns underlying tool processing in human adults are present in neonates and not in macaques

Tool understanding and use are supported by a dedicated left-lateralized, intrinsically connected network in the human adult brain. To examine this network's phylogenetic and ontogenetic origins, we compared resting-state functional connectivity (rsFC) among regions subserving tool processing in human adults to rsFC among homologous regions in human neonates and macaque monkeys (adolescent and mature). These homologous regions formed an intrinsic network in human neonates, but not in macaques. Network topological patterns were highly similar between human adults and neonates, and significantly less so between humans and macaques. The premotor-parietal rsFC had most significant contribution to the formation of the neonatal tool network. These results suggest that an intrinsic brain network potentially supporting tool processing exists in the human brain prior to individual tool use experiences, and that the premotor-parietal functional connection in particular offers a brain basis for complex tool behaviors specific to humans.