EZH2 suppresses ferroptosis in hepatocellular carcinoma and reduces sorafenib sensitivity through epigenetic regulation of TFR2.

Enhancing sensitivity to sorafenib can significantly extend the duration of resistance to it, offering substantial benefits for treating patients with hepatocellular carcinoma (HCC). However, the role of ferroptosis in influencing sorafenib sensitivity within HCC remains pivotal. The enhancer of zeste homolog 2 (EZH2) plays a significant role in promoting malignant progression in HCC, yet the relationship between ferroptosis, sorafenib sensitivity, and EZH2 is not entirely clear. Bioinformatic analysis indicates elevated EZH2 expression in HCC, predicting an unfavorable prognosis. Overexpressing EZH2 can drive HCC cell proliferation while simultaneously reducing ferroptosis. Further analysis reveals that EZH2 amplifies the modification of H3K27 me3, thereby influencing TFR2 expression. This results in decreased RNA polymerase II binding within the TFR2 promoter region, leading to reduced TFR2 expression. Knocking down EZH2 amplifies sorafenib sensitivity in HCC cells. In sorafenib-resistant HepG2(HepG2-SR) cells, the expression of EZH2 is increased. Moreover, combining tazemetostat-an EZH2 inhibitor-with sorafenib demonstrates significant synergistic ferroptosis-promoting effects in HepG2-SR cells. In conclusion, our study illustrates how EZH2 epigenetically regulates TFR2 expression through H3K27 me3, thereby suppressing ferroptosis. The combination of the tazemetostat with sorafenib exhibits superior synergistic effects in anticancer therapy and sensitizes the HepG2-SR cells to sorafenib, shedding new light on delaying and ameliorating sorafenib resistance.

Graphdiyne marries PEDOT:PSS to form high-stable heterostructure from 2-unstable components toward ultra-low detection limit of uric acid detection in sweat.

PEDOT: PSS has been used as a biomimetic uric acid (UA) sensor but suffers from unfortunate low detection limit (LOD), narrow detection range and poor stability. Herein, we get graphdiyne (GDY) marry PEDOT:PSS to create a very stable GDY@PEDOT:PSS heterostructure for a biomimetic UA sensor, which accomplishes the lowest LOD (6 nM), the widest detection range (0.03 µM-7 mM) and the longest stability (98.1% for 35 days) among the related UA sensors. The sensor was successfully used to in situ real-time detection of  UA in sweat. The enhancement mechanisms of the sensor were investigated, and results discover that C≡C of GDY and C = C of PEDOT:PSS can cross-link each other by π-π interactions, making not only the former strongly resistant against oxidation deterioration, but also causes the latter to efficiently prevent water swelling of polymer for poor conductivity, thereby leading to high stability from both components. While the stabilized heterostructure can also offer more active sites by enhanced absorption of UA via π-π interactions for highly sensitive detection of UA. This work holds great promise for a practical sweat UA sensor while providing scientific insight to design a stable and electrocatalytically active structure from two unstable components.

Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum.

The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI-) related to neurotransmission. A more interesting finding is that GCI- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.

Structural and functional connectivity abnormalities of the default mode network in patients with Alzheimer's disease and mild cognitive impairment within two independent datasets.

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by progressive dementia, and amnestic mild cognitive impairment (aMCI) has been defined as a transitional stage between normal aging and AD. Accumulating evidence has shown that altered functional connectivity (FC) and structural connectivity (SC) in the default mode network (DMN) is the prominent hallmarks of AD. However, the relationship between the changes in SC and FC of the DMN is not yet clear. In the present study, we derived the FC and SC matrices of the DMN with functional magnetic resonance imaging (fMRI) and diffusion-weighted imaging (DWI) data and further assessed FC and SC abnormalities within a discovery dataset of 120 participants (39 normal controls, 34 patients with aMCI and 47 patients with AD), as well as a replication dataset of 122 participants (43 normal controls, 37 patients with aMCI and 42 patients with AD). Disrupted SC and FC were found among DMN components (e.g., the posterior cingulate cortex (PCC), medial prefrontal cortex (mPFC), and hippocampus) in patients in the aMCI and AD groups in the discovery dataset; most of the disrupted connections were also identified in the replication dataset. More importantly, some SC and FC elements were significantly correlated with the cognitive ability of patients with aMCI and AD. In addition, we found structural-functional decoupling between the PCC and the right hippocampus in patients in the aMCI and AD groups. These findings of the alteration of DMN connectivity in neurodegenerative cohorts deepen our understanding of the pathophysiological mechanisms of AD.

Survey on Exact kNN Queries over High-Dimensional Data Space.

k nearest neighbours (kNN) queries are fundamental in many applications, ranging from data mining, recommendation system and Internet of Things, to Industry 4.0 framework applications. In mining, specifically, it can be used for the classification of human activities, iterative closest point registration and pattern recognition and has also been helpful for intrusion detection systems and fault detection. Due to the importance of kNN queries, many algorithms have been proposed in the literature, for both static and dynamic data. In this paper, we focus on exact kNN queries and present a comprehensive survey of exact kNN queries. In particular, we study two fundamental types of exact kNN queries: the kNN Search queries and the kNN Join queries. Our survey focuses on exact approaches over high-dimensional data space, which covers 20 kNN Search methods and 9 kNN Join methods. To the best of our knowledge, this is the first work of a comprehensive survey of exact kNN queries over high-dimensional datasets. We specifically categorise the algorithms based on indexing strategies, data and space partitioning strategies, clustering techniques and the computing paradigm. We provide useful insights for the evolution of approaches based on the various categorisation factors, as well as the possibility of further expansion. Lastly, we discuss some open challenges and future research directions.

Imbedding Pd Nanoparticles into Porous In2O3 Structure for Enhanced Low-Concentration Methane Sensing.

Methane (CH4), as the main component of natural gas and coal mine gas, is widely used in daily life and industrial processes and its leakage always causes undesirable misadventures. Thus, the rapid detection of low concentration methane is quite necessary. However, due to its robust chemical stability resulting from the strong tetrahedral-symmetry structure, the methane molecules are usually chemically inert to the sensing layers in detectors, making the rapid and efficient alert a big challenge. In this work, palladium nanoparticles (Pd NPs) embedded indium oxide porous hollow tubes (In2O3 PHTs) were successfully synthesized using Pd@MIL-68 (In) MOFs as precursors. All In2O3-based samples derived from Pd@MIL-68 (In) MOFs inherited the morphology of the precursors and exhibited the feature of hexagonal hollow tubes with porous architecture. The gas-sensing performances to 5000 ppm CH4 were evaluated and it was found that Pd@In2O3-2 gave the best response (Ra/Rg = 23.2) at 370 °C, which was 15.5 times higher than that of pristine-In2O3 sensors. In addition, the sensing materials also showed superior selectivity against interfering gases and a rather short response/recovery time of 7 s/5 s. The enhancement in sensing performances of Pd@In2O3-2 could be attributed to the large surface area, rich porosity, abundant oxygen vacancies and the catalytic function of Pd NPs.

Application of super-resolution and correlative double sampling in cryo-electron microscopy.

Developments in cryo-EM have allowed atomic or near-atomic resolution structure determination to become routine in single particle analysis (SPA). However, near-atomic resolution structures determined using cryo-electron tomography and sub-tomogram averaging (cryo-ET STA) are much less routine. In this paper, we show that collecting cryo-ET STA data using the same conditions as SPA, with both correlated double sampling (CDS) and the super-resolution mode, allowed apoferritin to be reconstructed out to the physical Nyquist frequency of the images. Even with just two tilt series, STA yields an apoferritin map at 2.9 Å resolution. These results highlight the exciting potential of cryo-ET STA in the future of protein structure determination. While processing SPA data recorded in super-resolution mode may yield structures surpassing the physical Nyquist limit, processing cryo-ET STA data in the super-resolution mode gave no additional resolution benefit. We further show that collecting SPA data in the super-resolution mode, with CDS activated, reduces the estimated B-factor, leading to a reduction in the number of particles required to reach a target resolution without compromising the data size on disk and the area imaged in SerialEM. However, collecting SPA data in CDS does reduce throughput, given that a similar resolution structure, with a slightly larger B-factor, is achievable with optimised parameters for speed in EPU (without CDS).

Prognostic models for prolonged disorders of consciousness: an integrative review.

Disorders of consciousness (DoC) are acquired conditions of severe altered consciousness. During the past decades, some prognostic models for DoC have been explored on the basis of a variety of predictors, including demographics, neurological examinations, clinical diagnosis, neurophysiology and brain images. In this article, a systematic review of pertinent literature was conducted. We identified and evaluated 21 prognostic models involving a total of 1201 DoC patients. In terms of the reported accuracies of predicting the prognosis of DoC, these 21 models vary widely, ranging from 60 to 90%. Using improvement of consciousness level as favorable outcome criteria, we performed a quantitative meta-analysis, and found that the pooled sensitivity and specificity of the hybrid model that combined more than one technique were both superior to those of any single technique, including EEG and fMRI at the tasks and resting state. These results support the view that any single technique has its own advantages and limitations; and the integrations of multiple techniques, including diverse brain images and different paradigms, have the potential to improve predictive accuracy for DoC. Then, we provide methodological points of view and some prospects about future research. Totally, in comparison to a great many diagnostic methods for the DoC, the research of prognostic models is sparse and preliminary, still largely in its infancy with many challenges and opportunities.

A dedicated eight-channel receive RF coil array for monkey brain MRI at 9.4 T.

The neuroimaging of nonhuman primates (NHPs) realised with magnetic resonance imaging (MRI) plays an important role in understanding brain structures and functions, as well as neurodegenerative diseases and pathological disorders. Theoretically, an ultrahigh field MRI (≥7 T) is capable of providing a higher signal-to-noise ratio (SNR) for better resolution; however, the lack of appropriate radiofrequency (RF) coils for 9.4 T monkey MRI undermines the benefits provided by a higher field strength. In particular, the standard volume birdcage coil at 9.4 T generates typical destructive interferences in the periphery of the brain, which reduces the SNR in the neuroscience-focused cortex region. Also, the standard birdcage coil is not capable of performing parallel imaging. Consequently, extended scan durations may cause unnecessary damage due to overlong anaesthesia. In this work, assisted by numerical simulations, an eight-channel receive RF coil array was specially designed and manufactured for imaging NHPs at 9.4 T. The structure and geometry of the proposed receive array was optimised with numerical simulations, so that the SNR enhancement region was particularly focused on monkey brain. Validated with rhesus monkey and cynomolgus monkey brain images acquired from a 9.4 T MRI scanner, the proposed receive array outperformed standard birdcage coil with higher SNR, mean diffusivity and fractional anisotropy values, as well as providing better capability for parallel imaging.

GABARAP promotes bone marrow mesenchymal stem cells-based the osteoarthritis cartilage regeneration through the inhibition of PI3K/AKT/mTOR signaling pathway.

Osteoarthritis (OA) is a degenerative disease of the cartilage prevalent in the middle-aged and elderly demographic. Direct transplantation of bone marrow mesenchymal stem cells (BMSCs) or stem cell-derived chondrocytes into the damaged cartilage is a promising therapeutic strategy for OA, but is limited by the poor survival and in situ stability of the chondrocytes. Autophagy is a unique catabolic pathway conserved across eukaryotes that maintains cellular homeostasis, recycles damaged proteins and organelles, and promotes survival. The aim of this study was to determine the role of the proautophagic γ-aminobutyric acid receptor-associated protein (GABARAP) on the therapeutic effects of BMSCs-derived chondrocytes in a rat model of OA, and elucidate the underlying mechanisms. Anterior cruciate ligament transection (ACLT) was performed in Sprague-Dawley rats to simulate OA, and the animals were injected weekly with recombinant human His6-GABARAP protein, BMSCs-derived differentiated chondrocytes (DCs) or their combination directly into the knee cartilage. The regenerative effects of GABARAP and/or DCs were determined in term of International Cartilage Repair Society scores and cartilage thickness. The combination treatment of DCs and GABARAP significantly increased the levels of the ECM proteins Col II and SOX9, indicating formation of hyaline-like cartilage, and decreased chondrocyte apoptosis and inflammation. DCs + GABARAP treatment also upregulated the mediators of the autophagy pathway and suppressed the PI3K/AKT/mTOR pathway, indicating a mechanistic basis of its therapeutic action.

Activation-based association profiles differentiate network roles across cognitive loads.

Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level-dependent (BOLD) response across several large-scale brain networks, the unique and shared contributions of each large-scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared-association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared-association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2-back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared-associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared-associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.

Core networks and their reconfiguration patterns across cognitive loads.

Different cognitively demanding tasks recruit globally distributed but functionally specific networks. However, the configuration of core networks and their reconfiguration patterns across cognitive loads remain unclear, as does whether these patterns are indicators for the performance of cognitive tasks. In this study, we analyzed functional magnetic resonance imaging data of a large cohort of 448 subjects, acquired with the brain at resting state and executing N-back working memory (WM) tasks. We discriminated core networks by functional interaction strength and connection flexibility. Results demonstrated that the frontoparietal network (FPN) and default mode network (DMN) were core networks, but each exhibited different patterns across cognitive loads. The FPN and DMN both showed strengthened internal connections at the low demand state (0-back) compared with the resting state (control level); whereas, from the low (0-back) to high demand state (2-back), some connections to the FPN weakened and were rewired to the DMN (whose connections all remained strong). Of note, more intensive reconfiguration of both the whole brain and core networks (but no other networks) across load levels indicated relatively poor cognitive performance. Collectively these findings indicate that the FPN and DMN have distinct roles and reconfiguration patterns across cognitively demanding loads. This study advances our understanding of the core networks and their reconfiguration patterns across cognitive loads and provides a new feature to evaluate and predict cognitive capability (e.g., WM performance) based on brain networks.

Three-photon light-sheet fluorescence microscopy.

We present the first demonstration of three-photon excitation light-sheet fluorescence microscopy. Light-sheet fluorescence microscopy in single- and two-photon modes has emerged as a powerful wide-field, low-photodamage technique for fast volumetric imaging of biological samples. We extend this imaging modality to the three-photon regime, enhancing its penetration depth. Our present study uses a conventional femtosecond pulsed laser at 1000 nm wavelength for the imaging of 450 µm diameter cellular spheroids. In addition, we show, experimentally and through numerical simulations, the potential advantages in three-photon light-sheet microscopy of using propagation-invariant Bessel beams in preference to Gaussian beams.

Neural decoding of visual stimuli varies with fluctuations in global network efficiency.

Functional magnetic resonance imaging (fMRI) studies have shown that neural activity fluctuates spontaneously between different states of global synchronization over a timescale of several seconds. Such fluctuations generate transient states of high and low correlation across distributed cortical areas. It has been hypothesized that such fluctuations in global efficiency might alter patterns of activity in local neuronal populations elicited by changes in incoming sensory stimuli. To test this prediction, we used a linear decoder to discriminate patterns of neural activity elicited by face and motion stimuli presented periodically while participants underwent time-resolved fMRI. As predicted, decoding was reliably higher during states of high global efficiency than during states of low efficiency, and this difference was evident across both visual and nonvisual cortical regions. The results indicate that slow fluctuations in global network efficiency are associated with variations in the pattern of activity across widespread cortical regions responsible for representing distinct categories of visual stimulus. More broadly, the findings highlight the importance of understanding the impact of global fluctuations in functional connectivity on specialized, stimulus driven neural processes. Hum Brain Mapp 38:3069-3080, 2017. © 2017 Wiley Periodicals, Inc.

The Human Brainnetome Atlas: A New Brain Atlas Based on Connectional Architecture.

The human brain atlases that allow correlating brain anatomy with psychological and cognitive functions are in transition from ex vivo histology-based printed atlases to digital brain maps providing multimodal in vivo information. Many current human brain atlases cover only specific structures, lack fine-grained parcellations, and fail to provide functionally important connectivity information. Using noninvasive multimodal neuroimaging techniques, we designed a connectivity-based parcellation framework that identifies the subdivisions of the entire human brain, revealing the in vivo connectivity architecture. The resulting human Brainnetome Atlas, with 210 cortical and 36 subcortical subregions, provides a fine-grained, cross-validated atlas and contains information on both anatomical and functional connections. Additionally, we further mapped the delineated structures to mental processes by reference to the BrainMap database. It thus provides an objective and stable starting point from which to explore the complex relationships between structure, connectivity, and function, and eventually improves understanding of how the human brain works. The human Brainnetome Atlas will be made freely available for download at http://atlas.brainnetome.org, so that whole brain parcellations, connections, and functional data will be readily available for researchers to use in their investigations into healthy and pathological states.

Towards higher sensitivity and stability of axon diameter estimation with diffusion-weighted MRI.

Diffusion-weighted MRI is an important tool for in vivo and non-invasive axon morphometry. The ActiveAx technique utilises an optimised acquisition protocol to infer orientationally invariant indices of axon diameter and density by fitting a model of white matter to the acquired data. In this study, we investigated the factors that influence the sensitivity to small-diameter axons, namely the gradient strength of the acquisition protocol and the model fitting routine. Diffusion-weighted ex. vivo images of the mouse brain were acquired using 16.4-T MRI with high (Gmax of 300 mT/m) and ultra-high (Gmax of 1350 mT/m) gradient strength acquisitions. The estimated axon diameter indices of the mid-sagittal corpus callosum were validated using electron microscopy. In addition, a dictionary-based fitting routine was employed and evaluated. Axon diameter indices were closer to electron microscopy measures when higher gradient strengths were employed. Despite the improvement, estimated axon diameter indices (a lower bound of ~ 1.8 µm) remained higher than the measurements obtained using electron microscopy (~1.2 µm). We further observed that limitations of pulsed gradient spin echo (PGSE) acquisition sequences and axonal dispersion could also influence the sensitivity with which axon diameter indices could be estimated. Our results highlight the influence of acquisition protocol, tissue model and model fitting, in addition to gradient strength, on advanced microstructural diffusion-weighted imaging techniques. © 2016 The Authors. NMR in Biomedicine published by John Wiley & Sons Ltd.

Brain tissue compartment density estimated using diffusion-weighted MRI yields tissue parameters consistent with histology.

We examined whether quantitative density measures of cerebral tissue consistent with histology can be obtained from diffusion magnetic resonance imaging (MRI). By incorporating prior knowledge of myelin and cell membrane densities, absolute tissue density values were estimated from relative intracellular and intraneurite density values obtained from diffusion MRI. The NODDI (neurite orientation distribution and density imaging) technique, which can be applied clinically, was used. Myelin density estimates were compared with the results of electron and light microscopy in ex vivo mouse brain and with published density estimates in a healthy human brain. In ex vivo mouse brain, estimated myelin densities in different subregions of the mouse corpus callosum were almost identical to values obtained from electron microscopy (diffusion MRI: 42 ± 6%, 36 ± 4%, and 43 ± 5%; electron microscopy: 41 ± 10%, 36 ± 8%, and 44 ± 12% in genu, body and splenium, respectively). In the human brain, good agreement was observed between estimated fiber density measurements and previously reported values based on electron microscopy. Estimated density values were unaffected by crossing fibers.

Visualization of mouse barrel cortex using ex-vivo track density imaging.

We describe the visualization of the barrel cortex of the primary somatosensory area (S1) of ex vivo adult mouse brain with short-tracks track density imaging (stTDI). stTDI produced much higher definition of barrel structures than conventional fractional anisotropy (FA), directionally-encoded color FA maps, spin-echo T1- and T2-weighted imaging and gradient echo T1/T2*-weighted imaging. 3D high angular resolution diffusion imaging (HARDI) data were acquired at 48 micron isotropic resolution for a (3mm)(3) block of cortex containing the barrel field and reconstructed using stTDI at 10 micron isotropic resolution. HARDI data were also acquired at 100 micron isotropic resolution to image the whole brain and reconstructed using stTDI at 20 micron isotropic resolution. The 10 micron resolution stTDI maps showed exceptionally clear delineation of barrel structures. Individual barrels could also be distinguished in the 20 micron stTDI maps but the septa separating the individual barrels appeared thicker compared to the 10 micron maps, indicating that the ability of stTDI to produce high quality structural delineation is dependent upon acquisition resolution. Close homology was observed between the barrel structure delineated using stTDI and reconstructed histological data from the same samples. stTDI also detects barrel deletions in the posterior medial barrel sub-field in mice with infraorbital nerve cuts. The results demonstrate that stTDI is a novel imaging technique that enables three-dimensional characterization of complex structures such as the barrels in S1 and provides an important complementary non-invasive imaging tool for studying synaptic connectivity, development and plasticity of the sensory system.

Acoustic tractor beam.

Negative radiation forces act opposite to the direction of propagation, or net momentum, of a beam but have previously been challenging to definitively demonstrate. We report an experimental acoustic tractor beam generated by an ultrasonic array operating on macroscopic targets (>1 cm) to demonstrate the negative radiation forces and to map out regimes over which they dominate, which we compare to simulations. The result and the geometrically simple configuration show that the effect is due to nonconservative forces, produced by redirection of a momentum flux from the angled sides of a target and not by conservative forces from a potential energy gradient. Use of a simple acoustic setup provides an easily understood illustration of the negative radiation pressure concept for tractor beams and demonstrates continuous attraction towards the source, against a net momentum flux in the system.

Individualized brain mapping for navigated neuromodulation.

ABSTRACT: The brain is a complex organ that requires precise mapping to understand its structure and function. Brain atlases provide a powerful tool for studying brain circuits, discovering biological markers for early diagnosis, and developing personalized treatments for neuropsychiatric disorders. Neuromodulation techniques, such as transcranial magnetic stimulation and deep brain stimulation, have revolutionized clinical therapies for neuropsychiatric disorders. However, the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques. Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems. Still, the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions. The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles, advantages, disadvantages, and future trends of these techniques. The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.